Evaluating the Determinants of Sub- National Growth in Indonesia. Neil Andrew McCulloch Bambang Suharnoko Sjahrir

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1 Evaluating the Determinants of Sub- National Growth in Indonesia Neil Andrew McCulloch Bambang Suharnoko Sjahrir

2 Outline of the Presentation The concentration of economic activity How has regional inequality changed over time? What drives sub-national growth What doesn t drive growth Income Convergence Spatial Divergence Model Regression results Growth acceleration/deceleration Difference regressions Conclusions

3 Uneven distribution of resources and economic activity Java represents 6% of the country s land area but accommodates almost 60% of the population Island Land Area* (in %) Population (in %) GDP (in %) Sumatra Java & Bali Kalimantan Sulawesi Maluku + Nusa Tenggara Papua

4 Indonesia Unity in Diversity I (District share of national GDP, 2003)

5 Indonesia - Unity in Diversity II (per capita GDP, 2003))

6 Richer provinces have more concentrated economic activity 2003 NAD NTT Maluku Sultra Sulsel Jateng NTB Lampung Sulteng Jambi Jatim Jabar Sulut Yogya Sumbar Kalteng Kalbar Sumsel Kalsel Sumut Bali Real GRDP Per Capita 2003 Top 20% district share of GRDP Fitted values

7 but per capita GDP is evenly distributed NTT Maluku Sultra NTB Jateng Lampung Sulsel Jambi Bkulu Sulteng Jatim Jabar Yogya Sulut NAD Kalbar Kalsel Bali Sumbar Sumut Sumsel Kalteng Real GRDP Per Capita Gini Coef of district GRDP PC Fitted values

8 Richer provinces do have more concentrated populations NTT Maluku Sultra Sulsel Lampung NTB Jateng Sulteng Bkulu Sulut Jatim Jabar Yogya Sumut Sumbar NAD Kalteng Sumsel Kalsel Kalbar Bali Jambi Real GRDP Per Capita Gini Coef of district population Fitted values

9 Inequality is higher between districts than provinces since the 1990s Gini Coefficient Provincial GDP PC Kabupaten/Kota GDP PC

10 Inequality is higher between provinces than within provinces GRDP PC GRDP non oil&gas PC GE(0) within province between province

11 Most provinces are becoming more concentrated Prov Name Top 20% district share of GRDP (1998) Top 20% district share of GRDP (2003) Change NTB Lampung Kalsel Sulteng Sulsel Jateng NTT Jatim Jambi Sumut Riau Yogya Sultra Bali Sumbar Jabar Kalteng Sulut Sumsel Kaltim Kalbar Papua Maluku NAD

12 Population is more dispersed Prov Name Top 20% district share of Population (1998) Top 20% district share of Population (2003) Change Kalteng Yogya Kalsel Sulut Lampung Kalbar NTB Maluku Sumbar Sumut Jatim Kaltim Bali Jambi NAD Sulsel Riau Sumsel Jabar Papua NTT Jateng

13 Are people following the jobs? Change in top 20% district share of GRDP Jateng NTT Papua Jabar Riau Sumsel Sulsel Jambi Jatim Sumut Yogya Bali Sumbar NAD Lampung Kalsel Sulut Kaltim Kalbar Maluku Change in top 20% district share of population Kalteng

14 Typical District performance (Median) Growth Rate in % Gini Coefficient Sumatra Kalimantan Java & Bali Sulawesi Maluku + Nusa Tenggara Papua Total Eastern Indonesia Western Indonesia Java Off Java

15 Indonesia s sub-national growth varies greatly across districts SULAWESI SUMATRA KALIMANTAN MALUKU JAVA BALI PAPUA NUSA TENGGARA

16 About the data A panel dataset of 292 districts in Indonesia that includes the following variables: GRDP Level and growth of GDP in constant and current prices GRDP by sector Indicate natural resource endowments Educational Level of human resource endowments Population Level and growth Infrastructure Extent and quality of roads and communications Ethnic diversity Ethno-linguistic factionalization Geographical Land locked or not Political Background of district leader, political supports from local parliament, margin of victory. Transport Distance to provincial town, major cities and Jakarta Investment Level and growth Governance Variety of investment related governance indicators

