95. Tinh Doan Khoa KTCT 2014 tài liệu, giáo án, bài giảng , luận văn, luận án, đồ án, bài tập lớn về tất cả các lĩnh vực...
ISSN 2339-5095 print / ISSN 2339-5206 electronic Journal of Southeast Asian Economies Vol 31, No (2014), pp 103–19 DOI: 10.1355/ae31-1g OTHER ARTICLES Impact of Household Credit on Education and Healthcare Spending by the Poor in Peri-Urban Areas, Vietnam Tinh Doan, John Gibson and Mark Holmes There is an ongoing debate about whether microfinance has a positive impact on education and health for borrowing households in developing countries To understand this debate, we use a survey designed to meet the conditions for propensity score matching (PSM) and examine the impact of household credit on education and healthcare spending by the poor in peri-urban areas of Ho Chi Minh City, Vietnam In addition to matching statistically identical non-borrowers to borrowers, our estimates also control for household pre-treatment income and assets, which may be associated with unobservable factors affecting both credit participation and the outcomes of interest The PSM estimates show a significant and positive impact of borrowing on education and healthcare spending However, further investigation of the effects of the treatment reveals that only formal credit has a significant and positive impact on education and healthcare spending, while informal credit has an insignificant impact on spending This paper contributes to the limited literature on peri-urban areas using evidence from one of the largest and most dynamic cities in Southeast Asia Keywords: Matching, education and healthcare spending, household credit, the poor, peri-urban I. Introduction Microfinance has increasingly attracted attention from the global development community because it is considered a powerful tool for alleviating Journal of Southeast Asian Economies poverty in developing countries An argument commonly made in support of microfinance is that it may help keep household production stable and mitigate adverse shocks, thus preventing school 103 Vo l , N o , A p r i l © 2014 JSEAE 07.indd 103 3/7/14 9:36:33 AM dropouts and a reduction in spending on healthcare (Armendariz and Morduch 2010; Dehejia and Gatti 2002; Edmonds, 2006; Jacoby and Skoufias 1997; Maldonado and Gonzalez-Vega 2008) Education and health are critical to sustainable poverty reduction since they affect the formation of quality human capital and the productivity of future generations There is ongoing debate about the impact of microfinance (Cull, Kunt and Morduch 2009) on borrowing households’ access to education and healthcare On the one hand, microcredit has a positive impact on education; girls received more schooling if households borrowed from the Grameen Bank (Pitt and Khandker 1998) On the other hand, some studies find no effects or adverse effects on children’s education (Hazarika and Sarangi 2008; Islam and Choe 2009; Morduch 1998) Likewise, in terms of health, Pitt et al (2003) find higher weight-for-age and height-forage levels amongst children of Grameen Bank borrowers, but Coleman (2006) observes a negative impact of microcredit on healthcare spending by households in northeast Thailand These differing outcomes are discussed in greater detail in the literature review (section II) One difficulty in evaluating the impact of microcredit is that borrowing and non-borrowing households typically differ in both observable and unobservable characteristics Borrowers may self-select into borrowing activities due to their “better” characteristics, thus making it challenging to form a counterfactual of what would have happened to borrowers in the absence of credit If studies fail to correct for this problem of selfselection, the estimates will be naïve and overstated (Coleman 2006) The propensity score matching (PSM) method may help avoid this problem In this approach, the effects of the treatment (i.e., borrowing/credit participation) are estimated by simulating a randomized experiment with a treatment and control group Households in the treatment group are matched, based on observable characteristics/factors, to other similar households that will then form the control group It is assumed that the matched households in the control group would have no systematic differences in response to Journal of Southeast Asian Economies 07.indd 104 the treatment, thus offering a valid counterfactual When used appropriately, PSM can replicate the advantages of a randomized experiment (Dehejia and Wahba 2002) In this paper, a survey, designed by the authors to meet the conditions under which PSM works well, is used to examine the impact of household credit on education and healthcare spending by the poor in the peri-urban areas of Ho Chi Minh City (HCMC), Vietnam In addition to matching statistically identical non-borrowers to borrowers, our estimates also control for pre-treatment household income and assets These pre-treatment variables may be associated with unobservable factors affecting both credit participation and the outcomes of interest; therefore the inclusion of these variables helps reduce bias Apart from the use of PSM, four other important features of the current analysis warrant comment First, the analysis takes both formal and informal credit into consideration Most studies have examined the impact of formal or programme credit but have not considered the effects of credit from other sources (Coleman 2006; Khandker 2005; Morduch 1998; Pitt and Khandker 1998) Our survey contributes to the exiting literature by capturing all sources of credit; the results reported below compare the effects of formal and informal credit (provided by relatives, friends, neighbours and informal moneylenders) Policy-makers often influence access to formal credit but have less leverage over informal credit; hence distinguishing their separate impact is of interest Secondly, we provide evidence from a newly industrializing peri-urban area (District 9) in one of the largest and most