UNIVERSITY OF ECONOMICS HOCHIMINH CITY VIETNAM –NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS    MULTIDIMENSIONAL POVERTY IN MEKONG RIVER DELTA By NGUYEN THI LAN ANH Ho Chi Minh, April 2014 UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS MULTIDIMENSIONAL POVERTY IN MEKONG RIVER DELTA A thesis submitted in partial fulfillment of the requirements for the degree of Master of Arts in Development Economics By NGUYEN THI LAN ANH Academic supervisor Dr NGUYEN HUU DUNG Ho Chi Minh City, April, 2014 DECLARATION I declare that "Multidimensional poverty in Mekong River Delta” is my own work; it has not been submitted to any degree at other universities I confirm that I have made by effort and applied all knowledge for finishing this thesis in the best way Ho Chi Minh City, April 2014 NGUYEN THI LAN ANH i ACKNOWLEDGEMENTS First of all, I would like to express our deepest gratitude to my supervisor, Dr Nguyen Huu Dung for his invaluable comments, suggestions and engagement through the learning process of the thesis I would also like to thank Dr Pham Khanh Nam for helpful remarks on my TRD Then I am much obliged to Dr.Tran Tien Khai and Dr Truong Dang Thuy for their enthusiasm which has encouraged me a lot to complete my dissertation I would also like to thank my Research Methodology module lecturer Dr Luca Tasciotti and Assoc Prof Dr Nguyen Trong Hoai who provided me with the background knowledge of research implementation It is no doubt that I am deeply indebted to my family members for all the kind understanding and spiritual support Finally, I would like to dedicate my concluding words to all class-mate who involved in this study ii ABSTRACT The study provides a critical review of poverty measurement in Mekong River Delta and arguments for utilizing a recent multi-dimensional methodology framework By applying Alkire & Foster’s methodology for Vietnamese Households Living Standards Survey data 2010 (VHLSS 2010), it produces a multidimensional poverty estimation of Mekong River Delta at the aggregate level, provincial level and identify the biggest contributors to multidimensional poverty Twelve indicators corresponding to four dimensions are considered for estimation The study also works out how policy-makers can prioritize the budget spending among provinces and within each province based on decomposable property of adjusted headcount ratio Lastly, the study investigates the effectiveness of using uni-dimension, per capita consumption, in poverty measurement and reaching a conclusion that consumption alone cannot capture deprivation experienced by the poor households Key words: Multidimensional poverty measurement, Deprivation, Alkire & Foster Methodology, Mekong River Delta iii TABLE OF CONTENTS DECLARATION i ACKNOWLEDGEMENTS ii ABSTRACT iii TABLE OF CONTENTS iv LIST OF TABLES vii LIST OF FIGURES vii ACRONYMS viii Chapter INTRODUCTION 1.1 Problem statement 1.2 Research objectives 1.3 Research questions .2 1.4 Research hypothesis 1.5 Justification of the study 1.6 Organization of the research study Chapter LITERATURE REVIEW 2.1 Key concepts of multidimensional poverty .4 2.1.1 Poverty 2.1.2 Poverty line 2.2 Economic theories of poverty 2.3 Approaches to poverty measurement 2.3.1 Monetary approach 2.3.2 Non-monetary approach .8 iv 2.4 Reviews of empirical studies 10 2.5 Chapter summary .14 2.5.1 Empirical literature summary 14 2.5.2 Problems and limitations of previous studies 17 2.5.3 Conceptual framework 18 Chapter METHODOLOGY .19 3.1 An overview of poverty in Vietnam and Mekong River Delta 19 3.2 Data 20 3.3 Methodology 20 3.3.1 Frequently used indicators of poverty measurement 21 3.3.2 Multidimensional poverty index of Alkire and Foster 22 3.4 3.3.2.1 Methodology 22 3.3.2.2 Choice of dimensions, indicators and deprivation cut off 23 3.3.2.3 Application