Abstract
Poverty has a geographic dimension. Geography, particularly the physical environment, plays a significant bearing on the state of poverty particularly in developing countries. However, this geographic dimension has not been given much attention in many poverty studies, especially in the Philippines. In an attempt to underscore its importance, this study explores the possible underlying determinants to poverty using two adjacent provinces of Albay and Camarines Sur as pilot site. Taking advantage of the analytical capability of GIS, the study incorporated spatial variables in the multiple regression analysis, namely: agro-climatic conditions, access to road infrastructure, and proximity to major markets; together with the influence of land distribution program, fiscal decentralization policy, and population growth, in order to analyze their likely influence on the incidence of poverty. Regression results revealed that access to road infrastructure, proximity to major markets, rate of land distribution, bias in fiscal decentralization policy, and aspects of agro-climatic condition, i.e. elevation, slope and rainfall, all have significant effects on poverty incidence within the study site. Geography and facets of public policy have a strong impact on the state of poverty, indeed.
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References
Albert, J. R. G., & Collado, P. M. (2004). Profile and determinants of poverty in the Philippines. Paper presented at the 9th National Convention on Statistics.
Amarasinghe, U., Samad, M., & Anputhas, M. (2005). Spatial clustering of rural poverty and food insecurity in Sri Lanka. Food Policy, 30, 493–509.
Balisacan, A. M. (2007). Why does poverty persist in the Philippines? Facts, fancies and policies. SEARCA Agriculture and Development Discussion Paper Series 1–32.
Balisacan, A. M., Edillon, R. G., & Ducanes, G. M. (2002). Poverty mapping and targeting for KALAHI-CIDDS Phase I: Final report prepared for the Department of Social Welfare and Development (DSWD), Republic of the Philippines.
Balisacan, A. M., & Fuwa, N. (2004). Going beyond crosscountry averages: Growth, inequality and poverty reduction in the Philippines. World Development, 32, 1891–1907.
Benson, T., Chamberlin, J., & Rhinehart, I. (2005). An investigation of the spatial determinants of the local prevalence of poverty in rural Malawi. Food Policy, 30, 532–550.
Biau, G., Zorita, E., von Storch, H., & Wackernagel, H. (1999). Estimation of Precipitation by kriging in the EOF space of the sea level pressure field. Journal of Climate, 12(4), 1070–1085.
Bigman, D., & Deichmann, U. (2000). Geographic targeting: A review of different approaches. In D. Bigman& H. Fofack (Eds.), Geographical targeting for poverty alleviation: Methodology and applications (pp. 43–73). Washington, DC: The World Bank.
Bigman, D., & Fofack, H. (2000). Introduction and overview. In D. Bigman & H. Fofack (Eds.), Geographical targeting for poverty alleviation: Methodology and applications (pp. 1–39). Washington, DC: The World Bank.
Coudouel, A., Hentschel, J. S., & Wodon, Q. T. (2002). Poverty measurement and analysis. In J. Klugman (Ed.), The poverty reduction strategies paper (PRSP) sourcebook (pp. 29–74). Washington DC: The World Bank.
Davis, B. (2003). Choosing a method for poverty mapping. Rome: Food and Agriculture Organization of the United Nations.
Deichmann, U. (1999). Geographic aspects of poverty. Retrieved June 21, 2010, from http://siteresources.worldbank.org/INTPGI/Resources/Pro-Poor-Growth/5319_povmap.pdf
Domingo, E. V. (2003). Poverty mapping in the Philippines. Paper presented at the UNESCAP Ad Hoc Expert Group Meeting on Poverty Mapping and Monitoring Using Information Technology.
Estudillo, J. P., Sawada, Y., & Hossain, M. (2005). Socioeconomic changes and the decline in poverty: A view from three villages in the Philippines, 1985–1997. Asian Economic Journal, 19(4), 383–406.
Fischer, G., van Velthuizen, H., Nachtergaele, F., & Medow, S. (2000). Global agro-ecological zones. Retrieved January 2007, from Food and Agriculture Organization of the United Nations and International Institute for Applied Systems Analysis: http://www.iiasa.ac.at/Research/LUC/GAEZ/index.htm
Fuwa, N. (2007). Pathways out of rural poverty: A case study in socio-economic mobility in the rural Philippines. Cambridge Journal of Economics, 31, 123.