dynamic Southeast Asian cities Our findings may therefore have external relevance for peri-urban areas in the rest of Southeast Asia Peri-urban areas are defined as places where industrialization has led to rapid urbanization and in-migration Rapid urbanization displaces poverty from rural to urban areas, resulting in the rapid urbanization of poverty In Vietnam’s urban areas, poverty levels have risen from 5.99 per cent in 1998 to 6.63 per cent in 2008 due to rapid population growth in these areas over the same period (VDR 2010) 104 Vo l , N o , A p r i l 3/7/14 9:36:33 AM Thirdly, most non-experimental research designs found in the existing literature on microcredit and human capital lack valid counterfactuals of the outcome for borrowers if they had not borrowed Further, they typically make comparisons between borrowers and non-borrowers without adopting a plausible mechanism for dealing with selfselection Finally, although a comparison of the outcomes for borrowers and non-borrowers using PSM is at risk of being unreliable if the groups that are being compared are too different, our empirical approach minimizes the differences between the two groups in order to enhance the validity of the PSM approach (Dehejia and Wahba 2002) Therefore, this paper also provides recommendations for overcoming the potential weakness of the PSM method The rest of this paper is organized as follows: Section II reviews the literature on household credit and its impact on education and healthcare; section III discusses the methodology employed; empirical results are reported in section IV; followed by concluding remarks in section V II. Literature Review As was mentioned above, credit may affect household demand for education and healthcare in two ways (Armendariz and Morduch 2010) Microcredit may help households earn higher incomes, in turn, raising consumption and increasing demand for healthcare and children’s education In a contrasting example, if access to credit raises female economic activity, children may be taken out of school to replace maternal input in the care of younger siblings or to work in expanded household businesses The evidence on these opposing effects of credit participation is mixed Dehejia and Gatti (2002), Edmonds (2006) and Jacoby and Skoufias (1997) find that inadequate schooling in poor countries can often be attributed to the lack of access to credit — households facing adverse shocks may pull children out of schools to reduce expenditure and increase household income by increasing children’s working hours Yet, in the same vein, Journal of Southeast Asian Economies 07.indd 105 Hazarika and Sarangi (2008) find that borrowing households may take children out of school to work in family businesses Because small loans are often associated with higher interest rates and short-term repayment conditions, poor borrowers may reduce costs by using their own labour, which may include child labour, in order to accrue high returns for the repayment of loans and interest For example, a study on Vietnam finds that households that borrowed from lenders with higher interest rates used more child labour (Beegle, Dehejia and Gatti 2004) There is also some interplay between healthcare and education If borrowing enables parents to promptly provide medication when children fall sick; children may recover faster and can stay in school Healthier children not only perform better in school but are more likely to stay in school for longer such that they grow up to be more productive adults In contrast, low school achievement and attendance rates are associated with malnutrition (Glewwe, Jacoby and King 2000) It has been observed that microfinance clients consume healthcare services such as pasteurization, health insurance, family planning and pregnant-mother care more often than non-clients (CGAP 2003) III. Analytical Framework III.1 Data Four hundred and eleven borrowing and nonborrowing households were interviewed in early 2008 in the peri-urban District (HCMC, Vietnam).1 Since our focus is on the impact of microcredit on poor households, our sample was selected from a list of households with an initial per capita income below the HCMC general poverty line of VND6 million (approximately US$1 per day).2 The sampling was carried out in two steps: we first selected wards within District and then selected households from those wards The target sample size was set at 500 households, including 100 reserves, to achieve a realized sample of 400 In fact, 411 households were successfully interviewed, accounting for 26 per cent of the total number of poor households in each of the selected 105 Vo l , N o , A p r i l 3/7/14 9:36:33 AM wards in the district The sample comprises 304 borrowing households and 107 non-borrowing households, with a total of 2,062 members: 955 (46.3 per cent) males and 1,102 (53.7 per cent) females More information on the sources of credit and the households’ per capita income prior to the treatment (borrowing) can be found in Appendix The survey was designed to collect data on household-level and individual-level variables: commune characteristics, household durable and fixed assets, borrowing and expenditure on healthcare, food and non-food items, and children’s schooling and education We also utilized GPS receivers to collect data on the location of the households and to measure their distances from facilities, such as schools III.2 Challenges to Evaluating Impact The main challenge to evaluating the impact of credit is the difficulty of separating the causal effect of credit from selection and reverse causation biases, which are common to nearly all statistical evaluations (Armendariz and Morduch 2010) In order to separate the effects of the treatment from other factors, we have to ask how borrowers would have managed without credit (Armendariz and Morduch 2010) This question is not easy to answer because researchers are unable to observe the virtual outcomes needed to construct such a counterfactual As