An asset based geographic targeting evidence from rural vietnam

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UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HOCHIMINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM – NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS AN ASSET-BASED GEOGRAPHIC TARGETING: EVIDENCE FROM RURAL VIETNAM A thesis submitted in partial fulfillment of the requirement for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By PHAM THI NGOC AI Academic Supervisor: Dr PHAM KHANH NAM HO CHI MINH CITY, MAY 2014 ACKNOWLEDGEMENT The thesis would not have been finished without the kind assistance and fruitful guidance of many people who have the contributions of different aspects for accomplishing the thesis First of all, I am specially grateful to Dr Pham Khanh Nam who encourages me at the beginning of title and help my deep understanding on literature theory as well as thesis writing In addition, I would like to express the sincere gratitude to Dr Truong Dang Thuy for sharing his knowledge for the technique of the model and some valuable advices for the methodology I would like to give special thank for my boss and colleagues who create conditions and assist working in order that I have more time for the research Finally, my most gratitude is for my family, especially my parents and husband who have been always side by side with me during learning this program and researching process ABSTRACT The purpose of this paper is to find out which asset is the most suitable for a particular region through calculating marginal return to a range of assets and then creating a serial of maps The data are taken from Vietnam Living Standard Survey in 2006 The Weighted Least Squares is used for running the regression and combining with technique bootstrap and stepwise iterative deletion with the threshold of 5% All targetable assets are focused on calculating marginal benefit It gives the reasonable findings that have very heterogeneous average marginal benefit across areas The results give suggestion for choosing which assets are suitable for a particular region, thus it makes increases their efficacy However, the governors and donors should consider the existence of trade-off equity and efficacy TABLE OF CONTENTS CHAPTER I: INTRODUCTION 1.1 Problem statement 1.2 Research objective 1.3 Research questions 1.4 Research contributions 1.5 Organization of the paper CHAPTER II: LITERATURE REVIEW 2.1 Geographic targeting theory 2.2 Household welfare function 2.3 The small estimation method 2.4 Transfer in-kind 2.5 The linkage between household welfare and return to assets 2.6 Review of empirical studies 12 CHAPTER III: OVERVIEW OF HOUSEHOLD WELFARE IN VIETNAM AND METHODOLOGY 15 3.1 Overview of household welfare in Vietnam 15 3.2 Econometric models 19 3.3 Data 22 3.3.1 Independent variables 23 3.3.2 Dependent variable 28 CHAPTER IV: EMPIRICAL RESULTS 29 4.1 Descriptive statistics 29 4.2 Econometric results 33 4.2.1 Statistics and value of marginal return of assets at national level 34 4.2.2 Analysis for average of mean marginal return of assets at provincial level 36 4.2.3 Kinds of maps for Vietnam 42 CHAPTER V:CONCLUSION, POLICY IMPLICATION, LIMITATION AND FURTHER RESEARCH 49 5.1 Conclusion 49 5.2 Policy implication 50 5.3 Limitation of this study 51 5.4 Direction for Further research 52 REFERENCES 53 LIST OF CHARTS Graph 3.1:The quintiles of income in urban and rural of Vietnam 16 Graph 3.2: The quintiles of expenditure in urban and rural of Vietnam 16 Graph 3.3: The Quintiles of income in the eight regions of Vietnam 17 Graph 3.4: The quintiles of expenditure in the eight regions of Vietnam 18 Graph 3.5: Poverty rate at different level of region in Vietnam (Unit: %) 18 Graph 4.1: Proportion of literate for each region 29 Graph 4.2: Distribution of educational level for each region 30 Graph 4.3: Distribution of expenditure for each educational level and each region 30 Graph 4.4: Distribution of ethnic minorities across regions 