ANALISIS TRIWULANAN: Perkembangan Moneter, Perbankan dan Sistem Pembayaran, Triwulan II - 2007
SUSUNAN PENGURUS BULETIN EKONOMI MONETER DAN PERBANKAN Departemen Riset Kebanksentralan Bank Indonesia Pelindung Dewan Gubernur Bank Indonesia Dewan Editor Prof. Dr. Anwar Nasution Prof. Dr. Miranda S. Goeltom Prof. Dr. Insukindro Prof. Dr. Iwan Jaya Azis Prof. Iftekhar Hasan Prof. Dr. Masaaki Komatsu Dr. M. Syamsuddin Dr. Perry Warjiyo Dr. Iskandar Simorangkir Dr. Solikin M. Juhro Dr. Haris Munandar Dr. M. Edhie Purnawan Dr. Burhanuddin Abdullah Dr. Andi M. Alfian Parewangi Pimpinan Editorial Dr. Perry Warjiyo Editor Pelaksana Dr. Darsono Dr. Siti Astiyah Dr. Andi M. Alfian Parewangi Sekretariat Nurhemi, S.E., M.A
Buletin ini diterbitkan oleh Bank Indonesia, Departemen Riset Kebanksentralan. Isi dan hasil penelitian dalam tulisan-tulisan di buletin ini sepenuhnya tanggungjawab para penulis dan bukan merupakan pandangan resmi Bank Indonesia. Kami mengundang semua pihak untuk menulis pada buletin ini paper dikirimkan dalam bentuk file ke Departemen Riset Kebanksentralan, Bank Indonesia, Menara Sjafruddin Prawiranegara Lt. 21; Jl. M.H. Thamrin No. 2, Jakarta Pusat, email :
[email protected] Buletin ini diterbitkan secara triwulan pada bulan April, Juli, Oktober dan Januari, bagi yang ingin memperoleh terbitan ini dapat menghubungi Unit Diseminasi – Divisi Diseminasi Statistik dan Manajemen Intern, Departemen Statistik, Bank Indonesia, Menara Sjafruddin Prawiranegara Lt. 2; Jl. M.H. Thamrin No. 2, Jakarta Pusat, telp. (021) 2981-8206. Untuk permohonan berlangganan: telp. (021) 2981-6571, fax. (021) 3501912.
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BULETIN EKONOMI MONETER DAN PERBANKAN
Volume 19, Nomor 3, Januari 2017
ANALISIS TRIWULAN: Perkembangan Moneter, Perbankan dan Sistem Pembayaran, Triwulan IV 2016 TM. Arief Machmud, Syachman Perdymer, Muslimin Anwar, Nurkholisoh Ibnu Aman, Tri Kurnia Ayu K, Anggita Cinditya Mutiara K, Illinia Ayudhia Riyadi The Role Of Interest Rates And Provincial Monetary Aggregate In Maintaining Inflation In Indonesia Chandra Utama, Miryam B.L. Wijaya, and Charvin Lim
241
267
Local Financial Development And Firm Performance: Does Financial Outreach Really Matters Within Indonesian Archipelago? Fickry Widya Nugraha
287
Financial Stability In Azerbaijan: The Application Of Fuzzy Approach G.C. Imanov, H.S.Alieva, R.A.Yusifzadeh
319
Pengaruh Finansialisasi Terhadap Ketimpangan Pendapatan Di Asean: Analisis Data Panel Pihri Buhaerah
335
Halaman ini sengaja dikosongkan
ANALISIS TRIWULAN: Perkembangan Moneter, Perbankan dan Sistem Pembayaran, Triwulan IV 2016
241
ANALISIS TRIWULAN: PERKEMBANGAN MONETER, PERBANKAN DAN SISTEM PEMBAYARAN, TRIWULAN IV, 2016 TM. Arief Machmud, Syachman Perdymer, Muslimin Anwar, Nurkholisoh Ibnu Aman, Tri Kurnia Ayu K, Anggita Cinditya Mutiara K, Illinia Ayudhia Riyadi1
Abstract The Indonesian economy recorded development in Quarter 4, 2016. The growth increased with more sound macroeconomic and financial system stability. The growth was supported by the growth of household consumption, better performance of investment, and the raise of export. On the other hand, the macroeconomic stability is well maintained as reflected on lower inflation, decreasing current account deficit, and stable Rupiah against foreign exchange. Domestic economy improves in accordance with the lower global financial risk and provides room for easing monetary policy on Quarter IV, 2016. The central bank lower the policy rate is well transmitted and is expected to strenghthen the growth momentum of economy ahead. Looking forward however, we still have to keep an eye on several external and domestic risks. For these reasons, Bank Indonesia keeps strengthening its monetary and macroprudential policy mix, and its coordination with the government in order to maintain the macroeconmoic stability, supporting the growth, and accelerate the structural reforms.
Keywords: macroeconomy, monetary, economic outlook. JEL Classification: C53, E66, F01, F41
1 Authors are researcher on Monetary and Economic Policy Department (DKEM). TM_Arief Machmud (
[email protected]); Syachman Perdymer (
[email protected]); Muslimin AAnwar (
[email protected]); Nurkholisoh Ibnu Aman (
[email protected]); Tri Kurnia Ayu K (
[email protected]); Anggita Cinditya Mutiara K (
[email protected]); Illinia Ayudhia Riyadi (
[email protected]). Authors would like to thank to Bambang Pramono, Rio Khasananda, and other unit for the great discussion to help enhancing this article.
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Buletin Ekonomi Moneter dan Perbankan, Volume 19, Nomor 3, Januari 2017
I. PERKEMBANGAN GLOBAL Perekonomian dunia membaik terutama didukung oleh AS dan Tiongkok, diikuti dengan harga komoditas global yang terus meningkat. Perbaikan ekonomi AS diperkirakan terus berlanjut didukung oleh konsumsi dan investasi yang meningkat. Perekonomian Tiongkok diperkirakan tetap tumbuh cukup kuat sejalan dengan proses rebalancing ekonomi yang berlangsung secara gradual. Sementara itu, harga komoditas dunia, termasuk harga minyak dan komoditas ekspor Indonesia terus menunjukkan peningkatan. Ekonomi AS mengalami perbaikan yang diperkirakan terus berlanjut. Perbaikan tersebut didukung oleh konsumsi dan investasi yang meningkat. Konsumsi AS cukup solid, tercermin dari konsumsi yang tumbuh sebesar 2,5% (yoy) pada triwulan IV 2016 (Grafik 1). Tetap kuatnya konsumsi AS juga tercermin dari kontribusi konsumsi pada pertumbuhan ekonomi yang tercatat sebesar 1,82% pada tahun 2016 (Grafik 2). Selain itu, rilis data pada Desember 2016 juga mengindikasikan masih solidnya konsumsi, antara lain terlihat dari peningkatan keyakinan konsumen dan pertumbuhan penjualan ritel riil dan tetap kuatnya pendapatan nominal. Pertumbuhan konsumsi yang masih solid tersebut didukung oleh kondisi ketenagakerjaan yang membaik, tercermin dari tingkat pengangguran yang menurun dan peningkatan average earning. Sementara itu, investasi mencatat kenaikan pertumbuhan sebesar 0,5% (yoy) pada triwulan IV 2016 dari -0,5% (yoy) pada triwulan III 2016. Meningkatnya investasi pada triwulan IV 2016 terutama didorong oleh investasi non-residensial (Grafik 3).
% (SAAR)
% (yoy, SA)
5
4,5
4,5
4,0
4
3,5
3,5
3,0
3
2,5
2,5
2,0
2
1,5
1,5
1,0
1
0,5
0,5
0,0
0
3,8
3,7
4,6
2,4
2,9
2,7
2,3
1,6
4,3
3,0
2,5
-0,5
Apr Jun Ags Okt Des Feb Apr Jun Ags Okt Des Feb Apr Jun Ags Okt Des 2014 2015 Konsumsi Riil (qtq, SAAR) Penjualan Ritel Riil (yoy, skala kanan)
2016 Konsumsi Riil (yoy, skala kanan) Pendapatan Riil Rata-rata (yoy, RHS)
% 4
3 2,16
2
1
1,95
1,82
1,00
Konsumsi (Kontribusi Tahunan) Pengeluaran Konsumsi Personal (Kontribusi) Pengeluaran Konsumsi Personal (YoY)
0 I ‘13 ‘14 ‘15 ‘16
II III IV I
II III IV I
II III IV I
II III IV
2013
2014
2015
2016
1st
Sumber: BEA, FRED, Bloomberg, diolah
Sumber: BEA, FRED, Bloomberg, diolah
Grafik 1. Konsumsi, Penjualan Ritel, dan Pendapatan
Grafik 2. Kontribusi dan Pertumbuhan Konsumsi PCE AS
ANALISIS TRIWULAN: Perkembangan Moneter, Perbankan dan Sistem Pembayaran, Triwulan IV 2016
243
% 20 15 10 5 0 -5 -10 -15
Inv. Inv. Non. Res. Inv. Res.
-20 -25 -30
Mar Sep Mar Sep Mar Sep Mar Sep Mar Sep Mar Sep Mar Sep Mar Sep 2009
2010
2011
2012
2013
2014
2015
2016
Sumber: BEA, FRED, Bloomberg, diolah
Grafik 3. Pertumbuhan Investasi
Perekonomian Tiongkok diperkirakan tetap tumbuh cukup kuat. PDB Tiongkok pada triwulan IV 2016 tumbuh sebesar 6,8% (yoy), sehingga secara keseluruhan tahun 2016 perekonomian Tiongkok tumbuh sebesar 6,7%. Hal ini sejalan dengan proses rebalancing ekonomi yang berlangsung secara gradual sebagaimana tercermin dari berlanjutnya tren perlambatan investasi, sementara tren konsumsi cenderung stabil. Pada Desember 2016, pertumbuhan penjualan ritel mencapai 10,9%, melampaui pertumbuhan Fixed Asset Investment yang tercatat sebesar 8,1% (Grafik 4). Perkembangan dari rebalancing ekonomi Tiongkok juga terlihat dari pertumbuhan kredit rumah tangga yang terus meningkat, sementara kredit korporasi menurun (Grafik 5).
%
%
40
Pertumbuhan Konsumsi - Pertumbuhan Investasi FAI Penjualan Ritel
30
Triliun CNY
60
120
Kredit rumah Tangga (skala kanan) Kredit Korporasi Nonfinansial (skala kanan) Pertumbuhan Kredit Rumah Tangga Pertumbuhan Kredit Korporasi Nonfinansial
50
100
20
40
80
10
30
60
0
20
-10
10
23,5
9,3
0
-20 Ags Des Apr Ags Des Apr Ags Des Apr Ags Des Apr Ags Des Apr Ags Des Apr Ags Des Apr Ags Des Apr Ags Des Apr Ags Des Apr Ags Des
20062007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Sumber: Bloomberg, diolah
Grafik 4. Pertumbuhan Konsumsi dan Pertumbuhan Investasi
40 20 0
JunOkt Feb Jun Okt Feb Jun Okt Feb Jun Okt Feb Jun Okt Feb Jun Okt Feb Jun Okt Feb Jun Okt
2009 2010
2011
2012
2013
2014
2015
2016
Sumber: Bloomberg, diolah
Grafik 5. Perkembangan Kredit Rumah Tangga dan Korporasi Tiongkok
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Buletin Ekonomi Moneter dan Perbankan, Volume 19, Nomor 3, Januari 2017
Sementara itu, harga komoditas dunia, termasuk harga minyak dan komoditas ekspor Indonesia, menunjukkan peningkatan. Rata-rata harga minyak nasional (Minas) pada triwulan IV 2016 meningkat menjadi 48 dolar AS per barrel, dari sebelumnya 42 dolar AS per barrel pada triwulan III 2016 (Grafik 6). Harga minyak mengalami gejolak selama triwulan IV 2016 seiring dengan faktor ketidakpastian yang berasal dari proses kesepakatan OPEC. Namun, kenaikan harga minyak dapat tertahan jika produksi minyak AS meningkat (Grafik 7). Produksi minyak AS mulai mendekati pertumbuhan positif, didorong oleh harga yang mulai naik. Jumlah pengeboran minyak (rig count) juga telah meningkat 50% dibandingkan jumlahnya di bulan Mei 2016.
mbpd
USD / barel 60
1950
56 55,5
55
9,5
52
1750
50
1550 9
45 Des-16 OPEC & non-OPEC deal pemotongan produksi 1,8 mbpd
40 35
1150
5,5
950
Sep-16 OPEC setuju membatasi produksi
30
1350
750
8
Produksi Minyak AS Rig Count AS (RHS)
25 20
550 350
7,5 Jan Feb Mar Apr Mei Jun
Jul
Ags Sep Okt Nov Des Jan
2016
Jan Mar Mei Jul Sep Nov Jan Mar Mei Jul Sep Nov Jan Mar Mei Jul Sep Nov Jan
2017
Sumber: Bloomberg Data terakhir: 26 Jan 2017
2014
2015
2016
2017
Sumber: Bloomberg
Grafik 6. Perkembangan Harga Minyak Brent
Grafik 7. Produksi Minyak dan Jumlah Rig AS
USD/MT 100 90 80
84
70 60 50 40
Data s.d. 07/02/2017
Jan MarMei Jun Sep Nov Jan MarMei Jun Sep Nov Jan MarMei Jun Sep Nov Jan 2014
2015
2016
Sumber: Bloomberg
Grafik 8. Perkembangan Harga Batubara
2017
ANALISIS TRIWULAN: Perkembangan Moneter, Perbankan dan Sistem Pembayaran, Triwulan IV 2016
245
Harga komoditas ekspor Indonesia juga menunjukkan peningkatan. Kenaikan harga batubara dipengaruhi tingginya impor Tiongkok, antara lain karena tingginya permintaan terkait musim dingin (Grafik 8). Selain itu, stok persediaan batubara di Tiongkok yang masih turun juga turut menyebabkan harga batubara berada di level yang tinggi.
II. DINAMIKA MAKROEKONOMI INDONESIA 2.1. Pertumbuhan Ekonomi Pertumbuhan ekonomi Indonesia pada triwulan IV 2016 didukung oleh pertumbuhan konsumsi rumah tangga, perbaikan kinerja investasi, dan peningkatan ekspor. Konsumsi RT masih tumbuh cukup kuat didukung oleh terkendalinya inflasi. Peningkatan kinerja investasi terutama didorong oleh pertumbuhan investasi nonbangunan dalam bentuk kendaraan dan peralatan lainnya. Perbaikan ini terindikasi pada kinerja sektor pertambangan dan perkebunan yang meningkat. Di sisi lain, investasi bangunan masih melambat sejalan dengan belum kuatnya dukungan investasi sektor swasta. Sementara itu, kinerja ekspor menunjukkan perbaikan yang signifikan seiring dengan mulai meningkatnya harga beberapa komoditas seperti harga batubara dan CPO.
Tabel 1. Pertumbuhan Ekonomi Sisi Pengeluaran (%,yoy) %Y-o-Y, Tahun Dasar 2010
Komponen
2014
2015 I
II
III
IV
5,01 -8,06 2,91 4,60 5,71
4,97 -7,98 2,61 4,01 4,72
4,95 6,57 7,09 4,93 6,11
4,93 8,33 7,12 6,43 7,78
2015
2016 I
II
III
IV
4,96 -0,62 5,32 5,01 6,11
4,97 6,40 3,43 4,67 6,78
5,07 6,71 6,23 4,18 5,07
5,01 6,64 -2,95 4,24 4,96
4,99 6,72 -4,05 4,80 4,07
2016
Konsumsi Rumah Tangga Konsumsi LNPRT Konsumsi Pemerintah Investasi Investasi Bangunan
5,15 12,19 1,16 4,45 5,52
Investasi NonBangunan
1,58
1,62
2,05
1,65
2,47
1,95
-1,20
1,70
2,16
7,07
2,45
1,07 2,12 5,01
-0,68 -2,63 4,82
-0,26 -7,37 4,74
-0,95 -6,65 4,77
-6,38 -8,75 5,17
-2,12 -6,41 4,88
-3,29 -5,14 4,92
-2,18 -3,20 5,18
-5,65 -3,67 5,01
4,24 2,82 4,94
-1,74 -2,27 5,02
Ekspor Barang dan Jasa Impor Barang dan Jasa PDB
5,01 6,62 -0,15 4,48 5,18
Sumber : BPS (diolah)
Konsumsi Rumah Tangga (RT) tetap tumbuh kuat dan menjadi motor pertumbuhan pada triwulan IV 2016. Konsumsi RT pada triwulan IV 2016 tumbuh stabil sebesar 4,99% (yoy) dibandingkan triwulan sebelumnya (5,01%, yoy) (Tabel 1). Konsumsi RT yang tetap kuat sejalan dengan keyakinan konsumen yang meningkat didukung oleh perbaikan keyakinan terhadap kondisi ekonomi (Grafik 9). Selain itu, terjaganya inflasi pada tingkat yang rendah berdampak positif pada daya beli masyarakat. Indikator penjualan ritel meningkat, terutama pada kelompok suku cadang dan clothing. Penjualan kendaraan bermotor khususnya mobil tumbuh tinggi pada triwulan IV 2016 (11,6% yoy), naik dari triwulan sebelumnya (5,1% yoy) (Grafik 10). Sementara,
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Buletin Ekonomi Moneter dan Perbankan, Volume 19, Nomor 3, Januari 2017
konsumsi Lembaga Non-Profit Rumah Tangga (LNPRT) tumbuh 6,7% (yoy) pada triwulan IV 2016 sedikit lebih tinggi dibandingkan triwulan sebelumnya, sejalan dengan meningkatnya kegiatan organisasi kemasyarakatan/partai politik terkait Pilkada serentak di berbagai daerah serta penyelenggaraan kegiatan beberapa organisasi berskala nasional. Konsumsi Pemerintah pada triwulan IV 2016 menurun sejalan dengan konsolidasi fiskal yang ditempuh melalui penghematan untuk memperkuat kredibilitas kebijakan fiskal. Penerimaan negara yang relatif terbatas mendorong pemerintah menempuh program penghematan belanja. Mulai semester kedua 2016, pemerintah melakukan pemotongan anggaran belanja. Secara keseluruhan belanja pemerintah mengalami kontraksi pertumbuhan.
Indeks
% 30
140 Indeks Ekspektasi Konsumen
130
Penjualan Ritel
20 10
120
Indeks Keyakinan Konsumen
0
110
-10 Indeks Keyakinan Saat Ini
100
Penjualan Mobil
-20
90
Penjualan Motor
-30 -30
80 I
II
III 2014
IV
I
II
III
IV
I
II
2015
Sumber: Bank Indonesia, diolah
Grafik 9. Indeks Keyakinan Konsumen
2016
III
IV
I
II
III 2014
IV
I
II
III
2015
IV
I
II
III
IV
2016
Sumber: Bank Indonesia, diolah
Grafik 10. Penjualan Ritel dan Kendaraan Bermotor
Investasi meningkat pada triwulan IV ditopang optimisme terhadap prospek ekonomi sejalan dengan kenaikan harga komoditas. Investasi naik 4,80% (yoy) dibandingkan triwulan sebelumnya (4,24% yoy) terutama didorong oleh investasi nonbangunan dalam bentuk kendaraan dan peralatan lainnya (Grafik 11). Kenaikan investasi tersebut sejalan dengan tren perbaikan harga komoditas global (khususnya batubara dan CPO) yang mendorong dilakukannya peremajaan alat angkutan di sektor pertambangan dan perkebunan. Hal tersebut terindikasi dari penjualan alat berat yang melonjak tinggi. Impor suku cadang dan perlengkapan alat angkutan juga tumbuh meningkat (Grafik 12). Namun, investasi bangunan melambat sejalan dengan masih terbatasnya investasi proyek konstruksi terkait pemotongan belanja modal pemerintah dan belum kuatnya dukungan investasi sektor swasta dalam pembangunan proyek konstruksi.
ANALISIS TRIWULAN: Perkembangan Moneter, Perbankan dan Sistem Pembayaran, Triwulan IV 2016
% yoy
%, yoy 10,0
8,62
8,0 6,0
25
4,0
-5,0
0
0,0
27,4
Investasi NonBangunan: Pengangkutan (sb kanan) 6,8
4,07 4,80
2,0
% yoy
50
7,07
-6,2
5
5,3 -0,2
-5 -15
-2,0 -25
-25
Impor Mobil Penumpang
-6,0
-35 -45
-50
-8,0 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 2013 2014 PTMB Non Bangunan excl. Haki & CBR
2015 Bangunan NonBangunan
25 15
14,8
7,3
Impor Suku Cadang
-4,0
247
Q1
Q2
Q3 2015
2016
Q4
Q1
Q2
Q3
Q4
2016
Sumber: Bank Indonesia
Sumber: BPS, diolah
Grafik 11. Pertumbuhan Investasi
Grafik 12. Impor Kendaraan dan Suku Cadang
Ekspor meningkat signifikan didorong oleh kenaikan harga komoditas dan perbaikan ekonomi global. Ekspor tumbuh positif pada triwulan IV 2016 sebesar 4,24% (yoy), meningkat dibandingkan triwulan sebelumnya yang terkontraksi 5,65% (yoy). Kenaikan harga komoditas menjadi faktor pendorong meningkatnya ekspor. Selain itu, pemulihan ekonomi global yang terus berlanjut meningkatkan permintaan dari negara mitra dagang utama seperti Tiongkok, India, dan AS. Berdasarkan kelompoknya, ekspor nonmigas meningkat baik ekspor komoditas primer (pertanian dan pertambangan) maupun manufaktur (Grafik 13). Ekspor CPO dan batubara meningkat didukung kenaikan harga dan permintaan khususnya dari negara Asia seperti India dan Tiongkok. Sementara itu, pendorong positifnya kinerja ekspor manufaktur utamanya adalah ekspor kendaraan bermotor, kimia organik dan tekstil. Sejalan dengan kenaikan ekspor dan stabilnya permintaan domestik, impor tumbuh positif pada triwulan IV-2016. Impor pada triwulan IV 2016 tumbuh sebesar 2,82% (yoy), membaik dibandingkan triwulan sebelumnya yang terkontraksi 3,67% (yoy) (Grafik 14). Kenaikan impor terutama ditopang oleh positifnya kinerja impor nonmigas di tengah pelemahan impor migas. Kenaikan impor nonmigas terutama didorong oleh positifnya impor bahan baku, terutama impor bahan baku untuk industri serta suku cadang dan perlengkapan barang modal, ditengah kontraksi impor barang modal.
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Buletin Ekonomi Moneter dan Perbankan, Volume 19, Nomor 3, Januari 2017
%, yoy
% 30,0
30
Pertanian
20
20,0
GDP Impor
10
Manufaktur
10,0
Total
Bahan Mentah
Konsumsi
Total
0 -10
0,0 Ekspor PDB
-10,0
-20 Investasi
-30 -20,0
Pertambangan
-40 -50
-30,0 Q1
Q2
Q3
Q4
2014
Q1
Q2
Q3
Q4
Q1
2015
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
2014
2016
Q2
Q3
Q4
Q1
2015
Q2
Q3
Q4
2016
Sumber: BPS, diolah
Sumber: BPS, diolah
Grafik 13. Pertumbuhan Ekspor Nonmigas Riil
Grafik 14. Pertumbuhan Impor Nonmigas Riil
Dari sisi sektoral, pertumbuhan kinerja Lapangan Usaha terkait ekspor meningkat sejalan dengan perbaikan harga komoditas, sementara Lapangan Usaha orientasi domestik tumbuh terbatas (Tabel 2). Sektor terkait ekspor seperti sektor pertanian (sub-Lapangan Usaha perkebunan) dan pertambangan (sub-Lapangan Usaha batubara dan bijih logam) menjadi motor pertumbuhan di triwulan IV-2016, sejalan dengan perbaikan ekspor. Lapangan Usaha manufaktur secara agregat tumbuh melambat dengan divergensi arah pertumbuhan berdasarkan orientasi produk. Industri berorientasi ekspor antara lain industri batubara, pengolahan migas,
Tabel 1.2 Proyeksi Pertumbuhan Ekonomi Sisi Lapangan Usaha (%,yoy) %Y-o-Y, Tahun Dasar 2010
Sektor
2014
2015 I
II
III
IV
2015
2016 I
II
II
IV
2016
Pertanian,Peternakan,Kehutanan,& Perikanan
4,24
3,76
6,54
2,88
1,64
3,77
1,47
3,44
3,03
5,31
3,25
Pertambangan & Penggalian
0,43
0,58
-3,59
-4,41
-6,03
-3,42
1,20
1,15
0,29
1,60
1,06
Industri Pengolahan
4,64
4,07
4,20
4,60
4,43
4,33
4,68
4,63
4,52
3,36
4,29
Listrik, Gas, Air Bersih, dan Pengadaan Air*
5,86
1,97
1,22
1,12
1,02
1,32
7,35
6,09
4,69
3,11
5,26
Konstruksi
6,97
6,03
5,35
6,82
7,13
6,36
6,76
5,12
4,95
4,21
5,22
Perdagangan dan Penyediaan Akomodasi dan Mamin**
5,29
3,70
1,95
1,97
4,03
2,90
4,43
4,25
3,79
4,01
4,11
Transportasi, Pergudangan, Informasi dan Komunikasi***
8,84
7,88
7,72
9,08
8,51
8,31
7,73
8,24
8,64
8,79
8,36
Jasa Keuangan, Real Estat, dan Jasa Perusahaan****
5,75
6,88
4,19
7,57
8,56
6,81
7,52
9,25
6,87
4,51
6,99
Jasa-jasa Lainnya*****
5,12
5,79
8,60
5,03
6,14
6,37
5,67
5,35
3,94
2,92
4,42
PDB
5,01
4,82
4,74
4,77
5,17
4,88
4,92
5,18
5,01
4,94
5,02
Sumber : BPS ^ Proyeksi Bank Indonesia * Penggabungan 2 lap. usaha: (i) Pengadaan Listrik dan Gas dan (ii) Pengadaan Air ** Penggabungan 2 lap. usaha: (i) Perdagangan Besar dan Eceran, Reparasi Mobil dan Motor, serta (ii) Penyediaan akomodasi dan makan minum *** Penggabungan 2 lap. usaha: (i) Transportasi dan Pergudangan serta (ii) Informasi dan Komunikasi **** Penggabungan 3 lap. usaha: (i) Jasa Keuangan, (ii) Real Estate, dan (iii) Jasa Perusahaan ***** Penggabungan 4 lap. usaha: (i) Adm. Pemerintahan, Pertahanan, Jaminan Sosial Wajib, (ii) Jasa Pendidikan, (iii) Jasa Kesehatan dan Kegiatan Lainnya, dan (iv) Jasa Lainnya
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dan tekstil tumbuh membaik. Sementara industri berorientasi domestik antara lain makananminuman (mamin) dan galian nonlogam/semen tumbuh melambat sejalan dengan permintaan domestik yang melambat. Di sisi lain, langkah konsolidasi fiskal tercermin pada Lapangan Usaha konstruksi dan sub-Lapangan Usaha jasa administrasi pemerintah yang tumbuh melambat. Secara spasial, ekonomi di Sumatera dan Kawasan Timur Indonesia (KTI) tumbuh meningkat sejalan dengan meningkatnya ekspor di tengah masih kuatnya pertumbuhan Jawa (Gambar 1). Perekonomian Sumatera yang meningkat ditopang kinerja ekspor seiring perbaikan harga berbagai komoditas utama wilayah Sumatera seperti CPO, karet, batubara, dan kopi. Peningkatan ekspor juga menjadi penopang peningkatan pertumbuhan ekonomi di KTI khususnya dalam bentuk komoditas utama seperti batubara, nikel, tembaga, emas, dan CPO. Sementara itu, pertumbuhan ekonomi Kalimantan Timur membaik meskipun masih kontraksi. Ekonomi Jawa masih tumbuh kuat ditopang menguatnya konsumsi rumah tangga, investasi, serta ekspor manufaktur. Ekspor yang meningkat menyumbang terjaganya daya beli konsumen di seluruh kawasan, sehingga konsumsi rumah tangga tetap tumbuh kuat.
SUMATERA
JAWA
4,19 4,47 4,03 4,49 I
II III 2016
5,38 I
IV
BALINUSRA
KALIMANTAN
5,82 5,70 II III 2016
5,45
I
II III 2016
6,02 5,56
6,74 6,83 5,22 4,87
1,97 1,62 1,21 2,22
IV
SULAMPUA
IV
I
II III 2016
IV
I
KTI
8,72 9,21
II III 2016
5,54 4,33 4,03 5,39
IV
I
II III 2016
IV
Nasional 5,18
ACEH 4,3
4,92 SUMUT 5,2
KEP. RIAU 5,2 RIAU 2,2
KALBAR 3,8
KALTARA SULTENG 4,27 3,8
JAMBI 6,4 SUMSEL 5,1 KEP. BABEL 4,9
SUMBAR 4,9
LAMPUNG 5 BANTEN 5,5
PDRB ≥ 7,0%
JABAR 5,4
6,0% ≤ PDRB < 7,0%
I
MALUT 6,5
II III 2016
PAPBAR 4,9
KALTIM (0,3)
KALTENG 8,6 DKI KALSEL JAKARTA 5,5 JATENG 5,3 5,3
BENGKULU 5,6
SULUT 6,5
5,01 4,94
DIY 4,7
JATIM 5,5
SULBAR 7,5 SULSEL 7,6 BALI 5,5
GORONTALO 7 MALUKU 5,9 NTT 5,2
SULTRA 7,6
NTB 3,8
5,0% ≤ PDRB < 6,0%
4,0% ≤ PDRB < 5,0%
0% ≤ PDRB < 4,0%
Sumber: BPS, diolah
Gambar 1. Peta Pertumbuhan Ekonomi Daerah Triwulan IV 2016 (%yoy)
PDRB < 0%
IV
PAPUA 21,4
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2.2. Neraca Pembayaran Indonesia Neraca Pembayaran Indonesia (NPI) triwulan IV 2016 mencatat surplus sebesar 4,5 miliar dolar AS. Kondisi tersebut didukung oleh defisit transaksi berjalan (TB) yang menurun dan surplus Transaksi Modal dan Finansial (TMF) yang cukup besar (Grafik 15). Secara keseluruhan tahun 2016, NPI mencatat surplus sebesar 12,1 miliar dolar AS, membaik secara signifikan dibandingkan tahun sebelumnya yang mencatat defisit 1,1 miliar dolar AS. Menurunnya defisit transaksi berjalan pada triwulan IV 2016 sejalan dengan perbaikan perekonomian dunia dan perekonomian Indonesia. Defisit transaksi berjalan triwulan IV 2016 tercatat sebesar 1,8 miliar dolar AS (0,8% dari PDB), lebih rendah dibandingkan dengan triwulan sebelumnya sebesar 4,7 miliar dolar AS (1,9% dari PDB), ditopang oleh perbaikan kinerja neraca perdagangan barang dan pendapatan primer (Grafik 16). Surplus neraca perdagangan barang tercatat meningkat didorong oleh peningkatan ekspor seiring dengan perbaikan ekonomi negara-negara mitra dagang dan meningkatnya harga komoditas global. Sementara itu, defisit neraca pendapatan primer menurun mengikuti jadwal pembayaran bunga surat utang pemerintah yang lebih rendah. Kinerja transaksi berjalan triwulan IV 2016 juga lebih baik dibandingkan dengan periode yang sama tahun 2015 yang mencatat defisit sebesar 4,7 miliar dolar AS (2,2% dari PDB) karena meningkatnya surplus neraca perdagangan barang dan menurunnya defisit neraca perdagangan jasa.