of Weights 31 Treatment of non-applicable population and missing data 33 3.4.1 Treatment of non-applicable population 33 3.4.2 Treatment of missing data 33 3.5 Analytical framework 34 3.6 Chapter summary .34 Chapter RESULTS 36 4.1 Indicator deprivation 36 4.2 “Across dimension” cut-off and MPI estimation .39 4.3 Poverty estimates at provincial level .40 4.4 Which indicator contributes the most to MPI? 44 4.5 Decomposition of adjusted headcount ratio and Policy implications 45 4.6 Comparison between Consumption poverty and Multidimensional poverty 50 v 4.6.1 Income poverty verses Multi-dimesional poverty .50 4.6.2 Correspondence of consumption poverty and multidimensional poverty 51 4.6.3 Correlation between Consumption and Multidimensional poverty .51 4.7 Chapter summary .51 Chapter CONCLUSION 52 5.1 Conclusion .52 5.2 Limitation and further researches 53 APPENDIX .55 REFERENCES .55 vi LIST OF TABLES Table 2.1: GSO – WB poverty lines Table 2.2 MOLISA poverty lines Table 2.3:Summary of empirical studies relating to multidimensional poverty 14 Table 3.1: Households surveyed in each province 20 Table 3.2: Dimensions, Indicators and cutoffs 30 Table 3.3: Weights assigned to dimensions and indicators 32 Table 4.1: Multidimensional poverty estimate on various cut off point 40 Table 4.2: Poverty estimates at provincial level 42 Table 4.3: Spearman correlation between Mo ranks by different k values 46 Table 4.4: Decomposing of adjusted headcount ratio by indicator 49 Table 4.5: Comparing monetary poor and multidimensional poor at aggregate level 51 Table 4.6: Spearman coefficient between indicators 50 LIST OF FIGURES Figure 2.1: Dimensions and indicators of the MPI ( by Alkire and Foster, 2010) 10 Figure 2.2: Dimensions and indicators of the MPI (by Cuong Nguyen, 2012) 12 Figure 2.3: Ten dimensions with 16 indicators representative for four livelihood assets (by Khai Tran & Danh Nguyen, 2012) 13 Figure 2.4 Conceptual framework 18 Figure 3.1: Analytical framework 34 Figure 4.1: Comparison percentage of households deprived in each indicator 37 Figure 4.2: Proportion of households deprived in each indicators 38 Figure 4.3: Proportion of household deprived in various numbers of indicators 38 Figure 4.4: Contribution by province of each dimension to MPI 44 Figure 4.5: Contribution to MPI by indicators at aggregate level 45 Figure 4.6: MPI compare to consumption poverty by provincial level 50 vii ACRONYMS GSO General Statistics Office HHP Household Prestige index HDI Human Development Index MDG Millennium Development Goal MPI Multidimensional Poverty Index VHLSS Vietnam Household Living Standard Survey MRD Mekong River Delta MOLISA Ministry of Labor, Invalids and Social Affairs viii contribution to overall poverty of Tien Giang province Hence, poverty in An Giang will be reduced if it spend larger share of budget for education and health insurance Table 4.4: Decomposing of adjusted headcount ratio by indicator Education Health Province Years of school Child enrolment Food Soc Trang 0.024 0.011 Breakdown 11.2% Tra Vinh Living standards Wealth Medical Affordability Insurance House quality Electricity Water Sanitation Asset Land PCE 0.014 0.014 0.013 0.017 0.005 0.009 0.021 0.014 0.036 0.033 0.212 5.1% 6.7% 6.7% 6.3% 8.0% 2.4% 4.4% 9.8% 6.7% 16.8% 15.7% 100% 0.015 0.009 0.012 0.013 0.023 0.017 0.001 0.004 0.020 0.012 0.021 0.030 0.177 Breakdown 8.5% 5.2% 6.5% 7.4% 13.0% 9.3% 0.8% 2.4% 11.2% 6.8% 11.8% 17.0% 100% Hau Giang 0.017 0.01 0.0065 0.0065 0.0083 0.0129 0.0007 0.0126 0.0165 0.0113 0.0306 0.0278 0.157 Breakdown 10.6% 4.4% 4.1% 4.1% 5.3% 8.2% 0.4% 8.0% 10.5% 7.2% 19.4% 17.7% 100% 0.018 0.00 0.0119 0.0047 0.0119 0.0143 0.0024 0.0019 0.0157 0.0152 0.0226 0.0333 0.155 11.5% 2.3% 7.6% 3.1% 7.6% 9.2% 1.5% 1.2% 10.1% 9.8% 14.6% 21.5% 100% 0.025 0.01 0.0025 0.0019 0.0094 