Academic journal article Journal of Southeast Asian Economies

Impact of Household Credit on Education and Healthcare Spending by the Poor in Peri-Urban Areas, Vietnam

Academic journal article Journal of Southeast Asian Economies

Impact of Household Credit on Education and Healthcare Spending by the Poor in Peri-Urban Areas, Vietnam

Article excerpt

I. Introduction

Microfinance has increasingly attracted attention from the global development community because it is considered a powerful tool for alleviating 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 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 self-selection, the estimates will be naive 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 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 existing 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. …

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