Miliar dolar AS
Miliar dolar AS
15 10
Persen
14
3
10
1
6
5
-1
2
0 -5
-2
-3
-6
-5
-10
-15 -20
Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4Q1Q2Q3Q4 2011
2012
2013
2014
2015
* angka sementara ** angka sangat sementara Sumber: Bank Indonesia
Grafik 1.15 Neraca Pembayaran Indonesia
Q1* Q2* Q3* Q4**
Transaksi Modal Finansial Transaksi Berjalan Neraca Keseluruhan
2016
-14 -18 -22
-7 Neraca Pendapatan Sekunder Neraca Perdagangan Transaksi Berjalan
Neraca Pendapatan Neraca Jasa CA/GDP (%) (rhs)
-9 -11 -13
-26 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
2011
2012
2013
2014
2015
* angka sementara ** angka sangat sementara Sumber: Bank Indonesia
Grafik 1.16. Neraca Transaksi Berjalan
Q1* Q2* Q3* Q4**
-10
2016
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Sementara itu, transaksi modal dan finansial pada triwulan IV 2016 mencatat surplus yang cukup besar dan melampaui defisit transaksi berjalan. Surplus transaksi modal dan finansial triwulan IV 2016 tercatat sebesar 6,8 miliar dolar AS, terutama bersumber dari surplus investasi lainnya sejalan dengan berlanjutnya repatriasi dana tax amnesty. Surplus transaksi modal dan finansial tersebut lebih rendah dibandingkan dengan surplus pada triwulan III 2016. Lebih rendahnya surplus di triwulan IV 2016 disebabkan oleh defisit investasi portofolio sebagai dampak keluarnya dana asing dari saham domestik dan SUN rupiah pasca-pengumuman Pemilu Presiden AS, serta surplus investasi langsung yang juga lebih rendah karena dipengaruhi outflow di sektor pertambangan.
Miliar dolar AS
Bulan 9,0
120 100
8,0
80
7,0
60 6,0
40
5,0
20 0
4,0 Feb Apr Jun Ags Okt Des Feb Apr Jun Ags Okt Des Feb Apr Jun Ags Okt Des
2014 2015 2016 Cadangan Devisa Bulan Impor dan Pembayaran Utang Pemerintah (Skala Kanan) Sumber: Bank Indonesia
Grafik 17. Perkembangan Cadangan Devisa
Perkembangan NPI tersebut pada gilirannya mendorong kenaikan posisi cadangan devisa. Posisi cadangan devisa pada akhir triwulan IV 2016 tercatat sebesar 116,4 miliar dolar AS, lebih tinggi dari 115,7 miliar dolar AS pada akhir triwulan III 2016 atau bila dibandingkan periode akhir triwulan IV 2015 yang sebesar 105,9 miliar dolar AS (Grafik 17). Peningkatan tersebut dipengaruhi penerimaan cadangan devisa, antara lain berasal dari penerbitan global bonds dan penarikan pinjaman luar negeri pemerintah, serta penerimaan pajak dan devisa migas, yang melampaui kebutuhan devisa untuk pembayaran utang luar negeri pemerintah dan SBBI valas jatuh tempo. Posisi cadangan devisa per akhir triwulan IV 2016 tersebut cukup untuk membiayai 8,8 bulan impor atau 8,4 bulan impor dan pembayaran utang luar negeri pemerintah, serta berada di atas standar kecukupan internasional sekitar 3 bulan impor. Bank Indonesia menilai cadangan devisa tersebut mampu mendukung ketahanan sektor eksternal dan menjaga kesinambungan pertumbuhan ekonomi Indonesia ke depan.
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2.3. Nilai Tukar Rupiah Nilai tukar rupiah mengalami tekanan pada triwulan IV 2016 di tengah peningkatan ketidakpastian terkait arah kebijakan AS. Pada triwulan IV 2016, secara point to point rupiah melemah sebesar 3,13% menjadi Rp13.473 per dolar AS (Grafik 18). Tekanan terhadap rupiah antara lain berasal dari meningkatnya ketidakpastian global terkait Pilpres AS, kenaikan FFR dan meningkatnya kebutuhan dolar AS untuk pembayaran utang luar negeri pada akhir tahun. Namun, tekanan terhadap rupiah tersebut berlangsung terbatas dan bersifat temporer. Untuk keseluruhan tahun 2016, secara point-to-point rupiah tercatat mengalami peningkatan sebesar 2,32% (Grafik 19).
YTD 2016 vs 2015
Rupiah 14.200 IDR/USD
14.000
Rata-rata triwulanan
Rata-rata bulanan
13.434
13.400
13.195
13.525
13.337
13.112
13.261
13.163
13.200 13.172
13.000
13.110
13.315
13.473
BRL
13.412
PHP
13.261
13,77
-13,10 -16,95
INR
13.505 13.313
13.600
12.600
TRY MYR
13.800
12.800
ZAR
-9,91
-4,28 -5,75 -2,61 -4,55
THB
IDR
4 13 22 2 12 23 3 15 24 5 14 25 4 1726 6 15 2412 21 1 10 22 31 9 21 30 11 20 31 9 18 29 8 2030
Jan Feb Mar Apr Mei Jun Jul
Ags
Sumber: Reuters
Grafik 18. Nilai Tukar Kawasan
Sep Okt Nov Des
0,57
-2,92 -2,55 -2,51 -3,74 -0,25
EUR Data s.d 30 Des-16
2,32 0,66
data s.d 30 Des-16
-20,0 -15,0 -10,0
-5,0
Rata-rata 21,68
-4,21 -5,43 -4,15
KRW
13.130
point-to-point
0,0
5,0
% 10,0
15,0
20,0
25,0
Sumber: Reuters
Grafik 19. Nilai Tukar Rupiah
Pada triwulan IV 2016, pelemahan Rupiah diikuti dengan volatilitas yang relatif meningkat, namun relatif lebih rendah dibandingkan negara kawasan. Meningkatnya volatilitas Rupiah pada triwulan IV 2016, terutama pada bulan November terjadi akibat dinamika pilpres AS dan kenaikan FFR. Volatilitas Rupiah pada triwulan IV 2016 relatif lebih rendah dari Rand (Afrika Selatan), Lira (Turki), Real (Brazil), Ringgit (Malaysia) dan Won (Korea Selatan) (Grafik 20)
ANALISIS TRIWULAN: Perkembangan Moneter, Perbankan dan Sistem Pembayaran, Triwulan IV 2016
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% 30 Tw3 - 2016 Tw4 - 2016
25 20 15 10 5 0 ZAR
TRY
BRL
MYR
KRW
IDR
SGD
INR
PHP
THB
Sumber: Reuters, diolah
Grafik 20. Volatilitas Triwulanan
2.4. Inflasi Inflasi IHK pada triwulan IV 2016 secara triwulanan meningkat dibandingkan triwulan sebelumnya namun masih terkendali pada rentang sasaran inflasi 4,0±1%. Pada akhir triwulan IV 2016, realisasi inflasi IHK tercatat sebesar 1,03% (qtq) atau sebesar 3,02% (yoy) (Grafik 1.21). Realisasi inflasi tersebut secara triwulanan lebih tinggi dibandingkan akhir triwulan sebelumnya yang sebesar 0,90% (qtq). Meningkatnya tekanan inflasi di triwulan IV 2016 terutama bersumber dari kelompok volatile food (VF) dan administered price (AP), sementara tekanan inflasi dari kelompok inti lebih rendah dibandingkan triwulan sebelumnya. Inflasi kelompok VF pada triwulan IV 2016 terutama dipengaruhi oleh naiknya harga aneka cabai akibat terbatasnya pasokan. Inflasi kelompok VF tercatat sebesar 2,06% (qtq), lebih tinggi dibandingkan triwulan III 2016 sebesar 0,30% (qtq). Lebih tingginya inflasi VF di triwulan IV 2016 didorong oleh inflasi komoditas cabai merah dan cabai rawit seiring dengan rendahnya pasokan. Inflasi cabai rawit dan cabai merah pada triwulan IV 2016 masing-masing mencatat kenaikan hingga sebesar 47,65% (qtq) dan 35,34% (qtq) antara lain karena tingginya intensitas hujan dan kendala produksi di sejumlah daerah sentra produksi. Meski demikian, secara tahunan (yoy) inflasi kelompok VF tercatat lebih rendah yakni menjadi 5,92% dibanding akhir triwulan sebelumnya yang sebesar 6,51%.
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%, yoy
Indeks
20
%, yoy
200 IHK Volatile Food
16
Inti Administered Prices
180
12
20 Inflasi IHK aktual (skala kanan) Inflasi Ekspektasi Harag Pedagang 3 bulan yad Inflasi Ekspektasi Harag Pedagang 6 bulan yad
15
160
8
10 5,92
4
140
3,07 3,02
0
5
120
0,21
-4
0
100 1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5 6 7 8 9 101112
2014
2015
2016
Sumber: BPS, diolah
Grafik 21. Perkembangan Inflasi Tahunan
1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5 6 7 8 9 101112 1 2 3 4 5
2014
2015
2016
2017
Sumber: BPS, diolah
Grafik 22. Ekspektasi Inflasi Pedagang Eceran
Kelompok AP juga mengalami kenaikan inflasi pada triwulan IV 2016 namun secara tahunan inflasi kelompok ini masih tercatat pada level yang rendah. Inflasi kelompok administered prices (AP) pada triwulan IV 2016 tercatat sebesar 1,68% (qtq) atau lebih tinggi dibandingkan triwulan III 2015 yang sebesar 0,93% (qtq). Pada triwulan ini, tekanan Inflasi kelompok AP didorong oleh kenaikan tarif angkutan udara, tarif listrik, rokok, dan bensin. Kenaikan tarif angkutan udara terjadi seiring dengan musim liburan menjelang hari raya Natal dan tahun baru 2017 serta mulainya liburan anak sekolah. Sementara kenaikan harga bensin didorong oleh kenaikan harga bensin nonsubsidi seperti Pertalite, Pertamax, dan Dexlite yaitu sebesar Rp150/liter pada Desember 2016. Sementara itu, kenaikan harga minyak dunia dan pelemahan nilai tukar diikuti oleh kenaikan tarif listrik pada akhir triwulan IV 2016. Perkembangan ini menyebabkan kelompok AP secara tahunan tercatat mengalami inflasi sebesar 0,21% (yoy) setelah pada triwulan sebelumya mengalami deflasi 0,38% (yoy). Sementara itu, inflasi kelompok inti pada triwulan IV 2016 menurun terutama dipengaruhi oleh rendahnya harga komoditas global dan terjaganya ekspektasi inflasi. Inflasi inti triwulan IV 2016 tercatat sebesar 0,48% (qtq), lebih rendah dibandingkan triwulan sebelumnya sebesar 1,03% (qtq). Pada triwulan ini, harga komoditas global mengalami penurunan sebesar 0,11% (qtq) dibandingkan dengan triwulan sebelumnya. Penurunan terutama terjadi pada komoditas emas internasional yang diikuti dengan turunnya harga perhiasan yang merupakan salah satu komoditas yang memiliki bobot cukup besar dalam keranjang kelompok inflasi inti. Selain itu, rendahnya tekanan inflasi inti turut dipengaruhi oleh faktor ekspektasi terhadap inflasi yang rendah sebagaimana terindikasi pada survei Desember 2016 (Grafik 22). Namun, adanya tekanan pelemahan terhadap nilai tukar rupiah pada Desember 2016 menahan berlanjutnya disinflasi kelompok inti.
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ANALISIS TRIWULAN: Perkembangan Moneter, Perbankan dan Sistem Pembayaran, Triwulan IV 2016
Ditinjau dari sisi spasial, inflasi tahunan (yoy) daerah secara agregat pada Triwulan IV 2016 tercatat lebih rendah dibanding Triwulan III 2016. Hal ini terutama dipengaruhi menurunnya inflasi di semua wilayah di KTI. Kembali normalnya harga tarif angkutan udara yang sempat melonjak di Triwulan III 2016 serta kenaikan permintaan pada Natal dan Tahun Baru yang terkompensasi dengan ketersedian stok pangan, membuat inflasi KTI pada Triwulan IV 2016 tercatat lebih rendah (Gambar 2).
ACEH 4
Inflasi Nasional: 3,02% (yoy) SUMUT 6,3
KEP. RIAU 3,5 RIAU 4
KALBAR 3,7
KALTIMRA 3,5 SULTENG 1,5
JAMBI 4,4 SUMSEL 3,6 KEP. BABEL 6,8
SUMBAR 4,9
KALTENG 2,1 DKI KALSEL JAKARTA 2,4 JATENG 3,6 2,4
BENGKULU 5 LAMPUNG 2,8 BANTEN 2,9
JABAR 2,7
Inf > 5,0%
DIY 2,3
JATIM 2,7
4,0% < Inf < 5,0%
SULBAR 2,2 SULSEL 2,9 BALI 5,5
SULUT 0,35
MALUT 1,9 PAPBAR 3,6
PAPUA 3,2
GORONTALO 1,3 MALUKU 3,3 NTT 2,5
SULTRA 2,7
NTB 2,6
3,0% < Inf < 4,0%
0% < Inf < 3,0%
Inf < 0%
Sumber: BPS (diolah)
Gambar 2. Peta Inflasi Daerah Desember 2016 (%, yoy)
III. PERKEMBANGAN MONETER, PERBANKAN, DAN SISTEM PEMBAYARAN 3.1. Moneter Transmisi kebijakan moneter melalui jalur suku bunga masih terus berjalan dengan kecepatan dan besaran yang bervariasi sepanjang triwulan IV 2016. Stance pelonggaran kebijakan moneter telah diikuti penurunan suku bunga PUAB, deposito, maupun kredit perbankan. Tekanan suku bunga PUAB di akhir tahun yang bersifat musiman juga cenderung lebih rendah dibanding periode yang sama tahun 2015 seiring dengan langkah antisipatif perbankan terhadap kebutuhan likuiditas di akhir tahun. Penurunan suku bunga masih terus berlanjut, baik pada suku bunga deposito maupun suku simpanan maupun kredit. Tren penurunan suku bunga deposito pada 2016 relatif lebih tinggi dibandingkan penurunan suku bunga deposito di 2015. Penurunan suku bunga kredit juga masih berlanjut pada semua jenis kredit disertai meningkatnya pertumbuhan kredit. Seiring dengan kenaikan pertumbuhan kredit tersebut, likuiditas perekonomian dalam arti luas (M2) juga tumbuh meningkat.
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Sepanjang triwulan IV 2016, kondisi likuiditas di pasar uang tetap terjaga meski sempat terjadi tekanan yang bersifat musiman pada akhir tahun. Suku bunga PUAB O/N pada triwulan IV 2016 mengalami penurunan menjadi 4,30% dari triwulan sebelumnya yang sebesar 4,76%. Penurunan BI 7-day RR Rate pada bulan Oktober 2016 turut mendorong penurunan suku bunga PUAB tenor pendek (Grafik 23). Penurunan suku bunga PUAB terjadi baik pada tenor O/N maupun tenor lebih panjang. Tekanan likuiditas sempat mengalami peningkatan seiring dengan meningkatnya kebutuhan akhir tahun. Hal ini tercermin dari rata-rata spread suku bunga max – min PUAB O/N yang sedikit meningkat menjadi 33 bps pada triwulan IV 2016 dari 32 bps pada triwuIan sebelumnya, turunnya volume rata-rata PUAB O/N menjadi Rp5,93 triliun dari triwulan sebelumnya sebesar Rp7,56 triliun dan Rp63,7triliun (Grafik 24). Namun demikian, tekanan likuiditas pada akhir tahun tersebut cenderung lebih rendah dibandingkan periode yang sama tahun 2015. Rata-rata spread suku bunga max-min PUAB O/N pada periode triwulan IV 2015 tercatat sebesar 33,36 bps.
%
% 10
8,3
rPUAB O/N DF Rate
7,8
7Days RR LF Rate
BI Rate
9 8
7,3
Rp Triliun Vol PUAB ON (rhs)
r PUAB ON
r DF
Vol DF (rhs)
BI Rate
7 days RR
200 180 160 140
7
6,8
120
6,3
6
58
5
5,3
4
4,8
3
40
4,3
2
20
3,8
1 8 1726 4 1312 2 112029 7 1625 4 132231 9 1827 6 1524 2 112029 7 1625 4 132231 9 1817 6 1524
Jan Feb
Mar
Apr
Mei
Jun Jul 2016
Ags
Sep
Okt Nov Des
Sumber: Bank Indonesia
Grafik 23. Perkembangan Suku Bunga PUAB O/N
100 80 60
Jan Feb Mar Apr Mei Jun Jul Ags Sep Okt Nov Des Jan 2016
2017
Sumber: Bank Indonesia
Grafik 24. Koridor Suku Bunga Operasional Moneter
Sementara itu, penurunan suku bunga deposito dan kredit juga masih berlanjut hingga akhir triwulan IV 2016. Suku bunga deposito tercatat turun sebesar 14 bps dari 6,9% pada triwulan III 2016 menjadi 6,7% pada triwulan IV 2016, sehingga untuk keseluruhan tahun 2016 suku bunga deposito telah turun sebesar 122 bps. Penurunan suku bunga deposito secara triwulanan terjadi pada semua tenor, dengan penurunan terbesar pada tenor panjang yakni 12 bulan dan 24 yang masing- masing turun sebesar 29 bps (qtq) dan 33 bps (qtq). Pada tenor yang lebih pendek (1, 3, dan 6 bulan), penurunan terkecil terjadi pada tenor 3 bulan yakni sebesar 15 bps (qtq). Suku bunga kredit juga turun yaitu sebesar 19 bps menjadi 12,04% pada
ANALISIS TRIWULAN: Perkembangan Moneter, Perbankan dan Sistem Pembayaran, Triwulan IV 2016
257
akhir triwulan IV 2016. Secara kumulatif, sepanjang tahun 2016 suku bunga kredit telah turun sebesar 79 bps atau lebih lambat dibandingkan penurunan suku bunga deposito. Penurunan suku bunga kredit secara triwulanan terjadi pada seluruh jenis kredit, dengan penurunan terbesar pada jenis kredit modal kerja (KMK) yang turun 25 bps (qtq), diikuti penurunan suku bunga Kredit Investasi (KI) sebesar 15 bps (qtq), dan penurunan suku bunga Kredit Konsumen (KK) sebesar 13 bps (qtq) (Grafik 25). Pada akhir triwulan IV 2016, spread antara suku bunga deposito dan suku bunga kredit turun 5 bps menjadi 532 bps (Grafik 26).
%
%
%
13,5
14,5 rKMK
14,0
rKI
rKK
RRT Sb Kredit 13,59
13,5
7,0 12,04
12,5
Spread Kredit -Depo (rhs) BI Rate RRT Sb Depo
11,5 10,5
13,0
9,5
12,5
8,5
12,0
12,04
11,5
12,04 11,21
7 Days LF Rate RRT Sb Kredit
5,0
Selisih rKredit - rDepo: 532bps
Feb Apr Jun Ags Okt Des Feb Apr Jun Ags Okt Des Feb Apr Jun Ags Okt Des Feb Apr Jun Ags Okt Des
2013
2014
2015
2016
Sumber: Bank Indonesia
Grafik 25. Suku Bunga Kredit: KMK, KI dan KK
4,0 3,0
7,5
6,72
6,5
11,0
6,0
2,0 1,0
5,5 4,5
0,0 Feb Apr Jun Ags Okt Des Feb Apr Jun Ags Okt Des Feb Apr Jun Ags Okt Des Feb Apr Jun Ags Okt Des
2013
2014
2015
2016
Sumber: Bank Indonesia
Grafik 26. Spread Suku Bunga Perbankan
Di sisi likuiditas, pertumbuhan likuiditas perekonomian M2 (uang beredar dalam arti luas) tumbuh meningkat. Pada akhir triwulan IV 2016, M2 tercatat tumbuh sebesar 10,0% (yoy), meningkat dari pertumbuhan pada triwulan sebelumnya yang sebesar 5,1% (yoy). Meningkatnya pertumbuhan M2 tersebut bersumber dari peningkatan M1, uang kuasi dan surat berharga selain saham (Grafik 27). Sementara itu, M1 pada triwulan IV 2016 tumbuh 17,3% (yoy), meningkat tinggi dari triwulan III 2016 yang sebesar 5,9% (yoy). Pertumbuhan M1 tersebut didorong oleh peningkatan giro terutama pada periode akhir tahun (Grafik 28). Sementara itu, berdasarkan faktor yang mempengaruhi, pertumbuhan M2 yang meningkat dipengaruhi oleh peningkatan pertumbuhan NDA dan NFA (Grafik 29).
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%, yoy
%, yoy
20
35 M1
30
Kartal
Giro
25
15
20 15
10
10 5
5
0 M1
Kuasi
-5
M2
0
-10 Jan Apr Jul Okt Jan Apr Jul Okt Jan Apr Jul Okt Jan Apr Jul Okt
Jan Apr Jul Okt Jan Apr Jul Okt Jan Apr Jul Okt Jan Apr Jul Okt 2013
2014
2015
2016
2013
Sumber: Bank Indonesia
2014
2015
2016
Sumber: Bank Indonesia
Grafik 27. Pertumbuhan M2 dan Komponennya
Grafik 28. Pertumbuhan M1 dan Komponennya
% 20 NFA
NDA
M2
15
10
5
0 Jan
Apr
Jul 2014
Oct
Jan
Apr
Jul 2015
Okt
Jan
Apr
Jul
Okt
2016
Sumber: Bank Indonesia
Grafik 29. Pertumbuhan M2 dan Faktor-faktor yang Mempengaruhinya
3.2. Industri Perbankan Ketahanan industri perbankan masih tetap kuat didukung oleh memadainya rasio kecukupan modal dan terkendalinya risiko kredit. Ketahanan permodalan industri perbankan masih berada pada level yang cukup kuat dan jauh diatas thresholdnya. Pada triwulan IV 2016 permodalan perbankan mengalami peningkatan, sebagaimana tercermin pada Capital Adequacy Ratio (CAR) yang tercatat sebesar 22,69%, lebih tinggi dibandingkan dengan 22,33% pada triwulan sebelumnya. Level kecukupan permodalan yang terus meningkat dibandingkan dengan tahuntahun sebelumnya diperkirakan masih mampu untuk menahan dampak negatif dari peningkatan
ANALISIS TRIWULAN: Perkembangan Moneter, Perbankan dan Sistem Pembayaran, Triwulan IV 2016
259
risiko kredit. Risiko kredit menunjukkan perbaikan pada akhir 2016, terindikasi dari rasio Non Performing Loan (NPL) gross yang turun dari 3,10% di triwulan sebelumnya menjadi 2,93% di triwulan IV 2016.
%
Listrik Pertambangan
40 35
Jasa Sosial
Total KMK KI KK
30 25
29,23
(6,61) (16,96)
1,27 3,44 15,59 11,32 11,18 14,58
Jasa Dunia Usaha Pertanian
24,18
Konstruksi Pengangkutan
20
19,66
(3,24) (3,62)
Industri
15
2,85
(0,08)
Perdagangan
5
(20)
0 Jun Sep Okt Mar Jun Sep Okt Mar Jun Sep Okt Mar Jun Sep Okt Mar Jun Sep 2012
2013
2014
2015
(10)
Des-16 Sep-16
8,27 7,49 6,40 7,67
Lain-lain
10
36,21
-
10
20
30
40
Sumber: Bank Indonesia
2016
Sumber: Bank Indonesia
Grafik 30. Pertumbuhan Kredit Menurut Penggunaan
Grafik 31. Pertumbuhan Kredit Sektoral
Sementara itu, pertumbuhan kredit terus membaik didukung oleh kredit produktif. Pertumbuhan kredit pada triwulan IV 2016 tercatat sebesar 7,9% (yoy), lebih tinggi dari pertumbuhan triwulan sebelumnya sebesar 6,5% (yoy). Pertumbuhan kredit tersebut bersumber dari peningkatan pertumbuhan kredit produktif yaitu kredit modal kerja (KMK) dan kredit Investasi (KI). Sementara itu, kredit konsumsi (KK) relatif masih stabil (Grafik 30). Untuk keseluruhan tahun 2016, kredit tumbuh 7,9% lebih rendah dari pertumbuhan tahun 2015 yang mencapai 10,5%. Secara sektoral, kredit pada triwulan IV 2016 di mayoritas sektor ekonomi mampu tumbuh positif seperti di sektor konstruksi dan industri seiring dengan kenaikan permintaan pada sektor-sektor tersebut (Grafik 31). Pertumbuhan Dana Pihak Ketiga (DPK) pada triwulan IV 2016 meningkat ditopang oleh deposito dan giro. DPK secara total tumbuh sebesar 9,6% (yoy), lebih tinggi dibandingkan dengan triwulan sebelumnya sebesar 3,1% (yoy) (Grafik 32). Berdasarkan jenisnya, pertumbuhan DPK pada triwulan IV 2016 terutama bersumber dari naiknya pertumbuhan deposito dan giro. Sedangkan, pertumbuhan tabungan masih cenderung stabil.
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25%
35% gDPK (skala kanan) gTabungan
30%
gGiro gDeposito
20%
25% 20%
9,6%
15%
15% 10%
10% 5%
5%
0% 0%
-5% Ags Nov Feb Mei Ags Nov Feb Mei Ags Nov Feb Mei Ags Nov Feb Mei Ags Nov
2012
2013
2014
2015
2016
Sumber: Bank Indonesia
Grafik 32. Pertumbuhan DPK
3.3. Pasar Saham dan Pasar Surat Berharga Negara Pasar saham domestik hingga akhir triwulan IV 2016 menunjukkan perkembangan positif sejalan dengan adanya sentimen positif yang berasal dari domestik maupun global. Indeks Harga Saham Gabungan (IHSG) pada akhir triwulan IV 2016 ditutup pada level 5.296,71. Secara kuartalan, meski posisi tersebut sedikit melemah dibandingkan akhir triwulan III 2016 yang sebesar 5.364,80 (-1,3%, qtq), namun secara keseluruhan tahun 2016 IHSG tersebut lebih tinggi 704 poin (15,32%, yoy) dibandingkan posisi akhir tahun 2015. Pelemahan indeks secara kuartalan terutama terjadi pada November 2016 akibat sentimen negatif sehubungan dengan ekspektasi kenaikan FFR, situasi politik AS menjelang pemilihan presiden, dan volatilitas harga minyak dunia. Selanjutnya, beberapa sentimen positif terkait kondisi makroekonomi Indonesia seperti inflasi yang terjaga, surplus neraca perdagangan, outlook Indonesia oleh Fitch, optimisme tax amnesty tahap kedua, dan kinerja emiten yang membaik dapat mendorong IHSG kembali meningkat di akhir Desember 2016. Kinerja saham domestik pada triwulan IV 2016 sejalan dengan pergerakan bursa saham global. IHSG secara kuartalan sedikit mengalami koreksi sebesar 1,3%, namun masih lebih baik dibanding beberapa negara kawasan seperti Filipina dan Hongkong yang mengalami koreksi masing-masing sebesar 10,3% dan 5,6%. Membaiknya kinerja perekonomian domestik mendorong terbentuknya sentimen positif sehingga mengakibatkan peningkatan kinerja IHSG pada Desember 2016 di tengah berbagai dinamika ketidakpastian global terutama terkait dengan kebijakan suku bunga The Fed dan volatilitas harga minyak. Perkembangan positif bursa saham domestik pada akhir triwulan IV 2016 terjadi di sebagian besar sektor ekonomi. Secara kuartalan, sektor properti dan sektor infrastruktur
ANALISIS TRIWULAN: Perkembangan Moneter, Perbankan dan Sistem Pembayaran, Triwulan IV 2016
261
masing-masing mengalami koreksi masing-masing sebesar -8,4% dan 6,7% (qtq). Di sisi lain, sektor pertambangan mengalami peningkatan sebesar 19,5% (qtq), terutama dipengaruhi oleh membaiknya harga komoditas sejak akhir triwulan III (Grafik 33). Kepemilikan saham oleh nonresiden mengalami penurunan pada triwulan IV 2016. Investor non residen tercatat melakukan net jual sebesar Rp18,29 triliun (qtq). Aksi jual investor non residen terutama berlangsung sejak awal triwulan IV 2016 dengan outflow tertinggi pada November 2016 yang mencapai Rp12,36 triliun akibat ketidakpastian global menjelang pemilihan presiden AS dan adanya ekspektasi kenaikan FFR. Dengan perkembangan tersebut, porsi investor non-residen di pasar saham pada Q4-2016 tercatat turun menjadi sebesar 30,9% (qtq) dari sebelumnya 36,2%.
Properti
-8,4%
Pertanian
4,0%
Perdagangan Konsumsi
1,0% -5,5%
Aneka Industri
0,1%
Industri Dasar
4,9%
Keuangan
1,0%
19,52%
Pertambangan Infrastruktur -6,75% IHDG -10%
-1,3%
-5%
0%
5%
10%
15%
20%
Sumber: Bank Indonesia
Grafik 1.33. Perkembangan Indeks Sektoral Triwulan IV 2016 (qtq)
Sejalan dengan kinerja pasar saham, kinerja pasar SBN juga menunjukkan peningkatan secara triwulanan. Pada akhir triwulan IV 2016, yield naik sebesar 89 bps (qtq) dari 6,98% menjadi 7,86% (Grafik 1.34). Adapun yield jangka pendek, menengah dan panjang naik masing-masing sebesar 85 bps (qtq), 92 bps (qtq) dan 87 bps (qtq) menjadi 7,41%, 7,93% dan 8,32%. Sementara itu, yield benchmark 10 tahun turun sebesar 91 bps (qtq) dari 7,06% menjadi 7,97%. Sejalan dengan penurunan yield, investor non residen tercatat melakukan net jual SBN pada triwulan IV 2016 yaitu sebesar Rp18,96 triliun (Grafik 1.35). Outflow terbesar tercatat terjadi pada November yang mencapai Rp19,57 triliun. Dengan demikian, kepemilikan investor non residen di pasar SBN pada November tercatat turun menjadi 36,65% dari sebelumnya 38,15% (qtq).
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%
%
bps 120
10
100
9
8
80
8
7,5
60
7
7
40
6
6,5
20
5
0
4
9
Perubahan Yield
Sep-16
Des-16
8,5
6 1
2
3
4
5
6
7
8
9
10
15
20
30
Rp T 45
Net Beli Jual Asing 10YR
35 25 15 5 -5 -15 -25
Feb Apr Jun Ags Okt Des Feb Apr Jun Ags Okt Des Feb Apr Jun Ags Okt Des Feb Apr Jun Ags Okt Des
2013
Tenor
2014
2015
2016
Sumber: Bank Indonesia
Sumber: Bank Indonesia
Grafik 34. Perubahan Yield SBN Triwulan IV 2016
Grafik 35. Yield SBN dan Net Jual/Beli Asing Triwulanan
3.4. Perkembangan Sistem Pembayaran Perkembangan pengelolaan uang rupiah secara umum sejalan dengan perkembangan ekonomi domestik, khususnya dari sektor konsumsi rumah tangga. Posisi Uang Kartal yang Diedarkan (UYD) pada akhir triwulan IV-2016 tercatat sebesar Rp612,5 triliun, meningkat sebesar Rp49,3 triliun atau 8,8% (qtq) dibandingkan posisi akhir triwulan sebelumnya yang mencapai Rp563,2 triliun. Meningkatnya posisi UYD tersebut seiring dengan peningkatan kebutuhan uang kartal perbankan/masyarakat menjelang periode Natal dan liburan akhir tahun 2016 (faktor musiman). Secara tahunan, posisi UYD pada periode laporan tumbuh 4,4% (yoy) dibandingkan dengan periode yang sama tahun sebelumnya yaitu sebesar Rp586,8 triliun (Grafik 36). Peningkatan UYD tersebut sejalan dengan perkembangan perekonomian nasional yang tetap tumbuh positif.