0.0128 0.0031 0.0098 0.0097 0.0114 0.0322 0.0246 0.152 Breakdown 16.2% 6.2% 1.7% 1.2% 6.2% 8.4% 2.1% 6.5% 6.4% 7.5% 21.2% 16.2% 100% Kien Giang 0.013 0.01 0.0078 0.0043 0.0092 0.0140 0.0032 0.0124 0.0167 0.0134 0.0224 0.0267 0.151 Breakdown 8.5% 5.6% 5.2% 2.8% 6.1% 9.2% 2.1% 8.2% 11.0% 8.8% 14.8% 17.6% 100% Dong Thap 0.018 0.01 0.0102 0.0048 0.0084 0.0121 0.0000 0.0121 0.0147 0.0091 0.0290 0.0190 0.145 Breakdown 12.5% 5.0% 7.1% 3.3% 5.8% 8.4% 0.0% 8.4% 10.1% 6.3% 20.0% 13.1% 100% 0.018 0.00 0.0052 0.0035 0.0121 0.0094 0.0010 0.0094 0.0125 0.0094 0.0339 0.0130 0.130 14.0% 2.0% 4.0% 2.7% 9.3% 7.2% 0.8% 7.2% 9.6% 7.2% 26.0% 10.0% 100% 0.012 0.00 0.0042 0.0048 0.0120 0.0082 0.0012 0.0118 0.0100 0.0091 0.0199 0.0145 0.110 10.7% 2.5% 3.8% 4.4% 10.9% 7.4% 1.1% 10.7% 9.1% 8.2% 18.1% 13.2% 100% 0.012 0.01 0.0099 0.0010 0.0059 0.0113 0.0012 0.0000 0.0119 0.0095 0.0119 0.0193 0.100 Breakdown 11.9% 6.0% 9.9% 1.0% 5.9% 11.3% 1.2% 0.0% 11.9% 9.5% 11.9% 19.4% 100% Tien Giang 0.014 0.01 0.0045 0.0040 0.0090 0.0065 0.0000 0.0068 0.0082 0.0071 0.0170 0.0162 0.098 Breakdown 13.9% 5.2% 4.6% 4.0% 9.2% 6.7% 0.0% 6.9% 8.4% 7.3% 17.3% 16.5% 100% Vinh Long 0.009 0.00 0.0095 0.0022 0.0087 0.0054 0.0004 0.0094 0.0093 0.0070 0.0143 0.0154 0.093 Breakdown 9.5% 2.4% 10.2% 2.4% 9.4% 5.8% 0.5% 10.2% 10.1% 7.6% 15.4% 16.6% 100% Long An 0.011 0.00 0.0013 0.0006 0.0071 0.0023 0.0004 0.0023 0.0070 0.0050 0.0145 0.0097 0.066 16.2% 7.4% 2.0% 1.0% 10.8% 3.5% 0.6% 3.5% 10.6% 7.7% 22.1% 14.7% 100% Ca Mau Breakdown An Giang Can Tho Breakdown Ben Tre Breakdown Bac Lieu Breakdown Source: Calculated from sub-sample set VHLSS 2010 (n= 1455) 49 Mo 4.6 Comparison between Consumption poverty and Multidimensional poverty 4.6.1 Income poverty verses Multi-dimesional poverty Figure 4.6 shows divergence between consumption poverty and multidimensional poverty The bar line presents multidimensional poverty headcount while the zic zag line displays consumption poverty headcount There are significant difference between consumption poverty and multidimensional poverty In almost provinces, consumption poverty is lower than multidimensional poverty There could be many reasons for the difference One of them is multidimensional poverty looks beyond consumption by directly measuring deprivation in health, education, living standards and all other aspect of human life In some cases, difference may come from the variance in households’ ability to convert income into education, health or living standards outcomes On the other hand, the result of using a recall data in measuring consumption is consumption poverty may be underestimated due to lack of data or forgotten of some kinds of consumption Besides, the shortcoming of health care services, schools and living conditions also contribute much to enlarge difference between multidimensional poverty and consumption poverty in MRD Figure 4.6: MPI compare to consumption poverty by provincial level 45 40 35 30 25 20 15 10 Soc Trang Tra Vinh Ca Mau Kien Giang Education Dong Thap Hau An Giang Giang Health Can Tho Bac Lieu Living standards Ben Tre Wealth Tien Giang Vinh Long Long An PCE Source: Calculated from sub-sample set VHLSS 2010 (n= 1455) 50 4.6.2 Correspondence of consumption poverty and multidimensional poverty Clearly, table 4.5 shows that multidimensional poor estimate is much higher than those in monetary poor There are 30 percent of households poor in multidimensional sense while only 22 percent of households fall below monetary poverty line The main reason is that monetary poverty reflects one deprivation – consumption deprivation while multidimensional poverty covers a much wider range of deprivation along with consumption Table 4.5 also reveals that the correspondence in declaring households as poor or non poor by consumption and multidimensional poverty is rather