(Luận văn) an asset based geographic targeting, evidence from rural vietnam

t to ng hi ep INSTITUTE OF SOCIAL STUDIES w UNIVERSITY OF ECONOMICS n THE HAGUE lo HOCHIMINH CITY ad VIETNAM THE NETHERLANDS ju y th yi VIETNAM – NETHERLANDS pl PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS n ua al n va fu AN ASSET-BASED GEOGRAPHIC TARGETING: EVIDENCE ll FROM RURAL VIETNAM m oi A thesis submitted in partial fulfillment of the requirement for the degree of nh MASTER OF ARTS IN DEVELOPMENT ECONOMICS at z By z k jm ht vb PHAM THI NGOC AI om l.c Dr PHAM KHANH NAM gm Academic Supervisor: n a Lu HO CHI MINH CITY, MAY 2014 n va y te re th t to ng hi ep w n ACKNOWLEDGEMENT lo ad The thesis would not have been finished without the kind assistance and fruitful y th guidance of many people who have the contributions of different aspects for ju accomplishing the thesis yi pl First of all, I am specially grateful to Dr Pham Khanh Nam who encourages me at thesis writing n ua al the beginning of title and help my deep understanding on literature theory as well as va In addition, I would like to express the sincere gratitude to Dr Truong Dang Thuy n ll for the methodology fu for sharing his knowledge for the technique of the model and some valuable advices oi m I would like to give special thank for my boss and colleagues who create conditions nh and assist working in order that I have more time for the research at z Finally, my most gratitude is for my family, especially my parents and husband who z k jm ht process vb have been always side by side with me during learning this program and researching om l.c gm n a Lu n va y te re th t to ng hi ep w n ABSTRACT lo ad The purpose of this paper is to find out which asset is the most suitable for a y th particular region through calculating marginal return to a range of assets and then ju creating a serial of maps The data are taken from Vietnam Living Standard Survey yi pl in 2006 The Weighted Least Squares is used for running the regression and al combining with technique bootstrap and stepwise iterative deletion with the n ua threshold of 5% All targetable assets are focused on calculating marginal benefit It gives the reasonable findings that have very heterogeneous average marginal benefit va n across areas The results give suggestion for choosing which assets are suitable for a ll fu particular region, thus it makes increases their efficacy However, the governors and oi m donors should consider the existence of trade-off equity and efficacy at nh z z k jm ht vb om l.c gm n a Lu n va y te re th t to ng hi ep w n TABLE OF CONTENTS lo ad CHAPTER I: INTRODUCTION y th Problem statement 1.2 Research objective 1.3 Research questions 1.4 Research contributions 1.5 Organization of the paper ju 1.1 yi pl n ua al va n CHAPTER II: LITERATURE REVIEW fu 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 ll 2.1 oi m at nh z z jm ht vb CHAPTER III: OVERVIEW OF HOUSEHOLD WELFARE IN VIETNAM AND Econometric models 19 3.3 Data 22 n 3.3.2 Dependent variable 28 a Lu 3.3.1 Independent variables 23 om 3.2 l.c Overview of household welfare in Vietnam 15 gm 3.1 k METHODOLOGY 15 n va y te re th t to ng hi ep w CHAPTER IV: EMPIRICAL RESULTS 29 n Descriptive statistics 29 4.2 Econometric results 33 lo 4.1 ad y th 4.2.1 Statistics and value of marginal return of assets at national level 34 ju yi 4.2.2 Analysis for average of mean marginal return of assets at provincial level 36 pl 4.2.3 Kinds of maps for Vietnam 42 al ua CHAPTER V:CONCLUSION, POLICY IMPLICATION, LIMITATION AND n FURTHER RESEARCH 49 va Conclusion 49 5.2 Policy implication 50 5.3 Limitation of this study 51 5.4 Direction for Further research 52 n 5.1 ll fu oi m at nh REFERENCES 53 z z k jm ht vb om l.c gm n a Lu n va y te re th t to ng hi ep w n LIST OF CHARTS lo ad Graph 3.1:The quintiles of income in urban and rural of Vietnam 16 y th Graph 3.2: The quintiles of expenditure in urban and rural of Vietnam 16 ju Graph 3.3: The Quintiles