Properti
-8,4%
Pertanian
4,0%
Perdagangan Konsumsi
1,0% -5,5%
Aneka Industri
0,1%
Industri Dasar
4,9%
Keuangan
1,0%
19,52%
Pertambangan Infrastruktur -6,75% IHDG -10%
-1,3%
-5%
0%
5%
10%
Sumber: Bank Indonesia
Grafik 36. Perkembangan UYD
15%
20%
ANALISIS TRIWULAN: Perkembangan Moneter, Perbankan dan Sistem Pembayaran, Triwulan IV 2016
263
Sebagai wujud komitmen menyediakan uang yang layak edar di masyarakat, salah satu langkah yang dilakukan Bank Indonesia secara rutin adalah kegiatan pemusnahan uang. Uang yang dimusnahkan oleh Bank Indonesia merupakan uang yang tidak layak edar baik berupa uang lusuh, uang rusak maupun uang Rupiah yang masih layak edar yang dengan pertimbangan tertentu tidak lagi mempunyai manfaat ekonomis dan/atau kurang diminati oleh masyarakat serta uang yang telah dicabut/ditarik dari perdaran. Selama triwulan IV-2016, Bank Indonesia melakukan pemusnahan uang tidak layak edar sebanyak 1,7 miliar lembar uang kertas, atau turun masing-masing sebesar 10,4% dibandingkan dengan triwulan sebelumnya (1,9 miliar lembar uang kertas). Secara umum, sistem pembayaran yang diselenggarakan baik oleh Bank Indonesia maupun industri berjalan dengan aman, lancar, efisien dan handal. Nominal transaksi Sistem Pembayaran Non Tunai oleh Bank Indonesia (SPBI) pada triwulan IV-2016 mencapai Rp47.700,08 triliun atau meningkat 19,6% (qtq) dibanding periode sebelumnya yang tercatat sebesar Rp39.900,34 triliun (Tabel 5). Peningkatan nominal transaksi tersebut didorong oleh meningkatnya transaksi BI-SSSS sebesar 29,9% (qtq) dan transaksi Sistem BI-RTGS sebesar 15,3% (qtq). Sementara itu, volume transaksi SPBI mencapai 35.907,41 ribu transaksi pada Triwulan IV 2016, atau meningkat sebesar 12,9% (qtq) dibandingkan triwulan sebelumnya (Tabel 3). Sumber utama peningkatan volume transaksi tersebut adalah meningkatnya volume transaksi Sistem Kliring Nasional Bank Indonesia (SKNBI) dan Sistem BI-RTGS (BI - Real Time Gross Settlement) untuk transaksi masyarakat dan pemerintah seiring dengan peningkatan aktivitas perekonomian pada periode akhir tahun, termasuk peningkatan transaksi pembayaran terkait tax amnesty tahap II. Tabel 3. Perkembangan Volume Sistem Pembayaran Non Tunai Volume (Ribu Transaksi)
Transaksi Sistem Pembayaran Non Tunai BI-RTGS
2015 Q-I 2.814,82
Q-II 2.917,79
Q-III 2.939,05
Q-IV 2.371,24
Total 2015 11.042,90
2016 Q-I 1.436,25
Q-II 1.523,86
Naik/(turun)
Q-III 2.131,25
Q-IV 2.566,09
QtQ 434,85
% Naik/(turun)
YoY QtQ 194,85 20,40%
YoY 8,22%
17,95
17,55
18,81
23,21
77,52
26,93
28,19
27,40
32,88
5,48
9,67 20,01% 41,64%
- Pemerintah
141,47
136,21
129,09
135,75
542,51
77,45
50,29
23,56
19,65
(3,91)
(116,10) -16,60% -85,53%
- Masyarakat
- Pengelolaan Moneter
2.328,44
2.439,37
2.449,87
1.856,97
9.074,65
979,47
1.050,57
1.699,33
2.085,10
385,77
228,13 22,70% 12,29%
- Pasar Modal
28,62
25,63
28,74
37,61
120,60
48,47
62,09
63,93
76,32
12,39
38,71 19,38% 102,92%
- Valas
33,69
33,84
35,86
32,75
136,14
37,36
37,27
33,68
34,85
1,17
- PUAB
19,62
20,48
19,22
22,22
81,53
20,52
22,10
20,21
18,52
(1,69)
(3,70) -8,37% -16,65%
245,04
244,72
257,46
262,74
1.009,95
246,05
273,34
263,15
298,79
35,64
36,05 13,54% 13,72%
45,60
46,36
39,78
51,91
183,65
68,91
80,46
67,46
72,31
4,85
27.120,50 27.868,97 27.855,16 30.688,25 113.532,88 29.372,08 32.271,09 29.617,04 33.269,01
3.651,97
- Lain-lain BI-SSSS SKNBI Debet - Cek - Bilyet Giro - Warkat Debet Lainnya Kredit Total Sumber: Bank Indonesia
9.725,46
9.459,81
8.743,21
9.151,56 37.080,03
8.664,63
8.695,86
7.728,27
8.125,02
2,10
20,40
3,48%
6,40%
7,18% 39,29%
2.580,76 12,33%
8,41%
396,75 (1.026,54)
5,13% -11,22%
873,25
840,02
762,62
819,05
3.294,94
759,68
763,60
687,54
731,60
44,06
(87,45)
6,41% -10,68%
8.651,77
8.434,42
7.839,28
8.190,65
33.116,11
7.785,64
7.826,68
6.950,83
7.319,79
368,96
(870,86)
5,31% -10,63%
200,44
185,37
141,31
141,86
668,98
119,32
105,58
89,90
73,62
(16,28)
(68,23) -18,10% -48,10%
17.395,05 18.409,16 19.111,95 21.536,69 76.452,85 20.707,45 23.575,23 21.888,77 25.143,99
3.255,22
3.607,30 14,87% 16,75%
29.980,93 30.833,13 30.833,98 33.111,40 124.759,44 30.877,25 33.875,40 31.815,75 35.907,41
4.091,66
2.796,00 12,86%
8,44%
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Nominal transaksi yang diselesaikan melalui Sistem BI-RTGS pada triwulan IV 2016 meningkat 15,29% (qtq) dari Rp26.926,33 triliun menjadi Rp31.043,73 triliun. Kondisi ini selaras dengan peningkatan di sisi volume transaksi, yang naik sebesar 20,4% (qtq) dari 2.131,25 ribu menjadi 2.566,09 ribu transaksi. Secara tahunan nominal transaksi melalui Sistem BI-RTGS di triwulan IV 2016 meningkat 11,9% (yoy) dibandingkan periode yang sama tahun lalu. Adapun dari sisi volume transaksi, terjadi peningkatan sebesar 8,2% (yoy) dibandingkan triwulan IV 2015. Transaksi melalui SKNBI pada triwulan IV 2016 meningkat baik dari sisi volume maupun nominal. Nominal transaksi melalui SKNBI meningkat sebesar 7,9% (qtq), yaitu dari Rp891,98 triliun menjadi Rp962,39 triliun. Sementara volume transaksi meningkat sebesar 12,3% (qtq), yaitu dari 29,6 juta transaksi menjadi 33,3 juta transaksi. Adapun nominal transaksi kliring kredit pada periode laporan mengalami peningkatan sebesar 9,3% (qtq), yaitu menjadi sebesar Rp602,91 triliun dari periode sebelumnya sebesar Rp551,86 triliun. Secara tahunan nominal transaksi melalui SKNBI di triwulan IV-2016 turun 6,2% (yoy) dibandingkan periode yang sama tahun lalu, namun dari sisi volume transaksi meningkat 8,4% (yoy) (Tabel 4).
Tabel 4. Perkembangan Nilai Sistem Pembayaran Non Tunai Nominal (Triliun Rp)
Transaksi Sistem Pembayaran Non Tunai
2015 Q-I
Q-II
Q-III
Q-IV
Total 2015
2016 Q-I
Q-II
Naik/(turun) Q-III
Q-IV
QtQ
% Naik/(turun)
YoY
QtQ
YoY
28.879,17 28.089,25 28.022,31 27.736,72 112.727,44 26.739,53 27.117,76 26.926,33 31.043,73
4.117,40
3.307,01 15,29% 11,92%
- Pengelolaan Moneter 14.847,78 13.430,31 13.538,63 12.612,32 54.429,03 11.960,33 10.975,31 11.008,30 14.630,02
3.621,72
2.017,70 32,90% 16,00%
BI-RTGS - Pemerintah
816,57
898,44
947,06
- Masyarakat
4.960,51
5.595,25
- Pasar Modal
1.043,74
- Valas
1.736,69
- PUAB
1.453,99
- Lain-lain BI-SSSS
3.752,81
1.159,52
1.043,66
1.257,81
1.270,44
12,63
5.111,47
5.400,70 21.067,93
4.603,10
5.232,32
5.304,77
5.991,29
686,51
590,59 12,94% 10,94%
963,96
1.122,07
1.261,89
4.391,66
1.431,28
1.623,57
1.846,98
1.693,98
(153,00)
432,09 -8,28% 34,24%
1.851,02
2.047,11
1.648,06
7.282,89
1.856,29
2.098,90
1.902,99
1.840,63
(62,36)
192,57 -3,28% 11,68%
1.556,38
1.411,41
1.681,29
6.103,07
1.584,27
1.746,17
1.609,17
1.409,69
(199,48)
(271,60) -12,40% -16,15%
4.019,88
3.793,89
3.844,56
4.041,73 15.700,05
4.144,73
4.397,85
3.996,31
4.207,70
211,38
8.758,28
7.697,54
8.025,62 10.703,05 35.184,49 12.994,90 11.777,14 12.082,03 15.693,96
3.611,92
1.090,74
179,70
165,97
1,00% 16,48%
5,29%
4,11%
4.990,91 29,90% 46,63%
732,49
743,01
739,33
1.026,24
3.241,07
1.110,34
1.199,35
891,98
962,39
70,41
(63,85)
7,89% -6,22%
Debet
395,36
383,12
373,52
395,80
1.547,81
371,00
372,81
340,12
359,48
19,36
(36,32)
5,69% -9,18%
- Cek
53,31
50,78
50,35
56,20
210,64
51,50
50,77
46,35
54,82
8,46
341,91
332,09
323,04
339,51
1.336,55
319,41
321,94
293,68
304,57
10,89
(34,94)
0,14
4,00
0,14
0,09
4,38
0,09
0,10
0,09
0,09
0,00
0,00
337,13
359,89
365,80
630,44
1.693,26
739,35
826,54
551,86
602,91
51,05
(27,53)
38.369,94 36.529,79 36.787,26 39.466,01 151.153,00 40.844,77 40.094,25 39.900,34 47.700,08
7.799,74
SKNBI
- Bilyet Giro - Warkat Debet Lainnya Kredit Total
(1,38) 18,26% -2,46% 3,71% -10,29% 1,70%
1,08%
9,25% -4,37%
8.234,08 19,55% 20,86%
Sumber: Bank Indonesia
Penyelenggaraan sistem pembayaran oleh industri pada triwulan IV 2016 berjalan aman dan lancar. Seiring dengan peningkatan preferensi masyarakat untuk bertransaksi secara non tunai, pada triwulan IV 2016 transaksi ritel masyarakat menggunakan instrumen Alat Pembayaran dengan Menggunakan Kartu (APMK) dan Uang Elektronik tumbuh positif. Nominal
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transaksi APMK meningkat 6,1% (qtq) menjadi Rp1.559 triliun, sementara dari sisi volume juga meningkat 5,9% (qtq) menjadi 1.449.763,9 ribu transaksi. Sementara nominal transaksi uang elektronik meningkat 25,7% (qtq) menjadi Rp2,17 triliun dan secara volume transaksi meningkat 23,0% (qtq) dibandingkan triwulan sebelumnya yaitu menjadi 206.839,4 ribu transaksi.
IV. PROSPEK PEREKONOMIAN Bank Indonesia memperkirakan perekonomian pada tahun 2017 tumbuh lebih tinggi. Kinerja investasi diperkirakan meningkat, didukung oleh berlanjutnya pembangunan infrastruktur Pemerintah dan perbaikan investasi swasta. Ekspor juga diperkirakan meningkat seiring membaiknya harga komoditas yang menjadi produk utama ekspor Indonesia. Dari sisi konsumsi, meningkatnya penghasilan masyarakat yang dibarengi dengan terkendalinya inflasi mendukung tetap kuatnya permintaan domestik pada tahun 2017. Sementara itu, sektor-sektor ekonomi utama diprakirakan tumbuh meningkat dan tetap menjadi pendorong perekonomian. Secara keseluruhan, perekonomian Indonesia pada 2017 diprakirakan tumbuh tinggi dibandingkan pencapaian tahun 2016 yaitu berada pada kisaran 5,0-5,4%. Selain itu, sejalan dengan peningkatan aktivitas ekonomi dan dampak pelonggaran kebijakan moneter dan makroprudensial yang telah dilakukan sebelumnya, pertumbuhan kredit dan DPK pada tahun 2017 diperkirakan lebih baik, masing-masing dalam kisaran 10-12% dan 9-11%. Pada tahun 2017, inflasi diperkirakan tetap terkendali dan berada pada kisaran sasaran inflasi. Inflasi pada tahun 2017 diprakirakan mengalami peningkatan dibanding tahun sebelumnya sejalan dengan penyesuaian administered prices seperti tarif tenaga listrik dan harga BBM yang merupakan kebijakan lanjutan reformasi subsidi energi oleh Pemerintah. Sementara itu, inflasi kelompok volatile food diprakirakan tetap terkendali dan inflasi inti juga diperkirakan tetap terjaga. Dengan demikian, meskipun mengalami peningkatan, inflasi tahun 2017 diperkirakan tetap terkendali dan berada dalam kisaran sasaran inflasi 2017 sebesar 4±1%. Bank Indonesia terus mencermati beberapa risiko dalam perekonomian ke depan. Dari sisi global, risiko berasal dari tren kenaikan harga komoditas yang berpotensi mendorong kenaikan inflasi. Rencana ekspansi fiskal pemerintah AS yang dibarengi dengan pengetatan kebijakan moneter dapat mendorong penguatan mata uang AS dan penyesuaian suku bunga FFR yang lebih cepat. Sementara itu, rencana relaksasi regulasi sektor keuangan di AS dapat meningkatkan risiko stabilitas sistem keuangan global dan potensi kebijakan proteksionis perdagangan AS dapat menekan volume perdagangan dunia. Dari sisi domestik, sumber risiko berasal dari rencana penyesuaian harga BBM sejalan dengan kebijakan lanjutan reformasi subsidi energi oleh Pemerintah yang berpotensi kembali mendorong kenaikan inflasi.
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Halaman ini sengaja dikosongkan
The Role of Interest Rates and Provincial Monetary Aggregate in Maintaining Inflation in Indonesia
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THE ROLE OF INTEREST RATES AND PROVINCIAL MONETARY AGGREGATE IN MAINTAINING INFLATION IN INDONESIA Chandra Utama, Miryam B.L. Wijaya, and Charvin Lim1
Abstract
Monetary policy may employ interest rate or money supply to derive the assigned national inflation target. In this manner, most studies investigate monetary policy effectiveness using national data. However, based on the idea that inflation is a regional phenomenon, the application of provincial data is more appropriate in explaining the relationship between monetary instrument and inflation. This study elaborates the impact of changes in provincial money supply, BI Rate (interest rates of central bank), and PUAB (money market interest rates) to regional inflation in Hybrid New Keynesian Phillips Curve (HNKPC) framework. This study employs Generalized Method of Moments (GMM) techniques on panel data of 32 provinces from 2005-III to 2013-III. The data is classified into 4 groups, which are Java-Bali (W1), Sumatera (W2), Kalimantan-Sulawesi (W3), and Papua-Maluku-Nusa Tenggara (W4). The estimation result shows that each monetary instrument has diverging effectiveness in different regions. Provincial monetary aggregate is only effective in Sumatera, while BI Rate can manage inflation in Sumatera and Kalimantan-Sulawesi. PUAB, on the other hand, is significantly affecting inflation in almost all Indonesian regions, except KalimantanSulawesi. We conclude that interest rate (BI rate and PUAB) is a more appropriate instrument, compared to provincial monetary aggregate, to control provincial inflation in Indonesia.
Keywords: Monetary policy, regional inflation, hybrid NKPC JEL Classification: E31, E52, R19
1 Authors are researcher on Center for Economic Studies (CES) Parahyangan Catholic University. Chandra Utama (chandradst@unpar. ac.id); Miryam B. L. Wijaya (
[email protected]); Charvin Lim (
[email protected]). Acknowledgement: The authors are grateful to participants of CES discussion sessions, the 2015 IRSA International Seminar in Denpasar, and the 2015 BEMP International Conference in Jakarta. They also especially thank BEMP anonymous referees for their comments and suggestions which improved this paper. All remaining errors are ours. Financial support from Parahyangan Catholic University, grant # III/LPPM/2015-02/47-P is acknowledged.
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I. INTRODUCTION Inflation has become the main concern in monetary authorities since it influences purchasing power and business climate, which in turn will determine macroeconomic variables such as national output and unemployment. Bank Indonesia (BI) has been granted an ultimate goal of achieving and maintaining the stability of rupiah toward the prices of goods and services, which are reflected in inflation (internal stability), and the exchange rate (external stability). In order to achieve the goal, BI implemented a monetary policy framework with inflation as its main target (Inflation Targeting Framework (ITF)) and free floating as its exchange rate system. In ITF inflation target is publicly announced to the public and designed in a forward looking manner, meaning that changes in monetary policy stance is done through an evaluation whether the development of inflation is still in line with the inflation target. In the ITF, the inflation target, the monetary policy operational target, and the measurement of success are all engaged in national level indicator. However, investigation for national data is inadequate, since Indonesia has a considerably large territory with different economic structures and performances among its regions. The application of regional data in assessing monetary policy effectiveness and inflation behavior is thus imperative. This regional approach is important since, currently, the aggregate national inflation is dominated by only several regions in Indonesia. The inflation in Java is weighted for 64.5% of the national indicator. Moreover, Java and Sumatera (two out of many regions in Indonesia) represented 84.3% of the national inflation (Bank Indonesia, 2009). It suggests that non-Java territory, which has 28 provinces and is appreciably larger in term of area, only has 35.5% of national inflation weight. Further, all provinces outside Java and Sumatera are only weighted for 15.7% of the national inflation. If we explore further we can find that the weighting of Jakarta-Bogor-Depok-Tangerang-Bekasi (known as Jabodetabek), as the main business district in Indonesia, have covered 37.65% of national inflation. Hence, based on this condition, if the inflation target, monetary adjustment, and policy evaluation are based on national inflation, they will dominantly be determined only by the condition of Java-Sumatera or Java, or particularly, Jabodetabek. The monetary stance in the ITF is reflected in the determination of BI Rate, which is expected to influence interest rates in money market, banking deposit, and loan market. The implementation of interest rate policy, including BI Rate and money market interest rates or Pasar Uang Antar Bank (PUAB O/N)2, reflect an effort to achieve the targeted inflation using national inflation through a “nationally constructed” operational target. In consequence, considering the different weighting and idiosyncrasies of each region, questions arisen: Are the “nationally constructed” operational target (the interest rate instruments) effective for managing inflation in every region in Indonesia? Or are they only effective to manage inflation
2 This is the operational target of the interest rate. Depending on inflation expectation and several sets of variables, Bank Indonesia will set the policy rate (BI Rate) to influence the interbank money market rate or PUAB O/N, which will affect the deposit and the lending rate within the banking. We will refer the PUAB O/N as PUAB for the rest of this paper.
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in Java and Sumatera? Is it more appropriate to regulate the provincial money supply to directly influence inflation in each region? This research applies the framework of Hybrid New Keynesian Phillips Curve (NKPC) to learn whether BI Rate, PUAB, or money supply is the most effective instrument in managing regional inflation. We also identify the time lag of regional inflation responses toward these monetary instruments.
II. THEORY 2.1. Controlling Inflation: Price or Quantity Based? In the early stage of monetary policy development, money supply was widely accepted as an instrument to maintain price stability. The rationale which advocates the relationship between monetary aggregate and inflation is explained by the quantity theory of money (QTM). The theory asserts that if monetary authority decides to change the amount of money supply in the economy, in the long run it will change the price level in the same proportion. This preposition suggests the effectiveness of using monetary aggregate as the operational target of monetary policy. Support for this approach is overlaid by Fisher hypothesis which conjectured constant state of real interest rate. Asserting certain commensurate movement between expected inflation and nominal interest rate, the preposition implies no real economic effect would occur by changes in the nominal interest rate. Fisher Hypothesis claims that there is no apparent relationship between expected inflation and real interest rate3. Michell-Innes (2006) stated that important studies regarding the Fisher Hypothesis are performed by Mishkin (1995) which provide comprehensive explanation for the transmission mechanism of monetary policy using monetary aggregate. In the recent development, however, monetary authority in various countries have justified the adoption of short-term interest rate as their operational target rather than monetary aggregate. Policy models started to set aside the relationship between money supply and inflation, and focus more on the relationship between interest rate and inflation. This phenomenon was reviewed by MacCallum and Nelson (2010) who have shown that most publications which contributed to monetary handbook are minimizing the role of monetary aggregate in the theory and analysis of monetary policy. Many economists had performed studies in order to identify the relationship between interest rate and inflation, one of them was Brzoza-Brzezina (2002). Later publication by Woodford (2003), based on the idea from Wicksellian, discussed the process of real interest rate and natural rates of interest4 in influencing inflation.
3 See Lucas (1980), Fried and Howitt (1983). 4 The level of interest rate which would maintain stable price.
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According to Goto and Torous (2003), the aggressive policy of anti-inflation was pioneered by Taylor in 1995 who formulated the famously known Taylor Rule5. Taylor Rule explains that nominal short-term interest rate needs to move quicker than the expected inflation (move more than one-for-one) in order to maintain price stability. The monetary conduct, therefore, yield a positive relationship between inflation and real value of interest rate. This view becomes the cornerstone of ITF implementation (Handa, 2009). The Taylor Rule contradicts Fisher Hypothesis which claimed the non-existent of more than one-for-one rule between inflation and interest rate. While Taylor Rule stated that changes in interest rate will determine the changes of inflation, Fisher Effect stated that it is money supply which cause the simultaneous changes between interest rate and inflation. A view supporting the Fisher Hypothesis comes from Monnet and Weber (2001) who stated that, while the monetary authority is able to adjust the interest rate, it will only change the controllable instrument such as bank reserves. The changes in this instrument influences money supply and then money market reacts to it, reflected by the changes in the interest rate. The view of Monnet and Weber (2001) supports money supply as the key element which would determine the interest rate and inflation. Shresta et al. (2002) also contradict Taylor Rule by asserting a negative correlation between expected inflation and interest rate. Further, Handa (2009) also gives support to Fisher Hypothesis, arguing that in the long term, the relationship between money supply and interest rate is very high (with 0.7 or more as their correlation) indicating that changes in money supply will determine the changes in interest rate and in turn will influence inflation. Moving to empirical aspect, there has been mixed results regarding the validity of Fisher Hypothesis. A study on developing country undertaken by Garcia (1993) in Brazil found that Fisher Hypothesis is occurring in the country. Other studies were conducted by Phylaktis and Blake (1993) and Carneiro et al. (2002) for Brazil, Mexico, and Argentina. While Phylaktis and Blake (1993) found that Fisher Effect is occurring in those three countries, Carneiro et al. (2002) only found the effect to be valid in Argentina and Brazil. For the alternative instrument, the history of the United States monetary conduct provides evidence for the effectiveness of maintaining price stability through aggressive interest rate rule (Clarida et al., 2000). The pre-Volcker period (before 1979) with moderate monetary stance are found to be less stabilizing compared to Volcker-Greenspan period, which put forward an aggressive monetary conduct. In the earlier era, Federal Reserve typically alter nominal interest rate in a lesser extent than the increase in expected inflation, leading to a fall in the real interest rate. Contrarily, Volcker-Greenspan systematically raise real interest rate in dealing with increasing inflation expectation. The latter approach is found to provide an era of greater price stability.
5 See Taylor (1995).
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2.2. Regional Inflation Variation and The Impact Of Monetary Policy on Regional Inflation Monetary policy is designed structurally and purposely for an ultimate national goal – price stability. Yet, the impact of monetary policy may differ among regions in a country. Differences may occur because of varying regional conditions, such as industrial competitiveness, financial structure, trading activities, and institutional environment. However, studies regarding the effectiveness of monetary policy are still commonly performed using national-level data. In this section, we review studies which exploits regional aspects in assessing monetary policy effectiveness. Studies regarding the effectiveness of ITF in provincial level had been undertaken in China. Mehrotra et al. (2007) found that in 1978-2004 there is a variation of inflation among China provinces. They also found that there are 22 out of 29 provinces where forward looking inflation component is statistically significant in determining the actual inflation. In ITF, the significance of forward looking inflation component will increase the effectiveness of monetary policy. Similar to China, Indonesia is a country with broad geographical territory and diverse social and economic condition. Ridhwan et al. (2011) shows that, while tight monetary policy might be conducive for the economy of Java, it may have a destructive effect on the economy of nonJava regions. This result indicates that one monetary conduct might bring about constructive effect on a region and a neutral or, even, destructive effect on the other. Another research by Chaban and Voss (2012) found that there is inflation variation in 10 provinces in Canada. All of the provinces, aside from Alberta, indicated the existence of anchored inflation expectation which supported the effectiveness of ITF. They assert that the success measure of ITF can be exhibited by its capability to certainly determine the expected inflation to be strictly around the inflation target and that the deviation cannot be predicted. In provincial-level, we can question the deviation of provincial inflation to its national target. If there is a considerable gap between provincial and national deviation, the nationally constructed inflation target is not equally successful in each region.
2.3. Hybrid NKPC model In this research, we estimate inflation using a theoretical framework developed by Gali and Gertler (1999) so called Hybrid NKPC model. According to Gali and Gertler, every firm adjust their prices in every periods with the fixed probability of . There are two types of firm, are firms with forward-looking behavior as Calvo (1983) stated in his study while the rest, , are firms with backward-looking behavior. Based on this condition, aggregate price can be constructed as equation (1). If the price set by forward looking firm is and backward looking firm is , hence the new price is:
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(1) Forward looking firms behave as Calvo’s (1983) assertion, therefore
can be derived as (2)
While denoted as
, equal to the average of adjusted price in the last period. The price can be
(3) Hence, the specification form of hybrid NKPC is (4) In empirical testing, equation (4) is estimated using non-linier instrumental variable (GMM) estimator. In their study, Gali and Gertler (1999) provided several strong findings regarding inflation behavior. First, the real marginal cost is statistically significant and is an important determinant of inflation (in this study we used output gap). Second, the behavior of forward looking is crucial because most of the firms have this kind of behavior. T; they found that 60-80% of the firms are forward looking behavior. Third, the behavior of backward looking is statistically significant. Therefore, even though forward looking behavior is plausible, pure forward looking model cannot be accepted. Last but not the least, it takes time for prices to change (sticky price).
III. METHODOLOGY In this research, we use quarterly panel data of 32 provinces in Indonesia covering the period of 2005-III to 2013-III. Most of the data are obtained in quarterly report of Perkembangan Perekonomian Daerah (Pekda) and Kajian Ekonomi Regional (KER) which are published by Kantor Bank Indonesia (K.BI) in provinces. Table 1 explains the definition of variable which are observed in this study.
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Table 1. Proxy and Measurement of Variables Variable Inflation
Sign πi,t
Measure
Explanation
Percentage
Year-on-year inflation
Actual output
yita
Trillion rupiah
Real regional output (base: year 2000)
Potential output
yit
Trillion rupiah
Estimated using Hodric-Prescott Filter
Output gap
yit
Percentage
Changes in real
*
mit
Trillion rupiah
yit =
( yita − yit* ) yit*
× 100%
mit is the changes in real currency money ( ∆Kit ) added by real demand depositn ( ∆Git ). ∆Kit is the net flow of currency in regional office central
money supply
bank. ∆Git is the changes in demand deposit in each province’s banking. BI Rate
r1t
Percentage
Central bank interest rate; national data
PUAB
r2t
Percentage
Money market interest rate; national data
We use year-on-year inflation to compare the development of inflation among provinces based on inflation in the previous year. Inflation is calculated using provincial consumer price index (CPI). Even though the original data of CPI is the general price in city, Pekda and KER have provided provincial CPI data. For Papua and Papua Barat, including Banten and Jakarta, the CPI data or inflation is compounded in a particular proportion based on Pekda and KER. For economic output indicator, we use real RGDP (Regional Gross Domestic Product) with year 2000 as its base year. Hodrick-Prescott Filter (HP Filter) is applied in order to obtain potential output, , based on the real PDRB data. The output gap, ,is the reduction of actual output to potential output, divided by potential output, multiplied by 100%. Because of the unavailability of provincial money supply data, we use the net flow of currency in provincial central bank branch offices (Kantor Bank Indonesia) as proxy of currency changes. If the outflow is greater than inflow (net outflow), there is an increase of currency supply in the region, vice versa. We also use demand deposit changes in commercial bank (conventional and Islamic banking) as the proxy of provincial demand deposit changes. The sum of currency changes and demand deposit changes is utilized as the changes of money supply in each province. For interest rate instruments, we use BI Rate, r1t, and PUAB, r2t. These variables are commonly used as the national monetary policy references. In contrast to mit, which is a provincial level data, r1t and r2t is a national-level data therefore the value for these variables are identical for each period in every provinces.6
6 Depending on the covariance structure of the equation block representing the panel data, using SUR is plausible when the set of exogenous variables are uniform across equations. However, in this paper we simply use one equation while the possible correlation across provinces may be adjusted during the estimation (i.e. white cross section). We thank to the anonymous reviewer of this journal for a good discussion and suggestion on this issue.
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To analyze the impact of policy variable toward provincial inflation we employ and modify Hybrid NKC framework developed by Gali and Gertler (1999): (5) We add policy variable,
,on the basic form of Hybrid NKPC, hence equation (5) become: (6)
where may contain , r1t, or r2t. Adapting Gali and Gertler (1999), we applied Generalized Method of Moments (GMM) as the estimation technique and employ πi,t-2 and πi,t-4 as the instrument variable. Estimating equation (6), we can identify the backward looking and forward looking behavior impacts by referring to and . We can also analyze the influence of output gap, , on inflation. In order to assess the impact of policy variable on inflation, we employ to . Equation (6) is also used for the estimation of policies impact with, taking into account, regional aspect consideration. Since it is most likely that monetary policies need time lag in order to take effect on the economy, we try to determine the best time lag by repeating the estimation of equation (6) using different time lags. The criteria used in determining the best time lag are the conformity of the impact direction with the theory and policy objective, which are positive , and the swiftness of effect.7 Based on equation (6) we for r1t and r2t, and negative for construct an inflation model which takes into account the influence-differences among regions
(7)
where d2, d3, and d4, are dummy variables for Sumatera (W2), Kalimantan-Sulawesi (W3), and Papua-Maluku-Nusa Tenggara (W4). Furthermore, d1, which represents Jawa-Bali region (W1), is not included in the model because of its role as the comparator and control dummy. Table 2 explains the classification of regions and its provinces.
7 The choice of exogenous policy lag impact is different from the autoregression process where the lag of the variable in question is the endogenous one; we use AIC or Scwartz on the letter.
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Table 2. Classification of Regions and Its Provinces8 Provinces Region 1 (W1)
Region 2 (W2)
Region 3 (W3)
Region 4(W4)
Banten and Jakarta
Jawa Tengah
Bali
Jawa Barat
Yogyakarta
JawaTimur
Aceh
Sumatera Barat
Sumatera Selatan
Sumatera Utara
Riau
Lampung
Kepulauan Bangka Belitung
Jambi
Kepulauan Riau
Bengkulu
Kalimantan Selatan
Kalimantan Tengah
Sulawesi Tenggara
Kalimantan Timur
Sulawesi Utara
Sulawesi Tengah
Kalimantan Barat
Sulawesi Selatan
Gorontalo
Nusa Tenggara Barat
Maluku
Nusa Tenggara Timur
Maluku Utara
Papua and Papua Barat
Based on equation (7), the impact of monetary instruments on inflation in Region 1, 2, 3, and 4 are consecutively ,( + ), ( + ), and ( + ). In order to identify the significance of each policy impact coefficient, we run the Wald test. Using equation (7) we can identify the coefficient differences between Jawa-Bali region (W1) and the other regions by learning coefficient , , and . However, we cannot identify the coefficient differences among regions except of using W1 as the comparator. For that purpose, we use Wald test in comparing the coefficient difference of each regions.
IV. RESULT AND ANALYSIS 4.1. Provincial Inflation Model Table 3 displays the estimation result of three equations using Hybrid NKPC framework. Each equation has different policy variable, which are money supply, policy rate (BI Rate), and interbank money rate (PUAB). The results indicate the existence of backward looking and forward looking behavior in the determination of Indonesian inflation. Forward looking tends to be more dominant, suggested by its significance and coefficient level. In concordance with the theoretical prediction, output gap has also a significant role in the construction of inflation.