similar There are 83 percent of surveyed households that are poor and non poor in the consumption poverty are also in the same status as in MPI However, table 4.5 provides strong evidence that MPI is a much more cute poverty measurement If we take up MPI as poverty measurement, there is only 4.9 percent of surveyed households who are monetary poor is disclosed as non multidimensional poor This shortage is not significant Meanwhile if monetary poverty is taken up, 12.2 percent of households who are multidimensional poor is disclosed as non monetary poor In this case, the shortage is 2.5 times higher Table 4.5: Comparing monetary poor and multidimensional poor at aggregate level Multidimesional poverty headcount ratio Poor Non Poor Monetary poverty headcount ratio Poor Non poor 17,3% 12,2% 4,9% 65,6% 4.6.3 Correlation between Consumption and Multidimensional poverty The relationship between two measurements is investigated by Spearman correlation The correlation coefficient is 0.57 Although it is statistically significant at percent, it is not very high Therefore, it does not support for the acceptance of using only consumption as a single indicator to measure poverty instead of multi-indicators Besides, the typical argument for estimating poverty level on the basis of consumption is that it is highly correlated with achievement in other indicator, in other words, it explains for the deprivations in every other indicators As a result, measuring poverty based on per capita 51 consumption automatically includes deprivation in other indicators However, that does not seem to be appropriate in MRD Table 4.6 presents the Spearman coefficient between different pairs of indicators which is used in measuring MPI As table shows, except for water deprivation, all of the coefficient between consumption deprivation and each other indicator deprivation are statistically significant at the percent level However, these are not very high, the highest rank correlation of consumption indicators are with food security, assets ownership, housing quality and sanitation with coefficient of 0.29, 0.24, 0.24 and 0.23 respectively This suggests that multidimensional poverty study is indeed needed and consumption alone cannot explain deprivation in multi aspects experienced by the poor 52 Table 4.6: Spearman coefficient between indicators Consumption Cultivated land Years of schooling Schooling enrollment Food security Medical affordability Insurance Housing quality Electricity Water Cultivated land 0.1887*** Years of schooling 0.1604*** 0.2756*** Schooling enrollment 0.1200*** 0.0659** -0.0291 Food security 0.2940*** 0.1474*** 0.1594*** 0.0283 Medical affordability 0.0939*** 0.0803*** 0.0800*** 0.0075 0.1761*** Insurance 0.1495*** 0.1418*** 0.1522*** 0.0006 0.1935*** 0.0719*** Housing quality 0.2430*** 0.1630*** 0.1637*** 0.048* 0.1827*** 0.1366*** 0.0477* Electricity 0.1032*** 0.1039*** 0.0943*** 0.0426 0.063** 0.0504* 0.0446* 0.1105*** Water 0.0435* 0.1531*** 0.0636** 0.0479* 0.0295 0.0225 0.029 0.0636** 0.0680*** Sanitation 0.2305*** 0.1003*** 0.1217*** 0.0691*** 0.141*** 0.0894*** 0.0232 0.4139*** 0.0656* 0.0726*** Asset ownership 0.2477*** 0.2061*** 0.2491*** 0.0132 0.1863*** 0.1204*** 0.1292*** 0.1968*** 0.1793*** 0.0309 Sanitation 0.1713*** * Correlation is significant at 10 percent ** Correlation is significant at percent ** * Correlation is significant at percent 50 4.7 Chapter summary This chapter has revealed that rural households in MRD is more deprived in most of indicators in comparing with rural households of Vietnam Among 13 province, Soc Trang is found to be the poorest and Long An is the least poorest province The result also shows that land possession and per capita consumption emerged as two biggest contributors of MPI at aggregated level Analysis at provincial level by