such, estimating the impact of credit participation requires measuring the difference in the outcome between the treatment and control groups: E(Y|D=1) – E(Y|D=0) Where, Y is the outcome and D is the treatment taking a value of if the treatment has been received, and if it has not The difference in the outcome, however, may result from either the treatment (credit participation), differences in observable characteristics or differences in unobservable characteristics Estimates will be biased if one does not control for the differences in observable and unobservable characteristics Differences in observable characteristics result Journal of Southeast Asian Economies 07.indd 106 in an “overt bias”, which can be removed by controlling for observables (Xi) in estimation models (Lee 2005) Thus, the estimation is now formulated as follows: E(Y| D=1, Xi) – E(Y|D=0, Xi) The estimated impact may also include a “hidden bias” resulting from unobservable characteristics Models using a randomized selection of treatment and control groups are helpful in this regard, as the randomization allows us to cancel out the differences in both observable and unobservable characteristics However, it is very hard to conduct a randomized test evaluating the impact of credit due to motivation and contamination problems (Mosley 1997) Hence, there are usually some problems with using non-experimental data due to the non-random implementation of credit programmes and self-selection into credit participation by borrowers Estimates of causality may include a selection bias if credit participation is correlated with unobserved characteristics that also affect the outcome For instance, households that are better motivated to invest in children’s schooling may have a greater demand for credit Without an adequate measure of motivation, this hidden factor may make an observable correlation between credit and schooling seem like a causal effect has taken place In the case of our sample, non-randomized credit participation is not a crucial concern because all the surveyed households have a per capita income of less than VND6 million This means that they are all eligible for preferred credit (i.e., subsidized interest and easy conditions) from government funds Selection by informal lenders and self-selection into credit borrowing due to unobservable factors, however, may still occur If data on pre-treatment variables of interest are available, researchers may examine differences in these variables in order to determine whether there is a positive or negative selection on unobserved characteristics (conditional on the observed characteristics) For instance, if YT0 and YC0 are the respective outcomes for the treatment and control groups at time (before the treatment), 106 Vo l , N o , A p r i l 3/7/14 9:36:34 AM and if E(YT0 | D=1, Xi) ≠ E(YC0 | D=0, Xi) is the result after controlling for observable factors, one should suspect that unobservable confounders are affecting the treatment and outcomes That is to say that a “hidden bias” has resulted from unobservable confounders Lee (2005, p 125) suggests that controlling for Y0 (together with Xi on the right-hand side) may to some extent reduce the hidden bias For the purposes of our study, pretreatment data on the variables of interest is not available but we can use (baseline) pre-treatment income per capita as a control variable (Mosley 1997; Heckman and Smith 1999): Yij,t-1 = a + b.Dij,t + I.Xij,t + eij,t-1 (1) Where, Yij is the outcome of interest of household i in ward j; D is a dummy variable representing (1) if a household borrows and (0) if it does not; X is a set of unchanged (or little changed) control variables over time, such as household characteristics The coefficient b shows whether borrowers had a higher or lower income per capita than non-borrowers prior to participating in borrowing activities (conditional on their observed characteristics) If b is positive, that means a positive selection on unobserved attributes exists: borrowers tend to be richer than non-borrowers, which will lead the non-experimental estimators to overstate the impact of credit participation We ran a regression of equation (1) and found a significantly positive b coefficient III.3 Emprical Method The propensity-matching method is the most suitable candidate for cross sectional nonexperimental datasets without good instrument variables This method forms a control group of non-participants with observed characteristics that are similar to participants (the treatment group) (Dehejia and Wahba 1999, 2002) The main advantage of the matching method is that one can draw on existing data sources and so it is quicker and cheaper to implement Nevertheless, matching does not control for unobservable characteristics that may cause a selection bias, and as a result, the Journal of Southeast Asian Economies 07.indd 107 reliability of estimates is reduced (Smith and Todd 2005) The most widely used matching method is propensity score matching (PSM) Other methods of matching each X (covariate matching) create a problem of high dimensionality, which requires large datasets The PSM method first estimates the propensity score for each participant and non-participant on the basis of observed characteristics It then compares the mean outcome for participants with the outcome for the matched (similar in terms of scores) nonparticipants In other words, the purpose of the PSM is to first select comparable non-borrowing households among all non-borrowing households to generate a control group, and then compare the outcomes for the treatment and matched control groups The crucial assumption is that the outcomes for non-borrowers in the matched control group represent what the borrowers would have experienced without credit participation This is referred to as unconfoundedness or a conditional independence assumption (CIA) (Rosenbaum and Rubin 1983) In summary, this is the underlying point of propensity score matching: control and treatment units