8 Jakarta and Banten is reported in the same account, and so, in this research, they are combined for all period. So do Papua and Papua Barat. Moreover, although Indonesia has 34 provinces in 2013, Kalimantan Utara and Sulawesi Barat are not included as observations because the data for those provinces are not available in all observation period (Sulawesi Barat was just established in 5th October 2004, while Kalimantan Utara was established in 25th October 2012). Table 2 shows the provinces included as observations in this research.
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Table 3. Estimation Result of Provincial Inflation Model π it Coef.
π it Prob.
Coef.
π it Prob.
Coef.
Prob.
π i,t−1
0.4429
0.0000
0.0887
0.0000
0.3252
0.0000
π i,t+1
0.6330
0.0000
0.2992
0.0000
0.6579
0.0000
yit
0.2020
0.0000
0.0364
0.0011
0.1063
0.0000
-1.4E-07
0.0001 1.5515
0.0000 0.4736
0.0000
Sit = mit Sit = r1it Sit = r2it
N
900
900
900
30
30
30.0000
J-stat.
29.96381
29.85828
29.6851
prob.
0.269113
0.273524
0.2809
Instrument rank
Note: We never use the money supply, policy rate, and the interbank money rate simultaneously. Within this limitation, we compare the effect of these three variables on inflation.
On the role of money supply, BI Rate, and PUAB in determining the level of inflation, Table 3 shows significant contradictive directions. The result suggests that an increase in money supply would lower inflation, while an increase in interest rate raises inflation. We suspect this peculiar result is in accordance with Friedman’s proposition on short-run monetary ineffectiveness. According to Friedman, monetary instruments need time in order to effectively affect the objective variable. Friedman argued that the long period of lag required by the economy to respond to monetary policy produce peculiar short-term relationship between monetary instrument and inflation. Batini and Nelson (2001) further confirm Friedman’s proposition of monetary policy ineffectiveness in the short-run using data from 1953 to 2001 in the United States and United Kingdom. Since the application of policy variables without time lag yield a conflictive result and that monetary policy need time to affect the economy, we try to re-estimate the model by adding time lag for the policy variables until we found each policy variable to have significant and corresponding effect to the policy objective. The lag would then demonstrate the nearest time in which monetary policy would produce the intended effect on inflation. The result is estimated and presented in Table 4. In concordance with the earlier result, the estimations show that backward looking and forward looking behavior have positive significant effects on inflation, with forward looking having greater influence. In accordance with the theory, output gap is also found to have a positive effect on inflation. By adding time lag worth one quarter, the estimation results of
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monetary policies influence have changed. Money supply is shown to have insignificant effect on inflation, while BI Rate a contradictive effect. However, the estimation result on PUAB has shown the expected result. PUAB has a negative significant effect on the future inflation, with a time lag of one quarter. It suggests that, different with the other two policies, PUAB will become an effective policy in three months after its implementation. The value of J-statistic probability professes that there is no over-identification in the model.
Table 4. Estimation Result of Provincial Inflation Model with1 Period Time Lag π it Coef.
π it Prob.
Coef.
π it Prob.
Coef.
Prob.
π i,t−1
0.444342
0.0000
0.202718
0.0000
0.468151
0.0000
π i,t+1
0.636456
0.0000
0.615281
0.0000
0.618738
0.0000
yit
0.158972
0.0000
0.165596
0.0000
0.155991
0.0000
-4.9E-09
0.7151 0.683177
0.0000 -0.07558
0.0000
Sit = mi,t-1 Sit = r1i,t-1 Sit = r2i,t-1
900
900
900
30
30
30
J-stat.
29.8360
29.7515
29.8454
prob.
0.2745
0.2780
0.2741
N Instrument rank
Table 5. Estimation Result of Provincial Inflation using 2 and 3 Periods of Time Lag π it
Coef.
π it
Prob.
Coef.
π it
Prob.
Coef.
π it
Prob.
Coef.
Prob.
πi,t−1
0.5094
0.0000
0.5392
0.0000
0.4903
0.0000
0.5669
0.0000
πi,t+1
0.4993
0.0000
0.5385
0.0000
0.5017
0.0000
0.5163
0.0000
yit
-0.0582
0.0000
-0.0308
0.0369
-0.0036
0.6988
-0.0322
0.0063
-2E-07
0.0000 2.1E-7
0.0000 0.0616
0.0000 0.12075
0.0000
Sit = mi,t-2 Sit = mi,t-3 Sit = r1i,t-2 Sit = r1i,t-3
870
840
870
30
30
30
J-stat.
29.5054
29.6032
29.9087
29.4934
prob.
0.2886
0.2844
0.2714
0.2891
N Instrument rank
840
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We applied two quarter and three quarter periods of time lag in order to find the best time lag for BI Rate and money supply. All of the estimation results indicate that inflation level in Indonesia is significantly affected by backward looking and forward looking behavior. In contrast with the earlier results, these estimation suggest that backward looking behavior has a greater impact on inflation, meaning that the economy take into account past inflation more considerably than expected future inflation in constructing their expected inflation. In Table 5, we show that money supply and BI Rate need 3 quarters to effectively influence the inflation.
4.2. Inflation Model with Regional Consideration In order to identify which monetary policy is better in maintaining regional inflation, we estimate each monetary policy’s impact on each region. We started by assessing monetary aggregate, followed by BI Rate and PUAB sequentially. In order to identify differences in each region’s response, we use dummy variable on monetary policy. Wald test is executed in order to evaluate the significance of monetary policy in each region and to diagnose if the policy effect is different. Table 6 shows the estimation result of Hybrid NKPC model with regional consideration and monetary aggregate as the policy variable. W1 is used as the base comparator in this equation. The estimation result suggest that forward looking and backward looking behavior have significant roles in determining future inflation, with forward looking being a slightly greater determinant. Output gap is found to have no significant effect in determining inflation.
Table 6. Estimation Result with Monetary Aggregate and Regional Response πit Coef.
Prob.
πi,t-1
0.5200
0.0000
πi,t+1
0.5515
0.0000
yit
0.0142
0.5505
mi,t-3
9.02E-08
0.1444
mi,t-3d2i,t
1.43E-06
0.0000
mi,t-3d3i,t
-3.8E-07
0.1934
mi,t-3d4i,t
-1.1E-06
0.0029
N Instrument rank J-statistic Prob(J-statistic)
840 30 29.32421 0.1698
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Table 7 displays the effect of monetary aggregate on each region’s inflation. The Wald Test result shows that money supply is only significantly affecting the inflation of W2 (Sumatera) and W4 (Papua-Maluku-Nusa Tenggara) regions. While it has a positive effect on W2’s inflation, it turns out to have negative effect on W4’s. It suggests that employing monetary aggregate as the monetary instrument will only be effective for managing inflation in W2 region.
Table 7. Wald Test: Effect of Monetary Aggregate on Each Region's Inflation Jawa-Bali Coef.
Sumatera
Kalimantan Sulawesi
Papua - Maluku Nusa - Tenggara
9.02E-08
1.52E-06
-2.87E-07
-1.00E-06
F - statistic
2.1345
76.9455
1.0900
8.4129
Prob.
0.1444
0.0000
0.2968
0.0038
Table 8. Wald Test: Monetary Aggregate’s Impact Differences Between Regions Jawa-Bali Jawa-Bali Sumatra Kal-Sul Pap-Mal_Nusa
Sumatera
Kalimantan Sulawesi
Papua - Maluku Nusa Tenggara
F-Stat
75.3142
1.6946
8.9352
(Prob.)
(0.0000)
(0.1934)
(0.0029)
75.3142
30.1851
40.8928
(0.0000)
(0.0000)
(0.0000)
1.6946
30.1851
4.3247
(0.1934)
(0.0000)
(0.0379)
8.9352
40.8928
4.3247
(0.0029)
(0.0000)
(0.0379)
Displayed by the estimation result in table 8, we found that money supply has no different impact on inflation in W1 and W3. The policy impact is especially unique in W2 and W4, where it is shown that there is no indifferent effect on other regions.
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Table 9. Estimation Result with BI Rate and Regional Response πit Coef.
Prob.
πi,t-1
0.5740
0.0000
πi,t+1
0.5094
0.0000
yit
-0.0463
0.0112
1.3058
0.1258
r1i,t-3d2i,t
-1.6213
0.0396
r1i,t-3d3i,t
-2.1693
0.0722
r1i,t-3d4i,t
-1.4775
0.1761
r1i,t-3
N
840
Instrument rank
30
J-statistic
28.9268
Prob. (J-statistic)
0.1827
Moving to BI Rate policy, table 9 displays the estimation result of Hybrid NKPC model with BI Rate as the shock variable. Different from the previous estimation, while backward looking and forward looking behavior still significantly affecting inflation, it is found that backward looking behavior has a slightly greater role in determining inflation. Contradicting the theory, we also found that an increase in output gap will lower inflation level. Depicted in table 10, we found that BI Rate as a monetary instrument is only significantly affecting W2 region (Sumatera). Furthermore, Table 11 suggests that BI Rate has indifferent effect on W1 and W4. It is also found that W2 has the same responses as W3 and W4 on BI Rate changes. The impact of BI Rate on W3’s inflation is also indifference to W4’s. While it indicates that BI Rate may be a better and fairer policy in managing regional inflation, the result in table 11 suggest that BI Rate is only effective to be implemented in Sumatera regions.
Table 10. WaldTest: Effect of BI Rate on Each Region's Inflation Jawa-Bali
Sumatera
KalimantanSulawesi
Papua-MalukuNusa Tenggara
(W1)
(W2)
(W3)
(W4)
Coef.
1.3058
-0.3155
-0.8635
-0.1717
F-statistic
2.3485
3.8237
4.4681
0.1852
Prob.
0.1258
0.0509
0.0348
0.6671
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Table 11. Wald Test: Diagnosis of BI Rate’s Impact Differences between Regions
Jawa-Bali
Jawa-Bali
Sumatera
KalimantanSulawesi
Papua-MalukuNusa Tenggara
(W1)
(W2)
(W3)
(W4)
F Stat.
4.2467
3.2403
1.8336
Prob.
0.0396
0.0722
0.1761
1.1297
0.0741
0.2882
0.7855
Sumatra
4.2467 0.0396
Kal-Sul
Pap-Mal_Nusa
3.2403
1.1297
2.0217
0.0722
0.2882
0.1554
1.8336
0.0741
2.0217
0.1761
0.7855
0.1554
The other alternative for monetary policy operational target is PUAB. Table 12 shows the estimation result of Hybrid NKPC model with PUAB as the shock variable. In this estimation result, we found that backward looking and forward looking behavior are significant in determining the level of inflation. We found forward looking behavior to be a greater determinant of inflation. It indicates that the expected future inflation has a bigger role in determining inflation than the inflation track records. In this equation result, we also found that the output gap impact on inflation is in line with theory.
Table 12. Inflation Model with PUAB and Regional Response πit Coef.
Prob.
πi,t-1
0.4697
0.0000
πi,t+1
0.6145
0.0000
yit
0.1463
0.0000
r2i,t-1
-0.8723
0.0110
r2i,t-1d2i,t
0.6892
0.0555
r2i,t-1d3i,t
1.6945
0.0025
r2i,t-1d4i,t
0.2270
0.3969
N Instrument rank J-statistic Prob(J-statistic)
900 30 29.3568 0.1688
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Table 13 presents the impact of PUAB on each observed region’s inflation. We found that PUAB stands as the best monetary instruments compared to the two others in this study. PUAB has significant and in-line-with-objective effect on W1 (Jawa-Bali), W2 (Kalimantan-Sulawesi) and W4 (Papua-Maluku-Nusa Tenggara) regions. Still to be put in our concern, PUAB has a positive effect on W3 region (Kalimantan-Sulawesi) which contradict the policy objective.
Table 13. Wald Test: Effect of PUAB on Each Region's Inflation
Coef.
Jawa-Bali
Sumatera
KalimantanSulawesi
Papua-MalukuNusa Tenggara
(W1)
(W2)
(W3)
(W4)
-0.8723
-0.1830
0.8222
-0.6453
F-statistic
6.4851
11.4296
8.3421
4.7564
Prob.
0.0110
0.0008
0.0040
0.0295
Diagnosing the impact differences of PUAB between each region, we found that W1, W2, and W3 are having indifferent response toward the changes of PUAB (see table 14). On the other hand, we found that the impact of PUAB on inflation in W4 is only indifferent with its impact on W3. Considering the result, we assert that PUAB is a fair monetary instrument in controlling inflation level across regions in Indonesia.
Table 14. Wald Test: Diagnosis of PUAB’S Impact Differences between Regions Jawa Bali Jawa-Bali
Sumatra
Kal-Sul
Pap-Mal_Nusa
Sumatra
Kal-Sul
Pap-Mal-Nusa
F-Stat
3.6769
9.2163
0.7184
Prob.
0.0555
0.0025
0.3969
3.6769
12.8614
1.9798
0.0555
0.0004
0.1598
9.2163
12.8614
7.0423
0.0025
0.0004
0.7184
1.9798
7.0423
0.3969
0.1598
0.0081
0.0081
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V. CONCLUSION Designing an effective and reliable monetary policy is necessary to ensure national price stability. To achieve the desired level of inflation, monetary authority can employ quantity-based approach, which make use of monetary aggregate as its instrument, or price-based approach, which exploit interest rate. The utilization of interest rate as the monetary operational target may have its own advantages in directing national inflation, but then again interest rate is a one-for-all ‘nationally-designed’ instrument. Different from interest rate, money supply can be regulated in regional level, accommodating each region’s economic needs. Since inflation is a regional phenomenon, a detailed assessment of each region’s idiosyncrasy is a necessity. If authorities are concerned about the inflationary gap among regions, they need policy instrument(s) which is or are capable of managing regional inflation evenly. Through this study, we found that each region in Indonesia have different responses on monetary policy instruments. Using Hybrid NKPC model, we found that backward looking and forward looking behavior have significant roles in determining inflation in Indonesia. Further, our finding supports Gali and Gertler (1999) in asserting forward looking behavior to have greater influence than backward looking. It suggests the importance and effectiveness of inflation targeting framework in directing societies’ expectation regarding future inflation. Conforming macroeconomics theory, output gap is also found to have a positive impact on inflation. Through regional analysis, we found that both monetary instruments (money supply and interest rate) are incapable of influencing inflation in the short run. Monetary policy needs time to effectively influence regional inflation. Our analysis suggests that PUAB instrument needs the least time lag to yield desirable influence on inflation. PUAB needs 1 quarter to effectively influence inflation while money supply and BI Rate needs 3 quarters. Furthermore, the estimation of PUAB alone delivers consistent backward and forward looking and output gap influence to regional inflation. Taking into account regional aspect in the model, we found that money supply has a significant impact on inflation in Sumatera (W2) and Papua-Maluku-Nusa Tenggara (W4). This instrument, however, is only effective in Sumatera region, since the estimation result suggests that it would have conflicting effect in Papua-Maluku-Nusa Tenggara. Moving to the second instrument, we found that BI Rate is an effective instrument in controlling the inflation in Sumatera (W2) and Kalimantan-Sulawesi (W3). In addition, through Wald test we found indifferent impact of BI Rate to inflation in these regions. Lastly, we found that PUAB is the most favourable instrument in controlling inflation in Indonesia. Based on the estimation result, we found that PUAB is potent to manage inflation in most of the observed regions, including Java-Bali (W1), Sumatera (W2), and Papua-MalukuNusa Tenggara (W4). In those regions, the influence of PUAB to inflation is in concordance with the policy objective. Figure 1 shows the favorable instrument(s) in controlling regional inflation in Indonesia.
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W2
W3 W4 Rp
W1
Rp
Provincial Money Supply
BI Rate
PUAB
Figure 1 Most Favorable Instrument in Controlling Regional Inflation in Indonesia
Each region has their own favorable monetary instrument in controlling inflation. However, in Java-Bali (W1) and Papua-Maluku-Nusa Tenggara (W4), inflation can only be managed using PUAB, while Kalimantan-Sulawesi’s (W3) inflation can only be controlled using BI Rate. Combining BI Rate and PUAB, monetary authority can effectively control inflation in every region in Indonesia. Hence, we conclude that the implementation of interest rate as the monetary instrument in Indonesia is more appropriate than money supply.
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REFERENCES Bank Indonesia, (2009), Kajian Ekonomi Regional Provinsi DKI Jakarta Triwulan IV 2009, Bank Indonesia, Jakarta. Batini, N., and Nelson, E. “The Lag from Monetary Policy Actions to Inflation: Friedman Revisited”, International Finance, 2001, 4(3), pp. 381-400. Brzoza-Brzezina, Michal. “The Relationship Between Real Interest Rates and Inflation”. National Bank of Poland Working Papers No. 23, 2002. Calvo, Guillermo, A. “Staggered Prices in Utility Maximizing Framework”, Journal of Monetary Economics, 1983, 12(3), pp. 383-398. Carneiro, F. G., Divino, J., and Rocha, C. H. “Revisiting the Fisher Hypothesis for Cases Argentina, Brazil and Mexico”, Applied Economics Letters, 2002, 9, 95-98. Chaban, M., and Voss, G. M. “National and Provincial Inflation in Canada: Experiences Under Inflation Targeting”. Department of Economics University of Victoria Discussion Paper No. DDP1201, 2012. Clarida, R., Gali, J., and Gertler, M. “Monetary Policy Rules and Macroeconomic Stability: Evidence and Some Theory”, Quarterly Journal of Economics, 2000, 115(1), pp. 147-180. Fried, J, and Howitt, P. “The Effects of Inflation on Real Interest Rates”, The American Economic Review, 1983, 73(5), pp. 968-980. Gali, J., and Gertler, M. “Inflation Dynamics: A Structural Econometric Analysis”, Journal of Monetary Economics, 1999, 44, pp. 195-222. Garcia, M. G. “The Fisher Effect in a Signal Extraction Framework: The Recent Brazilian Experience”, Journal of Development Economics, 1993, 41, pp. 71-93. Goto, S., and Torous, W. “Evolving Inflation Dynamics, Monetary Policy, and the Fisher Hypothesis”. AFA 2014 San Diego Meetings, 2014. Handa, J., (2009), Monetary Economics, Routledge, London and New York. Lucas, R. E. “Two Illustrations of the Quantity Theory of Money”, The American Economic Review, 1980, 70(5), pp. 1005-1014. MacCallum, B. T., and Nelson, E. “Money and Inflation: Some Critical Issues”. Finance and Economics Discussion Series No. 2010-57, 2010. Mehrotra, A., Peltonen, T., and Rivera, A. S. “Modeling Inflation in China: A Regional Perspective”. European Central Bank Working Paper Series No. 829/November 2007, 2007.
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Mishkin, F. S. “Symposium on the Monetary Transmission Mechanism”, Journal of Economic Perspectives, 1995, 9(4), pp. 3-10. Michell-Innes, H. A. “The Relationship Between Interest Rates and Inflation in South Africa: Revisiting Fisher’s Hyphotesis”. Master Thesis, Rhodes University, 2006. Monnet, C., and Weber, W. E. “Money and Interest Rates”, Federal Reserve Bank of Minneapolis Quarterly Review, 2001, 25(4), pp. 2–13. Phylaktis, K., and Blake, D. “The Fisher Hypothesis: Evidence From Three High Inflation Economies”, Weltwirtschaftliches Archiv, 1993, 129(3), pp. 591–599. Ridhwan, M. M., De Groot, H. L., F., Rietveld, P., and Nijkamp, P. “The Regional Impact of Monetary Policy in Indonesia”. Tinbergen Institute Discussion Paper No. TI2011-081/3, 2011. Shrestha, K., Chen, S., and Lee, C. “Are Expected Inflation Rates and Expected Real Rates NegativeLy Correlated? A Long-run Test of The Mundell-Tobin Hyphothesis”, The Journal of Financial Research, 2002, 25(3), pp. 305-320. Taylor, J. B. “The Monetary Transmission Mechanism: An Empirical Framework”, Journal of Economic Perspective, 1995, 9(4), pp. 11-26. Woodford, M., (2003), Interest and Prices: Foundations of a Theory of Monetary Policy, Princeton University Press, 2003.
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LOCAL FINANCIAL DEVELOPMENT AND FIRM PERFORMANCE: DOES FINANCIAL OUTREACH REALLY MATTERS WITHIN INDONESIAN ARCHIPELAGO? Fickry Widya Nugraha1
Abstract
This study attempt to examine whether firm’s performance depends on the local financial development across Indonesian provinces within 2010-2013. Combining data from the Indonesian survey of manufacturing firms with information from regional-level data, the study empirically documented several key findings. First, there are no consistent results that local bank availability is robustly associated with faster growth for firms in sectors with growth opportunities. Second, this paper documented a positive interplay between access to financial services to firm performance particularly within western part region of Indonesia. Third, access to the financial service appears to be closely connected with the level of economic development, particularly rate of poverty and inflation in the western part region of Indonesia. Taken these empirical finding together, this study imply that the level of inequality across Indonesian archipelago need to be reduced in order not only to provide a better access of financial services to the local stakeholders but also to improve small firms performance.
Keywords : Firm performance, local financial development, financial access JEL Classification : O16, L20
1 The author is analyst in Department of Strategic and Management Bank Indonesia. The views and opinions expressed in this paper do not necessarily reflect those of Bank Indonesia. Email address :
[email protected]
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I. INTRODUCTION A report published by the United Nations Industrial Development Organization recently documented that Indonesian economy has move toward industrialized nations. Figure 1 reports the trend of manufacturing industries share to GDP. Based on this figure, the Indonesian manufacturing industries has made a significant improvement provided the fact that share of manufacturing industries to GDP in 2015 has slightly increase to around 4%. This condition also made Indonesia manufacturing industries on par with several advance industrial countries such as United Kingdom, Russian Federation, Canada and Spain. The key contributions of sectors matters for economic growth and structural change since the technological opportunities between them vary significantly.2
China United States Japan Germany Republik of Korea India Italy France Brazil Indonesia United Kingdom Russian Federation Mexico Canada Spain
2000 2015
0,0
5,0
10,0
15,0
20,0
25,0
Source: UNINDO MVA Database
Figure 1. Share of Manufacturing in several Economies (constant price, 2010)
Meanwhile, on the other hand, the center point of Indonesia’s grand strategy toward inclusive and sustainable development path has been to increase the financial access service in manufacturing industry.3 This strategy cannot be separated from the global agenda and Lima Declaration, aiming for resilient infrastructure, promote inclusive and sustainable industrialization, and foster innovation within nations.4 This strategy is also designed rigorously in respond to
2 Industrial Development Report 2016 (see reference). 3 This can be seen from the long-term directions of Indonesian Financial Services Sector Master Plan 2015-2019 which is to build an inclusive access of financial services to foster economic development 4 In 2013, the Lima Declaration goal is correspond to the Sustainable Development Goal 9 (SDG 9) that aims to eradicate poverty through inclusive and sustainable industrial development. Inclusive and sustainable industrial development means that every country achieves a higher level of industrialization in their economies and benefits from the globalization of markets for industrial goods and services.
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the Indonesia’s business activity which is still portrayed by structural problem about bankability issues in particular within small Indonesia manufacturing firm. A previous study of Rosengard and Prasetyantoko (2012) pointed out that Indonesia is unbanked, especially for the micro, small, and medium enterprises (MSME). The study also stated that despite potentially lucrative unserved or under-served markets including the lowincome households and family business, the monetary policy and the regulatory regime in Indonesia set by the central bank has created barriers to the financial outreach and innovation for microfinance institutions, unintentionally. Back in the 70’s, it is widely recognized from literature that financial development is linked to economic growth. Goldsmith (1969) provided the first cross-country empirical study documenting the existence of a strong positive link between the functioning of the financial system and growth. A number of studies then followed, King and Levine (1993) showed that a country’s financial development matters for firm performance and aggregate growth. Afterwards, in countries with better functioning financial system, Rajan and Zingales (1998) showed that industries that depend mostly on external finance grew faster than industries that are not using external finance. In one hand, Levine (1997, 2005) shows through econometric studies that when banks manage to channeling capital to firms with the highest social returns and able to monitor the allocation of funds, this will encouraging entrepreneurship and economic growth. Furthermore, Levine (1997, 2005) also states that well-functioning banks will have positive effect to income distribution and poverty. Countries with better banks experience faster reductions in poverty as capital flows to those with the best projects, not simply to those with the most wealth and power, the reverse uphold for poor functioning banks. Generally, an important channel is linked to the access to financial services which represents by the availability of financial infrastructure that is more or less directly transmits the role of the financial sector to economic growth. In this sense, the better financial infrastructure should then help to foster economic development through many ways. This study will put Indonesia as a case since it represents an interesting aspect from the socioeconomics standpoint. First, Indonesia is the fourth most populous country where 67% of population is classified as the working ages5. Second, Indonesia is emerging countries in south-east asia where its GDP in 2013 account for $878,04 billion or the biggest in ASEAN economies6. Third, a report by the World Bank show an evidence that only 19.6% of Indonesia’s adult population that has access in a formal financial sector.7
5 Working ages define as people who have 15-64 ages 6 Association of South East Asian Nations (ASEAN) economic report 2011 7 The global Findex Database 2014 (see reference)
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By using the similar approach with Fafchamps and Schündeln (2013), this study will attempt to examine the impact of local financial outreach on firm performance in Indonesia. The outcome of this empirical research is expected to provide an important insight on the literature of financial inclusion in the context of emerging countries, specifically in Indonesia. Furthermore, the research is quite different in term of the research specification due to the fact that many of the previous studies had generally focused on the link between financial deepening and economic growth (Soedarmono, et al (2015), Trinugroho et al (2015), Subandono (2015), Jayaratne and Strahan (1996)). In order to bridge this gap, this study introduces two novelty fundamental aspects in comparison to the previous study. Firstly, this study emphasizes the interplay of access to the local financial development on firm performance across Indonesian archipelago. With 17,000 islands, the inequality issues between the Western and the Eastern part of Indonesia remains. In this case, taking into consideration the spatial aspect hopefully will provide a clear insight regarding the impact of financial access in these regions. Secondly, this paper merge the use of firm-level dataset from Indonesian manufacturing survey with regional-level dataset; we may expect better evidence in compare to the use of aggregated dataset only. The main empirical findings from this paper are as follows. First, there are no consistent result that local bank availability is robustly associated with faster growth for small firms, in particular for small firms in sectors with growth opportunities and in geographically concentrated region. Second, the paper found a positive interplay between local bank availability and firm performance. Third, level of development such as poverty rate, inflation rate and minimum wage are come hand in hand with the success of financial outreach to uphold firms performance within Indonesia’s province over the sample period. The remaining of the paper is organized as follows. Section 2 presents literature review on the theoretical ground between financial infrastructure and economic growth. Section 3 will briefly describes the data and methodology approach. Section 4 will briefly provide descriptive statistics on the dataset. Section 5 reports the main empirical results from this study. Finally, Section 6 presents a concluding remarks in regard to the overall findings and major implications that can be drawn from this study.
2. THEORY 2.1. Behavior of the Household and the Bank A theoretical approach related to this issue was proposed by Amable and Chatelain (2001). Let a fixed-size population of overlapping generations of agents live for two periods. There is a continuum of mass N=1 of these agents and they are uniformly distributed on a circle of circumference equal to unity. This formalizes horizontal differentiation and or spatial heterogeneity. During the first period, each agent inelastically supplies one unit of labor, saves
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a certain proportion of her earnings, and does not provide bequests to their offspring. The agents can invest their savings either in a storage technology, or put them as deposits in banks. More specifically, the model assume that the utility function of an agent living in t and t+1 is log-linear and depends on the levels of consumption at the end of each of the two periods :
(1)
Where ρ is the rate of time preference. The first period’s consumption is equal to the difference between wage income and savings, while second period’s consumption is equal to the revenues derived from savings, thus :
(2)
Where wt is the real wage, St is individual’s current amount of saving and Zt+1 is the real return on savings on any assets (deposit or storage technology). From this specification, saving function is assumed to be inelastic to the real return :
(3) There is imperfect competition in the banking sector because of horizontal (spatial) differentiation as in Salop’s (1979) model. In respond to this, the model considered where spatial competition takes place on a circle whose circumference is normalized to unity over which banks are equally distributed. The lender then deposits their money to the bank i only if the return (net) of the transactions cost of going to the bank is relatively higher than the return from storage technology µ. In this context, this analogy can be mathematically written as follow: (4) ri,td is the deposit rate of bank i at date t, l is the distance between a consumer’s location and bank i’s location, and dl is the lender transportation cost per unit of saving. The transportation cost dl is assumed to be a decreasing function of the services provided by financial infrastructure (G), which full depreciate after one period, as assumed for private capital. For homogeneity and simplicity, G is divided by Y in the specification of d, so that the distance factor is d(G/Y). The farther the consumer is from the bank located at l=0, the more costly it is for them to deposit their assets in that bank.
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2.2. Financial Infrastructure and Economic Growth In this framework, Amable and Chatelain (2001) consider that banks face spatial competition on the deposit market as in Salop’s (1979) model, but depositors’ transaction costs and banks’ intermediation costs are endogenous and depend on financial infrastructures. An increase in the number of banks will positively affect the level of aggregate savings. Public intervention will determine the amount of financial infrastructures in the economy and thus indirectly influence the extent of imperfect competition in the banking sector, which will in turn affect economic growth (gt)8.
(5)
One can see the influence of the financial intermediation sector on growth through the number of banks (n*) and the market share of a bank measured by the distance (2l*) which increases collected savings. The growth rate is constant over time, as is customary in AK-type endogenous growth models and depends positively on the saving rate (through the rate of time preference ρ) and on the productivity of capital, diminished by the marginal rate of unproductive taxation. In this particular case, imperfect competition in the banking sector adds the two negative effects on growth of operating costs of banks (f(τ3)) and the return of the alternative assets for depositors (μ). The positive effects of infrastructures on growth can appear clearly through the effect on d, as a decrease in the transaction cost incurred by the depositor d is beneficial to growth. Likewise, if infrastructure increase the level of private productivity (A) or decrease operating costs of banks (f(τ3)), it also beneficial to growth.
2.3. Access to Financial Services A study by Claessens (2006) showed that access to financial services has been recognized as an important aspect of development, and more emphasis is being given to extending financial services to low-income households. Access to finance is not the same as use of financial services. Access refers to the availability of a supply of reasonable quality financial services at reasonable costs, where reasonable quality and reasonable cost have to be defined relative to some objective standard, with costs reflecting all pecuniary and no pecuniary costs. On the other hand, the
8
Detail explanation regarding the mathematical proof can be seen on Amable and Chatelain (2001) page 487- 491
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use of financial services refers to the actual consumption of financial services. The difference between access and use can be analyzed in a standard demand and supply framework. Access refers to supply, whereas use is the intersection of the supply and demand schedules. Well-developed financial institutions will be crucial to an efficient allocation of resources in response to growth opportunities (Fisman and Love, 2007). A recent empirical study by Fafchamps and Schündeln (2013) in Morocco showed that local bank availability is robustly associated with faster growth for small-medium size firms in sectors with growth opportunitites. Paralleling these empirical findings suggest that development pathway need a better access of financial services to the local stakeholders (which can be either households or firms) in order to improve economic performance particularly in developing countries which has a significant amount of population. There are numerous potential channels, and recent research shows that finance is associated with all of them. According to the World Bank (2008), the availability of external finance is positively associated with the number of start-ups an important indicator of entrepreneurship as well as with firm dynamism and innovation. Finance is also needed if existing firms are to be able to exploit growth and investment opportunities and to achieve a larger equilibrium size. Firms can safely acquire a more efficient productive asset portfolio where the infrastructures of finance are in place, and they are also able to choose more efficient organizational forms such as incorporation.
2.4. Empirical Model Having explaining the theoretical ground, now we need to translate them into empirical model. Ideally, the model to estimate relates and is derived directly from the theoretical one; however this paper uses the ad-hoc model. This section will explain the construction of the empirical model, while its estimation and possible bias (e.g. endogeneity, reverse causation and selection bias) will be discussed on methodology. To assess the effect of local financial development on firm performance, we construct the structural model of interest as follows: (6) where ∆yfist is the outcome of performance for firm f in province i on sector s at period t; Bit is the total number of bank branch in location i at time t, Gs is growth opportunities at sector s available to firm f; Controlit is control variables for level of economic development in province i at time period t; μi is a region fixed-effect capturing the impact of unobservable time-invariant province characteristics, ωs and θt are dummies control for different growth rates across sectors and time, and εfist is error term. Firms that were used as reference group to calculate Gs are excluded from regression (6).