decomposing adjusted headcount ratio then is the base for policy implications The chapter closed on the comparison between monetary poverty and MPI indicates that in almost provinces, consumption poverty is lower than multidimensional poverty The correspondence in declaring households as poor or non poor by consumption and multidimensional poverty is rather similar However, the correlation between monetary poverty and multidimensional poverty suggests that multidimensional poverty study is indeed needed and consumption alone cannot explain deprivation in multi aspects experienced by the poor 51 Chapter CONCLUSION This chapter will serve to summarize the main findings of our research Also, general conclusion based on these findings will be described The chapter closes on a discussion of the study limitations and recommendation for further researches 5.1 Conclusion The study has set out to estimate multidimensional poverty in rural Mekong River Delta, the poorest region in Vietnam which highly needs intervention from Government For answering the question how width and depth of multidimensional poverty in MRD, multidimensional poverty index developed by Alkire and Foster (2007) was employed Twelve indicators covering four different dimensions: education, health, living standard and wealth were selected In the absence of empirical evidence, equal weight procedure was applied The study has also sought to know which indicators contribute the most to MPI, the relationship between consumption poor and multidimensional poor The result of the study showed that rural households in MRD is more deprived in most of indicators in comparing with overall poverty in the country Taking 30 percent cut-off poverty line, an estimation of 30 percent of households would be designated multidimensional poor, and they are on average deprived in 45 percent of weighted sum of indicators Cultivated land and per capita consumption are the ones with highest contribution to aggregate Mo, followed by sanitation and years of schooling When equal weighted system are used, cultivated land possession and PCE received a weight of 0.125 that is 2.5 times higher than the weight given to sanitation - the indicator with highest deprivation rate This is one of the reason why they have the highest shares of Mo although they are not the indicators with highest multiple deprivation rate From a policy perspective, the study also highlighted the advantage of using Alkire and Foster method to distribute budget among provinces as well as to prioritize budget within provinces The property of being decomposable in provinces and broken down into indicators is what exactly makes it get adapted for such purpose By decomposing Mo by provinces, the study indicates that Soc Trang, Tra Vinh and Hau Giang have highest Mo providing strong evidence that we need to 52 focus resource to reduce poverty in these provinces Mo of each province then would be broken down into indicators to find out which indicators are deserved to be given much effort In this study, we have also investigated the relationship between uni-dimensional poor and multidimensional poor to answer for the question “How is the difference between consumption poor and multidimensional poor” and “Can PCE alone measure poverty experienced by households?” The result attested that multidimensional poverty is much severe than consumption poverty in almost provinces of MRD Also, PCE alone fails to capture the more complex pattern of multidimensional poverty faced by poor households Overall, the evidence in this study revealed that Alkire and Foster method is innovative not only in changing the way of monitoring poverty, from the unidimensional approach which is focus on income/consumption poverty to multidimensional one which concentrates on most of aspects of human