with the same propensity score have the same probability of assignment to the treatment as in randomized experiments (Dehejia and Wahba 1999) The PSM method may produce estimates with low bias if datasets satisfy three conditions (Dehejia and Wahba 2002): (i) the data for treatment and control groups are collected using the same questionnaire; (ii) the treatment and control groups are drawn from the same locality; and (iii) the dataset contains a rich set of variables relevant to modelling credit participation and its outcomes Since all households surveyed in this study were poor prior to credit participation, the PSM method should produce less biased estimates than it would for a general sample of households with highly divergent per capita income rates While the PSM method also allows controlling for potential biases such as non-placement and self-selection (Dehejia 2005; Dehejia and Wahba 2002), it fails to control for unobservable characteristics which may create hidden biases because the scores are calculated on the basis of 107 Vo l , N o , A p r i l 3/7/14 9:36:34 AM observed characteristics only (Dias, Ichimura and Berg 2007) As was mentioned above, observable characteristics may not fully capture individual motivation, ability and skills — all of which may affect the treatment participation Ultimately, the success of the PSM method depends on how close the control and treatment group are in terms of space and time Further, the two groups should have as little baseline differences as possible (Lee 2005) IV. Empirical Results The PSM estimates of the impact on education and healthcare expenditure are presented in subsection IV.1 while subsection IV.2 looks at the impact of formal and informal credit on household expenditure IV.1 PSM Estimation Kernel (with the default bandwidth of 0.06) and radius matching (with the default radius of 0.1) results of the impact of microcredit on education and healthcare spending are discussed in this subsection.3 Imbens (2004), Lee (2005) and Rosenbaum and Rubin (1983) note that sets of controlling covariates should meet the conditions of matching controlling variables In this paper, the use of covariates in the score estimation stage follows discussions in Lee (2005) and Rosenbaum and Rubin (1983), and Caliendo and Kopienig (2008) and Bryson, Dorsett and Purdon (2002) In some cases, interaction terms were also used to balance the estimated propensity scores Impact on Education Expenditure Our base specifications (S1 and S3 in Table 1) use a set of covariates of household characteristics such as: the head of the household’s gender, age, education and marital status; school-aged child ratio, the number of children; and ward dummies to estimate the scores Although we not have panel data to apply the difference-in-difference matching estimator (believed to be considerably better than cross-sectional matching estimators), our attempts at reducing bias associated with unobservable Journal of Southeast Asian Economies 07.indd 108 characters by including pre-treatment household income and assets may help offset any disadvantage (Imbens and Wooldridge 2009; Mosley 1997) The effects that occur when pre-treatment income and assets are included in the matching are reported in the second (S2) and fourth rows (S4) of Table The changes in the model specifications between S1 and S3 and between S2 and S4, are to test for the sensitivity of the effect Figure displays the kernel densities of the propensity scores when pre-treatment income and assets are included alongside the other controlling variables (S4 in Table 1) The propensity scores range from 0.418 to 0.943 and from 0.174 to 0.940 for borrowers and non-borrowers, respectively,4 but the mean scores are not much different (0.761 and 0.675 for borrower and non-borrower groups) The figure also illustrates a substantial overlap in the distributions The following estimation of the average effect of the treatment is restricted to the area of common support, where the two distributions overlap Thus, some non-borrowers who are dissimilar to borrowers are not used in the comparison The estimates of the average effect of the treatment, on the treated (ATT), are reported in Table There is little difference in the results of the two matching approaches used When matching just household characteristics and location dummies (S1 and S3), the effect of credit participation on education spending is observed to be statistically significant at the per cent level After including the pre-treatment income and assets (S2 and S4) the estimated impact of credit participation declines, but is still significant at the per cent level According to these PSM estimates, the borrowers, on average, spent about VND81,000 to VND99,000 more on education per month than comparable non-borrowers Impact on Healthcare Expenditure Figure displays the kernel densities of the propensity scores estimated for evaluating the impact of credit participation on healthcare expenditure.5 The scores are from when the pre-treatment income and assets are included alongside the 108 Vo l , N o , A p r i l 3/7/14 9:36:34 AM Table The Average Effect of the Treatment on Monthly Average Education Expenditure in VND1,000 Using Matching Estimators for the Entire Sample Control variables in the propensity score estimation Kernel matching Radius matching Head’s gender, head’s age, head’s education, marital status, school-aged child ratio, and ward dummies (S1) 92.696 (31.967)** 98.696 (32.393)** S2=S1plus initial income in log, initial assets in logarithm 85.020 (34.027)* 93.022 (31.506)** Head’s gender, head’s age, head’s education, marital status, number of children from to 18, and ward dummies (S3) 87.447 (33.875)** 93.179 (34.182)** S4=S3 plus initial income in log, initial assets in logarithm 81.232 (34.621)* 86.861 (34.448)* Notes: Bootstrapped standard errors in parentheses with 1,000 repetitions, statistically significant at 10 per cent (+); per cent (*); and per cent (**) Only a few