Glasmeier, A. K. (2002). One nation, pulling apart: The basis of persistent poverty in the USA. Progress in Human Geography, 26, 155–173.
Henninger, N., & Snel, M. (2002). Where are the poor? Experiences with the development and use of poverty maps. Washington, DC and Arendal: World Resources Institute and UNEP-GRID.
Hyman, G., Larrea, C., & Farrow, A. (2005). Methods, results and policy implications of poverty and food security mapping assessments. Food Policy, 30, 453–460.
Jarvis, A., Reuter, H. I., & Guevarra, E. (2006). Hole-filled seamless SRTM data V3. Retrieved October 2006, from International Centre for Tropical Agriculture: http://srtm.csi.cgiar.org
Kam, S. -P., Hossain, M., Bose, M. L., & Villano, L. S. (2005). Spatial patterns of rural poverty and their relationship with welfare-influencing factors in Bangladesh. Food Policy, 30, 551–567.
Kristjanson, P., Radeny, M., Baltenweck, I., Ogutu, J., & Notenbaert, A. (2005). Livelihood mapping and poverty correlates at a meso-level in Kenya. Food Policy, 30, 568–583.
Legg, C., Kormawa, P., Maziya-Dixon, B., Okechukwu, R., Ofodile, S., & Alabi, T. (2005). Report on mapping livelihoods and nutrition in Nigeria using data from the National Rural Livelihoods Survey and the National Food Consumption and Nutrition Survey. Ibadan, Nigeria: International Institute for Tropical Agriculture.
Minot, N., Bob, B., & Epprecht, M. (2006). Poverty mapping and inequality in Vietnam: Spatial patterns and geographic determinants International Food Policy Research Institute Research Report, 146, 1–80.
NASA Landsat Program. (2003). Landsat ETM+. Retrieved October 2006, from United States Geological Survey.
NSCB. (2004). 2004 Regional and social economic trends in Bicol Region. Legaspi City: National Statistical Coordination Board, Regional Division V.
NSCB. (2005). Estimation of local poverty in the Philippines. Makati City: National Statistical Coordination Board and The World Bank.
NSCB. (2006). Overview of the Bicol region. Retrieved June 21, 2010, from http://www.nscb.gov.ph/ru5/overview/default.html
NSO. (2002). CY 2002 census of agriculture. Retrieved June 21, 2010, from http://www.census.gov.ph/data/sectordata/ag0411504.htm
NSO. (2000a). 2000 census of population and housing. Retrieved June 21, 2010, from http://www.census.gov.ph/data/sectordata/datapop.html
NSO. (2000b). Final results 2000 family income and expenditures survey. Retrieved June 21, 2010, from http://www.census.gov.ph/data/sectordata/2000/ie00fr09.htm
Rogers, D., Emwanu, T., & Robinson, T. (2006). Poverty mapping in Uganda: An analysis using remotely sensed and other environmental data. Pro-poor Livestock Policy Initiative Working Paper, 36, 1–60.
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Appendices
Appendix 1
a: Multiple regression model summary
R | R 2 | Adjusted R 2 | Standard error of the estimate |
|---|---|---|---|
0.912 | 0.832 | 0.794 | 0.0478069 |
b: ANOVA table of multiple regression analysis
| Â | Sum of squares | Degrees of freedom | Mean square | F | Sig. |
|---|---|---|---|---|---|
Regression | 0.499 | 10 | 0.050 | 21.812 | 0.001 |
Residual | 0.101 | 44 | 0.002 | ||
Total | 0.599 | 54 | Â |
c: Histogram of standardized residual
d: Normal P-P Plot of residuals
Appendix 2: Correlation Matrix for all of the Variables Used in Multiple Regression Analysis
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Vista, B.M., Murayama, Y. (2011). Spatial Determinants of Poverty Using GIS-Based Mapping. In: Murayama, Y., Thapa, R. (eds) Spatial Analysis and Modeling in Geographical Transformation Process. GeoJournal Library, vol 100. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0671-2_16
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DOI: https://doi.org/10.1007/978-94-007-0671-2_16
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