life, but also in providing a useful tool for allocating the resources among provinces and within province Base on these advantages, the study proposed that Alkire and Foster methodology is a useful instrument to capture multidimensional poverty in MRD 5.2 Limitation and further researches The most important limitation lies in the fact that our estimation is based on equal weight system which has neither reflected the importance of each dimensions/indicator to our life as suggested by Krujik and Rutten (2007) nor satisfied the recommendation of Nobel et al (2009) which supposed that the notion of separate dimensions explicitly empowers the researchers to control the weight of each dimension Because H rank and Mo rank are subject to the weight of each dimension/indicators, an advanced method in overcoming such limitation are needed to make the estimation more accuracy So far, due to data constraint from VHLSS 2010, our health data is rather weak and may overlook some deprived groups of households This weakness could have affected the measurement of multidimensional poverty which in turn might lead to the shortage of budget spending on households deprived in health Hence, it would be highly recommended for using more indicators pertaining to health dimension by doing research on another data Additionally, in this case, we need to consider new data to ensure that all important dimensions data are available 53 Finally, also due data constraint from VHLSS 2010, in declaring the household deprived in cultivated land possession, the study neglected the households which used to be agricultural households but shift to other activities due to the lack of land These households usually have a poor life because they are belongs to less skilled working forces and switching to other activities are their reluctant choices Hence, for more accurate estimation, an advance learning to overcome this limitation is needed Overall, the above arguments present some remaining issues which our study has not dealt with More researches and more data are hence needed to improve the estimation of multidimensional poverty With more precise estimation, the Government’s spending would be more effective and living of the poor households would be intrinsically improved 54 REFERENCES Alkire, S and Foster, J 2007 and 2009 Counting and Multidimensional Poverty Measurement OPHI Working Paper and 32 Alkire, S and Santos, M.E 2010 Acute Multidimensional Poverty: A New Index for Developing Countries OPHI Working Paper 38 Alkire, S and Foster, J (2011) Counting and multidimensional poverty measurement Journal of Public Economics Asselin, L M., and Vu, T A (2009), Analysis of Multidimensional Poverty, Theory and Case Studies, retrieved June 22, 2012 from http://idlbnc.idrc.ca/dspace/bitstream/10625/40247/1/128805.pdf, Basu, K and Foster, J (1998) On measuring Literacy Economic Journal 108(451), 1733-49 Be, L (2012) Mekong River Delta, the most poorest area in Vietnam (Vietnamese title: Đồng sông Cửu Long, vùng đất nghèo Việt nam Becker, G S (1964, ed 1975) Human Capital New York: Columbia University Press Blau, F., Ferber, M., & Winkler, A (1998) The economics of women, men and work Upper Saddle River, NJ: Prentice Hall Booysen, F., Van Der Berg, S., Burger, R., Von Maltitz, M & Du Rand, G (2008) Using an asset index to assess trends in poverty in seven Sub-Saharan African countries World Development, 36(6): 1113-1130 Burton, E (1992) The poverty debate: Politics and the poor in America Westport, CT:Greenwood Press Cuong, N (2010) Multidimensional urban poverty estimates in Vietnamese central cities HCM University of Economics, Vietnam-Netherlands Programme for M.A in Development economics, Vietnam Doeringer, P.B & Piore, M J (1971) Internal Labor Market and Manpower Analysis.Lexington, MA: Heath