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The main parameter of interest is β which we can interpret a positive coefficient as evidence for a positive effect of local financial development on firm performance after controlling for unobserved time-invariant and time-varying local heterogeneity. One approach to estimating β is to rule out μi, ωs and θt through a within-firm transformation to get (7) where
with
. Equation (7) will give consistent
estimates only if , which is a condition that is unlikely to hold since are likely to be correlated across neighboring sites, meaning that it also be likely to be correlated with unobserved time-varying local effects and hence violating the orthogonality condition. As consequence, this paper will estimate equation (6) in aggregate level i.e provincial level. Aside from that, this paper will also put emphasis on spatial issues, in essence that the baseline results will be divided into two geographical area which is categorized as Western part of Indonesia and Eastern part region of Indonesia. The rationale behind this strategy is firms are generally located in the Western part of Indonesia, in particular Java and Sumatera islands. Also, by dividing the region into these two categories we will be able to test whether a relative concentrated industrialized region have a different impact in term of the access to the financial services as proxied by branch density. Following Fafchamps and Schündeln (2013), the hypothesis in this study is as follow, small firms in locations where number of banks branches (Bi) are relatively high and financially less constrained therefore can grow faster.
III. METHODOLOGY 3.1. Data, Variable, and Proxies The brief description and definition regarding the variables and its sources are provided on the table. The main source of our data is Survey on Manufacturing Industry, Indonesian Statistical Bureau, and Bank Indonesia. Following the same approach by Fafchamps and Schündeln (2013) and Burgess and Pande (2005), variable access to financial services will be proxied by the number of bank branches. As pointed by by Burgess and Pande (2005), bank branch can be used as prompt indicator as they found that branch expansion into previously unbanked area in India had given a significant impact particularly to the reduction of poverty in the rural area. The finding also suggest that the Central Bank’s licensing policy could enabled the development of an extensive rural branch network, and this, in turn, allowed rural households to accumulate more capital and to obtain loans for longer-term productive investment.
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We measure the performance of the firm using three indicators; value added, output, and employment. However, according to by Fafchamps and Schündeln (2013), value added is a more satisfying proxy for growth opportunities since it measures returns to labor and capital. On the other hand, Fisman and Love (2007) argue that sales have less measurement error: sales are measured directly in the census, while value added is constructed from several variables. The same can be said for employment. Given that, over our study period, employment, sales, and value added moved in different directions, this study will elaborate all of these three indicators. In order to taken into account firm characteristics issues, this study will put emphasize on small-medium firm performance. Generally, the small-scale industrial enterprises tends to group together by geography and by economic subsector (Berry, Rodriguez and Sandee, 2001). Furthermore, they also pointed out that small-medium firms has an important role as the locus of most labor absorption in Indonesian manufacturing industries. This study will also put several control variables in order to capture the quality of economic development. As many previous study showed and argue, level of socioeconomic conditions need to represent some aspects of quality of life such as education, poverty, infrastructure, living standards, and employment (Fafchamps and Schündeln (2013), Guiso, Sapienza and Zingales (2004), Djankov et al., (2007), Japelli and Pagano (2002) and Petersen and Rajan, (2002)). Furthermore, as argued by Djankov et al., (2007), Japelli and Pagano (2002) and Petersen and Rajan, (2002), lenders would be more willing to deal with borrowers if they are well informed. The problem of asymmetric information and transaction cost considerations suggest that physical distance between lender and borrower is likely to affect access to finance. Hence, in the socioeconomically less developed regions banks have less incentives to the financial outreach as the information as well as the quality of borrowers are inadequate. Indeed, borrowers’ actions are harder to observe when lender and borrower are far apart, leading to adverse selection (of potential borrower) and moral hazard (for current borrower). Guiso, Sapienza and Zingales found (2004) found that social capital plays an important role in the degree of financial development across different parts of Italy. In line with this condition, this study believes that major control factor should potentially help to explain the interplay between firm performance and access to the financial services at the provincial level in Indonesia. Related to the growth opportunity, technological opportunities across sectors can vary significantly.9 To take into account this factors, we follow similar procedure of Fafchamps and Schündeln (2013) in calculating the sectoral growth opportunities (Gs = log(sum(vad2013))− log(sum(vad2010))10. The key assumption is that large firms are less likely to be financially constrained, and therefore are more able to take advantage of growth opportunities in their sector. See Guiso et al. (2004) for a similar assumption, which is based on findings by Berger et al. (2005) and Petersen and Rajan (2002). 9 Industrial Development Report 2016 (see reference) 10 The report s of growth opportunities calculation showed by the Table 3 in appendix. This study found a considerable variation across sectors over the sample period.
Firm size
Region
Sector
Period
Value Added Growth
Output growth
Workforce growth
Growth Opportunities
Financial development
Poverty rate
Household density
Inflation rate
Unemployment rate
Level of minimum wage
Distance
GDP Deflator
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
Proxy
1.
No
GDP_deflator
Dist
Wage
Unemployment
Inflation
Household
Pov_rate
Branch
gop_
Employment
Output
VAD
t
s
i
f
Variables
Symbol
Small firms (number of persons engaged 5-19) Households firms (number of workers 1-4 people)
4.
GDP_deflator = GDP(nominal/real) x 100
National road length (Km)
Province minimum wage
Rate of open unemployment
Year on Year (y.o.y) difference of Consumer Price Index (CPI)
Total number of household within the provinces divided by area of the provinces (Km2)
divided by the total number of population
Level
Regional-level
Sectoral-level
Firm-level
Data
Poverty rate is measured by total headcount of people who are classify below poverty line,
Banks branch are classify as number of branches in terms of commercial and rural banks.
Example gop_vad : log(sum(vad2013))-log(sum(vad2010))
This variable is computed for VAD, output and employment
Number of laborforce in each firms. The rate of workforce growth is computed using log
Output is total amount of production. The rate of output growth is computed using log
Value Added (VAD) is amount of output minus input. The rate of VAD growth is
Annual series of data started from 2010 to 2013
Sector of economy is divided into 17 sectors where its classification based on ISIC rev.4
Indonesia is divided into 34 provinces. Each of provinces are made up of regencies and
Medium firms (number of workers 20-99 people)
3.
Large firms (number of workforce of 100 people or more)
2.
1.
The manufacturing firms can be divided into four groups, namely:
Definition
Table 1. Variables and Data Proxies
Statistics Indonesia
Bank Indonesia : Banking Statistics
Survey Statistics Indonesia
Statistics Indonesia : Manufacturing
Source
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However, unlike Fafchamps and Schündeln (2013), this paper only use two possible cut-off point in identifying less constrained firms. First, firm size indicator, a firms which has threshold with at least 100 employees is categorized as big firms. Second, foreign ownership, complement with the manufacturing firm size, firms with more threshold than 50% in the foreign ownership is identified as big firms. The logic behind this threshold is foreign firms are less likely to be constrained by local financial markets. For simplicity purposes, less constrained firms will then label as big firms and firms that has workforce below 100 employees will be label as small firms.
3.2. Estimation We use panel data estimation on this paper. As stated previously on Chapter 2, the empirical model constructed should pay attention to several possible bias, particularly the endogeneity problem. Endogeneity problem occurs when an explanatory variable is correlated with the error term. This endogeneity bias can arise as a result of measurement error, autoregression with auto correlated errors, simultaneity and omitted variables. Two common causes of endogeneity are an uncontrolled confounder causing both independent and dependent variables of a model, and a series loop of causality between the independent and dependent variables of a model (see Hill, Griffths and Lim, 2012).11 Our empirical model is potentially suffered from the endogeneity problem. Banks may locate in regions, sectors or places that are expected to grow faster and hence where firms should perform better. To deal with this issue, we follow Fafchamps and Schündeln (2013) and use the local bank availability measured at t-1 period as a proxy for the individual firm’s access to financing. The above approach to deal with endogeneity bias is similar with Rajan and Zingales (1998). The main advantage of this approach is it allows us to control the location-specific growth trend, the expectation of which may have influenced bank placement. Rajan and Zingales (1998) documented that because of structural or technological reasons, there is variation across sectors in how much firms in as sector have to rely on external funds. Subsequent work by Fisman and Love (2007) provides reinterpretation of the original findings by Rajan and Zingales (1998). They argue that the test by Rajan and Zingales (1998) is implicitly a test about whether financial development facilitates firms’ investment in the presence of growth opportunities. Keeping production unchanged only requires replacement investment, which can typically be financed out of retained earnings. In contrast, if there are opportunities for growth, firms will need for expansion purposes. If funds cannot be found
11 R.C. Hill, W.E. Griffths, G.C. Lim, 2012. Principles of Econometrics, John Wiley & Sons, Inc., Chapters 10,11
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rapidly, opportunities will be seized by others. This mean that access to external finance is most critical for firms that face growth opportunities. Another important issue during the estimation is the reverse causality. In firm-level, the correlation between performance and access to finance is subject to reverse causation since banks are expected to lend to firms with high performance and prospects. To deal with this reverse causality issues, we follow the same strategy proposed by Fafchamps and Schündeln (2013). In short, in order to measure business activity within the regions, model (6) are only estimated for small firms and exclude the big ones. The idea behind this strategy is following. Among the possible theoretical reasons for larger firms’ better access to external financing are, for instance information issues, in a way that it is less costly for banks to obtain reliable and/ or independent information about larger firms’ income statements or balance sheets because information issues (less asymmetric information) and has to be collected by banks over time through relationship with firms (Petersen and Rajan, 2002).
IV. RESULT AND ANALYSIS 4.1. Descriptive Statistics This study use matching technique by firm establishment ID in merging the firm-level dataset with the regional-level dataset in order to obtain a panel dataset. For the purpose of analysis, if the data is missing and discontinue in the firm-level data, then it is remove from the sample. After merging the data set, the total observations is 43,855 or reduced by 48% due to the missing data at firm-level category over 2010-2013. In general, within the big firms category, the data show a straight upward trend value added. Meanwhile small firms exhibited a relative stagnant performance in compare to the big firms particularly in post 2011. At some point, this condition will be beneficial for this study due to the fact that this trend will generate an upward growth opportunities for small performance as framed by empirical previous study (Fafchamps and Schündeln (2013), Fisman and Love (2007)). As recorded in the Indonesian manufacturing firm survey 2013, of the total number 23,678 manufacturing firms, 70% are belong to the Micro-Small-Medium Enterprise (MSME) category. Now, If we closely examine within region as showed by Figure 3 on the right, we can see a clear evidence that number of firms that are located in the Western part of Indonesia is considerably higher than Eastern part of Indonesia (see grey shaded area). This condition support the empirical strategy in this paper in term of examining the impact of firms access between these two geography area.
Local Financial Development And Firm Performance: Does Financial Outreach Really Matters Within Indonesian Archipelago?
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28 27,9
6000
Big Firms
299
MSME
25
27,8
20
27,7 27,6
15
27,5
3000
10
27,4 27,3
5
Maluku
West Papua
Gorontalo
South Sulawesi
Central…
East Kalimantan
Bali
East Nusa…
East Java
Source : author calculation
0 DKI Jakarta
0 2013
Central Java
2012
Belitong Island
2011
Jambi
2010
Bengkulu
27,1
MSME Firms (rhs)
Aceh
Big Firms (Ihs)
West Sumatera
27,2
Source : author calculation
Figure 2. Trend of Firms Performance (log of sum(VAD)) in Indonesia over 2010-2013
Figure 3. Number of Firms by Province in 2013
In general, from descriptive statistics analysis (Appendix 1), this paper found that firm’s performances are generally lower in MSME category. This phenomenon can be seen from the mean of growth rate in the MSME category that are proxied by value added (VAD), output and employment. From all of these three main indicators of firm performance, MSME always exhibited a lower value than the big firms. For instance, over the period 2010-2013, the mean of VAD growth in the MSME category is 0.10. In contrast, mean of VAD growth rate of big firms are considerable more than twice in which recorded at 0.24 over the same period. The VAD-less growth phenomenon in MSME probably cannot be separated from the fact that these firms exhibited a lower mean of growth in output and employment over the sample period. Aside from this condition, the descriptive statistics show an important information regarding to the potential outlier problem as can be seen from the standard deviation in several variables such as branch density, population density and household density.12 In order to deal with this problem, this study compute DF-BETA threshold and check whether the variables above or below the threshold13. After carefully examining this method, a sample data than can be identified as an outlier is DKI Jakarta provinces. Table 1 reports the correlation matrix of all variables for Western part and Eastern part of Indonesia. As already explained in the methodology section previously, big firms are excluded
12 These variables are belong to the same provinces i.e DKI Jakarta. In line with this condition, this study then remove DKI Jakarta province from the estimation. 13 DF-BETA figure for all region are reported in Appendix 7. DF-BETA absolute threshold for 43,855 observations is 0.0095. This values is computed using +/- 2/sqrt(N). This study also use Variance Inflation Factors (VIF) test to detect severity in multicollinearity issues (Appendix 9). Explanatory variables that are potentially triggerd a high VIF then remove from the subsequent estimation i.e. lhousehold and ldist.
0.0586
0.0459
0.0318
0.0304
0.0597
0.0643
0.0192
-0.0576
-0.0246
growth opportunities by VAD
growth opportunities by output
growth opportunities by employemnt
poverty rate
population density
household density
distance
inflation rate
unemployment rate
0.069
0.0444
Branch density
real wage
0.1763
Employment growth
1
0.8447
Output growth
VAD growth
0.0801
-0.0505
-0.0768
0.0312
0.0833
0.0752
0.0402
0.0202
0.0386
0.0625
0.0594
0.235 1
0.016
-0.0758
-0.0212
-0.0364
0.0755
0.0771
-0.0399
-0.0587
-0.0534
-0.0147
0.0705
0.3177
-0.0706
-0.4024
-0.183
0.9512
0.9183
-0.4021
0.1811
0.1346
0.2191 1
0.0344
-0.0094
-0.0634
-0.1939
0.202
0.2269
-0.0875
0.3765
0.4631
1
0.0925
-0.0016
-0.1184
-0.1452
0.1172
0.1231
0.029
0.7136
1
0.0817
0.0223
-0.1039
0.0046
0.1452
0.1417
-0.0763
0.1605
-0.0928
-0.1459
-0.0398
-0.2379
-0.206
1
-0.0289
-0.0118
-0.0118
real wage
1
0.5477
-0.031
-0.0123
-0.0073
unemployment rate
1
0.0147
0.2037
0.0375
0.0178
0.0133
inflation rate
0.2906
-0.2303
-0.3967
-0.3217
0.9765
1
Population Density
0.0378
-0.0202
-0.3619
-0.0129
-0.0101
-0.0083
distance
Poverty Rate
0.0246
-0.1346
-0.0356
-0.7269
-0.0155
0.0011
-0.0011
household density
Growth Growth Growth Opportunities Opportunities Opportunities By Vad By Output By Employemnt
0.0024
-0.0998
-0.0691
0.0598
0.9673
-0.015
0.0017
-0.0008
population density
Branch Density
0.4551
-0.6386
-0.0256
0.1016
-0.0109
0.9612
0.0157
-0.0025
0.0009
poverty rate
Employment Growth
0.4251
-0.2939
0.0945
-0.0636
-0.0123
-0.4184
0.03
0.0247
0.0168
growth opportunities by employemnt
Output Growth
-0.2743
0.7112
-0.0816
-0.0689
0.0377
-0.0698
0.0056
0.0143
0.0095
growth opportunities by output
Vad Growth
-0.7545
-0.5067
-0.083
0.1438
0.2425
-0.0381
-0.0061
0.0031
0.0034
growth opportunities by VAD
Western Part Region Of Indonesia Eastern Part Of Indonesia
1 0.9987
1 -0.5406
1 0.0845
1 0.6055
1 0.4276
1 -0.0105
1 -0.0024
0.0051
0.0009
0.3856
-0.1819
-0.4879
-0.203
1
Household Density
0.4823
0.3948
-0.3134
-0.735
1
0.1484
0.2738
-0.1139
1
Distance
-0.1989
-0.4579
-0.1016
1
Population Household Distance Density Density
Branch density
Poverty Rate
1
Growth Growth Growth Opportunities Opportunities Opportunities By By Vad By Output Employment
0.3657
Branch Density
0.3623
Employment Growth
Employment growth
Output Growth
1 0.8712
Vad Growth
Output growth
VAD growth
Western Part Region Of Indonesia Eastern Part Of Indonesia
Table 2. Correlation Matrix at Firm-Level Data
-0.9613
0.2547
1
Inflation Rate
-0.8996
0.4147
1
Inflation Rate
-0.1946
1
Unemploy Ment Rate
-0.3522
1
Unemploy Ment Rate
1
Real Wage
1
Real Wage
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in this correlation matrix in order to avoid estimation biased. Based on Table 1 below, the main variable of interest show a positive correlation with all of the three firm performance indicators. However, the coefficient correlation is relatively weak in the Western part of Indonesia. Moreover, from Table 1, the correlation between growth opportunities (Gs) are positive across Western part of Indonesia except with value added growth opportunities with employment growth (-0,0061). A negative correlation indicates that if the number of workforce in the big firms is increasing then it will have a negative impact in small firms performance. Furthermore, branch density in general has positive correlation with firm performance. However the correlation magnitude tends to be weaker in Western part of Indonesia in compare to Eastern part. Growth opportunities have positive correlation on firm performance over the sample period although the magnitude is not so strong. Furthermore, this study find a relative small negative correlation on growth opportunities by value added to employment growth. This phenomenon indicates that big firm performance can trigger a labor dynamics i.e. labor movement from small to big firms. Branch density has negative correlation with poverty rate, meaning that the more regions has poverty rate the less branch would available. This phenomenon also supported by the correlation between real minimum wages and branch density which show positive correlation. In this case, richer provinces would have higher number of bank branches in compare to less rich provinces. Branch density has negative correlation with distance. The farther distance with household, the less number of bank branches available. Problem of asymmetric information captured from this correlation in essence that lenders would be willing to deal with borrowers if they are well informed. This evidence is in line with previous study by Fafchamps and Schündeln (2013), Djankov et al., (2007), Amable & Chatelain (2001). Also, the correlation matrix in the Table 1 shows that branch density and Inflation rate are negatively correlated, an indication that inflation represent a cost for Indonesian manufacturing firms.
4.2. Firm-Level Estimations 4.2.1. Baseline Result Table 2 reports the baseline results for the four considered outcomes at the firm-level sample. We start using the value added growth as dependent variable (∆Yfist), and then the branch density (Bit) and growth opportunities (Gs). The latter is calculated from the less constrained firms, hence this results already exclude big firms in the estimation sample. Based on the Table 2, the coefficient of interaction terms between branch density and growth opportunities (lbranch_gopvad) is not significant. The goodness of fit of the model is considerably low or close to zero for every scenario, even after we try to control for endogeneity bias and using fixed-effect terms to control time-invariant factors.
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If we separate this interaction terms, the coefficient of branch density at t-1 is positive and statistically significant in affecting firm performance. However, this condition only appears to be important in the Western part of Indonesia and not in Eastern one. Possible explanation to this case is many of the small firms are concentrated in the well-connected region of Western Indonesia. The magnitude of this coefficient is 1.49 for Western Indonesia; meaning a one percent point increase of the branch density will increase the firm performance by 1.49%. In this firm level estimation, the growth opportunities is not statistically different from zero.
Table 3. Firm-Level Baseline Results All regions Variables
Pool
Fixed Effect
(1) lbranch_gopvad lbranch1 gop_vad Constant
Observations R-squared
Western Part of Indonesia
-0.0876
Pool
Eastern Part of Indonesia
Fixed Effect
Pool
Fixed Effect
(2)
(3)
(4)
(1)
(2)
(3)
(4)
(1)
(2)
(3)
-0.347
-0.259
-0.261
-0.0550
-0.319
-0.182
-0.186
0.355
0.865
-0.323
(0.407)
(1.598)
(0.0607)
(0.279)
(0.272)
(0.270)
(0.0780)
(0.491)
(0.482)
(0.476)
0.0543
0.715***
0.659***
1.285***
0.0321
0.692**
0.603*
1.493***
(0.0397)
(0.217)
(0.214)
(0.379)
(0.0514)
(0.342)
(0.338)
(0.450)
-0.170 -0.00298 (0.254)
(1.167)
-0.286
-1.800
-1.379
-1.355
-0.174
-1.710
-0.772
-0.747
2.671
7.032
(0.226)
(1.096)
(2.215)
(2.201)
(0.283)
(1.793)
(2.784)
(2.756)
(2.596)
(11.53)
(4) -0.409
(2.984) (2.953) 0.809
0.120
(1.941)
(2.117)
-1.177
-2.258
(18.83) (18.70)
0.280*
3.129***
2.998**
5.388***
0.203
3.003**
2.529
5.840***
-1.252
-0.990
(0.148)
(0.847)
(1.437)
(1.889)
(0.187)
(1.250)
(1.799)
(2.119)
(1.636)
(8.250)
17,844
17,844
17,844
17,844
17,138
17,138
17,138
17,138
706
706
706
706
0.000
0.001
0.004
0.004
0.000
0.001
0.004
0.005
0.006
0.003
0.008
0.019
6,458
6,458
6,458
6,194
6,194
6,194
264
264
264
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
Number of id region FE sector FE time FE
yes
yes
4.899
0.924
(12.55) (13.69)
yes
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
4.2.2. Additional Controls for the Level of Development Afterwards, this study also investigate whether the local financial development as proxied by branch density also come hand in hand with level of economic development, in particular in the Western part of Indonesia. Table 3 below reports the estimation results. As a direct measure of the level of development, this study put emphasis on several variables such as the poverty rate (pov_rate), i.e. the proportion of the population living below the poverty line, inflation rate (inflation), real minimum wages (lrwage), unemployment rate (unemployment) and household density (lhousehold).
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Once again, after estimating with several control of level of development, this study do not find significant result on the interaction variables (lbranch_gopvad ) as empirically find for the case of Morocco in previous study by Fafchamps and Schündeln (2013). However, Table 3 provides an interesting insight for Indonesian case. At some point, after controlling timeinvariant factors, this study find that the level of development are closely connected with the level of branch density as can be seen from the stable increase in its magnitude when control variables are added into the core equation (6). This study also estimates equation for the case of the Eastern part of Indonesia and find that many of explanatory variables are not statistically significant even after controlling for fixed-effect factors14. Also from the Table 3, we can see that poverty rate (pov_rate) has the highest magnitude for all specification for all outcome scenario (column (2) to (6)). For instance, in column (6), if poverty rate in this region increase by one percentage point, the firm performance will decrease by 12.37%. Furthermore, real wages also has a significant and negative impact on firm performance during sample period. The coefficient on this variables is -5.21, meaning that an increase one percentage point in real wages will trigger a reduction in small performance by 5.21% over the sample period15. Another important variables that also significantly affected small firm performance is inflation rate (inflation). A one percentage point increase in inflation will decrease small firm performance by 0.45%. From this overall situation, this imply that small firm expansion within Indonesian provinces is closely connected with level of economic development i.e. inequality problems. Taken these empirical findings together, this indicates that level of economic development within regions are matter most than financial access on small firm performance. These empirical findings are contrast compare to the case for Morocco (Fafchamps and Schündeln, 2013), where most of these variables are not statistically significant after controlling fixed effect for commune and sector level16.
14 In order to save the space in this report, this results is reported in Appendix 3. 15 According to Priasto (2015), income and consumption inequality in Indonesia have been steadily increasing since 2000. After recovering from the 1997–1998 Asian financial crisis, Indonesia experienced a period of strong economic growth, driven in part by a commodities boom and strong domestic consumption. However, during this period the Gini coefficient also climbed in 2000 from 0.31 to 0.43 by 2013. These condition has made significant contribution to a high inequality within the country. 16 See page 20 on Table 3. Robustness : other controls for level of economic development in Fafchamps and Schündeln (2013)
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Table 4. Estimation Firm Performance (Value Added Growth)at Firm-Level in Western Part of Indonesia Variables lbranch_gopvad lbranch1 gop_vad
(1)
(2)
(3)
(4)
(5)
(6)
-0.186
-0.198
-0.208
-0.229
-0.230
-0.269
(0.476)
(0.472)
(0.475)
(0.471)
(0.469)
(0.467)
1.493***
1.764***
1.669***
1.411***
1.477***
1.326**
(0.450)
(0.465)
(0.479)
(0.491)
(0.504)
(0.563)
-0.747
-0.814
-0.825
-0.876
-0.899
-1.034
(2.756)
(2.749)
(2.748)
(2.735)
(2.730)
(2.724)
-8.564*
-9.724**
-11.37**
-10.83**
-12.37***
(4.374)
(4.430)
(4.496)
(4.530)
(4.641)
-0.0242
-0.462***
-0.508***
-0.453**
pov_rate inflation inflation1
(0.0288)
(0.156)
(0.168)
(0.176)
-0.0374
-0.0268
-0.0213
-0.0167
(0.0280) lrwage
(0.0285)
(0.0295)
(0.0297)
-5.305***
-5.604***
-5.214***
(1.859)
(1.908)
(1.937)
unemployment
0.0205
0.0106
(0.0264)
(0.0295)
lhousehold
0.459 (0.411)
Constant
Observations
5.840***
7.920***
8.198***
15.27***
15.87***
12.28**
(2.119)
(2.337)
(2.385)
(3.408)
(3.543)
(5.189)
17,138
17,138
17,138
17,138
17,138
17,138
R-squared
0.005
0.005
0.005
0.006
0.006
0.006
Number of id
6,194
6,194
6,194
6,194
6,194
6,194
region FE
yes
yes
yes
yes
yes
yes
sector FE
yes
yes
yes
yes
yes
yes
time FE
yes
yes
yes
yes
yes
yes
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
4.2.3. Channel of Financial Access As we have seen from previous analysis, over the sample period, access to bank branches is not associated with faster growth for small firms in sectors with growth opportunities across provinces in Indonesia. However, the local bank availability has a positive impact on small firms performance. The question then is: how can firms achieve higher value added growth if local banks is available? The underlying assumption in the finance and growth literature is that access to finance can facilitates investment and that this, in turn, generates growth in value added by increasing capital and raising productivity. In line with this condition. This paper will attempt to test whether firms with better access to bank will increase their labor productivity
Local Financial Development And Firm Performance: Does Financial Outreach Really Matters Within Indonesian Archipelago?
305
Table 5. Estimation Channel of Financial Access at Firm-Level in Western Part of Indonesia Channel of Financial Access Dep. Variables
Output growth
Employment growth
Labour Productivity
0.135 (0.166) 0.822* (0.436) 0.143 (1.143) -11.08*** (4.195) -0.637*** (0.148) 0.0103 (0.0278) -7.216*** (1.700) 0.0347 (0.0234)
-0.398** (0.162) 1.322*** (0.156) -1.240 (0.763) -1.765 (1.417) 0.0736 (0.0536) -0.0298*** (0.0100) 1.021 (0.625) 0.0245*** (0.00735)
0.133*** (0.0499) -0.119 (0.0835) 0.838*** (0.325) -1.620* (0.889) -0.00135 (0.00486) -0.116*** (0.0326) -0.00190 (0.00517) -1.222*** (0.373)
Constant
8.882* (4.587)
2.160 (1.418)
4.410*** (0.669)
Observations R-squared Number of id region FE sector FE time FE
17,138 0.007 6,194 yes yes yes
17,138 0.039 6,194 yes yes yes
17,138 0.048 6,194 yes yes yes
lbranch_gop lbranch1 gop_ pov_rate inflation inflation1 lrwage unemployment
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
and output. According to Fafchamps and Schündeln (2013), in case of investment in labor saving equipment, it is also conceivable that value added rises but output remains unchanged. Table 4 reports the estimation result for channel of financial access at firm level in Western part of Indonesia. This study follow the same procedure by dividing the channel into two main category, i.e. output and employment growth. However, this paper do not put emphasis on investment channel due to the information that is not available on the dataset. This study find similar finding with Fafchamps and Schündeln (2013), as reported by the Table 4, the channel in which access to financial services is works through labor productivity.17 From the estimation, the impact of branch is positive to the output growth, while the impact on employment is negative, if we estimated the sum of these two impact, this will channel to the higher growth in the productivity. This finding is confirmed by the significancy of positive coefficient on the interaction variables which has a magnitude 0.133 as reported in Table 4.
17 In this study, labour productivity is measured using basic calculation i.e. output per employment.
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This empirical finding is probably due to the investment in machine and/or productive equipment whereas also found as well in the case of Morocco by Fafchamps and Schündeln (2013). When small firms has a better access to the banking institutions, firms potentially will make an investment in productive assets. Essentially, if small firms has a better access to the financial services then firms will channel the capital (loan from the banking institutions) on asset productivity in order to achieve higher level performance as confirmed by this empirical findings within Indonesia provinces. Table 4 also indicates that if level of economic development such as inflation rate, poverty rate and unemployment rate are strongly connected with the small firm performance. In general, this study generally find that level of socioeconomic development in Indonesia are negatively link to small firm performance, for instance a higher in inflation rate will make an increase on marginal cost and therefore will lower small firms productivity. Taken these information with the previous analysis in subsection 5.1.2, generally this study find that level of economic development such as poverty and inflation is closely connected with the small firm performance.
4.3. Regional-Level Estimation Table 5 reports the estimation results on regional level. In this subsection, we estimate equation (6) but using aggregated level data to perform robustness checks. In general, this study find that once estimating at a more aggregated level and after rule out the fixed-effect time invariant factors, the variable in interest remain insignificant. Also, the branch’s density change its sign and no longer become significant with small firm performance, in particular for value added growth and output growth. But the branch density impact are statistically different from zero in term of the impact to growth of employment.17 Moreover, an important remarks can be seen from the positive sign of growth opportunities on the small firms performance within Western part of Indonesia. This mean that in sectors that has positive growth opportunities, small firms can make a business decision in to expand their activity within the Western part of Indonesia. The rationale behind these findings is when the small firms see a positive outlook in business opportunities, they will use financial institutions intermediation in order to grow their business, then it it will give an opportunities for small firms within the regions to enter the business related sectors.
17 After aggregating the sample into a regional-level, the number of observation reduce to 960. Theoretically we should have 1,972 observations because the sample data on this study consist 29 provinces, 17 sectors and 4 times periods. However, all of provinces samples do not constantly consist 17 sectors and hence resulting a reduction in the total of number of observations when the samples are aggregated.
307
Local Financial Development And Firm Performance: Does Financial Outreach Really Matters Within Indonesian Archipelago? Table 6. Estimation at Regional-Level Firm Performance in Western Part of Indonesia Firm Performance Variables lbranch_gopvad
lbranch1
gop_vad
pov_rate
inflation
-0.112
0.331
0.196
(0.467)
(0.366)
(0.131) 0.448***
-0.274
-0.281
(0.847)
(0.836)
(0.152)
8.051***
5.832***
0.733**
(2.188)
(1.384)
(0.329)
-22.00
-19.56
-3.035
(13.36)
(13.40)
(2.506)
-0.329
-0.654
-0.189
(0.466)
(0.127)
-1.719
-3.516
-2.141*
(4.561)
(4.882)
(1.255)
-0.0660
-0.0881
-0.00953
(0.0757)
(0.0746)
(0.0150)
(0.439) lrwage
unemployment
Constant
Employment growth
Output growth
VAD growth
4.619
5.282
4.232
(9.737)
(10.35)
(2.581)
564
564
564
0.188
0.226
0.161
region FE
yes
yes
yes
sector FE
yes
yes
yes
time FE
yes
yes
yes
Observations R-squared
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Furthermore, from the Table 5, we can also draw another important implication related to the impact of financial access on firm performance. The coefficient of branch density (lbranch1) on the employment growth is positive and statistically significant. This evidence indicates that if small firms have better access to the financial services, it only works through employment channel. This evidence is in line with the theoretical framework as proposed by Amable and Chatelain (2001), implying that when banks branch density within the region is increase, then small firms who has access to the financial services will channel their capital from banking institutions into a positive labor absorption in order to help country take off from poverty trap.
