Poverty, Inequality, Terrorism The Wealth of Villages

1. Poverty, Inequality, TerrorismThe Wealth of Villages -coauthor is John S. Felkner (post doc, NORC) Robert M. Townsend University of Chicago

2. 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

3. 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

6. 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

7. A Satellite View Of Industrialization

8. Wealth Index Spatial Distribution Chachoengsao, Lop Buri, Buriram and Sisaket 1986-1996

14. 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

15. Sisaket Province, - Road Network withAverage Road Speed

17. 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

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

22. 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

25. 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

27. 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

28. 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

29. 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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32. 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

33. SES Predicted Income per capita