Lexington Books., retrieved from 29 July, 2012 from http://www.trentu.ca/ids/documents/Q2_WP35_deKruijk_Rutten.pdf 55 Filmer, D., & Prichett, L (2001) Estimating wealth effects without expenditure data – or tears: An application of educational enrollment in states of India Demography, 38(1), 115-132 Gordon, D (2005) Indicators of Poverty and Hunger, retrieved on June 20, 2012 from www.un.org/esa/socdev/unyin/documents/ydiDavidGordon_poverty.pdf Greer J., Thorbecke E., 1986 A Methodology for Measuring Food Poverty Applied to Kenya, Journal of Development Economics, 24, pp 59-74, Elsevier, BV General Statistic Organization (2011) Result of Vietnamese household living standard survey 2010 Khai, T., & Danh, N (2012) Interrelation between livelihood assets and poverty in rural of Vietnam HCM University of Economics Kruijk, H D and M Rutten 2007 Weighting dimensions of poverty based on people’s priorities: Constructing a composite poverty index for Maldives, retrieved on 29 July, 2012 from https://www.trentu.ca/ids/documents/Q2_WP35_deKruijk_Rutten.pdf Noble, M., Barnes, H., Wright, G and Roberts, B (2009) Small area indices of multiple deprivation in South Africa Department of Social Policy and Social Work, University of Oxford UK retrieved on July 25, 2012 from http://download.springer.com/static/pdf/690/art%253A10.1007%252Fs11205-009-94607.pdf?auth66=1387377624_267942a2b72c78870e2cb3de89ca6a2c&ext=.pdf North, D (1990) Institutions, Institutional Change, and Economic Performance New York: Cambridge University Press Paul S., 1989 A model of constructing the poverty line, Journal of Development Economics, 30, p 129-144, Elsevier, BV http://dx.doi.org/10.1016/0304-3878(89)90054-0 Prakongsai P (2006 ) An application of Asset index for measuring household living standards in Thai Land International Health Policy Program (IHHP), Thailand retrieved on 27 July, 2012 from http://www.nesdb.go.th/econSocial/macro/TNCE/Download/5/Phusit.pdf 56 Prim Minister of Vietnam (2011) Decision No 09/2011/QĐ/Ttg dated 30 November 2011 issued by the Prime Minister on the issuance of Poverty line (Vietnamese title: Quyết định 09/2011/QĐ/TTg thủ tướng phủ việc ban hành chuẩn hộ nghèo, cận nghèo Rank, M (1994) Living on the edge: The realities of welfare in America New York: Columbia University Press Ravallion, M 1994 Poverty Comparision Chur, Switzerland: Harwood Academic Publishers Ravallion, M 1998 Poverty lines in theory and practice Living Standards Measurement Study Working Paper No 133 Washington, DC: World Bank Rowntree B S., 1901 Poverty: A Study of Town Life MacMillan, London, UK Santos, M E., & URa, K (2008) Multidimensional Poverty in Bhutan: Estimates and Policy Implications OPHI Working Paper 14 Sen, A.K (1976) Poverty: An Ordinal Approach to Measurement Econometrica, 44(2), 219-231 Schultz, T (1960) Capital formation of education Journal of Political Economy, 68(6), 571 -83 Schultz, T (1961) Investment in human capital Economic Review, 5(4), 1- 17 Schultz, T (1963) The Economic Value of Education New York: Columbia University Press Sherraden, M (1991) Assets and the poor: A new American welfare policy Armonk, New York: M E Sharpe, Inc United Nations Development Programme, 1990 Human Development Report 1990 United Nations, New York United Nations Development Programme, 1997 Human Development Report 1990 United Nations, New York United Nations Economic and Social Council (ECOSOC) “Statement of commitment for action to eradicate poverty adopted by administrative committee on coordination, May 20, 1998.” ECOSOC retrieved on 28 July, 2012 from http://www.unesco.org/most/acc4pov.htm World Bank (2000) Poverty and Inequality Analysis, In: The World Bank: Poverty Reduction & Equity, 09-11-2011, retrieved on 03 August, 2012 from http://go.worldbank.org/VFPEGF7FU0 57 APPENDIX Appendix 1: