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Poverty as a function of space: understanding the spatial configuration of poverty in Malaysia for Sustainable Development Goal number one

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Abstract

Poverty is one of the most common problems throughout the world. For this reason, United Nations Sustainable Development Goal (SDG) number one aims at its eradication from all countries around the globe. While this is an ambitious goal, it has reinforced the commitment of many countries, including Malaysia, towards increased poverty alleviation efforts. Peninsular Malaysia has over the past decade been making many efforts to reduce poverty among its population. Like many socioeconomic problems, poverty is a function of space, and spatial analysis can be key to deeper understanding and implementation of effectual intervention policies. In the era of advancement of geospatial techniques, many of our socio-economic problems can be explained through spatial analysis, mapping, and visualization. This study’s main objective is to illuminate the spatial distribution of poverty and assess the factors that most contribute to the spatial configuration, using hotspot analysis and geographically weighted regression. While the results demonstrate the complexity of poverty as an issue in Malaysia, they demonstrate a clear spatial pattern. Poverty rates in Malaysia are significantly clustered (p < 0.001), as most of the high poverty rate sub-districts are located in the northeastern states of Kelantan and Terengganu. Even though the SDG number one is an ambitious one, this paper has revealed important spatial dynamics that are worthy of consideration as the government implement policies that will eradicate poverty.

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References

  • Acemoglu, D., Johnson, S., & Robinson, J. (2006). Understanding prosperity and poverty: Geography, institutions, and the reversal of fortune. In Understanding poverty.

  • Acheampong, M., et al. (2018). Land use/cover change in Ghana’s oil city: Assessing the impact of neoliberal economic policies and implications for sustainable development goal number one—A remote sensing and GIS approach. Land Use Policy, 73(April), 373–384. http://www.sciencedirect.com/science/article/pii/S0264837716311875.

    Article  Google Scholar 

  • Akinyemi, F. O. (2007). Spatial data needs for poverty management. Research and Theory in Advancing Spatial Data Infrastructure Concepts, 5(1), 261–277.

    Google Scholar 

  • Akinyemi, F. O. (2008). In support of the millennium development goals: GIS use for poverty reduction tasks, 1331–1336.

  • Anselin, L. (1999). The future of spatial analysis in the social sciences. Geographic Information Sciences, 5(2), 67–76. http://www.tandfonline.com/doi/abs/10.1080/10824009909480516. Accessed 6 Dec 2016.

    Article  Google Scholar 

  • Babones, S. (2015). What is world-systems analysis? Distinguishing theory from perspective. Thesis Eleven, 127(1), 27–29.

    Article  Google Scholar 

  • Brunsdon, C., Fotheringham, S., & Charlton, M. (1998a). Geographically weighted regression. Journal of the Royal Statistical Society Series D The Statistician, 47(3), 431–443. http://doi.wiley.com/10.1111/1467-9884.00145..

    Article  Google Scholar 

  • Brunsdon, C., Fotheringham, S., & Chariton, M. (1998b). Geographically weighted regression-modelling spatial non-stationarity. Journal of Royal Statistical Society, 47(3), 431–443.

    Google Scholar 

  • Brunsdon, C., Fotheringham, S., & Charlton, M. E. (1996). Geographically weighted regression: A method for exploring spatial nonstationarity. Geographical Analysis, 28(4), 281–298. https://doi.org/10.1111/j.1538-4632.1996.tb00936.x.

    Article  Google Scholar 

  • Brunsdon, C., Fotheringham, S., & Charlton, M. (1999). Some notes on parametric significance tests for geographically weighted regression. Journal of Regional Science, 39(3), 497–524.

    Article  Google Scholar 

  • Bryceson, D. F., Bradbury, A., & Bradbury, T. (2006). Roads to poverty reduction? Dissecting rural roads’ impact on mobility in Africa and Asia. May: 1–38.

  • Burns, T. J., Kick, E. L., & Davis, B. L. (2003). Theorizing and rethinking linkages between the natural environment and the modern world-system: Deforestation in the late 20th century. World-Systems Research, IX(2), 357–390.

    Article  Google Scholar 

  • Cliff, A. D., & Ord, K. (1970). Spatial autocorrelation: A review of existing and new measures with applications. Economic Geography, 46(sup1), 269–292. https://www.tandfonline.com/doi/abs/10.2307/143144.

