The Effects of Agro-clusters on Rural Poverty:A Spatial Perspective for West Java of Indonesia

Dadan Wardhana, Rico Ihle, Wim Heijman

(Agricultural Economics and Rural Policy)

Prepared to be presented in EAAE 150th Seminar, Edinburgh, UK, October 22-23, 2015

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Presentation Outline:

Introduction

Theory

Data

Methodology

●Cluster measurements

●Model Specifications

Results and Discussion

Policy Remarks

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Introduction

Role of the agricultural sector in rural economies

Concentration of non-farm economy vs. concentrated agricultural sector

Shifting from agriculture to more developed non-farm to boost economies

However,

•Agricultural productivity growth associated with higher economic performance and lower poverty rates (de Janvry & Sadoulet, 2010)

•More investment in agricultural sector increases welfare (World Bank, 2008)

•GDP growth in agriculture has at least three times larger positive impact on poverty reduction than for the rest of the economy

How?

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What are agro-clusters?

Economic A

gg

lom

era

tion

Collective efficiency

(Schmitz & Nadvi, 1999)

Competitive advantages (Porter, 1990)

Pool of labourers,

information & knowledge exchange

(Krugman, 1995)

Farmer GroupManufacturing

Tourism

Restaurants

Exports

Source: Authors 4

Theory of Spatial Agglomeration Effects

Productivity curve

Cost curve

Number of labourers (Cluster size)

Productivity per hectare

Costs

Profits

Number of labourers (Cluster size) 

Profit per hectare

“Spatial concentration in agriculture reduces rural poverty” (Kiminami & Kiminami, 2009)

Source: Duraton, et al. (2010). p.34

Productivity CurveThe higher size of clusters, the higher productivity.

Cost CurveThe higher size of clusters, the higher production costs

Profit Curve•concave •profit vs. size of the cluster

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Description of West Java

¯

B

J

Note:B : Bandung Metropolitan, one of cities in West JavaJ : Jakarta Metropolitan, a capital city of Indonesia

Source: Authors

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Data

• The Indonesia programme for the census of agriculture of the office of national statistics, 2013.

• The Indonesia programme for the census of poverty of BPS, 2011

• District Statistical Yearbook published by BPS for all districts.

• The raw shapefile of West Java map.

We focus on 545 sub-districts of West Java of Indonesia by using aggregated data at sub-district level.

Poverty Rates (% of population)

Source: Authors 7

Cluster Measurements

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Spatial Regression Specifications

Models Specifications

SAR (Spatial Autoregressive Model) • Spatial lags in dependent variable (lnpov) for two baseline models

SDM (Spatial Durbin Model) • Spatial lags in both dependent variable and main explanatory variables (hcs and Ks)

SEM (Spatial Error Model) • Spatial lags in error terms for two baseline models

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Baseline models: poverty rates and agro-clusters

sq_hcs : squared cluster size;

Xi,s : control variables, including Growth of farmer number, % smallholders, % farmers with 55 years old, population density, total area, % wetland, productivity, distance and time travel to the nearest city.

D: dummy variable (1 = rural regions; 0 = urban areas)

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Results

Note: + and – are significant at 5%

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Conclusions

The higher number of farm labourers, the lower poverty is, but after reaching the optimal average number, the poverty rate rise.

The more specialization in agriculture or non-agriculture, sub-district may experience the lower poverty rate.

The direct effects of agro-clusters on poverty rates in the sub-district itself are larger than the spillover effects.

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Policy Remarks

In the sense of specialization

foster the specialisation of the sub-districts

establish production nuclei

control input availability and prices

improve market access

In terms of maintaining cluster size

•improve farmers’ capabilities and knowledge.

•enforce networks

Spatial spillover effects

improve the availability and quality of infrastructures

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Agriculture in West Java

Economic

Ag

glo

mera

tion

Thank you

Testing Spatial Dependence

OLS? Diagnostic tests on baseline models

• Contiguity spatial weight matrix

• Moran I’s spatial autoregression,

-2

-1

0

1

2

3

W ln

po

v

-2 -1 0 1 2 3 4 5 6 7 8 9 10

Poverty in Natural Logaritm

-2

-1

0

1

2

3

W C

luste

r siz

e

-2 -1 0 1 2 3 4 5 6 7 8 9 10cluster size

-2

-1

0

1

2

3

W S

pe

cia

lizatio

n In

dex

-2 -1 0 1 2 3 4 5 6 7 8 9 10

Specialization Index

Spatial Regression Specifications

Findings

Note: One, two and three asterisks denote significance at the 10%, 5% and 1% level, respectively.