of income in the eight regions of Vietnam 17 yi pl Graph 3.4: The quintiles of expenditure in the eight regions of Vietnam 18 al Graph 3.5: Poverty rate at different level of region in Vietnam (Unit: %) 18 n ua Graph 4.1: Proportion of literate for each region 29 Graph 4.2: Distribution of educational level for each region 30 va n Graph 4.3: Distribution of expenditure for each educational level and each region 30 ll fu Graph 4.4: Distribution of ethnic minorities across regions 31 m Graph 4.5: Expenditure of some ethnics 32 oi Graph 4.6: Distribution of livestock for each region 32 at nh Graph 4.7: Distribution of other assets across regions 33 z z k jm ht vb om l.c gm n a Lu n va y te re th t to ng hi ep w n LIST OF FIGURES lo ad Figure 4.1: Maps of AMB that is significantly greater than zero 43 y th Figure 4.2: Maps of proportion of positive AMB 44 ju Figure 4.3: Maps of maximum significant AMB 45 yi pl Figure 4.4: Maps of maximum proportion of positive 46 al Figure 4.5: Example of choosing cattle transferred to provinces which meet three n ua conditions: the magnitude of AMB at 0.035, 95% households have positive AMB and poverty rate 30% 48 n va ll fu oi m at nh z z k jm ht vb om l.c gm n a Lu n va y te re th t to ng hi ep w n LIST OF TABLES lo ad Table 3.1: Asset variables 26 y th Table 3.2: Control variables 270 ju Table 4.1: Average standard deviation of mean marginal return for national, yi pl regional and provincial level 34 al Table 4.2: Mean of AMB and proportion of provinces with positive AMB at n ua national level 36 Table 4.3: Values of AMB that are significantly greater than zero 38 va n Table 4.4: Data for proportion (%) of positive AMB of households in provinces 40 ll fu Table 4.5: Correlating between asset holdings and poverty with significant and oi m proportion of positive AMB 47 at nh z z k jm ht vb om l.c gm n a Lu n va y te re th t to ng hi ep w n lo ad ju y th yi pl n ua al n va ll fu oi m at nh z z k jm ht vb om l.c gm n a Lu n va y te re th t to ng hi ep w n lo CHAPTER I: INTRODUCTION ad 1.1 Problem statement y th Alleviating poverty is always the major targeting interested in by policy-makers in the ju developing countries There are many transfer programs made around the world from yi pl years to years However, increasing transfer program efficacy under the condition of al scarce resources is the extremely important issue which governors and donors consider n ua 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 va cost and easy administration Thus, the important rule of the geographic targeting for n ll fu 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 m oi higher when geographic units are smaller (Elbers et al 2007, Minot 2000, Bigman and nh Foback 2000) Commented [PKN1]: I’ve made a new paragraph here at Poverty map is a tool of geographic targeting It displays poverty indicator across z geography and answer the question where the poor people reside and who the poor z vb people are (Elbers et al 2003, Minot and Baulch 2005) as well as why the area has jm ht 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 for the poor and might create motivation for them out of poverty Besides, it gives a n va is better (Hoffmann, Barrett and Just, 2009) n places in the world, they prefer transferring in-kind and in some cases, transfer in-kind a Lu better, always consider by the researchers, donors and governors However, in many om programs with budget limitation Benefit of transferring whether in-kind or cash is l.c visual and practical tool for policy-makers and donors to manage their transfer gm donors should use in-kind transfer for a particular region to bring the highest benefit k asset-based approach It answers the extremely important question that governors and y te re th t to ng hi ep w n 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 lo ad y th holdings ju Other utility of targeting maps which we want to mention hereafter, we assume that yi chicken is chosen for transfer