31 Graph 4.5: Expenditure of some ethnics 32 Graph 4.6: Distribution of livestock for each region 32 Graph 4.7: Distribution of other assets across regions 33 LIST OF FIGURES Figure 4.1: Maps of AMB that is significantly greater than zero 43 Figure 4.2: Maps of proportion of positive AMB 44 Figure 4.3: Maps of maximum significant AMB 45 Figure 4.4: Maps of maximum proportion of positive 46 Figure 4.5: Example of choosing cattle transferred to provinces which meet three conditions: the magnitude of AMB at 0.035, 95% households have positive AMB and poverty rate 30% 48 LIST OF TABLES Table 3.1: Asset variables 26 Table 3.2: Control variables 270 Table 4.1: Average standard deviation of mean marginal return for national, regional and provincial level 34 Table 4.2: Mean of AMB and proportion of provinces with positive AMB at national level 36 Table 4.3: Values of AMB that are significantly greater than zero 38 Table 4.4: Data for proportion (%) of positive AMB of households in provinces 40 Table 4.5: Correlating between asset holdings and poverty with significant and proportion of positive AMB 47 CHAPTER I: INTRODUCTION 1.1 Problem statement Alleviating poverty is always the major targeting interested in by policy-makers in the developing countries There are many transfer programs made around the world from years to years However, increasing transfer program efficacy under the condition of scarce resources is the extremely important issue which governors and donors consider One of the methods used widely and popularly for researching and practicing is geographic targeting As it gives a visual and useful tool and performance with low cost and easy administration Thus, the important rule of the geographic targeting for poverty reduction is emphasized by Baker and Grosh(1994), Bigman and Foback (2000) Moreover, many papers have proven that the efficacy of transfer programs is higher when geographic units are smaller (Elbers et al 2007, Minot 2000, Bigman and Foback 2000) Poverty map is a tool of geographic targeting It displays poverty indicator across geography and answer the question where the poor people reside and who the poor people are (Elbers et al 2003, Minot and Baulch 2005) as well as why the area has high incidence poverty which is driven by natural resources (Szonyi et al 2010) However, the greatly advanced step of geographic targeting is targeting map with asset-based approach It answers the extremely important question that governors and donors should use in-kind transfer for a particular region to bring the highest benefit for the poor and might create motivation for them out of poverty Besides, it gives a visual and practical tool for policy-makers and donors to manage their transfer programs with budget limitation Benefit of transferring whether in-kind or cash is better, always consider by the researchers, donors and governors However, in many places in the world, they prefer transferring in-kind and in some cases, transfer in-kind is better (Hoffmann, Barrett and Just, 2009) Commented [PKN1]: I’ve made a new paragraph here Water -0,60 -0,44 Motorbike -0,21 -0,39 0,32 0,78 “-” the scope for benefit of water gets 100% for the whole regions, not depend asset holdings Other utility of targeting maps which we want to mention hereafter, we assume that chicken is chosen for transfer program We select the provinces with the following conditions: (1) Provinces have expected mean marginal return higher than 0.035 (2) Provinces have 95% of positive AMB (3) Provinces have poverty rate of 30% at least We find out provinces that can meet the above conditions and plot at the Figure 4.5 hereafter: ( % ) ( % ) Figure 4.5: Example of choosing cattle transferred to provinces which meet three conditions: the magnitude of AMB at 0.035, 95% households have positive AMB and poverty rate 30% 50 CHAPTER V: CONCLUSION, POLICY IMPLICATION, LIMITATION AND FURTHER RESEARCH 5.1 Conclusion: Geographic targeting with asset-based approach is the method that gives a