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V. CONCLUSION This paper analyzes the impact of local financial development on firm performance across provinces in Indonesia. Combining data from the Indonesian survey of manufacturing firms with information from regional level data, the study empirically found two key findings. First, we do not find consistent results that the local bank availability is robustly associated with faster growth for micro-small-medium firms in sectors with growth opportunities. This evidence is contrast with the previous study by Fafchamps and Schündeln (2013) in Morocco where the local bank availability was robustly associated with the firm located in sector with growth opportunities. Second, there are positive interplay between access to financial services and firm performance across Indonesian archipelago. However, this interplay only works significant within the western part region of Indonesia. Third, access to the financial service appears to be closely connected with the level of economic development, in particular rate of poverty and inflation within the western part region of Indonesia. In regard to the channel to financial access, this study finds a significant link between the access to financial services and the productivity. In this case, small firms that have better access to finance will potentially make an investment in productive assets. Taken these empirical findings together suggest, this study imply that level of inequality across Indonesian archipelago need to be reduced in order not only to provide a better access of financial services to the local stakeholders but also to improve small firms performance through investment channel.
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REFERENCES Amable, Bruno and Jean-Bernard Chatelain. (2001). Can Financial Infrastructures Foster Economic Development. Journal of Development Economics. 64, 481-498. Beck,Thorsten, Asli Demirgüç-Kunt, Maria Soledad Martinez Peria. (2007). Reaching out : Access to and use of Banking Services Across Countries. Journal of Financial Economics. 85, 234-266. Berry, Albert, Edgard Rodriguez and Henry Sandee. (2001). Firm and Group Dynamics in the Small Medium Enteprise sector in Indonesia. World Bank Institute. Burgess, Robin and Rohini Pande. (2005). Do Rural Banks Matter ? Evidence from the Indian Social Banking Experiment. American Economic Review. 95 (3), 780-795. Claessens, Stijn. (2006). Access to Financial Services : A review of The Issues and Public Policy Objectives. Oxford University Press. Djankov, Simeon, Caralee Mcliesh and Andrei Shleifer. (2007). Private Credit in 129 Countries. Journal of Financial Economics. 84, 299-329. Demirgüç-Kunt Asli, Lero Klapper, Dorothe Singer and Peer Van Oudheusden. (2014). The Global Findex Database 2014. Measuring Financial Inclusion around the World. Policy Research Working Paper 7255. World Bank. Fafchamps, Marcel and Matthias Schündeln. (2013). Local financial Development and Firm Performance : Evidence from Morocco. Journal of Development Economics. 103, 15-28. Fisman, Raymond and Inessa Love. (2007). Financial Dependence and Growth Revisited. Journal of The European Economic Association. 5 (2-3), 470-479. Goldsmith, R. (1969). Financial Structure and Development. Yale Univ. Press, New Haven, CT. Guiso, Luigi, Paola Sapienza and Luigi Zingales. (2004). Does Local Financial Development Matter?. Quarterly Journal of Economics. 119 (3), 929-969. Hill, R. Carter, William E. Griffiths and Guay C. Lim. (2012). Principles of Econometrics, 4th Edition. King, Robert and Levine Ross. (1993). Finance and Growth : Schumpeter Might be Right. Quarterly Journal of Economics. 108 (3), 717-737. Levine, Ross. (1997). Financial Development and Economic Growth : Views and Agenda. Journal of Economic Literature. 30, 596-620. Levine, Ross. (2005). Finance and Growth : Theory and Evidence. Chapter 12 Handbook of Economic Growth. Amsterdam : North-Holland Elsevier Pubslisher.
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Priasto, Aji. (2015). Summary of Indonesia’s Povery Analysis. Asian Development Bank Papers No.04 October 2015. Pagano, Marco and Tullio Jappelli. (2002). Information Sharing, Lending and Defaults. Crosscountry Evidence. Journal of Banking and Finance. 26, 2017-2045. Petersen, Mitchell and Raghuram Rajan. (2002). Does Distance Still Matter ? The Information Revolution in Small Business Lending. Journal of Finance. 57 (6), 2533-2570. Rajan, Raghuram G. and Luigi Zingales. (1998). Financial Dependence and Growth. American Economic Review. 88, 559-586. Rosengard, Jay K. and Agustinus Prasetyantoko. (2012). Regulatory Constraints to Financial Inclusion in Indonesia. East Asia Forum : Economics, Politics and Public Policy in East Asia and The Pacific. (accessible from http://www.eastasiaforum.org/2012/06/11/ regulatoryconstraints-to-financial-inclusion-in-indonesia) Subandono. (2015). Institutions, Croissance Economique et Entrepreneuriat: Causes et Conséquences des Activités Entrepreneuriales Sur Le Développement Économique des Regions Indonésiennes. These doctoral Universite Paris 1 Pantheon Sorbonne. Soedarmono, Wahyoe, Iftekhar Hasan and Nuruzzaman Arsyad. (2015). Finance-Growth Nexus: Evidence from Indonesia. (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2570737)). Trinugroho, Irwan, Agusman Agusman, Moch Doddy Ariefianto, Darsono Darsono, Amine Tarazi. (2015). Determinants of Cross Regional Disparity in Financial Deepening. Evidence from Indonesian Province. (accessible from https://hal-unilim.archives-ouvertes.fr/hal-01114255). United Nations Industrial Development Organizations. (2015). The Role of Technology and Innovation in Inclusive and Sustainable Industrial Development. World Bank. (2008). Finance for All : Policy Pitfalls in expanding access. World Bank. (2014). The Global Findex 2014. Measuring financial access around the World (accessible from http://www.worldbank.org/en/programs/globalfindex).
firm level
regional level
log transformed
970
1,270,000,000
3
12
7
2
289
0.52
0.12
93
806,239
1,908,083
90.15
0.11
1,852.91
487.93
8.17
7.55
886.08
73.27
0.45
1,377
15.43
10.90
16.39
11.87
4.55
2.33
6.83
5.49
6.75
4.29
7.03
0.16
-4.40
-0.01
43,885
43,885
43,885
43,885
43,885 175,000,000 43,885 73,500,000
0.66
43,885
43,885
43,885
43,885
43,885
43,885
43,885
43,885
43,885
43,885
43,885
43,885
43,885
43,885
43,885
43,885
43,885
43,885
43,885
43,885
43,885
43,885
43,885
43,885
43,885
43,885
43,885
43,885
32,912
32,912
32,912
time code
Province
Manufacturing sectors
Firm size
Employment
Output
Value added
growth opportunity vad
growth opportunity output
growth opportunity employment
GDP deflator (2010=100)
vad / GDP deflator
output / GDP deflator
Branch density
poverty rate
population density
household density
inflation rate (y.o.y)
unemployment rate
wage (local currency)
human development index (hdi)
employment rate
distance
log of vad
log of real vad
log of output
log of real output
log of employment
log of branch density
log of population density
log of household
log of wage
log of hdi
log of distance
growth rate of vad
growth rate of output
growth rate of employment
0.51
0.97
1.03
0.76
0.03
0.26
1.11
1.08
1.38
1.30
2.18
2.18
2.13
2.13
620
0.05
1.86
278.18
2.76
0.89
884.14
3,395.45
0.04
218.85
14,200,000
7,700,809
6
0.15
0.28
0.18
675,000,000
0
5
4
1
1
2012
43,885
year
15,615
Std. Dev.
31,485
Mean
43,885
Obs
Firm Identifier
Variables
All Firms
-5.60
-12.99
-9.29
4.96
4.18
6.45
0.31
1.79
-1.55
3.00
3.88
8.44
2.86
7.45
143
0.21
65.20
630.00
1.83
3.39
1.36
6.00
0.03
0.06
49
17
70
-0.21
-0.12
0.24
1,720
4,625
20
1
1
1
1
2010
1,761
Min
6.22
3.05
8.29
7.72
4.36
7.70
8.27
9.62
6.76
10.82
20.93
25.27
20.27
24.61
2,250
0.57
78.59
2,200.00
13.74
11.32
3,922.61
15,015.00
0.34
966.87
1,230,000,000
638,000,000
129
0.37
0.98
1.06
49,000,000,000
94,700,000,000
50,000
2
17
29
4
2013
54,215
Max
13,567
13,567
13,567
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
18,195
Obs
0.09
(4.32)
0.24
7.00
4.29
6.77
5.42
6.77
2.27
5.85
13.66
18.19
12.65
17.18
1,332
0.45
73.27
903.61
7.86
8.27
477.49
1,818.04
0.11
87.08
4,349,333
1,849,935
92.96
0.12
0.51
0.67
169,000,000
399,000,000
638
1
7
12
2
2,011
29,295
Mean
0.55
0.98
1.07
0.76
0.03
0.26
1.19
1.17
1.41
0.96
1.69
1.69
1.67
1.67
621
0.04
1.88
279.20
2.78
0.96
874.25
3,355.69
0.04
216.04
21,700,000
11,900,000
6.09
0.15
0.29
0.18
1,040,000,000
1,950,000,000
1,436
-
5
4
1
1
15,676
Std. Dev.
Big Firms
Appendix 1. Descriptive Statistics
(4.49)
(12.99)
(9.29)
4.96
4.18
6.45
0.31
1.79
(1.55)
4.61
5.65
10.14
2.86
7.45
143
0.21
65.20
630.00
1.83
3.39
1.36
6.00
0.03
0.06
285
17
69.94
(0.21)
(0.12)
0.24
1,720
25,213
100
1
1
1
1
2,010
1,761
Min
6.22
3.05
8.29
7.72
4.36
7.70
8.27
9.62
6.76
10.82
20.93
25.27
20.27
24.61
2,250
0.57
78.59
2,200.00
13.74
11.32
3,922.61
15,015.00
0.34
966.87
1,230,000,000
638,000,000
129.34
0.37
0.98
1.06
49,000,000,000
94,700,000,000
50,000
1
17
29
4
2,013
54,215
Max
19,345
19,345
19,345
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
25,690
Obs
-0.09
-4.46
0.10
7.06
4.29
6.74
5.54
6.87
2.38
3.63
10.60
15.12
9.66
14.19
1,408.18
0.46
73.27
873.66
7.32
8.09
495.32
1,877.61
0.11
92.32
179,063
67,039
92.63
0.13
0.53
0.46
0.96
1.00
0.76
0.02
0.25
1.04
1.01
1.35
0.47
1.49
1.49
1.44
1.43
616.96
0.05
1.85
276.78
2.72
0.84
891.03
3,423.18
0.04
220.80
836,405
340,215
6.12
0.15
0.28
0.17
31,000,000
0.66
77,300,000
6,144,134
21
0
5
4
1
1
16,500,000
42
2
7
12
3
2,012
15,384
Std. Dev.
Small Firms 33,036
Mean
-5.60
-10.87
-5.97
4.96
4.18
6.45
0.31
1.79
-1.55
3.00
3.88
8.44
2.94
7.50
143.00
0.21
65.20
630.00
1.83
3.39
1.36
6.00
0.03
0.06
49
19
69.94
-0.21
-0.12
0.24
1,800
4,625
20
2
1
1
1
2,010
1,763
Min
1.53
1.93
7.01
7.72
4.36
7.70
8.27
9.62
6.76
4.60
17.52
22.13
16.80
21.32
2,250.00
0.57
78.59
2,200.00
13.74
11.32
3,922.61
15,015.00
0.34
966.87
40,600,000
19,800,000
129.34
0.37
0.98
1.06
1,820,000,000
4,060,000,000
99
2
17
29
4
2,013
54,213
Max
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Appendix 2: Growth Opportunities No
Sector
Growth Opportunities VAD
Output
Employment
1
Food processing and beverages
0.69
0.78
2
Tobacco
0.83
0.70
0.12
3
Textiles
0.94
0.63
-0.04
4
Garment
0.72
0.54
0.09
5
Leather
0.74
0.63
0.14
6
Wood and wood products
0.64
0.51
0.07
7
Paper and printing
0.41
0.40
0.10
8
Coke and refined petroleum
0.55
0.98
0.01
9
Chemical and pharmaceutical products
0.75
0.85
0.19
10
Rubber and plastics products
0.68
(0.12)
0.00
11
Metal transformation
0.58
0.23
0.08
12
Mechanical equipment
0.85
0.87
0.37
13
Computer, communication and optical products
0.49
0.46
-0.03
14
Electrical equipment
1.06
0.77
0.17
15
Transport equipment
0.27
0.32
0.23
16
Furniture
0.24
0.33
-0.21
17
Repair machinery and recycling
0.53
0.48
0.01
0.33
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Local Financial Development And Firm Performance: Does Financial Outreach Really Matters Within Indonesian Archipelago?
Appendix 3: Estimation at Firm-Level with Controls in Eastern Part of Indonesia Variables lbranch_gopvad lbranch1 gop_vad
(1)
(2)
(3)
(4)
(5)
(6)
-0.409
-0.530
-0.518
-0.561
-0.704
-0.683
(2.953)
(2.887)
(2.822)
(2.800)
(2.773)
(2.841)
0.120
1.001
0.933
1.022
1.381
1.525
(2.117)
(2.297)
(2.285)
(2.262)
(2.269)
(2.326)
-2.258
-2.949
-2.882
-3.091
-3.837
-3.681
(18.70)
(18.31)
(17.88)
(17.76)
(17.60)
(17.99)
pov_rate
27.32
29.85
29.82
35.35*
29.10
(19.08)
(20.66)
(20.01)
(20.75)
(20.53)
inflation inflation1
-0.0284
0.164
0.112
0.144
(0.0708)
(0.182)
(0.170)
(0.168)
0.0485
0.0560
0.0418
0.0543
(0.0764)
(0.0764)
(0.0779)
(0.0809)
lrwage
1.313
1.231
1.347
(1.162)
(1.105)
(1.088)
unemployment
0.0958
0.0821
(0.0613)
(0.0621)
lhousehold
-1.322 (1.442)
Constant
Observations
0.924
3.535
2.672
0.407
1.862
7.091
(13.69)
(14.06)
(13.86)
(13.43)
(13.39)
(15.51)
706
706
706
706
706
706
0.019
0.025
0.027
0.030
0.034
0.035
Number of id
264
264
264
264
264
264
region FE
yes
yes
yes
yes
yes
yes
sector FE
yes
yes
yes
yes
yes
yes
time FE
yes
yes
yes
yes
yes
yes
R-squared
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
VAD growth Output growth Employment growth Branch density growth opportunities by VAD growth opportunities by output growth opportunities by employemnt poverty rate population density household density distance inflation rate unemployment rate real wage
Variables (3)
(2) 1 0.4828 0.0085 0.0304 0.0689 0.231 -0.0021 0.0041 0.0032 0.0058 -0.039 -0.008 0.0543
(1)
1 0.9333 0.4474 0.0001 0.0041 0.063 0.2122 0.0021 -0.0025 -0.003 0.0098 -0.0254 -0.0159 0.0443 1 -0.0018 -0.0275 -0.0849 -0.0157 0.0029 -0.0013 -0.0021 0.0144 0.0092 0.0055 -0.0211
Employment Growth
Output Growth
Vad Growth
1 0.026 0.0579 0.0011 -0.4395 0.953 0.9493 -0.7231 -0.2696 0.3038 0.3032
(4)
Branch Density
1 0.4504 0.3655 0.0132 0.035 0.0339 0.0356 0.0045 0.0461 -0.0092
(5)
Growth Opportunities By Vad
1 0.4837 0.0428 0.0835 0.0829 0.0214 -0.0043 0.0738 -0.0012
(6)
1 0.0578 0.003 0.0026 0.016 -0.0178 0.0356 0.0265
(7)
Growth Growth Opportunities Opportunities By By Output Employemnt
1 -0.3241 -0.2978 0.4306 -0.3189 -0.3632 0.1719
(8)
Poverty Rate
1 0.999 -0.6368 -0.292 0.38 0.2456
(9)
1 -0.6381 -0.3181 0.3519 0.261
(10)
Population Household Density Density
Appendix 4: Correlation Matrix at Regional-Level in Western Part of Indonesia
1 0.1886 -0.1999 -0.2756
(11)
Distance
1 0.3191 -0.9063
(12)
Inflation Rate
1 -0.2309
(13)
UnemployMent Rate
(14)
1
Real Wage
314 Buletin Ekonomi Moneter dan Perbankan, Volume 19, Nomor 3, Januari 2017
VAD growth Output growth Employment growth Branch density growth opportunities by VAD growth opportunities by output growth opportunities by employemnt poverty rate population density household density distance inflation rate unemployment rate real wage
Variables
Output Growth (2) 1 0.2308 0.0266 -0.0466 -0.0283 0.1801 -0.045 0.0104 0.0157 0.0372 0.0013 -0.0836 0.0128
Vad Growth
(1)
1 0.9305 0.2081 0.0067 -0.0431 -0.0294 0.1376 -0.0317 0.0016 0.004 0.0224 -0.018 -0.0949 0.0336 1 -0.0112 0.0823 -0.0339 -0.0449 0.0007 -0.0088 -0.0072 0.0091 0.007 -0.0483 -0.0008
(3)
Employment Growth
1 0.136 0.0065 0.0251 -0.2616 0.8198 0.8756 -0.2371 -0.3819 0.0388 0.2956
(4)
Branch Density
1 0.4177 0.3233 -0.0566 0.1449 0.1383 -0.0926 -0.0402 0.134 0.0116
(5)
Growth Opportunities By Vad
1 0.5205 0.1477 -0.033 -0.0428 -0.12 -0.1532 0.0458 0.1313
(6)
1 -0.0459 0.0492 0.0418 0.0157 -0.0346 0.0354 -0.0051
(7)
Growth Growth Opportunities Opportunities By By Output Employemnt
1 -0.2886 -0.3299 -0.1718 -0.3184 -0.1764 0.3385
(8)
Poverty Rate
1 0.986 -0.3656 -0.2907 -0.1976 0.1446
(9)
1 -0.3062 -0.3204 -0.1666 0.1746
(10)
Population Household Density Density
Appendix 5: Correlation Matrix at Regional-Level in Eastern Part of Indonesia
1 0.2441 0.251 -0.2253
(11)
Distance
1 0.3922 -0.9407
(12)
Inflation Rate
1 -0.2955
(13)
UnemployMent Rate
(14)
Real Wage
1
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Appendix 6: Estimation at Regional-Level in Eastern Part of Indonesia Variables lbranch_gopvad lbranch1 gop_vad pov_rate inflation lrwage unemployment Constant
Observations
Firm Performance VAD growth
Output growth
Employment growth
-0.0135
0.375
-0.212
(0.969)
(0.618)
(0.349)
-0.627
-0.449
0.363
(0.934)
(0.817)
(0.278)
-1.351
0.891
-0.701
(2.102)
(1.275)
(0.469)
-11.93
-16.53
1.595
(10.78)
(10.64)
(3.850)
0.309
0.307
0.0251
(0.246)
(0.245)
(0.0731)
2.126
1.785
0.0607
(1.671)
(1.737)
(0.472)
-0.152*
-0.155**
-0.0365
(0.0818)
(0.0746)
(0.0251)
-1.050
-6.361
-0.115
(3.988)
(3.859)
(1.058)
264
264
264
0.200
0.247
0.123
region FE
yes
yes
yes
sector FE
yes
yes
yes
time FE
yes
yes
yes
R-squared
Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Appendix 7. Scatter Plot of DF-BETA log(branch) and Standardize values of log(branch)
lbranch
-2 0 2 4 6
-2 0 2 4 6
-2 0 2 4 6
-2 0 2 4 6
-2 0 2 4 6
0
2
Graphs by region_province
-2
West_Kalimantan
Riau
4
-2
0
2
West_Nusa_Tenggara
South_East_Sulawesi
Kep. Bangka Belitung
4
2
4
-2
Standardized values of (lbranch)
0
West_Sumatera
West_Papua
0
South_Sulawesi
South_Kalimantan
-2
Maluku
2
East_Kalimantan
Bengkulu
Lampung
East_Java
DKI_Jakarta
Central_Sulawesi
Jambi
Banten
Bali
Aceh
4
-2
0
Yogyakarta
2
South_Sumatera
North_Sulawesi
East_Nusa_Tenggara
Central_Java
Appendix 8. Scatter Plot of Standardize values of log(branch) by provinces in Indonesia over 2010-2013
4
-2
0
West_Java
2
North_Sumatera
Gorontalo
Central_Kalimantan
4
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Appendix 9. Variance Inflation Factor (VIF) Variable
Variance Inflation Factors (VIF) (1)
(2)
(3)
lhousehold
43.17
lbranch1
42.98
2.08
1.33
unemployment
3.68
1.91
1.90
ldist
3.44
3.15
inflation1
3.08
3.07
2.90
pov_rate
2.64
2.64
2.08
lrwage
2.56
2.54
2.40
gop_vad
1.00
1.00
1.00
Mean VIF
12.82
2.34
1.94
Financial Stability In Azerbaijan: The Application Of Fuzzy Approach
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FINANCIAL STABILITY IN AZERBAIJAN: THE APPLICATION OF FUZZY APPROACH1 G.C. Imanov, H.S.Alieva, R.A.Yusifzadeh2
Abstract
This paper develops an aggregate financial stability index (AFSI) for Azerbaijan financial system, over the period 2005-2015. The data used includes four composite indices; consisting nineteen individual indicators in total. We use intuitionistic fuzzy to set the weights of sub-indices and define the level of financial stability in Azerbaijan. Determining the weights appropriately across different years is important. This paper concludes the fuzzy assessment of the index is more capable compared to standard approach in capturing the dynamics of financial stability during the observed period.
Keywords: financial stability, intuitionistic fuzzy sets, financial development index, financial vulnerability index, financial soundness index JEL Classification: C43, C51, C53, E58
1 Earlier version of this paper was presented on 10th International Conference, the Bulletin of Monetary Economics and Banking, Bank Indonesia. Authors thank to reviewers and panelist for the great discussion during the conference, and let us improve this paper. 2 Authors are researchers on Institute of Control Systems of the National Academy Sciences of Azerbaijan Republic, korkmazi2000@ gmail.com (corresponding author);
[email protected]; and
[email protected].
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I. INTRODUCTION Financial stability is a broad concept, included different aspects of financial system, such as institutions, infrastructure, and markets. This is an important phenomenon in terms of the real economic growth. The financial system is stable only when it is able to promote the productivity of the economy and to prevent financial imbalances that arise endogenously or as a result of adverse and unforeseen events, Shinasi (2004) Consequences of the financial and economic crises of the late XX and early XXI extended the necessity for researches on financial stability in central banks, financial institutions and independent experts at the national and international levels. The purpose of these researches was to develop appropriate approaches and evaluation methods for timely identification of sources of financial instability and to design a correct appropriate response to them. The major objective of analysis of financial stability is to examine the different relationships, detecting negative trends, as well as economic, regulatory and institutional determinants for assessing state of the financial system and its vulnerabilities. Considering the financial stability of the system as a phenomenon within a particular state or region is commonly used a set of indicators that reflect the state of not only the financial sector institutions, infrastructure and the market in general, but also real, public, external sectors of the economy. So that it takes into account changes in the macroeconomic environment, which have a significant impact on the financial system. From the international comparability of indicators have been developed guidelines for the compilation of financial soundness indicators3, by IMF and the monetary authorities of countries. Furthermore, the European Central Bank has developed a list of indicators for macro prudential monitoring financial stability of the European Union banking system. In order to assess and monitor the financial stability for individual country an independent experts and the monetary authorities of the European Union have developed indicators taking into consideration the features of national economies (Gersl (2004)- National Bank of Czech Republic, Van den End (2005)- Central Bank of Netherlands, Rouabah (2007) - Central Bank of Luxembourg and etc.). The aim of this research is to add new input to the financial stability literature by examining the case of emerging country like Azerbaijan. Specifically, the main objective of the paper is to provide a new approach in weighting procedure to estimate an aggregate financial stability index. To the best of our knowledge the paucity of studies on financial stability index is very striking in the case of Azerbaijan. Moreover, this is the first attempt to fuzzy estimation of this index.
3 Financial Soundness Indicators: Compilation guide, IMF, 2006. This compilation includes 39 indicators into two groups. First group reflects set of “base “ indicators for banking sector, second group of indicators are called “recommended set”, included 27 indicators.
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In this paper first on the base of yearly data for the period 2005-2015 was used standard method to estimate an aggregate index (AFSI) and corresponding composite indices, where the weights of sub-indices were taken equal to 0.25. Then to compute the weights we applied intuitionistic fuzzy sets, which allow avoiding subjectivity. However it is worth to note that fuzzy approach allow to acquire some following advantages; first, in a standard calculation of financial stability index, the value “0” indicates instability and “1” recorded as a stability of the financial system. But fuzzy approach is convenient to avoid this kind of disjunction by determining a distributed terms such as “very low stability”, “low stability” and etc. Secondly, diversified terms are obtained not only for aggregated index, but also for composite indices. This gives an opportunity to establish a stability level for sub-indices individually. The third advantage of using fuzzy approach is that in a large empirical literature the weights for individual indicators and for AFSI have assigned equally, but some of them have been defined differently depending on the author’s judgments. As mentioned in Albulescu (2004) latter requires complete data and it is difficult to justify and demonstrate the choice of the statistical weight. In our case we assumed an impact of individual indicators on financial stability equally, but composite indexes are weighted by intuitionistic fuzzy sets, which provide obtaining the different weights according to years. For the deepening of the research we are dealing with extension of the list of individual indicators and then to apply fuzzy approach to all indicators in order to obtain different weight for the individual indicators. The remainder of the paper is structured as follows. A brief theory and related literatures are reviewed in section two. Next we present methodology of the construction of the stability indices in the third section. Section four contains the calculation result of the aggregate stability index for Azerbaijan financial system. The last section points out of the findings of this study and conclude the presentation of this paper.
II. THEORY Recent global financial crisis and changes in world economy have re-kindled the interest of central bankers and policy makers on the financial system stability assessment. From the empirical prospects a large body of literature has applied various indexes in measurement of financial stability. Illing and Liu (2003) developed the Financial Stress Index (FSI) for Canada. In the study using daily data from the survey have been chosen variables reflecting banking sector, foreign exchange market and debt market. A standard method and credit weighting techniques were used for calculation.
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Experts from Netherlands Central Bank Van den End (2006) created a Financial Condition Index (FCI) for Netherlands and six OECD countries. FCI index was built based on interest rates, effective exchange rate, real estate prices, and stock prices, solvency of financial institutions and volatility of financial institutions stock index. Then the FCI index has been extended to Financial Stability Condition index (FSCI). Weighting of the indicators have been employed by combining backward-looking IS curve and VAR (Vector Autoregressive Model). In the paper for the Romanian financial system stability, Albulescu (2009) developed a synthetic index in which aggregates different indicators for financial stability. For the purpose of constructing aggregate index based on quarterly data, twenty individual indicators were incorporated into four composite indices: (i) financial development index, (ii) financial vulnerability index, (iii) financial soundness index, and (iv) world economic climate index. The aggregation of these indicators employed standard approach with equal weighting for individual indicators. In the research developed by Morris (2010) for Jamaica applied normalization and aggregation procedures to create financial stability index. In their study weight of the subindices are determined by judgmental approach. The next paper from a large literature on FSI assessment is Gersl and Hermanek (2006) aggregate index for the Czech Republic banking sector, which was called Banking Stability Aggregate Index (BSAI). The indicators were selected based on current international practice and weights established based on authors experience and judgments. Nelson and Perli (2005) have constructed a Financial Fragility Index (FFI) for United States financial system in two-step process. First step involves three group indicators which take into consideration the level, volatility and correlation of twelve individual variables. Second step present logit model estimation to obtain the probability that the behavior of financial markets corresponds to previous financial crisis. For the Azerbaijan there are a few papers developed for the financial stability assessment. One of them is employed by Yusifzade and Mammadova (2015). However in the paper is developed panel estimation for developing and developed countries, which aggregated data are obtained by using principle of components. The study captures four aspects of financial system as depth, access, efficiency and stability. Then aggregated index is used to estimate relationship between financial development and economic growth. According to panel estimation results show that as financial stability reaches some intermediate level it starts to ensure economic development. However, economic development reverses if financial system is excessively stable and financial intermediaries keep more capital and liquidity than what is needed. In the following section, we will describe construction method of financial stability index for Azerbaijan financial system, using standard procedure with fuzzy approach in weighting of sub-indices.
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III. METHODOLOGY 3.1. The construction of the stability index for the Azerbaijan financial system. To measure the aggregated financial stability index, we use four sub-groups of indicators. This includes the Financial Market Index (FMI), Financial Vulnerability Index (FVI), Financial Soundness Index (FSI), and World Economic Climate Index (WEI). Indicators within each sub-indices are explained in detail below.
The indicators of Financial Market Index - FMI 1. Total credit to GDP ratio (DC) - provides information about the ability of credit institutions in performing their intermediation functions. High value of this indicator increases the value of sub-index. 2. Interest Spread (IS) - defined as the difference between credit rates and deposits rates. The high spreads interpreted as incompetence of intermediation and allocation of resources, and low spreads are an indicator of the effectiveness of the banking system. High interest spreads have a negative impact on financial stability. 3. Herfindahl–Hirschman Index (HHI) in assets - demonstrates the concentration level of financial market. US Department of Justice considers markets with HHI value equal to less than 1,000-unconcentrated, 1000-800 - moderately, above 1000 - highly concentrated markets. 4. Market capitalization data was unavailable for analyzed period, thus we were satisfied with data represented above
The indicators of Financial Vulnerability Index - FVI 1. Fiscal deficit to GDP ratio (FD) - is taken as an indicator of financial system stress. High value of the indicator has a negative impact on economic development. 2. Current account (CA). The indicators of balance of payments allow tracking up the coming external shocks. A significant deficit in current account may indicate to increasing possibility of a currency crisis and reducing the liquidity of the national financial system. 3. Inflation rate (IN) - rising inflation distorts price proportions and profitability indicators of economic processes, which leads to inefficient use of financial resources; deters the inflow of foreign investment; devalues national currency savings. 4. Real Effective Exchange Rate (REER) - this indicator reflects the exports competitiveness. The increase in this indicator expresses the competitiveness of the sector. High volatility negatively affects the financial system.
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5. Public Debt to GDP ratio (PD) - a high level of this indicator negatively affects financial stability. 6. International Reserves to Import ratio (IR) - a sufficient level of international reserves allows the monetary authorities to conduct an independent and flexible monetary and currency policy by adjusting the level and volatility of the exchange rate of national currency and provide liquidity to the economic agents of financial markets in a stressful and crisis periods. High value of this indicator positively affects the financial system. 7. Non-government Credit to Total Credit ratio (NGC) - Reduction in the value of this indicator has a negative impact. 8. Ratio of M2 to International Reserves (MIR) - the increase adversely affects the adequacy of reserves. 9. M2 multiplier (MM) - High level of value has a negative influence to financial stability.
Indicators of Financial Soundness Index - FSI 1. Return on Assets (ROA) - High value refers to effectiveness of banking system. 2. Bank Capital to Assets Ratio (BCA) - increase in this indicator has a positive effect on performance of the banking system. 3. Liquid Assets to Total Assets ratio (LAA) - The growth indicates increasing liquidity, while reduction shows decline in the liquidity of banking sector. 4. Bank regulatory capital to risk weighted assets (RCRA) - the growth in the value of this indicator negatively affects banking system.
Indicators of World Economic Climate Index (WEI)4 1. World Economic Growth (WEG) - Azerbaijan has new formulated financial system and growth in the global economy positively impact on financial system of country 2. Oil Price in the world market (OPR)[9] - due to Azerbaijan is resource rich country and economy more supported by oil revenues, rise in oil prices has a positive effect in the economy as a whole. All financial systems are interconnected and deterioration of these indicators such as, world economic growth, world inflation and oil prices has negative impact at national level for economic and financial stability, Albulescu (2010).
4 CESifo, calculated by Munich group
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3.2. Standard Approach on Measuring Aggregated Financial Stability Index A standard procedure of calculation an aggregate index of financial stability includes the normalization of individual indicators. For this purpose has been used the formula as following:
where, Xtn is normalized value of indicator X in year t, Xt - average value of the indicator X, σt - is standard deviation of the indicator X during the period. After the normalization an individual indicators were grouped into respective four subindices by the following formula:
Aggregate index of financial stability is computed as follows:
where, wi (i=1,..,4) - are weights of corresponding sub-indices.