program We select the provinces with the following pl conditions: ua al (1) Provinces have expected mean marginal return higher than 0.035 (2) Provinces have 95% of positive AMB n va (3) Provinces have poverty rate of 30% at least n We find out provinces that can meet the above conditions and plot at the Figure 4.5 ll fu hereafter: oi m nh ( at z % z ) vb jm ht ( % k ) n households have positive AMB and poverty rate 30% a Lu meet three conditions: the magnitude of AMB at 0.035, 95% om l.c gm Figure 4.5: Example of choosing cattle transferred to provinces which n va y te re 50 th t to ng hi ep w n CHAPTER V: CONCLUSION, POLICY IMPLICATION, lo ad LIMITATION AND FURTHER RESEARCH y th 5.1 Conclusion: ju Geographic targeting with asset-based approach is the method that gives a potential to yi improve the efficiency of in-kind transferred programs in poverty reduction Basing on pl calculating expected marginal benefit at household level across geography to find out al ua in-kind transfer program which is the most suitable for a particular region The paper n takes the data of Vietnam Household Living Standard Survey and some geographic va variables in 2006 The model regression is the weighted Least Squares based on the n logarithmic function of the relationship between household welfare and asset return fu ll Combining bootstrap technique, stepwise iterative deletion and take derivatives to m estimate expected marginal return for each assets at household level Then, we oi nh synthesize for provincial, regional and national level and finally create the serial maps at for visual viewing We focus mainly on all private targetable assets The findings have z answered the question relating to magnitude and scope of benefit for each asset as well z as making a view of allocating their transfer which brings the most beneficial to a vb particular region Due to the lack of the second data of Agricultural and Fishery ht observations at provincial level have at least 45 households However, the findings benefit, but proportion of positive expected marginal benefit is lower other assets such 100% of positive AMB for all the regions, the second is motorbike Comparing to the n n va size of benefit is motorbike arranged at the second order The size and scope of return a Lu coast and delta To be different with literate, water gets less value of benefit but have om levels are very different across each region The regions that get great benefit, are at l.c water and motorbike This proves that impact of education is always efficient but its gm seem reasonable It suggests that education (literate) brings the largest magnitude of k jm Census, the results are at provincial level for reference, although a number of y te re 51 th t to ng hi ep w n for livestock assets lower than the above assets Some assets such road and bank not lo get return as per expected In general, the spatial distribution of asset return is very ad heterogeneous, thus it suggests a powerful tool to choose the most suitable assets for a y th determined area To explore the meaning of the results, we create a serial of maps that ju show the magnitude and scope of asset return through regions Especially when we yi combine with poverty map, we can create a map which can show the best mean of pl transfer and poverty indicator, it is a strong tool of geographic targeting for poverty al ua reduction intervention The findings also suggest that certainly existing some areas n which have the large benefit but not need to transfer and the opposite Thus, the va governors and donors should consider the trade-off between efficiency and equity to n meet the final purpose of poverty alleviation The method gives a flexible and easily fu ll visual tool to create the targeting map with particular assets m oi 5.2 Policy implication: nh The purpose of the paper estimates the marginal expected return of a range to assets at across geography The findings show that education brings the most