potential to improve the efficiency of in-kind transferred programs in poverty reduction Basing on calculating expected marginal benefit at household level across geography to find out in-kind transfer program which is the most suitable for a particular region The paper takes the data of Vietnam Household Living Standard Survey and some geographic variables in 2006 The model regression is the weighted Least Squares based on the logarithmic function of the relationship between household welfare and asset return Combining bootstrap technique, stepwise iterative deletion and take derivatives to estimate expected marginal return for each assets at household level Then, we synthesize for provincial, regional and national level and finally create the serial maps for visual viewing We focus mainly on all private targetable assets The findings have answered the question relating to magnitude and scope of benefit for each asset as well as making a view of allocating their transfer which brings the most beneficial to a particular region Due to the lack of the second data of Agricultural and Fishery Census, the results are at provincial level for reference, although a number of observations at provincial level have at least 45 households However, the findings seem reasonable It suggests that education (literate) brings the largest magnitude of benefit, but proportion of positive expected marginal benefit is lower other assets such water and motorbike This proves that impact of education is always efficient but its levels are very different across each region The regions that get great benefit, are at coast and delta To be different with literate, water gets less value of benefit but have 100% of positive AMB for all the regions, the second is motorbike Comparing to the size of benefit is motorbike arranged at the second order The size and scope of return 51 for livestock assets lower than the above assets Some assets such road and bank not get return as per expected In general, the spatial distribution of asset return is very heterogeneous, thus it suggests a powerful tool to choose the most suitable assets for a determined area To explore the meaning of the results, we create a serial of maps that show the magnitude and scope of asset return through regions Especially when we combine with poverty map, we can create a map which can show the best mean of transfer and poverty indicator, it is a strong tool of geographic targeting for poverty reduction intervention The findings also suggest that certainly existing some areas which have the large benefit but not need to transfer and the opposite Thus, the governors and donors should consider the trade-off between efficiency and equity to meet the final purpose of poverty alleviation The method gives a flexible and easily visual tool to create the targeting map with particular assets 5.2 Policy implication: The purpose of the paper estimates the marginal expected return of a range to assets across geography The findings show that education brings the most benefit compared to other assets although magnitude and scope of education is various across geography Thus, education as an engine helps the poor out of poverty Its efficacy increases more for the region which have relative educational level Thus, the governors and donors should have programs which support education like tuition and tool assistance for learning or/and other policies to encourage to go to school We also pay attention on ownership of motorbike and water which may bring the economic efficacy highly and the scope gets 100% in the whole country For other assets like bank, road, land, efficacy only focus some areas The governors should be more careful for invest as a few scattered regions get high results For three kinds of livestock, pig has widespread scope and magnitude which are larger than other stock and the next to cattle and its values is especially high at North East and 52 North West Cattle seem to be efficient in the North and Central Highlands