Questionnaires used in VHLSS 2010 Seq Variable Question Years of schooling Which grade [name] has finished? School enrolment In the past 12 months, has [name] attend school? Food security In the past 30 days, has your household had sufficient food? Medical treatment Can [name] afford for medical treatment? affordability Medical insurance Do [name] has any type of insurance coverage? Housing quality What is the main material of your house’s column? What is the main material of your house’s roof? What is the main material of your house’s wall? Electricity What is the main source of your light? Drinking water What is the main source of your drinking water? Asset Kindly let us know what kind of following assets you have? 10 Sanitation What type of toilet does your household have? 11 Consumption 12 Cultivated land possession - Expenditure on foods & drinks during holidays - Daily expenditure on foods & drinks - Expenditure on non-food & non-drink and other expenditure - Annual expenditure - Other expenses included in expenditure - Housing expenditure - Durable asset expenditure Which kind of the following land are your household manage and use? How much area? 58 Appendix 2: Source of data from VHLSS 2010 Seq Variable Filename Question Years of schooling muc2a1.dta m2ac1 School enrolment muc2a1.dta m2ac5 Food security muc8.dta m8c11a, m8c11b Medical treatment affordability muc3a.dta m3c7 Medical insurance muc3b.dta m3c9 Housing quality muc7.dta m7c3, m7c4, m7c5 Electricity muc7.dta m7c18 Drinking water muc7.dta m7c14 Asset muc6b.dta m6c2 10 Sanitation muc7.dta m7c17 11 Consumption muc5a1.dta m5a1c2b muc5a2.dta m5a2c2b muc5b1.dta m5b1c2 muc5b2.dta m5b2c2 muc5b3.dta m5b3c2 muc4b0.dta m4b0ma, m4b0c3 12 Cultivated land possession Appendix 3: Variance in deprivation in each indicator (Mekong River Delta and Vietnam) Indicators Years of schooling School enrolment Food security Medical affordability Insurance House Quality Electricity Water Sanitation Asset ownership Consumption Cultivated land Vietnam 10 15 10 36 19 38 44 36 27 27 Mekong River Delta Percentage variance 14 11 -4 -1 22 -14 45 26 -1 40 66 21 40 22 -5 28 -1 59 Appendix 4: Types of toilet using in rural households of Mekong River Delta Type of toilet Septic tank toilet Sulabh flush toilet compartment latrine On water surface Others No toilet Total Freq Percent 446 30.65 31 2.13 23 1.58 799 54.91 91 6.25 65 4.48 1455 100.00 Cum 30.65 32.78 34.36 89.28 95.53 100 Appendix 5: Consumption water source in rural households of Mekong River Delta Consumption water source Tab water Public tab water Drilling well Protected dug well Unprotected dug well Protected river canal Unprotected river canal Buying water Raining water Other Total Freq Percent 235 16.15 24 1.65 597 41.03 11 0.76 0.62 0.27 0.07 16 1.1 245 16.84 313 21.51 1455 100.00 Cum 16.15 17.8 58.83 59.59 60.21 60.48 60.55 61.65 78.49 100.00 Appendix 6: Correspondence of consumption poor and housing quality poor Consumption Non poor Poor Total Housing quality Non poor Poor Total 699 433 1,132 107 216 323 806 649 1,455 Appendix 7: Correspondence of consumption poor and asset poor Consumption Non poor Poor Total Asset Non poor Poor 760 372 123 200 883 572 Total 1,132 323 1,455 60 ... study poverty in wider and deeper sense through various dimensions The study aims at mapping and measuring of multidimensional poverty in rural areas of Mekong River Delta by estimating incidence... poverty in each province from the multidimensional perspectives 1.2 Research objectives i To measure the state of multidimensional poverty by provinces in rural of Mekong River Delta ii To find... main kinds of poverty lines: Relative poverty line is set by putting one income in relation to the rest of population This establishes poverty lines in terms of a percentage mean or median of income