benefit compared z z to other assets although magnitude and scope of education is various across geography vb Thus, education as an engine helps the poor out of poverty Its efficacy increases more jm ht 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 k learning or/and other policies to encourage to go to school more careful for invest as a few scattered regions get high results n than other stock and the next to cattle and its values is especially high at North East and a Lu For three kinds of livestock, pig has widespread scope and magnitude which are larger om assets like bank, road, land, efficacy only focus some areas The governors should be l.c economic efficacy highly and the scope gets 100% in the whole country For other gm We also pay attention on ownership of motorbike and water which may bring the n va y te re 52 th t to ng hi ep w n North West Cattle seem to be efficient in the North and Central Highlands Thus, pig lo and cattle are more suitable than chicken in the mountainous areas and highlands ad Only bicycle does not seem mostly to be worth for poverty reduction y th For visual view, the results are plotted on the map, the policy-makers can ju recognizewhich place on the map the asset has high magnitude of benefit and large yi scope of benefit for map compared to other regions As well, when comparison among pl assets, they can know among many assets which they can get, which assets is the most ua al beneficial n Based on the information details of each region and available resources as well as va thought about trade-off between efficacy and equity, policy-makers can decide which n assets are chosen for transfer scheme for a particular region Besides, they can combine fu ll with other tools like poverty maps and criteria to choose the rightest region for a m available assets oi nh However, here has not yet been the end results to solve the question about final benefit at and influence of asset transfer Policy-makers need to know more cost of asset such as z cost of purchasing, maintenance and procurement as well as consider life span of z assets Based on that, we can conclude that which assets bring the most beneficial for a vb particular region This study has two main shortcomings ofthe method and data which we can be study only brings the meaning of displaying the method, literature and gives the idea n va equilibrium cannot It exists the endogenous concern as welfare household and assets n For the method,the analysis just accounts for partial equilibrium, but general a Lu about a tool for asset-based geographic targeting for poverty reduction intervention om regional level such as provinces, districts are not sure about degree of explain This l.c For the data, there is no the data of the census The value of AMBs at level lower than gm unavoidable k jm ht 5.3 Limitation of this study: y te re 53 th t to ng hi ep w n have dual causality But according to Lang, Barrett and Naschold (2013) and Nguyen lo Viet Cuong, Tran Ngoc Truong and Roy Van Der Weide (2010), we have to accept this ad issue as we only want to determine the asset taken part in expenditure, not care for y th effect Finally, transferring a huge number of assets to a given area which makes its ju market changed, the benefit of assets is changeable In some case, transferring a huge yi quantity makes positive impact on externalities, for instance, mobile phone or pl infrastructure In other cases, this makes negative impact on benefit of asset, for al ua instance: cow If a large number of cattle are transferred into a particular region, it n makes the price of milk lower This leads the benefit if cow’s benefit reduces Thus, we n va have to assume that “aggregate asset transfer will typically be marginal in magnitude and therefore that partial equilibrium assumption suffice” Lang, Barrett and Naschold fu ll (2013) m Although there are some shortcomings which can be unavoidable, we are believable oi at improving program transfers and