    Article  Google Scholar 

  • Dunaway, W. A. (2003). Emerging issues in the 21st century world-system: Crises and resistance in the 21st century world-system. Contributions in Economics and Economic History.

  • Ellis, S. D. (1997). Key issues in rural transport in developing countries. Transport Research Laboratory Report 1, Vol. 260, pp. 1–27.

  • Fealy, G. (2005). Islamisation and politics in southeast Indonesia. In Islam in World Politics (pp. 152–169). London: Routledge. https://www.taylorfrancis.com/books/e/9781134347179. Accessed 30 Aug 2017.

  • Fingleton, B. (2009). Spatial autoregression. Geographical Analysis, 41(4), 385–391.

    Article  Google Scholar 

  • Haining, R. P. (2009). Spatial autocorrelation and the quantiative revolution. Geographical Analysis, 41(4), 1–11.

    Google Scholar 

  • Holt, J. B. (2007). The topography of poverty in the United States: A spatial analysis using county-level data from the community health status indicators project. Preventing Chronic Disease, 4(4), A111. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2099276&tool=pmcentrez&rendertype=abstract. Accessed 6 Dec 2016.

  • Idris, K., et al. (2016). Quality of life in rural communities: Residents living near to Tembeling, Pahang and Muar Rivers, Malaysia. PLoS ONE, 11(3), 1–16.

    Article  Google Scholar 

  • Kanbur, R., & Sumner, A. (2012). Poor countries or poor people? Development assistance and the new geography of global poverty. Journal of International Development, 24(6), 686–695.

    Article  Google Scholar 

  • Kick, E. L., McKinney, L. A., & Thompson, G. H. (2011). Intensity of food deprivation: The integrative impacts of the world system, modernization, conflict, militarization and the environment. International Journal of Comparative Sociology, 52(6), 478–502.

    Article  Google Scholar 

  • Kissling, W. Daniel, & Carl, G. (2008). Spatial autocorrelation and the selection of simultaneous autoregressive models. Global Ecology and Biogeography, 17(1), 59–71.

    Google Scholar 

  • Lee, O. A. (2010). Coastal resort development in Malaysia: A review of policy use in the pre-construction and post-construction phase. Ocean and Coastal Management, 53(8), 439–446.

    Article  Google Scholar 

  • Lin, C.-H., & Wen, T.-H. (2011). Using geographically weighted regression (GWR) to explore spatial varying relationships of immature mosquitoes and human densities with the incidence of dengue. International Journal of Environmental Research and Public Health, 8(7), 2798–2815. http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3155330&tool=pmcentrez&rendertype=abstract. Accessed 6 Dec 2016.

  • Meadows, D. (1986). Poverty causes population growth causes poverty, 1–3. http://www.sustainer.org/dhm_archive/index.php?display_article=vn126manupured.

  • Miller, D. (2001). The poverty of morality. Journal of Consumer Culture, 1(2), 225–243.

    Article  Google Scholar 

  • Murshed, S. M. (2007). What turns a blessing into a curse? The political economy of natural resource wealth. In Pakistan Development Review.

  • Olaniyi, A. O., Abdullah, A. M., Ramli, M. F., & Alias, M. S. (2012). Assessment of drivers of coastal land use change in Malaysia. Ocean and Coastal Management, 67(October), 113–123.

    Article  Google Scholar 

  • Osman, M. M., Bachok, S., Muslim, S. A., & Bakri, N. I. M. (2015). Unemployment issues and problems in Kinta, Manjung and Kuala Kangsar, Perak, Malaysia. ProcediaSocial and Behavioral Sciences, 168, 389–399. http://linkinghub.elsevier.com/retrieve/pii/S1877042814057036. Accessed 27 Mar 2018.

  • Population Distribution and Basic Demographic Characteristics. (2011). Department of Statistics, Malaysia.

  • Rafee Majid, M., et al. (2016). Mapping poverty hot spots in Peninsular Malaysia using spatial autocorrelation analysis. Journal of the Malaysian Institute of Planners. https://doi.org/10.21837/pmjournal.v14.i4.144.

    Article  Google Scholar 

  • Ross, M. L. (1999). The political economy of the resource curse. World Politics, 51(2), 297–322.

    Article  Google Scholar 

  • Rosser, A. (2006). Escaping the resource curse. New Political Economy, 11, 557–570.