Thus, pig and cattle are more suitable than chicken in the mountainous areas and highlands Only bicycle does not seem mostly to be worth for poverty reduction For visual view, the results are plotted on the map, the policy-makers can recognizewhich place on the map the asset has high magnitude of benefit and large scope of benefit for map compared to other regions As well, when comparison among assets, they can know among many assets which they can get, which assets is the most beneficial Based on the information details of each region and available resources as well as thought about trade-off between efficacy and equity, policy-makers can decide which assets are chosen for transfer scheme for a particular region Besides, they can combine with other tools like poverty maps and criteria to choose the rightest region for a available assets However, here has not yet been the end results to solve the question about final benefit and influence of asset transfer Policy-makers need to know more cost of asset such as cost of purchasing, maintenance and procurement as well as consider life span of assets Based on that, we can conclude that which assets bring the most beneficial for a particular region 5.3 Limitation of this study: This study has two main shortcomings ofthe method and data which we can be unavoidable For the data, there is no the data of the census The value of AMBs at level lower than regional level such as provinces, districts are not sure about degree of explain This study only brings the meaning of displaying the method, literature and gives the idea about a tool for asset-based geographic targeting for poverty reduction intervention For the method,the analysis just accounts for partial equilibrium, but general equilibrium cannot It exists the endogenous concern as welfare household and assets 53 have dual causality But according to Lang, Barrett and Naschold (2013) and Nguyen Viet Cuong, Tran Ngoc Truong and Roy Van Der Weide (2010), we have to accept this issue as we only want to determine the asset taken part in expenditure, not care for effect Finally, transferring a huge number of assets to a given area which makes its market changed, the benefit of assets is changeable In some case, transferring a huge quantity makes positive impact on externalities, for instance, mobile phone or infrastructure In other cases, this makes negative impact on benefit of asset, for instance: cow If a large number of cattle are transferred into a particular region, it makes the price of milk lower This leads the benefit if cow’s benefit reduces Thus, we have to assume that “aggregate asset transfer will typically be marginal in magnitude and therefore that partial equilibrium assumption suffice” Lang, Barrett and Naschold (2013) Although there are some shortcomings which can be unavoidable, we are believable that this method creates the useful knowledge to fill up the large hole that prevents improving program transfers and policy intervention 5.4 Direction for further research: Targeting performance increases the efficacy when the region is researched at small region Thus, it is necessary for combine information from the census population or agricultural census with household survey We should research about actual cost of each assets (only livestock mentioned in household survey) to compare benefit From that, we can understand fully about the benefit which each asset actually brings back One thing which we should understand that geographic targeting with asset-based approach is not a only and final tool for choosing the in-kind transfer We can combine with other tools of targeting map as well as use the panel data for studying more about expected benefit of assets on poverty incidence 54 REFERENCES Allessandro Tarozzi and Angus Deaton (2009) Using census and survey data to estimate poverty and in equity for small areas The review of Economics and Statistics, November 2009, 91(4): 773-792 Aronson, Richard J (1985) Public finance New York: McGraw Hill