households (10 households) have or more children aged to 18 years old To simplify the process, we grouped them with households having kids Si are model specifications Figure Propensity of Scores for Borrowers and Non-borrowers to Estimate the Effects of the Treatment on the Treated for Education Expenditure Borrowers Non-borrowers –4 –6 Predicted Probability –8 Note: The propensity scores of control units outside the area of common support are cut off Journal of Southeast Asian Economies 07.indd 109 109 Vo l , N o , A p r i l 3/7/14 9:36:35 AM Figure Propensity of Scores for Borrowers and Non-borrowers to Estimate ATT for Healthcare Expenditure Borrowers Non-borrowers –2 –4 –6 Predicted Probability –8 Note: The propensity scores of control units outside the area of common support are cut off other controlling variables when constructing the matches (S4 in Table 2) The propensity scores range from 0.348 to 0.989 for borrowers and from 0.195 to 0.962 for non-borrowers Once again, the estimation of the average treatment effect is restricted to the area of common support where the distributions overlap The estimates of the impact of credit participation on healthcare expenditure are reported in Table The results show that credit participation positively affects healthcare expenditure They are also statistically significant regardless of the sets of covariates and the types of matching approaches used Borrowers spent about at least VND93,000 more on healthcare than their non-borrowing counterparts Journal of Southeast Asian Economies 07.indd 110 IV.2 Impact of Formal and Informal Household Credit In this subsection, the effects of multiple treatments are estimated to contrast the impact of informal and formal credit on education and healthcare expenditure (Lee 2005) When applying these multiple treatments, we treat credit from formal sources (F) as a full “dose” and credit from informal sources (I) as a partial “dose”.6 Here, we directly compare the formal and informal credit groups, taking the informal credit group as the control group Estimates of the effects of the multiple treatments on education expenditure are reported in Table The estimation procedure used here is similar to 11 Vo l , N o , A p r i l 3/7/14 9:36:38 AM Table Average Effect of the Treatment on Monthly Average Healthcare Expenditure in VND1,000 Using Matching Estimators Control variables in the propensity score estimation Kernel matching Radius matching S2=S1 plus initial income in log, initial assets in logarithm 93.082 (55.382)+ 94.016 (56.441)+ Specification (S3) 122.047** (46.442)** 131.161 (44.413)** S4=S3plus initial income in logarithm, initial assets in log 108.313 (50.301)* 112.895 (48.612)* Specification (S1) 112.277 (48.711)* 111.267 (49.422)* Notes: Bootstrapped standard errors in parentheses with 1000 repetitions, statistically significant at 10 per cent (+); per cent (*); and per cent (**) S1: Head’s gender, head’s age, head’s education, marital status, household size in log, head’s age*gender, ward dummies S3: Head’s gender, head’s education, marital status, dummy of child below 6, number of children from to 18 years old, persons from 18 to 60 years old, dummy of person older than 60 years old, head’s age*education, and ward dummies the one discussed in subsection IV.1: household characteristics are first used to construct the scores (S1 and S3) and thereafter, pre-treatment income and assets are controlled for (S2 and S4) The estimated effects of informal credit are reported in columns and 3, and the effects of formal credit effect are reported in columns and The estimates show that informal credit has no significant effect on household education expenditure In contrast, formal credit strongly affects education expenditure Both kernel and radius matching estimators display similar estimates that are statistically significant at the per cent level To test the results further, we directly compared the impact of formal credit to informal credit Estimates of the difference between the impact of formal and informal credit are shown in the last column of Table The estimates are consistent across the specifications of the matching variables Journal of Southeast Asian Economies 07.indd 111 The higher credit level (or treatment level) leads to a greater positive impact Likewise, we looked at the impact of formal and informal credit on healthcare spending The estimates for informal credit and formal credit on health care expenditure are reported in Table The results of the difference in the impact of formal and informal credit are presented in the last column of Table Informal credit has a positive but only marginally significant impact at the 10 per cent level of statistical significance In contrast, the impact of formal credit is more than double that of informal credit (statistically significant at the 10 per cent level) The use of multiple ordered treatments shows that the higher treatment level (formal credit) has a greater positive impact on healthcare and education expenditure, thus confirming the positive effects of credit on healthcare and education 111 Vo l , N o , A p r i l 3/7/14 9:36:38 AM Table The Average Effect of Treatment on Monthly Average Education Expenditure in VND1,000 using Matching Estimators for the Entire Sample Control variables in the propensity score estimation Informal credit vs Non-borrowers ATTK Formal credit vs Non-borrowers ATTR ATTK ATTR Formal vs Informal ATTR Specification (S1) 35.283 (38.173) 26.968 (37.641) 152.813 (47.642)** 159.717 (46.162)** 111.607 (44.662)* Specification (S2) 10.963 (40.052) 13.056 (39.539) 148.027 (46.321)** 146.784 (48.596)** 117.417 (48.373)* Specification (S3) 33.991 (37.867) 24.652 (36.579) 144.884 (46.097)** 159.113 (44.351)** 108.720 (42.935)* Specification (S4) 7.750 (39.834) 13.440 (38.605) 145.492 (45.875)** 148.440 (48.368)** 118.657 (50.221)* Notes: Bootstrapped standard errors in parentheses with 1,000 replications, statistically significant at 10 per cent (+); per cent (*); per cent (**) S1: Head’s gender, head’s age, head’s education, marital status, ward dummies, school-aged child ratio, and head’s age*head’s gender S2: Head’s gender, head’s age, head’s education, marital status, ward dummies, school-aged child ratio, head’s age*head’s education, initial income in logarithm, initial assets in logarithm S3: Head’s gender, head’s age, head’s education, marital status, ward dummies, number of children aged to 18 years old, and head’s age*head’s gender S4: Head’s gender, head’s age, head’s education, marital status, ward dummies, number of children aged to 18 years old, head’s age*education, initial income in logarithm, initial assets in logarithm In some cases to meet the balancing property in estimation of propensity scores we added interaction terms in specifications of the propensity score ATTK: Average Treatment Effect on the Treated, Kernel matching ATTR: Average Treatment Effect on the Treated, Radius matching V. Finding Summary and Concluding Remarks This paper estimates the impact of credit participation on education and healthcare expenditure by the poor in the peri-urban District of Ho Chi Minh City, Vietnam, using data from a survey designed to meet the conditions for propensity score matching.7 The results illustrate that borrowers spent more on education and Journal of Southeast Asian Economies 07.indd 112 healthcare than their non-borrowing counterparts, thus credit participation has a highly positive and significant effect on healthcare and education spending We focussed on the poor in order to ensure that disparities between the treatment and control groups would be minimal We also controlled for pre-treatment income levels, which, in turn, control for unobservable attributes such as motivation, 11 Vo l , N o , A p r i l 3/7/14 9:36:38 AM Table The Average Effects of the Treatment on the Monthly Average Healthcare Expenditure in VND1,000 Using Matching Estimators Control variables in the propensity score estimation Informal credit vs Non-borrowers ATTK Formal credit vs Non-borrowers ATTR ATTK ATTR Formal vs Informal ATTR Specification (S1) 77.197 (45.833)+ 77.037 192.648 198.287 175.762 (41.612)+ (95.163)* (98.337)* (85.766)* S2=S1plus initial income in log, initial assets in log 65.709 (43.060) 68.638 165.153 183.121 178.730 (40.846)+ (96.364)+ (92.470)* (92.515)+ Specification (S3) 59.844 (45.626) 71.473 200.227 198.616 162.392 (42.105)+ (97.934)* (97.505)* (92.509)+ S4=S3plus initial income in log, initial assets in log 60.404 (44.646) 66.845 195.088 194.632 161.437 (44.254) (97.652)* (96.055)* (97.067)+ Notes: Bootstrapped standard errors in parentheses with 1,000 replications, statistically significant at 10 per cent (+); per cent (*); per cent (**) S1: Head’s gender, head’s education, marital status, head’s age, household size in logarithm, ward dummies, head’s age*gender S3: Head’s gender, head’s education, marital status, dummy of child below years old, number of children aged to 18 years old, number of persons aged 18 to 60 years old, dummy of older than 60 years old, ward dummies, marital status*head’s gender In some cases to meet the balancing property in estimation of propensity scores we added interaction terms in specifications of the propensity score ATTK: Average Treatment Effect on the Treated, Kernel matching ATTR: Average Treatment Effect on the Treated, Radius matching entrepreneurial ability and skills Our estimation strategy, therefore, sought to reduce bias and enhance the reliability of the PSM estimates Our findings are in line with those of Nguyen (2008) and Quach and Mullineux (2007), who have found that access to credit programmes has a positive impact on household welfare and poverty reduction in rural Vietnam Spending on education Journal of Southeast Asian Economies 07.indd 113 and healthcare affects the formation of human capital Given their limited physical and financial assets, human capital becomes the most important asset for the poor in peri-urban areas Therefore, policies aiming at reducing poverty in these areas should consider enhancing subsidized credit to the poor and/or providing subsidized or free universal education and healthcare services 11 Vo l , N o , A p r i l 3/7/14 9:36:39 AM APPENDIXES Appendix Pre-treatment Income per Capita of the Poor and Their Sources of Loans Mean income is VND3.57 million with standard deviation as VND0.844 million, and median of VND3.6 million (see Figure 3) Monthly income per capita is just about VND300,000 (or US$18), slightly higher than the national official poverty line (equivalent to US$ 16.4).8 Density 1.0e-04 2.0e-04 3.0e-04 4.0e-04 5.0e-04 Figure Pre-treatment per Capita Income 1000 2000 3000 INCOME 4000 5000 6000 Note: Unit of horizontal axis is VND1,000 Journal of Southeast Asian Economies 07.indd 114 11 Vo l , N o , A p r i l 3/7/14 9:36:39 AM Table Sources of Loans Sources of loans No of loans Percentage Formal VBSP Commercial banks JCSF Social political organizations HEPRF Informal Moneylenders, ROSCAs, pawnbrokers, others Friends, relatives, neighbors 336 37 26 29 62 182 272 51 221 Overall 608 55% 6% 4% 5% 10% 30% 45% 8% 36% 100% Mean of loans (VND1,000) 9,327 9,622 54,923 4,564 4,564 5,176 5,229 9,218 4,308 7,494 Source: Calculation from authors’ survey; VBSP- Vietnam Bank for Social Policies; JCSP — Job Creation Support Fund; HEPRF — The Hunger Elimination and Poverty Reduction Funds; ROSCAs- Rotating savings and credit associations Appendix The Average Treatment Effect on Monthly Average Education Expenditure in VND1,000 Using Matching Estimators for the Entire Sample with Reduced Bandwidth (0.01) and Radius (0.05) Control variables in the propensity score estimation Treated/controls Head’s gender, head’s age, head’s education, marital status, school-aged child ratio, and ward dummies (S1) 86.58 (35.71)* 95.63 (33.35)** S2=S1plus initial income in log, initial assets in logarithm 304/101 78.95 (36.53)* 91.25 (33.46)** Head’s gender, head’s age, head’s education, marital