    Article  Google Scholar 

  • Saari, M. Yusof, Alias, E. F., & Chik, N. A. (2013). The importance of the agricultural sector to the Malaysian economy: Analyses of inter-industry linkages. Pertanika Journal of Social Science and Humanities, 21(September), 173–188.

    Google Scholar 

  • Saari, M. Yusof, Dietzenbacher, E., & Los, B. (2015). Sources of income growth and inequality across ethnic groups in Malaysia, 1970–2000. World Development, 76(December), 311–328. https://doi.org/10.1016/j.worlddev.2015.07.015.

    Article  Google Scholar 

  • Sachs, J. D., Mellinger, A. D., & Gallup, J. L. (2001). The Geography of poverty and wealth. Scientific American, 284(3), 70–75.

  • Sinding, S. W. (2009). Population, poverty and economic development. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1532), 3023–3030. http://rstb.royalsocietypublishing.org/cgi/doi/10.1098/rstb.2009.0145. Accessed 27 Mar 2018.

    Article  Google Scholar 

  • Starkey, P., & Hine, J. (2014). Poverty and sustainable transport: How transport affects poor people with policy implications for poverty reduction.

  • The East Coast Economic Region (ECER). (2008). Transforming the east coast.

  • The Office of Chief Statistician, and Department of Statistics Malaysia. (2013). Department of Statistics Malaysia Press release report of household income and basic amenities survey 2012. Report of household income and basic amenities survey, 2012, 11–97. http://www.statistics.gov.my/portal/index.php?option=com_content&view=article&id=1640&Itemid=111&lang=bm. Accessed 26 Apr 2018.

  • The Office of Chief Statistician, and Department of Statistics Malaysia. (2015). Department of Statistics Malaysia Press release report of household income and basic amenities survey 2014. Report of household income and basic amenities survey, 2014, 5. https://www.dosm.gov.my/v1/index.php?r=column/pdfPrev&id=aHhtTHVWNVYzTFBua2dSUlBRL1Rjdz09. Accessed 26 Apr 2018.

  • The Office of Chief Statistician, and Department of Statistics Malaysia. (2017). Department of Statistics Malaysia Press release report of household income and basic amenities survey 2016. Report of Household Income and Basic Amenities Survey, 2016, 7. https://www.dosm.gov.my/v1/index.php?r=column/pdfPrev&id=aHhtTHVWNVYzTFBua2dSUlBRL1Rjdz09. Accessed 26 Apr 2018.

  • Thirtle, C., Lin, L., & Piesse, J. (2003). The impact of research-led agricultural productivity growth on poverty reduction in Africa, Asia and Latin America. World Development, 31(12), 1959–1975.

    Article  Google Scholar 

  • Tilak, J. B. G. (2002). Education and poverty. Journal of Human Development, 3(2), 191–207. http://www.tandfonline.com/doi/abs/10.1080/14649880220147301.

    Article  Google Scholar 

  • UN. (2007). 46248 Implementation of the first United Nations decade for the eradication of poverty (19972006).

  • UN. (2018). Sustainable Development Goals: Goal 1—End poverty in all its forms everywhere. https://unchronicle.un.org/article/goal-1-end-poverty-all-its-forms-everywhere. Accessed 26 Apr 2018.

  • Voss, P. R., Long, D. D., Hammer, R. B., & Friedman, S. (2006). County child poverty rates in the US: A spatial regression approach. Population Research and Policy Review, 25(4), 369–391.

    Article  Google Scholar 

  • Wallerstein, I. (1974). Theory, culture & society. The Modern World System.

  • Wallerstein, I. M. (1979). Studies in modern capitalism. The Capitalist World-Economy: Essays

  • Wegener, M. (2014). Handbook of regional science. http://link.springer.com/10.1007/978-3-642-23430-9.

  • Wright, R. E. (1996). Standardized poverty measurement. Journal of Economic Studies, 23(4), 3–17. http://www.emeraldinsight.com/10.1108/01443589610149889.

    Article  Google Scholar 

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Correspondence to Mehrdad Vaziri.

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Vaziri, M., Acheampong, M., Downs, J. et al. Poverty as a function of space: understanding the spatial configuration of poverty in Malaysia for Sustainable Development Goal number one. GeoJournal 84, 1317–1336 (2019). https://doi.org/10.1007/s10708-018-9926-8

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