3.3. Fuzzy Approach to Measuring Financial Stability Index Measurement performs two quite distinct roles. One is to help ensure the accountability of the authorities responsible for performing the task. The other is to support the implementation of the chosen strategy to achieve the goal in the real time. The former calls for ex post measurement of financial instability, i.e. for assessments of whether financial instability prevailed or not at some point in the past. The latter relies on ex ante measurement, i.e. on assessment of whether the financial system is fragile or not today. While both ex ante and ex post measurement are “fuzzy”, the challenges in supporting strategy implementation are tougher (Borio and Drehmann (2009))
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The literature mentions several methods for determining the weights of the variables in the FSI. These are econometric estimations with a macroeconomic model, a reduced form aggregate demand function (backward-looking IS curve), or a Vector Autoregression Model (VAR). The weights can also be determined by the way of economic arguments, such as a variable’s importance for the financial system. Alternatively, each variable in the index can be given in equal weights. In some studies, the above methods are combined (Van den End, 2006). In determining the weights of sub-indices are mainly used expert assessments. However, it should be noted that the values of these weights depend not only on time but also on situation existing in the various financial markets and global economy. In order to define the weights of individual sub-indices of aggregated index, we have used intuitionistic fuzzy set technique. The intuitionistic fuzzy set, proposed by K.Atanassov (1986), is a generalization of L. Zadeh’s fuzzy set. The intuitionistic fuzzy set is defined as:
where,
if
numbers indicate the degree of membership and nonmembership of x to a respectively. For each intuitionistic fuzzy set X, there is intuitionistic index x in A.
In this study in order to define weights of financial stability sub-indices, we used generalized entropy measure of intuitionistic fuzzy set F, composed of n elements, proposed by E. Szmidt and J. Kacprzyk (2001):
where,
Financial Stability In Azerbaijan: The Application Of Fuzzy Approach
327
The weights of each individual index are defined on the basis of the following formula:
IV. RESULT AND ANALYSIS 4.1. Standard Procedure of Measuring AFSI Following the standard method on calculating the aggregate financial stability index, we firstly normalize every single of individual indicators. Table 1 shows the normalized value of those indicators for 2005-2015 years. Table 1. Normalized Velues of The Indicators of Financial Stability in Azebaijan during 2005-2015 Years
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Financial Market Indicators - FMI DC
-1.3
-1.08
-0.6
-0.72
0.01
0.071
-0.37
0.27
0.36
1.33
2.04
IS
1.81
0.79
0.20
1.08
0.20
-0.9
-1.12
-1.27
-1.05
-0.02
0.29
HHI
-6.69
-6.69
0.24
1.23
1.36
0.81
0.98
-0.75
-1.04
-1.12
0.24
Financial Vulnerability Indicators FD
-1.12
1.031
-0.2
0.49
-1.12
-1.48
1.21
0.672
1.21
-0.76
0.08
CA
-1.68
-0.11
0.75
1.36
0.40
0.86
0.64
0.21
-0.15
-0.5
-1.76
IN
0.37
0.17
1.46
2.09
-0.88
-0.23
0.105
-0.94
-0.74
-0.90
-0.50
REE
-1.57
-1.4
-1.10
0.24
-0.08
0.53
0.86
0.66
0.72
1.48
-0.35
PD
0.76
-0.52
-1.20
-1.72
0.14
-0.15
-0.56
0.06
0.96
1.83
0.39
IR
-1.69
-1.29
-0.50
-0.15
-0.19
0.221
0.43
0.67
1.32
1.59
-0.40
NGC
-1.45
-0.24
0.28
-0.32
-1.31
-0.94
-0.07
0.26
1.28
1.57
0.93
MFR
-2.18
-0.73
-0.50
-0.78
0.42
0.95
-0.25
0.48
0.39
0.96
1.21
MM
-1.62
-0.35
0.92
-0.35
-0.35
-0.35
-0.35
-0.35
0.92
2.20
-0.35
ROA
0.32
-0.09
0.99
0.45
0.86
-1.16
-1.03
-1.43
-0.49
-0.22
1.80
BCA
0.9
0.02
0.24
0.46
0.90
0.24
-0.57
-0.35
0.46
0.39
-2.69
LAA
1.96
1.86
-0.20
-0.33
-0.85
-0.10
-0.18
0.08
-0.85
-0.53
-0.89
RCRA
1.53
0.49
1.12
-0.02
0.03
-0.44
-1.58
-0.44
0.18
0.75
-1.63
Financial Soundness Indicators
World Economic Index WEG
0.67
1.02
0.96
-0.27
-2.54
0.90
0.14
-0.27
-0.21
-0.15
-0.27
OPR
-1.28
-0.76
-0.40
0.77
-0.88
-0.07
1.10
1.14
1.10
0.73
-1.40
ECI
0.73
1.14
0.73
-2.35
0.02
0.58
-0.95
-0.67
0.58
0.30
-0.11
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The results of calculation for sub-indices and aggregate index of Azerbaijan during 20052015 are given in Table 2 and Figure 1. We have assumed each of the sub-indices have received the weights equal to 0.25.
Table 2. Sub-indices and Aggregate Index of Financial Stability of Azerbaijan during 2005-2015 2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
FMI
-2.06
-2.33
-0.05
0.53
0.53
-0.01
-0.82
-0.58
-0.58
0.06
0.85
FVI
-1.13
-0.38
-0.01
0.10
-0.33
-0.06
0.22
0.19
0.66
0.83
-0.08
FSI
1.18
0.57
0.54
0.14
0.23
-0.36
-0.84
-0.53
-0.17
0.10
-0.85
WEI
0.04
0.47
0.41
-0.62
-1.13
0.47
0.10
0.07
0.49
0.29
-0.59
AFSI
-0.49
-0.42
0.22
0.04
-0.18
0.01
-0.34
-0.21
0.10
0.32
-0.17
2,5 2
FMI
FVI
FSI
WEI
AFSI
1,5 1 0,5 0 -0,5 -1 -1,5 -2 -2,5 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Figure 1. Sub-indices and Aggregated Index of Financial Stability of Azerbaijan
Conforming with standard method, the outcome describes that AFSI has not received the value of “1” during 2005-2015 period. This indicates the financial system of Azerbaijan has not been stable along this period. This results leave in doubt because after 2006, Azerbaijan enjoy the oil boom era where the banking sector performance was satisfactorily.
4.2. Fuzzy Approach Result on Measuring AFSI If we use uniform weights, we may not be able to examine the changes in the economy for each single year. Fortunately the Fuzzy approach is competent to close this gap. In a fuzzy approach, to aggregate index of financial stability, we classify the values of sub-indices into four categories below. The matrix of linguistic variables for the years of 2005-2015 is provided in Table 3.
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• Very low stability – VLS = (-2.43, -2.43, -1.20); • Low stability – LS = (-1.23,0.00,0.00); • Stable – S = (0.00,0.00, 0.65); • High stability – HS = (0.63; 1.28; 1.28). Table 3. Matrix of Linguistic Values of Sub-indices in The Period 2005-2015 Sub-indices
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
FMI
VS
VL
LS
ST
ST
LS
LS
LS
LS
ST
HS
FVI
LS
LS
LS
ST
LS
LS
ST
ST
HS
HS
LS
FSI
HS
ST
ST
ST
ST
LS
LS
LS
LS
ST
LS
WEI
ST
ST
ST
LS
LS
ST
ST
ST
ST
ST
LS
Note : VLS = Very low stability; LS = Low Stability; S = Stable; HS = High Stability
Based on fuzzy method, the calculation of each sub-indices of financial stability in Azerbaijan during 2005-2015 years, is provided on Table 4.
Table 4. Indicators of Intuitionistic Fuzzy Set Sub-indices
FMI μ1 t
ν1 t
FVI π1 t
μ2 t
ν2 t
FSI π2 t
μ3 t
ν3 t
WEI π3 t
μ4 t
ν4 t
π4 t
Years 2005
0.70
0.30
0
0.08
0.92
0
0.85
0.15
0
0.94
0.06
0
2006
0.92
0.08
0
0.69
0.31
0
0.12
0.88
0
0.28
0.72
0
2007
0.96
0.04
0
0.99
0.01
0
0.16
0.84
0
0.36
0.64
0
2008
0.18
0.82
0
0.85
0.15
0
0.78
0.22
0
0.50
0.50
0
2009
0.20
0.80
0
0.73
0.27
0
0.64
0.36
0
0.08
0.92
0
2010
0.99
0.01
0
0.95
0.05
0
0.70
0.30
0
0.27
0.73
0
2011
0.33
0.67
0
0.66
0.34
0
0.32
0.68
0
0.85
0.15
0
2012
0.53
0.47
0
0.70
0.30
0
0.57
0.43
0
0.89
0.11
0
2013
0.53
0.47
0
0.04
0.96
0
0.86
0.14
0
0.24
0.76
0
2014
0.89
0.11
0
0.31
0.69
0
0.85
0.15
0
0.54
0.46
0
2015
0.35
0.65
0
0.93
0.07
0
0.30
0.70
0
0.52
0.48
0
The calculation of entropy for each individual sub-indices during the year of 2005 is given below:
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The entropy for each individual sub-index in 2005-2014 is:
The weights of individual sub-indices for the year of 2015 are calculated as follows:
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Using weights of individual sub-indices and their linguistic values (Table 3), the aggregated index of financial stability is calculated for the year of 2015:
For the whole period observed, the weights of sub-indices and aggregated index of financial stability are given in Table 5.
Table 5. Weights of Sub-indices and Aggregated Indices Indicators W1
W2
2005
0.18
2006
0.31
W3
W4
0.25
0.28
0.29
0.19
0.29
0.21
AFSI
Years
2007
0.3
0.31
0.25
0.14
2008
0.34
0.35
0.31
0
2009
0.28
0.23
0.16
0.33
2010
0.32
0.3
0.18
0.2
2011 2012 2013 2014 2015
0.22 0.06 0.04 0.37 0.23
0.21 0.32 0.37 0.23 0.45
0.23 0.14 0.32 0.34 0.28
0.35 0.49 0.26 0.06 0.04
(-0.57,-0.08,0.33) LS - ST (-0.98,-0.75,-0.04) LS (-0.75,0,0251) LS - ST (0,0,0.65) ST (-0.69,0,0.228) LS - ST (-1,0,0.13) LS - ST (-0.55,0,0.37) LS - ST (-0.24,0,0.53) LS - ST (-0.2,0.47,0.64) LS - ST (0.15,0.29,0.79) ST - HS (-0.79,0.29,0.29) LS - ST
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Having the result above, now we can compare the financial stability from the two approaches; the standard and the fuzzy approach. With respect to the dynamics of AFSI, we may conclude the fuzzy approach is more satisfactory than the standard method. This is because the fuzzy approach can capture the changes in economy. Considering the fact that Azerbaijan is in transition with the new financial system, the results are generally represent low stability during the period of 2005-2007 and 2009-2013. The aggregate index is higher in 2008 and 2014 which reflects the higher stability of the Azerbaijan financial system. The reason for this was the presence of significant economic growth supported by stimulated banking sector due to the oil boom. With the “Contract of the Century, 1994”, Azerbaijan economy is gifted with oil revenues and 2007 year was an oil boom for this country. The changes provided significant oil revenues and contributed to the economy and the financial system. As a result of global economic crises since 2009, the stability decreased to low stable level. Starting 2010, the economy was in recovery stage and the increasing of oil prices committed to higher stability performed in 2014. However, the declining oil price by the end of 2014 and devaluation of exchange rate in 2015 affected the activity and the performance of banking sector. The result was a low stable level of financial stability. These dynamics are well captured by the aggregated financial stability obtained with Fuzzy approach. This finding may contribute to the way we construct the financial stability index for any possible use, including forecasting it based on indicators constructing it.
V. CONCLUSION This paper has developed a method to calculate the weights of composite indices and the quality level of financial stability in Azerbaijan, covering the period of 2005-2015. Main step on this approach is appropriately determining the weights for the years. It is worth to note that uniformly apply equal weights to the aggregate index is not capable to cover the economic changes in 2005-2015. Taking the advantage of using Fuzzy approach, this paper constructed the weights representing each year separately. Moreover instead of using disjunction of the term “stability” and “instability”, we manage to categorize the stability index onto four categories, starting from “very low stability” to “very high stability”. This paper concludes that the fuzzy approach is superior to the standard one because it is able to capture key periods of financial stability during the sample period. For future research, one may enhance the result and by extending the list of individual indicators and then apply the fuzzy approach to obtain different weights for all individual indicators.
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REFERENCES Albulescu, C.T. (2008). Assessing Romanian Financial Sector Stability by Means of an Aggregate Index. Oeconomica, 17(2): 67-87. Albulescu, C.T. (2010). Forecasting the Romanian Financial System Stability Using a Stochastic Simulation Model. Romanian Journal of Economic Forecasting, (1): 81-98. Morris, V.C. (2011). Measuring and Forecasting Financial Stability: The Composition of an Aggregate Financial Stability Index for Jamaica, Journal of Business, Finance and Economics in Emerging Economies, 6(2): 34-51 Nasreen, S. and Anwar, S. and Shahzadi, H. (2015). Financial Stability and Macroeconomic Environment: Evidence from Panel Data Analysis of South Asian Countries. Pakistan Journal of Social Sciences, 35(1):145-160 Atanassov, K. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1): 87-96 Szmidt, E. and Kacprzyk, K. (2001). Entropy for intuitionistic fuzzy sets. Fuzzy Sets and Systems,(118): 467-477 G.J.Shinasi, G.J. (2004). Defining Financial Stability, International Monetary Fund WP/187 Illing, M. and Y, Liu. (2003). An Index of Financial Stress for Canada. National Bank of Canada Working Paper /14: 63p. Van den End, J.W. (2006). Indicator and Boundaries of Financial Stability. De Nederlandsche Bank Working Paper /97: 24 p. Borio, C. and Drehmann, M. (2009). Towards an operational framework for financial stability: “fuzzy” measurement and its consequences, BIS Working Papers /284: 50 p. Jordan, A and Smith, L. (2014). Measuring the Level of Financial Stability in the Bahamas. 46th Annual Monetary Studies Conference: Macro-Prudential Supervision, Financial Stability And Monetary Policy, Central Bank of Trinidad and Tobago Karanovic, G. and Karanovic, B. (2015). Developing an Aggregate Index for Measuring Financial Stability in the Balkans. 7th International Conference, The Economies of Balkan and Eastern Europe Countries in the changed world, EBEEC: 3-17 BP. (2015). Statistical Review of World Energy, 64th edition, June 2015, 45 p. CESifo Group Munich, www.ifo.de/w/4LsApn939
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PENGARUH FINANSIALISASI TERHADAP KETIMPANGAN PENDAPATAN DI ASEAN: ANALISIS DATA PANEL Pihri Buhaerah1
Abstract
This paper examines the impact of financialization on income inequality in ASEAN-5 countries for the period of 1990-2013 by employing panel data analysis. The data was collected from various secondary sources by undertaking fixed effect model and generalized method moment. The result shows that there is a significant relationship between all financialization indicators and income distribution. Generalized method moment analysis using Arellano-Bond estimator also shows that all financialization indicators have a significant relationship with income distribution. There is no different sign estimator both in fixed model effect and generalized method moment analysis. This paper revealed that financialization indicators such as stock market capitalization and return on assets contribute positively to worsen income inequlality. In contrast, domestic private debt securities have a negative effect on gini coefficient in ASEAN-5 countries indicating that increasing domestic private debt securities will improve income distribution in the region.
Keywords: Financialization, inequality, fixed effect model, generalized method moment JEL Classification: C23, D31
1 Author is Researcher Associate at Jakarta Institute for Financial Policy (JIFP), and Economist at the Indonesian National Commission on Human Rights (Komnas HAM) ; (
[email protected])
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I. PENDAHULUAN Isu kesenjangan ekonomi baik antar kawasan dan negara maupun antar kelompok pendapatan dalam dalam satu negara tengah menjadi sorotan tajam dalam dua dekade terkahir. Terlebih lagi, saat ini situasi perekonomian global kian berisiko dan tidak pasti. Dalam batas dan konteks tertentu, beberapa pihak meyakini, ketimpangan bisa mendongkrak kinerja pertumbuhan ekonomi suatu negara. Namun, di sisi yang lain, ketimpangan justru cenderung berevolusi menjadi mesin yang merusak proses akumulasi modal fisik, pembangunan sumber daya manusia dan pertumbuhan ekonomi yang berkelanjutan. Bahkan, dalam beberapa kasus, ketimpangan terbukti telah memicu ketidakstabilan politik di mana ujungnya malah berdampak pada volatilitas ekonomi yang menyebabkan situasi perekonomian kian sulit diprediksi dari waktu ke waktu. Sehubungan dengan hal itu, Laporan PBB Tahun 2013 mengungkapkan bahwa tingkat ketimpangan secara global masih tergolong tinggi. Alasannya, pada 2010, negara-negara berpendapatan tinggi ditaksir menikmati pendapatan 55 persen dari total pendapatan global. Padahal, negara-negara tersebut hanya didiami 16 persen dari total populasi dunia. Ironisnya, negara-negara berpendapatan rendah yang dihuni sekitar 72 persen dari populasi global justru hanya menikmati 1 persen dari keseluruhan pendapatan global. Laporan tersebut juga mengungkapkan bahwa nilai koefisien gini internasional sebagai refleksi ketimpangan internasional pada 2010 relatif tetap lebih tinggi dibandingkan nilai koefisien gini pada 1980. Lebih lanjut, tingkat kesenjangan ekonomi antar kawasan menurut laporan UNDP Tahun 2013 yang bertajuk “Humanity Divided: Confronting Inequality in Developing Countries”, menyebutkan bahwa hampir seluruh kawasan mengalami peningkatan nilai koefisien gini terkecuali Kawasan Amerika Latin, Karibia, dan Afrika. Afrika menjadi kawasan yang mengalami penurunan tingkat ketimpangan yang paling tinggi yakni sebesar 7 persen, diikuti Kawasan Amerika Latin (Argentina, Brazil, dan Meksiko) dan Karibia (5 persen). Sementara itu, negaranegara di Zona Eropa dan Kelompok Negara Persemakmuran menjadi wilayah dengan penigkatan koefisien gini yang paling tinggi (35 persen) dibandingkan kawasan lainnya, diikuti Kawasan Asia dan Pasifik (13 persen). Menariknya, laporan tersebut juga mengungkapkan bahwa tingkat ketimpangan pendapatan rumah tangga di negara-negara berpendapatan tinggi justru terindikasi lebih rendah (9 persen) ketimbang negara-negara berpendapatan rendah dan menengah (11 persen). Selain itu, penelitian yang mengupas tentang ketimpangan pendapatan dua dekade terakhir juga sudah tak terhitung banyaknnya. Sejumlah kajian yang seringkali dirujuk oleh para akademisi dan praktisi pembangunan sejauh ini masih terpusat pada relasi antara pertumbuhan ekonomi dan ketimpangan (Dollar & Kraay (2002), Benhabib (2003), Adam (2003), Barro (2008), Berg & Ostry (2011), Dollar, Kleineber, & Kraay (2013), Kraay, Dollar, & Kleineberg (2014)). Dalam hal ini, perdebatan relasi antara keduanya terpolarisasi ke dalam dua kutub. Kutub pertama, relasi dari pertumbuhan menuju ketimpangan yang mengikuti hipotesis yang dibangun oleh
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Kuznets. Kutub kedua, pengaruh ketimpangan terhadap pertumbuhan ekonomi sebagai anti tesis dari hipotesis yang dibangun oleh Kuznets. Dari sejumlah kajian yang mengupas tentang ketimpangan ekonomi sejauh ini secara umum menyebutkan bahwa faktor-faktor penyebab kesenjangan ekonomi dapat dikelompokkan ke dalam dua hal, yakni faktor eksogen (dari luar negeri) dan faktor endogen (dari dalam negeri). Faktor eksogen yang memacu kesenjangan ekonomi meliputi globalisasi perdagangan, keuangan, dan perubahan teknologi (UNDP, 2013). Adapun faktor domestik (endogen) yang berkontribusi terhadap ketimpangan distribusi pendapatan adalah kebijakan ekonomi makro, kebijakan pasar tenaga kerja, ketimpangan kekayaan, kebijakan perpajakan dan transfer, dan belanja pemerintah. Sementara itu, sejumlah kajian juga mencoba mengupas pertautan pembangunan keuangan dengan ketimpangan pendapatan (Clarke et.al (2003), Beck et.al (2004), Claessens & Perotti (2005), Canavire-Bacarreza & Rioja (2008), Demirguct-Kunt & Levine (2009), Kappel (2010), Jauch & Watzka (2012), dan Park & Shin (2015)). Sayangnya, studi yang secara empiris mengkaji pengaruh finansialisasi terhadap kesenjangan pendapatan masih belum banyak dilakukan. Belum tersedianya data yang memadai baik dalam bentuk lintas negara maupun deret waktu menjadi salah satu faktor penyebab masih kurangnya kajian yang terkait dengan isu finansialisasi dan ketimpangan pendapatan. Meski sedikit lebih kompleks, beberapa penelitian mencoba memulai membedah isu ini secara lebih sistematis dan mendalam. Sebagai contoh, hasil kajian Hou Lin dan TomaskovicDevey (2013) menunjukkan bahwa kenaikan ketergantungan terhadap pendapatan keuangan (financial income), dalam jangka panjang, menyebabkan penurunan porsi buruh atas pendapatan, peningkatan bagian eksekutif puncak atas kompensasi, dan pendapatan antar pekerja melebar di Amerika Serikat (AS). Dengan menggunakan data deret waktu mulai 1970 sampai 2008, Lin dan Tomaskovic-Devey (2013) menemukan bahwa finansialisi menyebabkan porsi buruh atas pendapatan menurun lebih dari setengahnya, kenaikan pertumbuhan kompensasi ekstekutif sebesar 9,6 %, dan peningkatan pertumbuhan perbedaan pendapatan antar pekerja sebesar 10,2 %. Hal senada juga ditemukan dalam studi yang dilakukan oleh Kus (2012) tentang finansialisasi dan ketimpangan pendapatan di negara-negara OECD. Dengan menggunakan data 1995-2007 dari 20 negara OECD, hasil temuan Kus (2012) mengindikasikan bahwa finansialisasi terbukti berkontribusi secara positif terhadap kenaikan ketimpangan pendapatan. Kus (2012) juga menemukan bahwa pada negara yang memilki serikat buruh yang lemah, efek finansialisasi cenderung lebih kuat dalam meningkatkan kesenjangan pendapatan ketimbang negara dengan serikat buruh yang lebih kuat. Hasil penelitian yang lebih terkini seperti Dunhaup (2014) juga mengkonfirmasi temuan ketiga hasil kajian sebelumnya yakni finansialisasi memainkan peran yang penting dalam meningkatkan ketimpangan di negara-negara maju.
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Sayangnya, kesemua hasil penelitian tersebut pada umumnya hanya mengambil sampel dari negara-negara maju di Kawasan Eropa dan Amerika. Padahal, negara-negara lain seperti negara-negara di Kawasan ASEAN juga menarik untuk dikaji karena isu ini bukan hanya persoalan negara-negara maju. Kawasan ASEAN sendiri dianggap sebagai salah satu kawasan pertumbuhan yang relatif stabil dan dinamis dibanding Kawasan Amerika Latin, termasuk dalam hal pembangunan sektor keuangan di mana menunjukkan kinerja pertumbuhan yang cukup signifikan. Sayangnya, kawasan ini juga menyimpan potensi instabilitas politik dan sosial yang tinggi karena kesenjangan yang kian menganga dari tahun ke tahun. Terkait hal itu, nilai koefisien gini negara-negara ASEAN seperti Singapura, Brunei, Malaysia, Thailand, Indonesia, dan Filipina terindikasi cukup bervariasi. Studi yang dilakukan oleh Bock (2014) menemukan bahwa Brunei, Malaysia, dan Singapura cenderung memiliki koefisien gini yang lebih tinggi dibanding negara ASEAN lainnya. Nilai koefisien gini ketiga negara tersebut telah menembus batas psikologis (0.40) karena telah mencapai angka 0.45. Adapun tren ketimpangan di Thailand, Indonesia, dan Filipina memiliki pola yang sedikit berbeda (Bock, 2014). Pada awalnya, Thailand dan Filipina memang identik dengan tingkat distribusi pendapatan yang tinggi karena nilai koefisien gininya mencapai 0.45. Namun, beberapa tahun terakhir, kedua negara tersebut berhasil menurunkan nilai koefisien gininya. Sebaliknya, Indonesia awalnya memiliki nilai koefisien gini yang tergolong rendah. Namun, nilai koefisien gini Indonesia belakangan cenderung mengalami peningkatan dari tahun ke tahun. Karena ASEAN-5 identik dengan masalah ketimpangan yang tinggi dalam proses pertumbuhan ekonominya, maka analisis tentang faktor-faktor kunci yang memacu ketimpangan pendapatan di kawasan ini menjadi penting untuk dikaji. Berbeda dengan kajiankajian ketimpangan sebelumnya yang lebih difokuskan pada hubungan antara instrumen kebijakan sosial dan fiskal terhadap ketimpangan, kajian ini lebih diarahkan pada relasi antara instrumen kebijakan keuangan dan distribusi pendapatan. Menariknya lagi, kajian ini juga akan mengupas secara khusus mekanisme transmisi dan dampak aktivitas finansialisasi korporasi terhadap distribusi pendapatan di Kawasan ASEAN khususnya di Singapura, Malaysia, Thailand, Indonesia, dan Filipina yang biasanya dikenal sebagai negara ASEAN-5. Bagian kedua dari paper ini mengulas kerangka konseptual yang digunakan untuk membedah peran finansialisasi terhadap distribusi pendapatan. Bagian ketiga menguraikan model ekonometrik yang digunakan untuk mengestimasi kaitan finansialisasi dengan ketimpangan. Bagian kelima menampilkan hasil temuan beserta pembahasan. Bagian terakhir akan menyarikan poin-poin kunci dalam penelitian beserta implikasi kebijakan dari penelitian ini.
II. TEORI Finansialisasi dalam arti luas didefinisikan sebagai peningkatan peran industri keuangan dalam kegiatan perekonomian,yang meliputi pengendalian keuangan dalam pengelolaan perusahaan, aset keuangan terhadap total aset, surat berharga yang diperdagangkan dan khususnya ekuitas
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terhadap total aset keuangan, pasar saham sebagai pasar untuk kontrol perusahaan dalam menentukan strategi perusahaan, dan fluktuasi di pasar saham sebagai penentu siklus bisnis (Dore, 2000 dikutip Falkowski, 2011). Finansialisasi lebih popular dipahami sebagai meningkatnya pola akumulasi keuntungan yang diperoleh terutama melalui saluran keuangan daripada melalui perdagangan dan produksi komoditas (Krippner, 2005; Arrighi, 2009). Finansialisasi juga didefinisikan sebagai dua proses yang saling terkait (Hou Lin & Tomaskovic-Devey, 2013). Proses pertama, melalui peningkatan dominasi sektor keuangan dan juga kontrol sektor tersebut dalam perekonomian. Proses kedua, melalui peningkatan partisipasi industri non-keuangan dalam jasa keuangan dan pasar investasi. Artinya, finansialisasi merujuk pada peningkatan peran dan dominasi industri keuangan termasuk pasar keuangan dan instituasi keuangan dalam menjalankan roda perekonomian (Davis & Kim, 2015). Meski beragam, secara sederhana, istilah finansialisasi menjadi popular untuk menandai adanya pergeseran perubahan peran dan ketergantungan antara sektor keuangan dan sektor riil dalam perekonomian. Untuk memudahkan, definisi finansialisasi yang digunakan dalam penelitian ini merujuk pada definisi finansialiasi yang dibangun oleh Epstein. Epstein (2005) mendefinisikan finansialisasi sebagai berikut.
“Financialization means the increasing role of financial motives, financial markets, financial actors and financial institutions in the operation of the domestic and international economies.”
Dalam perspektif Epstein (2005), finansialisasi dipersepsikan sebagai peningkatan peran motif keuangan, pasar keuangan, dan aktor serta institusi keuangan dalam aktivitas perekonomian domestik dan internasional. Dengan demikian, secara umum, finansialisasi dapat dipahami dan diasosiasikan sebagai peningkatan peran sektor keuangan ketimbang sektor riil dalam perekonomian baik dalam level perekonomian domestik maupun dalam tataran perekonomian global. Menurut Palley (2009), saluran finansialisasi dapat dibagi ke dalam tiga saluran utama yakni melalui perilaku pasar keuangan, perilaku korporasi non-keuangan, dan perubahan struktur pasar dan regulasi. Saluran pertama melalui perubahan dalam pasar keuangan yang memberikan dampak terhadap perekonomian secara makro yang mencakup perubahan dalam nilai ekuitas, peningkatan akses terhadap utang, kredit, dan lain-lain. Saluran kedua melalui perubahan perilaku korporasi non-keuangan yang mencakup perubahan kebijakan keuangan korporasi terkait pembayaran kepada para pemegang saham dan perubahan dalam leverage perusahaan dan perilaku pembiayaan. Saluran ketiga melalui perubahan kebijakan ekonomi untuk kepentingan sektor keuangan yang meliputi deregulasi pasar keuangan dan tenaga kerja serta globalisasi. Perubahan kebijakan ekonomi tersebut pada gilirannya mempengaruhi parameter struktur yang penting seperti pembagian keuntungan dan komposisi gaji.
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Finansialisasi
Perilaku pasar keuangan
Perilaku korporasi nonkeuangan
Perubahan struktur pasar dan regulasi
Pengaruh finansialisasi terhadap distribusi pendapatan dirangkum dan dikelompokkan oleh Stockhammer (2010) menjadi tiga saluran. Saluran pertama, adanya peningkatan pendapatan dari aktivitas ekonomi rente. Saluran kedua, adanya kenaikan pendapatan dalam sektor keuangan, yang biasanya berbentuk bonus, menyebabkan jurang distribusi pendapatan menjadi melebar. Saluran ketiga, finansialisasi telah menggeser perimbangan kekuatan antara pemodal dan pekerja dalam berbagai cara mulai dari perubahan dalam pengaturan korporasi hingga peningkatan kesempatan yang dibuka ke perusahaan-perusahan akibat globalisasi keuangan. Hal senada juga dikemukakan oleh Hou Lin dan Tomaskovic-Devey (2013). Hasil studi mereka menunjukkan bahwa proses ketimpangan pendapatan melalui finansialisasi perekonomian dapat dilihat dari tiga hal. Pertama, meningkatnya ketergantungan pendapatan dari sektor keuangan melalui penurunan porsi kontribusi buruh dalam sektor inti seperti manufaktur, transportasi, dan konstruksi terhadap pendapatan nasional. Kedua, meningkatnya ketergantungan pendapatan dari sektor keuangan melalui peningkatan yang signifikan dalam pemberian kompensasi eksekutif puncak.Terakhir, meningkatnya ketergantungan pendapatan dari sektor keuangan melalui peningkatan kesenjangan pendapatan diantara pekerja seperti ketimpangan pendapatan antara pekerja di divisi manajerial dan keuangan dibandingkan dengan pekerja produksi dan penjualan. Sementara itu, Kus (2012) membagi efek finansialisasi terhadap ketimpangan pendapatan ke dalam empat saluran. Saluran pertama, perkembangan industri keuangan dalam beberapa dekade terakhir dibiayai oleh pengorbanan sektor riil yang produktif. Artinya, telah terjadi penurunan tingkat profitabilitas sektor non-keuangan yang mengakibatkan penurunan upah bersih kelas menengah dan pekerja kerah biru yang bekerja di sektor industri produktif. Saluran kedua, adanya perpindahan sumber utama pencetak laba dari sektor riil ke sektor keuangan telah melemahkan pengaruh kebijakan dan lembaga tertentu dalam mengurangi ketimpangan
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ekonomi seperti undang-undang serikat pekerja dan upah minimum.Saluran ketiga, adanya ketergantungan yang tinggi perusahaan non-keuangan terhadap sektor keuangan menyebabkan pengaturan perusahan lebih ditujukan untuk melayani kepentingan pemilik modal dan manajer perusahaan yang cenderung berorientasi pada pencarian keuntungan jangka pendek. Implikasinya, ongkos pengeluaran untuk pekerja akan dipotong sementara pada saat yang sama eksekutif puncak diganjar dengan bonus yang tinggi. Saluran terakhir, pasar saham mendorong terjadinya konsentrasi pendapatan pada kelompok masyarakat yang berpendapatan tinggi terutama ketika pasar saham mengalami masa keemasan. Kelompok tersebut memiliki kemapauan keuangan untuk berinvestasi secara besar-besaran pada periode awal masa keemasan pasar saham. Sementara itu, kelompok masyarakat berpendapatan lebih rendah baru bisa memasuki pasar saham belakangan ketika periode keeamasan sudah berlangsung cukup lama yang mengakibatkan mereka menderita kerugian. Hal ini terkonfirmasi dari naiknya proporsi pendapatan dari investasi, properti, dan modal dalam beberapa dekade terakhir. Ironisnya, sebagain besar pendapatan itu terindikasi justru hanya dikenakan dikenakan pajak di bawah tarif pajak yang seharusnya dibayarkan sebagaimana yang dikenakan pada sumber-sumber pendapatan lainnya.