17 Outline of Model Basic growth model i.e. gip = β 0 + β1yi0 + β1gnborp + β2popeduc + β3geog + β4infr + ε i0 Look at three periods: , , OLS and panel approach Include a range of spatial variables Consider spatial autocorrelation

18 What is good for GDP isn t always good for growth Share of population had junior high school education S h are p eo ple eve r/be ing in ju nio r Se con d ary S ch ool pe r to tal p opu lation ;S U S9 3 R ea l GR D P p er cap ita (in m io Rp ), BP S Fitted valu es S h are p eo ple eve r/be ing in ju nio r Se con d ary S ch ool pe r to tal p opu lation ;S U S9 3 G eo m etric A verag e Grow th R eal GR D P Fitted v alue s

19 What is good for GDP isn t always good for growth Share of HH with telephone Share of HH with telephone, PODES 1993 Real GRDP per capita (in mio Rp), BPS 1993 Fitted values Share of HH with telephone, PODES 1993 Geometric Average Growth Real GRDP Fitted values

20 Agriculture based districts are poorer, but it doesn t mean lower growth Share of agriculture to total GRDP, 1993 GRDP Per Capita 1993 (in Rupiah) Fitted values Share of agriculture to total GRDP, 1993 Geometric Average Growth Real GRDP Fitted values

21 Manufacturing based districts are richer, but doesn t mean higher growth Share of non oil & gas manufacturing to total GRDP, 1993Share of non oil & gas manufacturing to total GRDP, 1993 GRDP Per Capita 1993 (in Rupiah) Fitted values Geometric Average Growth Real GRDP Fitted values

22 Income Convergence? Ln per capita Real GDP, 1993 Geometric Average Growth Real GRDP Fitted values

23 Spatial Divergence? Growth performance is influenced by growth performance of neighboring districts Weighted Average Growth of neighbouring districts Geometric Average Growth Real GRDP Fitted values

24 OLS Period Regressions Geometric Average Growth in real per capita GDP Ln per capita Real GDP (4.91)** Weighted Average Growth of neighbouring districts during the period Share people ever/being in junior Secondary School per total population Share of population that is urban (3.56)** Labour Force (millions) (2.74)** Share of manufacturing to total GRDP (2.85)** (2.37)* Oil and Gas is the main sector (2.37)* Gini coefficient of sectoral employment

25 OLS Period Regressions contd. Geometric Average Growth in real per capita GDP Farmer's terms of trade index Distance district to Province's Capital Have to get on a boat to get to Province's Capital Telelphones per household (2.09)* Ethno-linguistic fragmentation Java dummy (2.86)** (1.98)* Constant (4.01)** Observations R-squared Robust standard errors in parentheses * significant at 5%; ** significant at 1%

26 Fixed Effect Regressions Ln per capita Real GRDP, initial year Weighted Average Growth of neighboring districts during the period Share people ever/being in junior Secondary School per total population (0.026)*** (0.026)*** (0.015)*** (0.030)*** (0.025) (0.023)*** (0.187) (0.106)*** (0.113) Labor force, initial year (0.032)*** (0.019)*** (0.009)** Gini coefficient of sectoral employment (0.096) (0.042) (0.052) Constant (0.476)*** (0.441)*** (0.241)*** Observations Number of groups R-squared

27 Accelerators and Decelerators Geometric Average Growth ; pre-crisis income percapita Geometric Average Growth ; post-decentralization income percapita

28 Difference Regression ΔgiP = β0 + β1δx i + ΔβX i0 + Δε i Look at change between and If coefficient on initial condition is zero then this is the initial model Results Model does a poor job of explaining changes in growth

29 Summary of Key Messages Economic activity is highly concentrated in Indonesia Inequality between provinces has been falling consistently Inequality between districts rose before the crisis, but has fallen since There is evidence for simultaneous income convergence AND for spatial divergence Sectoral effects appear to stimulate growth differently in different periods However, education, geographic, infrastructure variables are not strongly associated with growth

30 Future Work Using an alternative (and less noisy) measure of district performance Incorporating measures of economic governance Doing the analysis at the provincial level

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