Baker, Judy L., Margaret E., (1994) Poverty reduction through geographic targeting: How well does it work? World Development 22 (7), 983-995 Bedi, Tara, David, Fofact, Hippolyte (2000) Geoghraphic targeting for poverty alleviation: methodology and applications Washington Dc, World Bank Regional and Sectoral Studies Chris Elbers, Jean O Lanjouw, and Peter Lanjouw (2003) Micro-level estimation of poverty and inequality Econometrica, Vol.71, No , 355-364 David Coady, Margaret Grash and John Hoddinotts (2004) Targeting of Transfer in Developing Countries: Review of Lessons and Experience The World Bank Washington, D.C Demombynes, G., Elbers, C., Lanjouw, J.O., & Lanjouw, P (2007) How good a map? Putting small area estimation to the test World Bank working papers 4155 Felix Naschold and Christopher B Barrett (2011) Do Short-Term Observed Income Changes Overstate Structural Economic Mobility ? Oxford Bulletin of Economics and Statistics, 73, (2011) 0305-9049 Fleisher, Belton M., Edward J Ray and Thomas J., Keisner (1987) Principles of Economics Dubuque, Iowa; William C Brown Frank Ellis and H Ade Freeman (2006) Rural livelihoods and poverty reduction strategies in four African countries.The Journal of Development Studies, 40:4, 1-30 Gallup, J.L., Sachs, J.D., (1999) Geographic and Economic development, CAER II Discussion Paper No.39 Harvard Institute for International Development 55 Corey Lang, Christopher B Barrett and Felix Naschold (2013) Targeting Maps: An Asset-Based Approach to Geographic Targeting World Development Vol.41, pp.232244 Michelle Adato, Michael R Carter and Julian May (2006) Exploring poverty traps and social exclusion in South Africa using qualitative and quantitative data The Journal of Development Studies, 42:2, 226-247 Nguyen Viet Cuong (2011) Poverty projection using a small area estimation method: Evidence from Vietnam Journal of Comparative Economics 39 368-382 Nguyen Viet Cuong, Tran Ngoc Truong and Roy Van Der Weide (2010) Poverty and Inequality Maps in Rural Vietnam: An application of Small Area Estimation Asian Economic Journal 2010, Vol 24 No 335-390 Nicolas Minot (2000) Generating disaggerated poverty maps: An application to Vietnam World Development Vol.28, No.2, pp 319-331 Okwi, P.O., Ndeng’e, G., Kristjanson, P., Arunga, M., Notenbaert, A., Omolo A., Henninger, N, Benson, T., Kariuki, P., Owour, J., (2007) Spatial determinants of poverty in rural Kenya In: Proceedings of the National Academy of Sciences of the United States of America (PNAS), 104:43 (16769-16774) URL: http://www.pnas.org/cgi/reprint/104/43/16769 ” Roy D Adams and Ken McCormick (1993) The Traditional Distinction between Public and Private Goods Needs to Be Expanded, Not Abandoned Journal Theoretical Politics 5: 109 Robert S Pindyck and Daniel L Rubinfeld (2009) Microeconomics Household Equivalence Scales: Theory versus Policy?Author(s): Julie NelsonSource: Journal of Labor Economics, Vol 11, No (Jul., 1993), pp 471-493 56 A Shenggen Fan, Connie Chan-Kang (2004) Returns to investment in less-favored areas in developing countries: a synthesis of evidence and implication for Africa Food Policy 29 (2004) 431-444 Suan-Pheng Kam, Mahabub Hossain, Manik Lal Bose, Lorena S Villano (2005) Spatial patterns of rural poverty and their relationship with welfare-influencing factors in Bangladesh Food Policy 30 (2005) 551-567 Vivian Hoffmann, Christopher B Barret and David R Just (2009) Do free Goods Stick to Poor Households? Experimental Evidence on Insecticide Treated Bednets World Development Vol.37, No 3, pp 607-617, 2009 .World Bank (2003) Vietnam Development Report 2004 Poverty Poverty Reduction and Economic Management Unit, East Asia and Pacific Region http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTPOVERTY/EXTPA/0,, contentMDK:20205001~menuPK:435735~pagePK:148956~piPK:216618~theSitePK: 430367,00.html APPENDIX: Example of regressing the equation model once as follows: sw reg lnexp mot bic lan wat cat chi pig lit edu roa ban pop eth dis mar tem ran dry motedu