III. METODOLOGI 3.1. Pemilihan Variabel dan Sumber Data Untuk mengukur pengaruh finansialisasi terhadap distribusi pendapatan, studi ini menggunakan data panel dengan periode tahunan dari 1999 sampai 2013. Adapun indikator yang digunakan dalam studi ini adalah indikator pembangunan keuangan dan indikator distribusi pendapatan. Indikator distribusi pendapatan yang digunakan dalam studi ini adalah koefisien gini. Data koefisien gini didapat dari beberapa sumber antara lain ILO Global Wage Database, UN World Income Inequality Database (WIID), World Development Indicators (WDI) Bank Dunia, Global Financial Development Database (GFDD), Singapore Department of Statistics,National Statistical Office of Thailand, Philippine Statistics Authority, Economic Planning Unit of Malaysia, dan Badan Pusat Statistik Indonesia. Adapun variabel, definisi, satuan, dan sumber data yang digunakan dalam studi ini ditunjukkan pada Tabel 1 di bawah ini.
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Tabel 1. Jenis dan Sumber Data Variabel
Definisi
Satuan
Satuan
GINI
Gini coefficient
%
WDI, WIID, ILO, Singapore Department of Statistics, National Statistical Office of Thailand, Philippine Statistics Authority, Economic Planning Unit of Malaysia, dan Badan Pusat Statistik Indonesia
ROA
Bank return on assets before tax
%
GFDD 2016
SMC
Stock market capitalization
% of GDP
GFDD 2016
DPDS
Outstanding domestic private debt securities
% of GDP
GFDD 2016
UNEM
Unemployment rate
% of total labor force
WDI 2016
EMPA
Employment in Agriculture
% of total employment
WDI 2016
VEM
Vulnerable Employment
% of total employment
WDI 2016
3.2. Model Ekonometrika Menurut Afsar et.al (2014), proses dan hasil finansialisasi dapat diukur dengan menggunakan tiga indikator yakni rasio nilai kapitalisasi pasar terhadap PDB, tingkat profitabilitas bank yang dinyatakan sebagai pendapatan bank sebelum pajak, dan nilai efek dari aset perbankan. Sementara itu, Kus (2012) menggunakan variabel nilai keseluruhan saham yang diperdagangkan, tingkat profitabilitas bank sebelum pajak, dan sekuritisasi atas aset perbankan untuk mengukur proses finansialisasi dalam perekonomian. Selanjutnya, karena pertimbangan ketersediaan data antar negara di kawasan ASEAN, maka variabel yang digunakan dalam studi mengalami perubahan sedikit. Sebagai gambaran, variabel yang dianggap bisa mewakili proses finansialisasi antara lain bank return on assets before tax (ROA), stock market capitalization to gdp (SMC), dan outstanding domestic private debt securities (DPDS). Selain keempat variabel tersebut, studi ini juga menggunakan tiga variabel tambahan yakni unemployment rate, employment in agriculture, dan vulnerable employment. Lebih lanjut, guna menganalisis pengaruh finansialisasi terhadap distribusi pendapatan, studi ini menggunakan variabel koefisien gini (GINI) sebagai variabel dependen dan sejumlah indikator pembangunan di sektor keuangan seperti ROA, SMC, DPDS, dan beberapa variabel kontrol (VC) sebagai variabel independen. Model ekonometrika dasar yang digunakan dalam penelitian ini untuk mengukur pengaruh finansialisasi terhadap distribusi pendapatan sebagai berikut:
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LogGiniit=β1i+β2LogGini2it-1+β3ROA3it+β4LogSMC4it+β5Log DPDS5it+β6LogVC7it+εit Model di atas diadaptasi dari model yang dikembangkan oleh Kus (2012) dan Afsar et.al (2014) dan diestimasi dengan menggunakan analisis data panel. Kesemua variabel yang digunakan terkecuali ROA diestimasi dalam bentuk log linear untuk mendapatkan gambaran elastisitas. Adapun ringkasan statistik untuk variabel-variabel yang digunakan dalam model di atas ditunjukkan pada Tabel 2.
Tabel 2. Ringkasan Statistik Variabel LGINI ROA
Obs
Mean 77
3,724035
Std.Dev 0,1315671
Min 3,427515
Max 3,88609
95
0,7568719
2,97577
-16,44494
3,55372
LSMC
130
4,151707
0,939095
0,1909509
5,581855
LDPDS
111
1,80002
1,906348
-3,479916
4,173406
LUNEM
120
1,329995
0,7050554
-0,356675
2,476538
LEMPA
119
2,933082
1,535888
-1,609438
4,198705
LVEM
100
3,436741
0,7306146
2,104134
4,268298
Teknik estimasi yang digunakan adalah analisis data panel yang memberikan kemudahan dan fleksibilitas dalam melakukan pemodelan antar waktu dan antar individu secara bersamaan. Implikasinya, hasil estimasi akan lebih akurat karena data panel secara struktur lebih mendekati realita dibandingkan data runtun waktu atau data silang saja. Selain itu, secara teoretis, dengan jumlah observasi yang semakin banyak (N) sehingga memperbesar derajat kebebasan dan menurunkan kemungkinan adanya kolinearitas antar variabel bebas (Greene, 2005; serta Hsio, 2003 dan Klevmarken, 1989 dalam Baltagi, 2005). Tergantung pada struktur matriks kovarian dan sifat variabel yang ada dalam model, kita harus memilih model terbaik diantara pilihan Pool Least Square (PLS), Fixed Effect Model (FEM), dan Random Effect Model (REM). Langkah selanjutnya adalah kembali model tersebut untuk kemungkinan satu atau beberapa variabel independen yang tidak betul-betul bersifat eksogen. Untuk mengatasi masalah tersebut, teknik estimasi Generalised Method of Moment (GMM) diperlukan. Karenanya, dalam penelitian ini akan digunakan model data panel dinamis Arellano-Bond. Teknik ini dianggap cocok untuk mengecek estimator yang memiliki variabel independen yang bersifat tidak eksogen, bermodel efek tetap, dan berpola heteroskedastisitas dan serial korelasi yang spesifik per individu (Roodman, 2006).
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IV. HASIL DAN ANALISIS 4.1. Deskripsi Anatomi Finansialisasi ASEAN - 5 Diantara indikator pembangunan keuangan global, ada beberapa indikator yang lazim digunakan untuk melihat seberapa dalam finansialisasi di negara ASEAN-5. Indikator-indikator tersebut antara lain nilai kapitalisasi pasar (stock market capitalization), rasio profitabilitas (Return on Asset/ROA), efek utang swasta domestik (domestic private debt securities), dan pendapatan nonbunga sektor perbankan (bank non-interest income) Terkait hal itu, sejak 2009, nilai kapitalisasi saham di pasar keuangan atau lebih sering dinamakan sebagai pola dan kecenderungan nilai kapitalisasi pasar diantara negara ASEAN-5 terus naik dari waktu ke waktu terkecuali Singapura (lihat Grafik 1). Grafik 1 juga menunjukkan bagaimana nilai kapitalisasi pasar Singapura selalu berada di peringkat teratas hingga 2010. Sayangnya, sejak 2011, Malaysia berhasil menyalip Singapura dalam hal nilai kapitalisasi pasar. Adapun posisi negara ASEAN-5 lainnya seperti Thailand, Indonesia, dan Filipina tidak mengalami perubahan dalam periode 2000-2012. Menariknya lagi, nilai kapitalisasi pasar Singapura dan Malaysia selalu berada di atas nilai pendapatan nasionalnya (PDB) dalam kurun waktu 20002012. Sebaliknya, dalam periode tersebut, nilai kapitalisasi pasar Thailand, Indonesia, dan Filipina masih di bawah nilai pendapatan nasionalnya.
Stock Market Capitalization 250
IDN MLY THAI PHL SIN
200 150 100 50 0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Sumber: Global Financial Development Database, World Bank (2015)
Grafik 1. Nilai Kapitalisasi Pasar Saham, (% PDB)
Adapun pola dan kecenderungan nilai imbal hasil atas aset (Return on Asset/ROA) terlihat berbeda dengan indikator nilai kapitalisasi pasar. Secara umum, nilai ROA tertinggi masih dipegang oleh Indonesia kendati pernah disalip oleh Malaysia pada 2011. Sebaliknya, Thailand justru berada pada posisi terbawah untuk nilai ROA. Menariknya, Singapura yang dikenal sebagai
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pusat keuangan di kawasan ASEAN justru memiliki nilai ROA yang lebih rendah dibandingkan Indonesia dan Malaysia. Menariknya lagi, Malaysia dan Singapura memiliki nilai ROA dengan tingkat volatilitas relatif yang lebih tinggi diantara negara ASEAN-5 lainnya.
Return on Asset 70
Domestic Private Debt Securities IDN MLY THAI PHL SIN
60 50 40
70 60 50 40
30
30
20
20
10
10
0
10
-10
IDN MLY THAI PHL SIN
0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Sumber: Global Financial Development Database, World Bank (2015)
Grafik 2. Perkembangan Nilai ROA (sebelum pajak, %)
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Sumber: Global Financial Development Database, World Bank (2015)
Grafik 3. Perkembangan Nilai Efek Utang Swasta (%)
Di samping itu, finansialisasi dinilai tidak melulu soal nilai kapitalisasi pasar dan imbal hasil atas aset. Nilai efek utang swasta sebagai bagian dari proses sekuritisasi dan kebijakan keuangan perusahaan juga penting untuk diperhatikan. Grafik 3 di atas menunjukkan nilai efek utang swasta di Malaysia menempati ranking tertinggi diantara negara ASEAN-5. Sebaliknya, Filipina dan Indonesia menempati posisi paling buncit dalam hal nilai efek utang swasta. Menariknya, jurang nilai efek utang swasta antara Malaysia dan Singapura kian melebar pasca krisis keuangan global 2008. Menariknya lagi, nilai efek utang swasta Malaysia dan Thailand terus meningkat sejak 2004. Sementara itu, nilai efek utang swasta Singapura justru bergerak ke turun sejak 2004 dan mengalami penurunan yang cukup signifikan pasca 2008. Analisa deskriptif ini dipersandingkan dengan hasil estimasi model ekonometrik berikut. Perbandingan ini penting untuk memberikan penjelasan logis dan argumen yang saling menguatkan, atau justru argumen yang saling bertentangan. Keduanya penting sebagai bagian dari robustness test dalam penelitian ini.
4.2. Pemilihan Model dan Hasil Estimasi Pemilihan model menggunakan uji Breusch-Pagan Lagrange Multiplier sebagaimana ditampilkan pada Tabel 3. Hasilnya, menunjukkan REM tidak dapat dijalankan. Dengan demikian, kesemua model yang digunakan dalam analisis data panel disarankan menggunakan model efek tetap (FEM).
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Tabel 3. Hasil Uji Breusch-Pagan Lagrange Multiplier Jenis Model
Prob>chibar2
Keputusan
Kesimpulan
Model 1
1.0000
H0 diterima
Menggunakan model efek tetap
Model 2
1.0000
H0 diterima
Menggunakan model efek tetap
Model 3
1.0000
H0 diterima
Menggunakan model efek tetap
Model 4
1.0000
H0 diterima
Menggunakan model efek tetap
Untuk melihat apakah terdapat otokorelasi pada model data panel dinamis, digunakan uji autokerelasi Arellano-Bond. Hasilnya, dari Tabel 6 di bawah terlihat bahwa secara statistik hipotesis awal (null hypothesis) yang menyatakan tidak terdapat autokerelasi diterima atau tidak dapat ditolak. Dengan demikian, dari semua model yang dilibatkan, tidak ada satupun yang memiliki autokerelasi.
Tabel 4. Hasil Uji Autokolerasi Jenis Model Model 1
Model 2
Model 3
Model 4
Prob > z
Keputusan
Order 1
0,1901
Order 2
0,2847
Order 1
0,2098
Order 2
0,3267
Order 1
0,1848
Order 2
0,9850
Order 1
0,1367
Order 2
0,5917
Kesimpulan
H0 diterima
Tidak terdapat otokorelasi
H0 diterima
Tidak terdapat otokorelasi
H0 diterima
Tidak terdapat otokorelasi
H0 diterima
Tidak terdapat otokorelasi
Hasil estimasi pengaruh finansialisasi terhadap distribusi pendapatan dengan menggunakan analisis data panel ditampilkan pada Tabel 4. Jenis analisis data panel yang digunakan pada Tabel 1 adalah model efek tetap. Hasilnya, keempat variabel yang digunakan untuk melihat pengaruh finansialisasi terhadap ketimpangan dengan menggunakan model efek tetap secara statistik terbukti signifikan. Nilai koefisien variabel Lag GINI, ROA dan LSMC bertanda positif yang sementara variabel LDPDS bertanda negatif. Hal ini mengindikasikan bahwa kenaikan nilai Lag Gini, ROA dan LSMC akan memperburuk kesenjangan pendapatan. Sebaliknya, kenaikan nilai LDPDS justru memperbaiki tingkat distribusi pendapatan di kawasan ini.
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Tabel 5. Finansialisasi dan Koefisien Gini: Analisis Data Panel (1989-2014) (Variabel Dependen=Ginii,t) Variabel
Model 1
Model 2
Model 3
Model 4
Variabel Lag Log GINIi,t-1
0.5523*** (0.1309)
0.5471*** (0.1297)
0.3837*** (0.0845)
0.2243** (0.0873)
0.0031** (0.0013) 0.0943*** (0.0147) -0.0326*** (0.0030)
0.0036* (0.0020) 0.0516*** (0.0198) -0.0166** (0.0068)
0.0398 (0.0307)
0.0511* (0.0282) -0.1978 (0.1449)
1.3184*** (0.4405)
2.4436*** (0.9262)
.0098605 .0532646 -0.5009** (0.2428) -0.0185 0.0576) 4.1802*** (1.2566)
Variabel Kunci ROA LSMC LDPDS
0.0046** (0.0021) 0.0612** (0.0280) -0.0184*** (0.0066)
0.0041** (0.0021) 0.0784** (0.0307) -0.0148** (0.0071) Variabel Kontrol
LUNEM LEMPA LVEM CONS Catatan: Standard Error (dalam kurung), *p<0.10, **p<0.05, ***<0.01
Tabel 5 menunjukkan nilai koefisien lag gini sebesar 0.55 yang artinya kenaikan 10 persen nilai koefisien gini tahun sebelumnya akan meningkatkan tingkat kesenjangan pendapatan sesudah periode tersebut sebesar 5,5 persen. Selanjutnya, nilai koefisien ROA dan LSMC masing-masing sebesar 0,005 dan dan 0,06 yang mengindikasikan bahwa kenaikan nilai ROA dan LSMC masing-masing sebesar 10 persen menyebabkan kenaikan nilai koefisien gini berturut-turut sebesar 0,05 persen dan 0,6 persen. Adapun nilai koefisien LDPDS adalah -0,02 yang mengindikasikan kenaikan nilai LDPDS sebesar 10 persen akan menurunkan nilai koefisien gini sebesar 0,2 persen. Dari ketiga variabel finansialisasi yang digunakan menunjukkan bahwa meski signifikan, namun pengaruhnya terhadap distribusi pendapatan di kawasan ASEAN belum memiliki pengaruh yang dominan. Sebagaimana telah diuraikan sebelumnya, salah satu permasalahan yang mengemuka dalam analisis data panel adalah jika terdapat lag dari variabel terikat sebagai variabel bebas, maka kemungkinan akan terdapat korelasi antara variabel terikat dengan residu. Atas dasar itu, maka analisis panel data dengan menggunakan model efek tetap perlu dilanjutkan dengan menggunakan analisis Generalized Method of Moments (GMM) guna mendapatkan analisis yang lebih baik. Menurut Roodman (2006), analisis GMM dibutuhkan karena seperti dalam kasus OLS, analisis panel data dengan lag variabel dependen dan error yang saling berautokorelasi berpotensi menghasilkan parameter yang inkonsisten. Karena itu, analisis data
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panel akan menjadi lebih baik jika dilanjutkan dengan analisis dynamic panel data model yang dikembangkan oleh Arellano-Bond (1991) atau lebih populer dikenal sebagai Arellano-Bond estimator. Atas dasar itu, maka penggunaan metode GMM melibatkan beberapa variabel instrumen untuk menyelesaikan permasalahan adanya korelasi antara lag dependen variabel dengan residual dan adanya hubungan antara regressor lag variabel terikat dengan residual. Adapun yang dimaksud dengan variabel instrumen adalah variabel yang tidak memiliki hubungan atau korelasi dengan residual. Atau, variabel yang memiliki korelasi dengan variabel bebas namun tidak memiliki pengaruh langsung terhadap variabel terikat. Karena itu, variabel instrumen yang dilibatkan adalah bank credit to bank deposit (BCBD) dan bank non-intererst income to total income (BNII). Tabel 6. Finansialisasi dan Koefisien Gini: Analisis GMM (1989-2014) (Variabel Dependen=Ginii,t) Variabel
Model 1
Model 2
Model 3
Model 4
Variabel Lag Log GINIi,t-1
0.5676*** (0.0501)
0.5330*** (0.0059)
0.3837*** (0.0845)
0.2243*** (0.0873)
0.0031** (0.0013) 0.0943 *** (0.0147) -0.0326*** (0.0030)
0.0036* (0.0020) 0.0516*** (0.0198) -0.0166** (0.0068)
0.0501 (0.0153)
0.0511* (0.0282) -0.1978 (0.1449)
1.360*** (0.0427)
2.4436*** (0.9262)
0.0099 (0.0533) -0.5009 (0.2428)** -0.0185 (0.0576) 4.1802*** (1.2566)
Variabel Kunci ROA LSMC LDPDS
0.0050*** (0.0008) 0.0620** (0.0244) -0.0057 (0.0151)
0.0038*** (0.0011) 0.0822*** (0.0165) -0.0243** (0.0106) Variabel Kontrol
LUNEM LEMPA LVEM CONS
1.3463*** (0.1330)
Catatan: Standard Error Robust (dalam kurung), *p<0.10, **p<0.05, ***<0.01
Hasil estimasi dengan menggunakan teknik estimasti GMM ditampilkan pada Tabel 6. Secara umum, tabel tersebut menunjukkan bahwa finansialisasi secara statistik terbukti memiliki pengaruh yang signifikan terhadap distribusi pendapatan. Ketiga indikator finansialisasi yang digunakan untuk mengukur pengaruh proses finansialisasi terhadap nilai koefisien gini menghasilkan tanda koefisien yang konsisten dengan hasil estimasi model efek tetap. Hasilnya, koefisien estimasi untuk variabel ROA dan LSMC terbukti memiliki pengaruh yang
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searah terhadap koefisien gini. Artinya, kenaikan ROA dan LSMC akan memberikan efek yang buruk terhadap perbaikan kesenjangan pendapatan. Sebaliknya, koefisien estimasi variabel LDPDS konsisten negatif yang mengindikasikan bahwa kenaikan nilai variabel tersebut akan menurunkan nilai koefisien gini atau akan memperbaiki tingkat ketimpangan pendapatan.
V. KESIMPULAN Tujuan dari studi ini adalah untuk mengukur pengaruh proses finansialisasi dalam perekonomian terhadap distribusi pendapatan di negara ASEAN-5. Untuk mengukurnya, data yang digunakan berupa data panel periode 1989-2014. Dengan menggunakan model efek tetap dan model data panel dinamis Arellano-Bond, hasil estimasi kedua jenis analisis data panel tersebut membuktikan bahwa ketiga variabel finansialisasi yang dipilih memiliki pengaruh yang signifikan terhadap distribusi pendapatan. Variabel nilai kapitalisasi pasar dan imbal hasil atas aset sebelum pajak berkorelasi positif dengan distribusi pendapatan. Artinya, jika nilai kedua variabel tersebut meningkat maka tingkat distribusi pendapatan cenderung akan memburuk. Sebaliknya, variabel nilai efek utang swasta domestik memiliki hubungan yang negatif dengan kesenjangan pendapatan. Hal ini mengindikasikan bahwa kenaikan variabel nilai efek utang swasta domestik dapat berperan sebagai instrumen perbaikan tingkat distribusi pendapatan.
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REFERENSI Adams Jr, Richard H. (2003). Economic Growth, Inequality, and Poverty: Findings from a New Data Set. The World Bank, Policy Research Working Paper No. 2972, February 2003. Afsar, Muharrem, Afsar, Asli, dan Mecik, Oytun. (2014). Financialization Process and Outcomes in Developed Countries, International Journal of Economics and Finance. 6(12), 192-200. Arellano, Manuel dan Bond, Stephen. (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equation. The Review of Economic Studies, April 1991. 58 (2), 277-297. Badan Pusat Statistik. (2015). diakses Desember 2015, http://www.bps.go.id/Subjek/view/ id/23#subjekViewTab3|accordion-daftar-subjek1 Badi H. Baltagi. (2005). Econometric Analysis of Panel Data. John Wiley &Sons Ltd, West Sussex. Barro, Robert J. (2008). Inequality and Growth Revisited. Asian Development Bank (ADB), Working Paper Series on Regional Economic Integration No. 11, January 2008. Beck, Thorsten dan Levine, Ross. (2004). National Bureau of Economic Research (NBER). Working Paper No. 10979. December 2004. Benhabib, Jess. (2003). The Tradeoff Between Inequality and Growth, Annals of Economics and Finance. Vol.4, 329-345. Berg, Andrew,dan Ostry, Jonanthan D. (2011). Inequality and Unsustainable Growth: Two Sides of the Same Coin?. International Monetary Fund (IMF), IMF Staff Discussion Note, April 8. Bock, Matthew J. (2014). Income Inequality in ASEAN: Perceptions on Regional Stability from Indonesia and the Philippines. ASEAN-Canada Research Partnership, Working Paper No.1, April 2014. Bureau for Development Policy. (2013). Humanity Divided: Confronting Inequality in Developing Countries, United Nations Development Programme (UNDP). Canavire-Bacarreza, Gustavo dan Rioja, Felix. (2008). Financial Development and the Distribution of Income in Latin America and the Caribbean. The Institute for labor Study (IZA). Discussion Paper No.3796, October 2008. Clarke, George, Xu, Lixin Colin, dan Zou, Heng-fu. (2003). Finance and Income Inequality: Test of Alternative Theories. The World Bank. Policy Research Working Paper No.2984, March 2003. Davis, Gerald F. dan Kim, Suntae. (2015). Financialization of the Economy. Draft Chapter for Annual Review of Sociology. January 13.
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Demirguc-Kunt, Asli dan Levine, Ross. (2009). Finance and Inequality: Theory and Evidence. National Bureau of Economic Research (NBER), Working Paper No. 15275. August 2009. Singapore Department of Statistics. (2016). diakses Desember 2016. http://www.singstat.gov. sg/statistics Department of Economic and Social Affairs. (2013). Inequality Matters: Report of the World Social Situation, United Nations. Dollar, David dan Kraay, Aart. (2002). Growth is Good for the Poor. Journal of Economic Growth. September 2002. 7(3); hal. 195-225. Dore, R., (2000). Stock Market Capitalism: Welfare Capitalism: Japan and Germany versus the Anglo-Saxons. Oxford University Press, 2000. Dikutip oleh Michal Falkowski. (2011). “Financialization of Commodities’. Journal of Contemporary Economics. Vol.5 (4),hal 4-17. Dunhaupt, Petra. (2010). Financialization and the Rentier Income Share: Evidence from the USA and Germany. Macroeconomic Policy Institute, Working Paper No.2/2010. February 9. Dunhaupt, Petra. (2014). An Empirical Assessment of the Contribution of Financialization and Corporate Governance to the Rise in Income Inequality. Institute for International Political Economy Berlin, Working Paper No.41. Economic Planning Unit of Malaysia. (2016). diakses Desember 2016, http://www.epu.gov. my/en/home Gerald A. Epstein. (2005). Financialization and the World Economy. Edward Elgar. Giovanni Arrighi. (2010). The Long Twentieth Century. Verso. ILO. (2016). Key Indicators of Labor Market, diakses Desember 2016, http://www.ilo.org/global/ statistics-and-databases/research-and-databases/kilm/lang--en/index.htm ILO. (2016). Global Wage Database, diakses Desember 2016, http://www.ilo.org/global/research/ global-reports/global-wage-report/2014/lang--en/index.htm Jauch, Sebastian dan Watzka, Sebastian. (2012). Financial Development and Income Inequality: A Panel Data Approach. CESifo Working Paper No. 3687. October 2012. Kraay, Aart, Dollar, David, dan Kleineberg, Tatjana. (2014). Growth, Inequality, and Social Welfare: Cross-Country Evidence. Makalah dipresentasikan dalam Sixtieth Panel Meeting on Economic Policy, the Einaudi Institute for Economics and Finance (EIEF). Rome. 24-25 October 2014. Kappel, Vivien. (2010). The Effects of Financial Development on Income Inequality and Poverty. Swiss Federal Institute of Technology Zurich, Economics Working Paper No. 10/27. March, 29. Krippner, Greta R. (2005). The Financialization of the American Economy. Socio-Economic Review. Vol.3, hal. 173-208.
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Kus, Busak. (2012). Financialization and Income Inequality in OECD Nations: 1995-2007. The Economic and Social Review, Winter 2012. Vol. 43(4), hal. 477-495. Lin, Ken-Hou dan Tomaskovic-Devey, Donald. (2013). Financialization and U.S. Income Inequality: 1970-2008. American Journal of Sociology, March. Vol. 118(5), hal. 1284-1329. National Statistical Office of Thailand. (2016). diakses Desember 2016, http://web.nso.go.th/. Palley, Thomas I. (2016). The Macroeconomics of Financialization: A Stages of Development Approach. Presented at the 5th International Conference, Developments in Economic Theory and Policy. held in Bilbao, Spain, July 10 and 11, 2008. Park, Cyn-Young dan Mercado Jr, Rogelio V. (2015). Financial Inclusion, Poverty, and Income Inequality in Developing Asia. Asian Development Bank (ADB), Economics Working Paper No.426. January 2015. Park, Donghyun dan Shin, Kwanho. (2015). Economic Growth, Financial Development, and Income Inequality. Asian Development Bank (ADB), Economics Working Paper No.441. August 2015. Philippine Statistics Authority. (2016). diakses Desember 2016, http://psa.gov.ph/ Roodman, David. (2006). How to Do xtabond2: An Introduction to “Difference” and “System” GMM in Stata. Center for Global Development, Working Paper No.103. December 2006. Stockhammer, Engelbart. (2010). Financialization and the Global Economy. Political Economy Research Institute, Working Paper No.240. 13 October 2010. The World Bank. (2016). World Development Indicators, diakses Desember 2016, http://data. worldbank.org/data-catalog/world-development-indicators The World Bank. (2016). Global Financial Development Database, diakses Desember 2016, http://data.worldbank.org/data-catalog/global-financial-development United Nations University. (2016). World Income Inequality Database, diakses Desember 2016, https://www.wider.unu.edu/project/wiid-world-income-inequality-database William H. Greene. (2003). Econometric Analysis. Prentice Hall. New Jersey.
PETUNJUK PENULISAN 1. Naskah harus merupakan karya asli penulis (perorangan, kelompok atau institusi) yang tidak melanggar hak cipta. Naskah yang dikirimkan, belum pernah diterbitkan dan tidak sedang dikirimkan ke penerbit lain pada waktu yang bersamaan. Hak cipta atas naskah yang diterima, TETAP menjadi hak penulis. 2. Setiap naskah yang disetujui untuk diterbitkan, akan mendapatkan kompensasi finansial sebesar Rp 5.000.000,-. 3. Naskah dapat dikirimkan dalam bentuk softcopy (file). Sangat disarankan untuk mengirimkan softcopy anda ke:
[email protected] (Cc. to:
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Jika tidak memungkinkan, file tersebut dapat disimpan dalam disket atau CD dan dikirimkan melalui pos ke alamat redaksi berikut: BULETIN EKONOMI MONETER DAN PERBANKAN Departemen Riset Kebanksentralan, Bank Indonesia Menara Sjafruddin Prawiranegara, Lt. 21, JI. M. H. Thamrin No.2 Jakarta Pusat, INDONESIA Telpon: 62-21-2981-4119, Fax: 62-21-3501912
4. Naskah dibatasi.+ 25 halaman berukuran A4, spasi satu (1), font Times New Roman dengan ukuran font 12. 5. Persamaan matematis dan simbol harap ditulis dengan mempergunakan Microsoft Equation. 6. Setiap naskah harus disertai abstraksi, maksimal satu (1) halaman ukuran A4. Untuk naskah yang ditulis dalam bahasa Indonesia, abstraksi-nya ditulis dalam Bahasa Inggris, dan sebaliknya. 7. Naskah harus disertai dengan kata kunci (Keyword) dan dua digit nomor Klasifikasi Journal of Economic Literature (JEL). Lihat klasifikasi JEL pada, http://www.aeaweb.org/journal/ jel_class_system.html. 8. Naskah ditulis dengan penyusunan BAB secara konsisten sebagai berikut, I. JUDUL BAB I.1. Sub Bab I.1.1. Sub Sub Bab
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9. Rujukan dibuat dalam footnote (catatan kaki) dan bukan endnote. 10. Sistem referensi dibuat mengikuti aturan berikut, a. Publikasi buku: John E. Hanke dan Arthur G. Reitsch, (1940), Business Forecasting, PrenticeHall, New Jersey. b. Artikel dalam jurnal:
Rangazas, Peter. “Schooling and Economic Growth: A King-Rebelo Experiment with Human Capital”, Journal of Monetary Economics, Oktober 2000,46(2), hal. 397-416.
c. Artikel dalam buku yang diedit orang lain: Frankel, Jeffrey A. dan Rose, Andrew K. “Empirical Research on Nominal Exchange Rates”, dalam Gene Grossman dan Kenneth Rogoff, eds., Handbook of International Economics. Amsterdam: North-Holland, 1995, hal. 397-416. d. Kertas kerja (working papers): Kremer, Michael dan Chen, Daniel. “Income Distribution Dynamics with Endogenous Fertility”. National Bureau of Economic Research (Cambridge, MA) Working Paper No.7530, 2000. e. Mimeo dan karya tak dipublikasikan: Knowles, John. “Can Parental Decision Explain U.S. Income Inequality?”, Mimeo, University of Pennsylvania, 1999. f. Artikel dari situs WEB dan bentuk elektronik lainnya: Summers, Robert dan Heston, Alan W. “Penn World Table, Version 5.6” http:// pwtecon.unpenn.edu/, 1997. g. Artikel di koran, majalah dan periodicals sejenis: Begley, Sharon. “Killed by Kindness”, Newsweek, April 12, 1993, hal. 50-56. 11. Naskah harus disertai dengan biodata penulis, lengkap dengan alamat, telepon, rekening Bank dan e-mail yang dapat dihubungi. Disarankan untuk menulis biodata dalam bentuk CV (curriculum vitae) lengkap.