status, number of children from to 18, and ward dummies (S3) 304/107 83.34 (33.07)* 85.99 (34.69)* S4=S3 plus initial income in log, initial assets in logarithm 304/101 77.98* (36.53) 85.07* (35.46) Journal of Southeast Asian Economies 07.indd 115 304/107 Kernel matching Radius matching 11 Vo l , N o , A p r i l 3/7/14 9:36:39 AM Appendix The Average Treatment Effect on Monthly Average Healthcare Expenditure in VND1,000 Using Matching Estimators for the Entire Sample with Reduced Bandwidth (0.01) and Radius (0.05) Control variables in the propensity score estimation Specification (S1) Treated/controls 304/103 Kernel matching Radius matching 95.36 (49.21)* 109.33 (51.07)* S2=S1 plus initial income in log, initial assets in logarithm 304/97 94.85 (50.91)+ 99.086 (53.83)+ Specification (S3) 304/107 118.799 (45.74)** 129.242 (44.56)** S4=S3plus initial income in logarithm, initial assets in log 304/102 103.15 (49.87)* 114.571 (49.24)* Appendix The Average Treatment Effect on Monthly Average Education Expenditure in VND1,000 Using Matching Estimators for the Entire Sample with Reduced Bandwidth (0.01) and Radius (0.05) Control variables in the propensity score estimation Informal credit vs Non-borrowers ATTK ATTR ATTK ATTR Formal vs Informal ATTR Specification (S1) 1.64 (45.59) 22.07 (37.07) 151.72 (47.33)** 163.46 (51.03)** 101.26 (44.89)* Specification (S2) 14.42 (38.72) 12.94 (41.57) 140.52 (49.89)** 143.62 (53.94)** 116.54 (53.33)* Specification (S3) 39.05 (44.19) 27.92 (39.01) 142.33 (50.62)** 158.14 (46.47)** 103.75 (46.77)* Specification (S4) 3.59 (42.27) 13.17 (40.86) 138.56 (48.50)** 145.36 (49.80)** 109.70 (49.06)* Notes: ATTK: Average Treatment Effect on the Treated, Kernel matching ATTR: Average Treatment Effect on the Treated, Radius matching Journal of Southeast Asian Economies 07.indd 116 Formal credit vs Non-borrowers 11 Vo l , N o , A p r i l 3/7/14 9:36:39 AM Appendix The Average Treatment Effect on Monthly Average Healthcare Expenditure in VND1,000 Using Matching Estimators for the Entire Sample with Reduced Bandwidth (0.01) and Radius (0.05) Control variables in the propensity score estimation Informal credit vs Non-borrowers ATTK ATTR Formal credit vs Non-borrowers ATTK ATTR Formal vs Informal ATTR Specification (S1) 76.74 (44.28)+ 82.27 (42.20)+ 195.91 (92.66)* 203.89 (95.21)* 182.58 (101.87)+ S2=S1plus initial income in log, initial assets in log 62.94 (46.35) 65.15 (44.34) 171.79 (102.53)+ 177.72 (98.07)+ 190.70 (93.12)+ Specification (S3) 67.11 (42.33) 68.71 (44.44) 210.84 (93.08)* 195.42 (100.89)* 171.16 (105.08)+ S4=S3plus initial income in log, initial assets in log 81.99 (46.22)+ 90.09 (48.36)+ 205.71 (91.63)* 195.77 (98.01)* 179.03 (102.92)+ Notes: ATTK: Average Treatment Effect on the Treated, Kernel matching ATTR: Average Treatment Effect on the Treated, Radius matching NOTES The authors thank, without implicating, Andrea Menclova, Asadul Islam and the participants of the Sixth Australasia Development Economics Workshop for their helpful comments and suggestions Any remaining errors are those of the authors HCMC has twenty-four districts District has the fifth lowest population density, with a population of 227,816 (in 2008) The list was provided by the District Department of Labour, Invalids and Social Affairs Poor households in the list excluded temporary migrants When we reduced the bandwidth and radius to 0.01 and 0.05 for kernel and radius matching respectively, the results are robust See Appendixes to Some studies suggest that the estimation should be in the range of 0.1 to 0.9, but there are 44 observations with scores greater than 0.9 (about 11 per cent of the sample); if these are dropped, the estimates will be misleading (Crump et al 2009) The sets of variables used for estimating scores to draw Figures and are different Each set of variables should affect both credit participation and outcomes The impact on education expenditure is featured in Figure and the impact on healthcare expenditure is featured in Figure That is why the two figures are slightly different Basically, formal credit amounts are larger than informal ones, hence formal credit is considered as consituting a higher level of treatment or a full treatment We expect that if loans have an effect on outcomes (i.e., education and health care expenditure), borrowing greater amounts will have a stronger impact on the respective outcomes The mean of accumulated loans per household is VND8,317 thousand (about US$500) and VND15,135 thousand (about US$920) for informal and formal credit, respectively The average size per loan is VND5,229 thousand (about US$317) and VND9,327 thousand (about US$566) for informal and formal credit, respectively The impact on both expenditure and school enrolment and/or attendance should be investigated to provide a more complete picture of the effects on education The impact on child schooling was examined in a separate paper in International Development Planning Review (forthcoming) Average exchange rate in 2008, US$1=VND16,481 Journal of Southeast Asian Economies 07.indd 117 11 Vo l , N o , A p r i l 3/7/14 9:36:39 AM References Armendariz, de Aghion B and Johnathan Morduch The economics of microfinance Cambridge, MA and London: The MIT Press, 2010 Beegle, K., Dehejia, R and Gatti, R “Why should we care about child labor? The education, labor market, and health consequences of child labor” NBER Working Paper Series, Working paper 10980 (2004) Bryson, A., R Dorsett and S Purdon “The use of propensity score matching in the evaluation of active labor market policies” Policy Studies Institute and National Centre for Social Research, University of Westminster, 2002 Caliendo, M and S Kopeinig “Some practical guidance for the implementation of propensity score matching” Journal of Economic Surveys 22, no (2008): 31–72 Coleman, B E “Microfinance in Northeast Thailand: Who benefits and how much?” World Development 34, no (2006): 1612–38 Consultative Group to Assist the Poor (CGAP) ”Is