motroa motban motpop moteth motdis motmar mottem motran motdry motmmo bicedu bicroa bicban bicpop biceth bicdis bicmar bictem bicran bicdry bicmbi watedu watroa watban watpop wateth watdis watmar wattem watran watdry watmwa lanedu lanroa lanban lanpop laneth landis lanmar lantem lanran landry lanmla catedu catroa catban catpop cateth catdis catmar cattem catran catdry catmca pigedu pigroa pigban pigpop pigeth pigdis pigmar pigtem pigran pigdry pigmpi chiedu chiroa chiban chipop chieth chidis chimar chitem chiran chidry chimch litedu litroa litban litpop liteth litdis litmar littem litran litdry litmli eduroa eduban edupop edueth edudis edumar edutem eduran edudry edumed 57 roapop roaeth roadis roamar roatem roaran roadry banpop baneth bandis banmar bantem banran bandry fixphone tele fridge per_house semiper_house otherhouse clean_water otherwater flushtoilet othertoilet notoilet elec otherelec mem_school if rand>0.3, pr(0.05)robust (per_house dropped due to estimability) begin with full model p = 0.9938 >= 0.0500 removing watroa p = 0.9926 >= 0.0500 removing pigran p = 0.9814 >= 0.0500 removing litmar p = 0.9711 >= 0.0500 removing watran p = 0.9557 >= 0.0500 removing catmar p = 0.9549 >= 0.0500 removing liteth p = 0.9248 >= 0.0500 removing catmca p = 0.9103 >= 0.0500 removing lanedu p = 0.9061 >= 0.0500 removing watdry p = 0.8960 >= 0.0500 removing bandry p = 0.8938 >= 0.0500 removing litdis p = 0.8877 >= 0.0500 removing watban p = 0.8778 >= 0.0500 removing motdry p = 0.8646 >= 0.0500 removing motmmo p = 0.8554 >= 0.0500 removing roadis p = 0.8559 >= 0.0500 removing litran p = 0.8356 >= 0.0500 removing otherwater p = 0.8201 >= 0.0500 removing catban p = 0.8076 >= 0.0500 removing catdis p = 0.7874 >= 0.0500 removing motban p = 0.7824 >= 0.0500 removing lanmar p = 0.8007 >= 0.0500 removing lanban 58 p = 0.7725 >= 0.0500 removing otherelec p = 0.7732 >= 0.0500 removing pigdry p = 0.7556 >= 0.0500 removing motran p = 0.7523 >= 0.0500 removing roaran p = 0.7488 >= 0.0500 removing bandis p = 0.7296 >= 0.0500 removing landry p = 0.7243 >= 0.0500 removing fridge p = 0.7105 >= 0.0500 removing pigdis p = 0.6679 >= 0.0500 removing roadry p = 0.6652 >= 0.0500 removing edumar p = 0.6697 >= 0.0500 removing mar p = 0.6631 >= 0.0500 removing biceth p = 0.6572 >= 0.0500 removing chieth p = 0.6449 >= 0.0500 removing roapop p = 0.6323 >= 0.0500 removing lanpop p = 0.7910 >= 0.0500 removing laneth p = 0.5887 >= 0.0500 removing roa p = 0.5955 >= 0.0500 removing catran p = 0.5930 >= 0.0500 removing tele p = 0.5676 >= 0.0500 removing bicdis p = 0.5664 >= 0.0500 removing litban p = 0.5755 >= 0.0500 removing eduban p = 0.5811 >= 0.0500 removing edudry p = 0.5828 >= 0.0500 removing watedu p = 0.5522 >= 0.0500 removing litedu p = 0.5089 >= 0.0500 removing wattem p = 0.6067 >= 0.0500 removing watpop 59 p = 0.7553 >= 0.0500 removing wateth p = 0.4842 >= 0.0500 removing bicedu p = 0.4809 >= 0.0500 removing litdry p = 0.4834 >= 0.0500 removing watmwa p = 0.4561 >= 0.0500 removing banran p = 0.4505 >= 0.0500 removing chiroa p = 0.4222 >= 0.0500 removing lanroa p = 0.4018 >= 0.0500 removing landis p = 0.3670 >= 0.0500 removing bicmbi p = 0.3535 >= 0.0500 removing pigmpi p = 0.3554 >= 0.0500 removing flushtoilet p = 0.3233 >= 0.0500 removing watdis p = 0.2799 >= 0.0500 removing catedu p = 0.2454 >= 0.0500 removing dis p = 0.2885 >= 0.0500 removing edupop p = 0.2193 >= 0.0500 removing cateth p = 0.6368 >= 0.0500 removing catpop p = 0.2149 >= 0.0500 removing pigroa p = 0.2011 >= 0.0500 removing chimar p = 0.2097 >= 0.0500 removing mottem p = 0.3684 >= 0.0500 removing moteth p = 0.1864 >= 0.0500 removing lantem p = 0.1896 >= 0.0500 removing elec p = 0.2014 >= 0.0500 removing clean_water p = 0.1514 >= 0.0500 removing litmli p = 0.1636 >= 0.0500 removing eduroa p = 0.1781 >= 0.0500 removing motedu 60 p = 0.1466 >= 0.0500 removing bicban p = 0.1381 >= 0.0500 removing eduran p = 0.1450 >= 0.0500 removing pigban p = 0.1156 >= 0.0500 removing catdry p = 0.1217 >= 0.0500 removing ran p = 