policy intervention nh that this method creates the useful knowledge to fill up the large hole that prevents z z 5.4 Direction for further research: vb ht Targeting performance increases the efficacy when the region is researched at small agricultural census with household survey We should research about actual cost of that, we can understand fully about the benefit which each asset actually brings back n expected benefit of assets on poverty incidence a Lu with other tools of targeting map as well as use the panel data for studying more about om approach is not a only and final tool for choosing the in-kind transfer We can combine l.c One thing which we should understand that geographic targeting with asset-based gm each assets (only livestock mentioned in household survey) to compare benefit From k jm region Thus, it is necessary for combine information from the census population or n va y te re 54 th t to ng hi ep w n REFERENCES lo Allessandro Tarozzi and Angus Deaton (2009) Using census and survey data to ad estimate poverty and in equity for small areas The review of Economics and Statistics, y th November 2009, 91(4): 773-792 ju Aronson, Richard J (1985) Public finance New York: McGraw Hill yi pl Baker, Judy L., Margaret E., (1994) Poverty reduction through geographic targeting: ua al How well does it work? World Development 22 (7), 983-995 Bedi, Tara, David, Fofact, Hippolyte (2000) Geoghraphic targeting for poverty n n Sectoral Studies va alleviation: methodology and applications Washington Dc, World Bank Regional and fu ll Chris Elbers, Jean O Lanjouw, and Peter Lanjouw (2003) Micro-level estimation of oi m poverty and inequality Econometrica, Vol.71, No , 355-364 nh David Coady, Margaret Grash and John Hoddinotts (2004) Targeting of Transfer in at Developing Countries: Review of Lessons and Experience The World Bank z Washington, D.C z Demombynes, G., Elbers, C., Lanjouw, J.O., & Lanjouw, P (2007) How good a map? jm ht vb Putting small area estimation to the test World Bank working papers 4155 Felix Naschold and Christopher B Barrett (2011) Do Short-Term Observed Income Frank Ellis and H Ade Freeman (2006) Rural livelihoods and poverty reduction n va Discussion Paper No.39 Harvard Institute for International Development n Gallup, J.L., Sachs, J.D., (1999) Geographic and Economic development, CAER II a Lu strategies in four African countries.The Journal of Development Studies, 40:4, 1-30 om Economics Dubuque, Iowa; William C Brown l.c Fleisher, Belton M., Edward J Ray and Thomas J., Keisner (1987) Principles of gm Statistics, 73, (2011) 0305-9049 k Changes Overstate Structural Economic Mobility ? Oxford Bulletin of Economics and y te re 55 th t to ng hi ep w n Corey Lang, Christopher B Barrett and Felix Naschold (2013) Targeting Maps: An lo Asset-Based Approach to Geographic Targeting World Development Vol.41, pp.232- ad 244 y th Michelle Adato, Michael R Carter and Julian May (2006) Exploring poverty traps and ju social exclusion in South Africa using qualitative and quantitative data The Journal of yi pl Development Studies, 42:2, 226-247 ua al Nguyen Viet Cuong (2011) Poverty projection using a small area estimation method: Evidence from Vietnam Journal of Comparative Economics 39 368-382 n va Nguyen Viet Cuong, Tran Ngoc Truong and Roy Van Der Weide (2010) Poverty and n Inequality Maps in Rural Vietnam: An application of Small Area Estimation Asian ll m Nicolas Minot (2000) fu Economic Journal 2010, Vol 24 No 335-390 Generating disaggerated poverty maps: An application to oi nh Vietnam World Development Vol.28, No.2, pp 319-331 at Okwi, P.O., Ndeng’e, G., Kristjanson, P., Arunga, M., Notenbaert, A., Omolo A., z Henninger, N, Benson, T., Kariuki, P., Owour, J., (2007) Spatial determinants of z States of America (PNAS), 104:43 (16769-16774) Roy D Adams and Ken McCormick (1993) The Traditional Distinction between Politics 5: 109 Equivalence Scales: Theory versus Policy?Author(s): Julie n a Lu NelsonSource: Journal of Labor Economics, Vol 11, No (Jul., 1993), pp 471-493 A om l.c Robert S Pindyck and Daniel L Rubinfeld (2009) Microeconomics Household gm Public and Private Goods Needs to Be Expanded, Not Abandoned Journal Theoretical k jm http://www.pnas.org/cgi/reprint/104/43/16769 ” URL: ht United vb poverty in rural Kenya In: Proceedings of the National Academy of Sciences of the n va y te re 56 th t to ng hi ep w n Shenggen Fan, Connie Chan-Kang (2004) Returns to investment in less-favored areas lo in developing countries: a synthesis of evidence and implication for Africa Food ad Policy 29 (2004) 431-444 y th Suan-Pheng Kam, Mahabub Hossain, Manik Lal Bose, Lorena S Villano (2005) ju Spatial patterns of rural poverty and their relationship with welfare-influencing factors yi pl in Bangladesh Food Policy 30 (2005) 551-567 ua al Vivian Hoffmann, Christopher B Barret and David R Just (2009) Do free Goods Stick to Poor Households? Experimental Evidence on Insecticide Treated Bednets n va World Development Vol.37, No 3, pp 607-617, 2009 n World Bank (2003) Vietnam Development Report 2004 Poverty Poverty Reduction fu ll and Economic Management Unit, East Asia and Pacific Region oi m http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTPOVERTY/EXTPA/0,, contentMDK:20205001~menuPK:435735~pagePK:148956~piPK:216618~theSitePK: nh 430367,00.html at z APPENDIX: z ht vb 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 jm motedu motroa motban motpop moteth motdis motmar mottem motran motdry catdry catmca pigedu pigroa pigban pigpop pigeth pigdis pigmar pigtem pigran n va eduroa eduban edupop edueth edudis edumar edutem eduran edudry edumed n chidry chimch litedu litroa litban litpop liteth litdis litmar littem litran litdry litmli a Lu pigdry pigmpi chiedu chiroa chiban chipop chieth chidis chimar chitem chiran om landry lanmla catedu catroa catban catpop cateth catdis catmar cattem catran l.c watdry watmwa lanedu lanroa lanban lanpop laneth landis lanmar lantem lanran gm bicmbi watedu watroa watban watpop wateth watdis watmar wattem watran k motmmo bicedu bicroa bicban bicpop biceth bicdis bicmar bictem bicran bicdry y te re 57 th t to ng hi ep w n roapop roaeth roadis roamar roatem roaran roadry banpop baneth bandis banmar lo bantem banran bandry fixphone tele fridge per_house semiper_house otherhouse ad clean_water otherwater flushtoilet othertoilet notoilet elec otherelec mem_school if y th rand>0.3, pr(0.05)robust ju (per_house dropped due to estimability) begin with full model yi p = 0.9938 >= 0.0500 removing watroa pl p = 0.9926 >= 0.0500 removing pigran al ua p = 0.9814 >= 0.0500 removing litmar n p = 0.9711 >= 0.0500 removing watran va p = 0.9557 >= 0.0500 removing catmar n p = 0.9549 >= 0.0500 removing liteth fu ll p = 0.9248 >= 0.0500 removing catmca m p = 0.9103 >= 0.0500 removing lanedu oi p = 0.8960 >= 0.0500 removing bandry p = 0.7824 >= 0.0500 removing lanmar n n va p = 0.8007 >= 0.0500 removing lanban a Lu p = 0.7874 >= 0.0500 removing motban om p = 0.8076 >= 0.0500 removing catdis l.c p = 0.8201 >= 0.0500 removing catban gm p = 0.8356 >= 0.0500 removing otherwater k p = 0.8559 >= 0.0500 removing litran jm p = 0.8554 >= 0.0500 removing roadis ht p = 0.8646 >= 0.0500 removing motmmo vb p = 0.8778 >= 0.0500 removing motdry z p = 0.8877 >= 0.0500 removing watban z p = 0.8938 >= 0.0500 removing litdis at nh p = 0.9061 >= 0.0500 removing watdry y te re 58 th t to ng hi ep w n p = 0.7725 >= 0.0500 removing otherelec lo p = 0.7732 >= 0.0500 removing pigdry ad p = 0.7556 >= 0.0500 removing motran y th p = 0.7523 >= 0.0500 removing roaran ju p = 0.7488 >= 0.0500 removing bandis yi p = 0.7296 >= 0.0500 removing landry pl p = 0.7243 >= 0.0500 removing fridge al ua p = 0.7105 >= 0.0500 removing pigdis n p = 0.6679 >= 0.0500 removing roadry va p = 0.6652 >= 0.0500 removing edumar n p = 0.6697 >= 0.0500 removing mar fu ll p = 0.6631 >= 0.0500 removing biceth m p = 0.6572 >= 0.0500 removing chieth oi p = 0.6323 >= 0.0500 removing lanpop p = 0.5089 >= 0.0500 removing wattem n n va p = 0.6067 >= 0.0500 removing watpop a Lu p = 0.5522 >= 0.0500 removing litedu om p = 0.5828 >= 0.0500 removing watedu l.c p = 0.5811 >= 0.0500 removing edudry gm p = 0.5755 >= 0.0500 removing eduban k p = 0.5664 >= 0.0500 removing litban jm p = 0.5676 >= 0.0500 removing bicdis ht p = 0.5930 >= 0.0500 removing tele vb p = 0.5955 >= 0.0500 removing catran z p = 0.5887 >= 0.0500 removing roa z p = 0.7910 >= 0.0500 removing laneth at nh p = 0.6449 >= 0.0500 removing roapop y te re 59 th t to ng hi ep w n p = 0.7553 >= 0.0500 removing wateth lo p = 0.4842 >= 0.0500 removing bicedu ad p = 0.4809 >= 0.0500 removing litdry y th p = 0.4834 >= 0.0500 removing watmwa ju p = 0.4561 >= 0.0500 removing banran yi p = 0.4505 >= 0.0500 removing chiroa pl p = 0.4222 >= 0.0500 removing lanroa al ua p = 0.4018 >= 0.0500 removing landis n p = 0.3670 >= 0.0500 removing bicmbi va p = 0.3535 >= 0.0500 removing pigmpi n p = 0.3554 >= 0.0500 removing flushtoilet fu ll p = 0.3233 >= 0.0500 removing watdis m p = 0.2799 >= 0.0500 removing catedu oi p = 0.2885 >= 0.0500 removing edupop p = 0.1636 >= 0.0500 removing eduroa n n va p = 0.1781 >= 0.0500 removing motedu a Lu p = 0.1514 >= 0.0500 removing litmli om p = 0.2014 >= 0.0500 removing clean_water l.c p = 0.1896 >= 0.0500 removing elec gm p = 0.1864 >= 0.0500 removing lantem k p = 0.3684 >= 0.0500 removing moteth jm p = 0.2097 >= 0.0500 removing mottem ht p = 0.2011 >= 0.0500 removing chimar vb p = 0.2149 >= 0.0500 removing pigroa z p = 0.6368 >= 0.0500 removing catpop z p = 0.2193 >= 0.0500 removing cateth at nh p = 0.2454 >= 0.0500 removing dis y te re 60 th t to ng hi ep w n p = 0.1466 >= 0.0500 removing bicban lo p = 0.1381 >= 0.0500 removing eduran ad p = 0.1450 >= 0.0500 removing pigban y th p = 0.1156 >= 0.0500 removing catdry ju p = 0.1217 >= 0.0500 removing ran yi p = 0.1176 >= 0.0500 removing pig pl p = 0.5479 >= 0.0500 removing pigtem al ua p = 0.1217 >= 0.0500 removing pigeth n p = 0.1031 >= 0.0500 removing banmar va p = 0.0876 >= 0.0500 removing motroa n p = 0.0958 >= 0.0500 removing pigmar fu ll p = 0.0726 >= 0.0500 removing bictem m p = 0.1252 >= 0.0500 removing bicdry oi p = 0.1045 >= 0.0500 removing bicran z p = 0.0795 >= 0.0500 removing watmar z p = 0.1559 >= 0.0500 removing bicroa at nh p = 0.1383 >= 0.0500 removing bicmar vb p = 0.0613 >= 0.0500 removing roaeth ht regression obs Prob > F = R-squared = 0.3008 Root MSE = 0.47144 t P>t Robust lnexp Coef Std Err [95% Conf Interval] n 33.89 a Lu = om F( 49, 3438) l.c 3488 gm = k Linear jm Number of n va y te re 61 th t to ng hi ep w 0.1334927 0.0516291 2.59 0.01 0.032266 0.23472 bic -0.2093429 0.0552354 -3.79 -0.3176404 -0.10105 -0.221544 0.0602927 -3.67 -0.3397571 -0.10333 wat 0.0872175 3.9 0.0433856 0.13105 n mot lo ad lan 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 ua 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 baneth -0.8039507 0.2267872 -3.54 -1.248602 edutem -0.000747 0.0001828 -4.09 -0.0011055 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 ju cat y th 0.0223558 yi pl al n n va ll fu oi m at nh z z -0.00039 vb -0.3593 ht k jm -0.00039 om l.c gm n a Lu n va y te re 62 th t to ng hi ep w n _house lo chiran 0.0002027 -3.4 0.001 -0.0010861 -0.00029 -0.0089073 0.0028814 -3.09 0.002 -0.0145567 -0.00326 0.0013108 3.43 0.001 0.0005605 0.002061 ad bantem -0.0006887 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 ua -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 chiedu -0.0041763 0.0017808 -2.35 0.019 -0.0076677 litroa 0.3097412 0.0921097 3.36 0.001 0.129146 notoilet -0.3790739 0.0330137 -11.48 -0.4438023 -0.31435 5.110816 0.509132 10.04 4.112584 6.109048 ju 0.0003827 bicpop y th lanran yi pl al n n va ll fu oi m at nh z z 0.107757 vb -0.00068 ht k jm 0.490336 om l.c gm _cons n a Lu n va y te re 63 th t to ng hi ep w n lo ad ju y th yi pl n ua al n va ll fu oi m at nh z z k jm ht vb om l.c gm n a Lu n va y te re 64 th