microfinance an effective strategy to reach the Millennium Development Goals?” CGAP FocusNote no 24 (2003) Washington, D.C.: Littlefield, E., Morduch, J., & Hashemi, S Retrieved from Crump, R., V Hotz, G Imbens and O Mitnik “Dealing with limited overlap in estimation of average treatement effects” Biometrika 96, no (2009): 187–99 Cull, R., A.D Kunt and J Morduch “Microfinance meets the market” Journal of Economic Perspectives 23, no (2009): 167–92 Dehejia, R.H “Practical propensity score matching: a reply to Smith and Todd” Journal of Econometrics 125 (2005): 355–64 Dehejia, R and Gatti, R “Child labor, the role of income variability and access to credit in a cross-section of countries” The World Bank, Policy Research Working Paper no 2767, 2002 Dehejia, R and Wahba, S “Casual effects in non-experimental studies: Re-evaluating the evaluation of training programs” Journal of the American Statistical Association 94, no 448 (1999): 1053–62 ——— “Propensity score matching methods for nonexperimental causal studies” Review of Economics and Statistics 84, no (2002): 151–61 Dias, M.C., H Ichimura and G Berg “The matching method for treatment evaluation with selective participation and ineligibles” Centre for Microdata Methods and Practice CEMMAP working paper CWP33/07 (2007) Edmonds, E “Child labor and schooling responses to anticipated income in South Africa” Journal of Development Economics 81, no (2006): 386–414 Glewwe, P., H Jacoby and E King “Early childhood nutrition and academic achievement: a longitudinal analysis” Journal of Public Economics 81 (2000): 345–68 GSO “Population and housing census, April 2009” Complete Report and Major Findings, General Statistical Office Hanoi, Vietnam (2010) Hazarika, G and S Sarangi “Household access to microcredit and child labor in rural Malawi” World Development 36, no (2008): 843–59 Heckman, J and J Smith “The pre-program earnings dip and the determinants of participation in a social program: Implications for simple program evaluation strategies” NBER Working Paper Series, Working paper 6983 (1999) Heckman, J., H Ichimura and P Todd “Matching as an econometric evaluation estimator: Evidence from evaluating a job training program” Review of Economic Studies 64, no (1997): 605–54 Imbens, G “Nonparametric estimation of average treatment effects under exogeneity: A review” Review of Economics and Statistics 86, no (2004): 4–29 Imbens, G and J Wooldridge “Recent developments in the econometrics of program evaluation” Journal of Economic Literature 47, no (2009): 5–86 Islam, A and C Choe “Child labour and schooling responses to access to microcredit in rural Bangladesh” MPRA Working Paper no 16842 (2009) Retrieved from Munich Personal RePEc Archive Jacoby, H and E Skoufias “Risk, financial markets, and human capital in a developing country” Review of Economic Studies 64, no (1997): 311–35 Khandker, S “Microfinance and poverty: Evidence using panel data from Bangladesh” World Bank Economic Review 19, no (2005): 263–86 Lee, M.J Micro-econometrics for policy, program and treatment effects New York: Oxford University Press, 2005 Maldonado, J and C Gonzalez-Vega “Impact of microfinance on schooling: Evidence from poor rural households in Bolivia” World Development 36, no 11 (2008): 2440–55 Journal of Southeast Asian Economies 07.indd 118 11 Vo l , N o , A p r i l 3/7/14 9:36:40 AM Morduch, J “Does microfinance really help the poor? New evidence from flagship programs in Bangladesh”, 1998 Mosley, P “The use of control groups in impact assessment for microfinance” International Labor Office, Geneva, Working Paper no 19, 1997 Nguyen, C “Is a Governmental Microcredit Program for the Poor really Pro-poor? Evidence from Vietnam” Developing Economies 46, no (2008): 151–87 Quach, H and A Mullineux “The Impact of Access to Credit on Household Welfare in Rural Vietnam” Research In Accounting In Emerging Economies (2007): 279–307 Pitt, M and Khandker, S “The impact of group-based credit programs on poor households in bangladesh: does the gender of participants matter?” Journal of Political Economy 106, no (1998): 958–92 Pitt, M., S Khandker, O Chowdhury and D Millimet “Credit programs for the poor and the health status of children in rural Bangladesh” International Economic Review 44, no (2003): 87–118 Rosenbaum, P and D Rubin “The central role of the propensity score in observational studies for causal effects” Biometrika 70 (1983): 41–55 ———.“Constructing a control group using multivariate matched sampling methods that incorporate the propensity” American Statistician 39 (1985): 33–38 Smith, J and P Todd.“Does matching overcome Lalonde’s critique of nonexperimental estimators?” Journal of Econometrics 125, no 1–2 (2005): 305–53 United Nations World urbanization prospects: The revision 2007 New York: United Nations, 2007 VDR “Vietnam Development Report 2010: Modern Institutions” Joint Donor Report to the Vietnam Consultative Group Meeting, Hanoi, Vietnam, 3–4 December 2009 Tinh Doan is Research Analyst at the Ministry of Business, Innovation and Employment, and Fellow at University of Economics and Business, Vietnam National University, Hanoi John Gibson is Professor in the Economics Department, University of Waikato, New Zealand Mark Holmes is Professor in the Economics Department, University of Waikato, New Zealand Journal of Southeast Asian Economies 07.indd 119 11 Vo l , N o , A p r i l 3/7/14 9:36:40 AM ... ATTK ATTR Formal vs Informal ATTR Specification (S1) 76.74 (44.28)+ 82.27 (42.20)+ 195.9 1 (92.66)* 203.89 (95.2 1)* 182.58 (101.87)+ S2=S1plus initial income in log, initial assets in log 62.94 ... 68.71 (44.44) 210.84 (93.08)* 195.4 2 (100.89)* 171.16 (105.08)+ S4=S3plus initial income in log, initial assets in log 81.99 (46.22)+ 90.09 (48.36)+ 205.71 (91.63)* 195.7 7 (98.01)* 179.03 (102.92)+... Joint Donor Report to the Vietnam Consultative Group Meeting, Hanoi, Vietnam, 3–4 December 2009 Tinh Doan is Research Analyst at the Ministry of Business, Innovation and Employment, and Fellow at