Poverty, Inequality, TerrorismThe Wealth of Villages

-coauthor is John S. Felkner (post doc, NORC)Robert M. TownsendUniversity of Chicago

TODAY, ONE PART, ONLY

• TO UNDERSTAND POVERTY, UNEVEN DEVELOPMENT AND THE POTENTIAL FOR TERRORISM LOCALLY

• NEED ECONOMIC MODELS TO UNDERSTAND UNDERLYING FORCES WITH FINE TUNED PREDICTIVE POWER

• ASSESS POLICY CHANGE

Data:• Socio-Economic Data: Thai Community Development

Department (CDD) biannual census data• More than 3000 villages in four provinces, 1986-1996• Focus on four Thai provinces specifically chosen to

represent a cross-section of Thai economic development: fertile central plains versus poorer northeast- same as Townsend Thai project. Adding South/unrest

• Supplemental: GIS spatial data collected from a variety of sources, including a number of Thai government agencies. Also utilized an archive of Landsat satellite imagery from 1979-2004

1986-1996: Thai high growth period

Thai economy experienced some of the highest growth rates in the world, ranging from 7 to 12 percent, often attributed to financial liberalization

• Average wealth doubled, rapid industrialization

• Extensive deforestation and urbanization

A Satellite ViewOf Industrialization

Wealth Index Spatial Distribution

Chachoengsao, Lop Buri, Buriram and Sisaket1986-1996

GIS, Road Networks, and “Accessibility”:

• Highly detailed geo-referenced data on road networks was used to calculate travel-time along road networks taking into account varying road speeds

• This allowed for the creation of variables as proxies for “access” to economic agglomerations, which could then be used in the testing and correction of simulation models

Sisaket Province, - Road Network withAverage Road Speed

Dynamic Simulation of the Occupational Choice Model:

• villages as the data points• Simulation begins with base year wealth distribution

1986 and produces results through 1996 • Financial intermediation “index” imposed or not

exogenously in each year of the simulation (binary from CDD)- occupation choice and end of period wealth a function of initial and talent (costs)

• The credit sector is weighted according to the exogenous intermediation fraction, and an equilibrium obtained giving a common market clearing wage and interest rate in credit mkt

• trace path of individual villages given the prices

Figure 9: Occupational Choice Simulated Vs. Actual Means

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Occupational Choice Simulated 3-Bin Spatial Modification5-Bin Spatial Modification Actual Entrepreneurial Activity

Spatial and Temporal Testing of the Financial Deepening Model: The simulation did an excellent job of capturing overall dynamic trends

Figure 12A: Financial Deepening Simulation - Actual Vs. Simulated Financial Credit Access

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Actual Access Simulated Access

Figure 12B: Financial Deepening Simulation - Actual Vs. Simulated Wealth

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1986 1988 1990 1992 1994 1996

Actual Wealth Simulated Wealth

Residuals structural models regressed onto covariates:

• Occupation choice onto – wealth, education, an intermediation access and the

agglomeration access proxies• Results:

– Wealth and education are never significant– However, time-travel to nearest major intersections is

positive and significant as model is over predicting with distance

– credit intermediation index is positive, as if in the model credit/saving access is too good

Part 2: Agglomeration Proxies for Individual Provinces(Coefficient values in bold, probability values in italics.)

Sisaket Dependent VariablesIndependent Occupational Choice Spatially Modified 3-Bin Spatially Modified 5-Bin

Variables Residuals Residuals ResidualsNorth-South -0.0194 -0.0026 -0.0050

Regimes 0.0460 0.7882 0.6205Two-Agglomeration 0.0082 0.0134 0.0255

Regimes 0.3998 0.1677 0.0100Three-Agglomeration -0.0105 -0.0054 0.0093

Regimes 0.2792 0.5816 0.3526Distance To -0.0001 -0.0001 -0.0001

Two-Agglomerations 0.0419 0.0157 0.0005Distance To 0.0000 -0.0001 -0.0001

Three-Agglomerations 0.9034 0.8430 0.1437

Buriram Dependent VariablesIndependent Occupational Choice Spatially Modified 3-Bin Spatially Modified 5-Bin

Variables Residuals Residuals ResidualsDistance To 0.0000 -0.0001 -0.0001

Three-Agglomerations 0.6099 0.5685 0.8250Distance To 0.0000 0.0000 -0.0001

Four-Agglomerations 0.3582 0.4936 0.5871

Lop Buri Dependent VariablesIndependent Occupational Choice Spatially Modified 3-Bin Spatially Modified 5-Bin

Variables Residuals Residuals ResidualsEast-West -0.0370 -0.0310 -0.0209Regimes 0.0105 0.0440 0.2057

Distance To 0.0000 0.0000 0.0000One-Agglomeration 0.0059 0.0792 0.4588

Distance To 0.0000 0.0000 -0.0001Two-Agglomerations 0.0730 0.3756 0.8741

Chachoengsao Dependent VariablesIndependent Occupational Choice Spatially Modified 3-Bin Spatially Modified 5-Bin

Variables Residuals Residuals ResidualsEast-West -0.0219 -0.0139 0.0067Regimes 0.4981 0.6627 0.8324

Distance To -0.0086 -0.0005 0.0151Agglomeration 0.7754 0.9865 0.6055

An Experiment:• Policy Simulation: create new, hypothetical road networks and

impose spatially varying estimated costs via m parameter – – does superior accessibility increase simulated entrepreneurial activity

for villages close to new roads?• Roads intersections were created using the GIS according to 2 criteria:

– Located far from existing roads and major intersections– Located in areas with low levels of entrepreneurial activity

• Model was re-simulated using the spatially modified model (with new estimated m parameter values with distance to new road intersections)

• Result: dramatically higher levels of entrepreneurial activity near to the new major road intersections

Financial deepening model• Model over predicts closer to spatial

agglomerations• Confirmed with Local Moran spatial statistical

cluster detection• Residuals also regressed onto agglomeration

proxies, wealth and education, and significant and negative results for all 3 direct agglomeration proxy variables, and significant and positive results for wealth and education

• In sum, the simulation is over-predicting close to economic agglomerations- both wealth and credit

Spatial Modification

• Again, full sample stratified into bins – 3 bins by equal number of villages – along the axis of time-travel to major intersections

• Also, model simulated separately for commercial banks only, and then for BAAC only

• This allowed for the estimation across space of the variation in costs of using each major financial provider as captured by the q parameter

Figure 15: Financial Deepening Simulation - k^defined by actual wealth distribution and participation rate

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BAAC - Bin 1Commercial Banks - Bin 1BAAC - Bin 2Commercial Banks - Bin 2BAAC - Bin 3Commercial Banks - Bin 3

Commercial Banks (bin 1)

Commercial Banks (bin 2)

BAAC (bin 1)

BAAC (bin 2)

BAAC (bin 3)

Commercial Banks (bin 3)

•Graph above displays relative costs by bin (results plotted in data wealth units)

•Note that for BAAC, costs are systematically lower than for commercial banks

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Conclusions:• We begin with the assumption that spatial proximity acts to minimize

transmission costs for ideas: can we test whether spatial proximity to economic agglomerations facilitates the spread of entrepreneurial activity, wealth or access to credit?

• Consequently, we estimate transaction costs as a function of decreasing accessibility to economic agglomerations

• For the entrepreneurial choice model, the testing reveals that spatial proximity matters greatly in determining the cost of going into entrepreneurial activities – the model performs much better after estimation of spatially varying entrance costs

• For the financial deepening model, the testing reveals an apparently policy distortion due to government support of the public credit provider, resulting in higher estimated costs closer to agglomerations

SES Predicted Income per capita