0.1176 >= 0.0500 removing pig p = 0.5479 >= 0.0500 removing pigtem p = 0.1217 >= 0.0500 removing pigeth p = 0.1031 >= 0.0500 removing banmar p = 0.0876 >= 0.0500 removing motroa p = 0.0958 >= 0.0500 removing pigmar p = 0.0726 >= 0.0500 removing bictem p = 0.1252 >= 0.0500 removing bicdry p = 0.1383 >= 0.0500 removing bicmar p = 0.1045 >= 0.0500 removing bicran p = 0.1559 >= 0.0500 removing bicroa p = 0.0795 >= 0.0500 removing watmar p = 0.0613 >= 0.0500 removing roaeth Number of Linear regression obs = 3488 F( 49, 3438) = 33.89 Prob > F = R-squared = 0.3008 Root MSE = 0.47144 t P>t Robust lnexp Coef Std Err 61 [95% Conf Interval] mot 0.1334927 0.0516291 2.59 0.01 0.032266 0.23472 bic -0.2093429 0.0552354 -3.79 -0.3176404 -0.10105 lan -0.221544 0.0602927 -3.67 -0.3397571 -0.10333 wat 0.0872175 0.0223558 3.9 0.0433856 0.13105 cat 0.2697319 0.0722978 3.73 0.1279809 0.411483 chi 0.4234352 0.1203405 3.52 0.187489 0.659381 lanmla 0.2724486 0.0698226 3.9 0.1355507 0.409347 lit -1.842227 0.5652367 -3.26 0.001 -2.950461 -0.73399 edu 0.2847328 0.0513652 5.54 0.1840234 0.385442 chitem -0.0016865 0.0004772 -3.53 -0.0026222 -0.00075 ban 2.428568 0.7756367 3.13 0.002 0.9078129 3.949324 pop 0.0003099 0.0000891 3.48 0.001 0.0001352 0.000485 eth 0.4612814 0.1069528 4.31 0.2515839 0.670979 edumed -0.0091657 0.0011518 -7.96 -0.011424 -0.00691 edudis -0.00013 0.0000382 -3.41 0.001 -0.0002048 -5.5E-05 tem 0.0084695 0.0020411 4.15 0.0044677 0.012471 roatem -0.0012558 0.0003049 -4.12 -0.0018536 -0.00066 dry -0.0008768 0.0002494 -3.52 -0.0013657 -0.00039 baneth -0.8039507 0.2267872 -3.54 -1.248602 -0.3593 edutem -0.000747 0.0001828 -4.09 -0.0011055 -0.00039 banpop -0.0002014 0.000082 -2.46 0.014 -0.0003622 -4.1E-05 motpop -0.0000978 0.0000481 -2.03 0.042 -0.000192 -3.53E-06 pigedu 0.0071295 0.0014584 4.89 0.00427 0.009989 motdis 0.0025985 0.0009696 2.68 0.007 0.0006974 0.0045 motmar -0.1207308 0.0494985 -2.44 0.015 -0.2177802 -0.02368 pigpop -0.0000472 0.0000146 -3.24 0.001 -0.0000758 -1.9E-05 chidis 0.0004917 0.0002066 2.38 0.017 0.0000867 0.000897 fixphone 0.2191638 0.0648489 3.38 0.001 0.0920174 0.34631 Semiper -0.1055767 0.023777 -4.44 -0.1521951 -0.05896 62 _house chiran -0.0006887 0.0002027 -3.4 0.001 -0.0010861 -0.00029 bantem -0.0089073 0.0028814 -3.09 0.002 -0.0145567 -0.00326 lanran 0.0013108 0.0003827 3.43 0.001 0.0005605 0.002061 bicpop 0.000132 0.0000631 2.09 0.037 8.25E-06 0.000256 edueth -0.0319978 0.0146858 -2.18 0.029 -0.0607916 -0.0032 catroa 0.0344384 0.0131269 2.62 0.009 0.0087011 0.060176 otherhouse -0.2761955 0.0311201 -8.88 -0.3372113 -0.21518 cattem -0.0011605 0.0003137 -3.7 -0.0017756 -0.00055 chipop 0.000084 0.0000275 3.06 0.002 0.0000301 0.000138 othertoilet -0.2776404 0.0253574 -10.95 -0.3273574 -0.22792 chiban 0.1652758 0.0287047 5.76 0.1089958 0.221556 litpop -0.0002566 0.0000995 -2.58 0.01 -0.0004516 -6.2E-05 mem_school 0.0908762 0.0076644 11.86 0.075849 0.105904 littem 0.007471 0.0023008 3.25 0.001 0.00296 0.011982 chimch 0.0337395 0.0117003 2.88 0.004 0.0107992 0.05668 chidry 0.0012164 0.0005416 2.25 0.025 0.0001545 0.002278 roamar 0.0628306 0.022914 2.74 0.006 0.0179042 0.107757 chiedu -0.0041763 0.0017808 -2.35 0.019 -0.0076677 -0.00068 litroa 0.3097412 0.0921097 3.36 0.001 0.129146 0.490336 notoilet -0.3790739 0.0330137 -11.48 -0.4438023 -0.31435 5.110816 0.509132 10.04 4.112584 6.109048 _cons 63 64 ... +β28watban +β29watpop +β30wateth +β31watdis +β32watmar +β33wattem +β34watran +β35watdry +β36watmwa +β37lan +β38lanedu +β39lanroa +β40lanban +β41lanpop +β42laneth +β43landis +β44lanmar +β45lantem... Quintile Urban Quintile Quintile Mean Rural Graph 3.2: The quintiles of expenditure in urban and rural of Vietnam (Unit: thousand dong) 17 There are remarkable distances in welfare among the Vietnamese... greatly advanced step of geographic targeting is targeting map with asset- based approach It answers the extremely important question that governors and donors should use in-kind transfer for
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