The last classification group is composed of a set of works with miscellaneous approaches from a methodological point of view.
In Thailand, through structural general equilibrium models of growth, Kaboski and Townsend (2005) study the effect of the presence of (pseudo) financial institutions on households that otherwise would have limited access to credit or savings facilities. These economic models are different from all the previous techniques that are also known as reduced-form estimations. They study the direct relationship between an intervention and the outcome variable in the population under examination. The economic models include the interrelationships among different endogenous and exogenous variables and provide a schematic view of the effects of interventions within this created framework. They can show a more holistic view of the possible effects of the policies. They can also become very complex as in the case of macroeconomic frameworks modelling financial regulation or taxes, as the effects are dynamic and heterogeneous in different sectors of the population (Khandker et al., 2010).
The institutions studied are Production Credit Groups (PCGs), which lend mostly cash and also provide saving facilities, women’s groups and rice and buffalo banks. PCGs are the most similar to an MFI. The outcome variables under study are growth in assets, the probabilities of consumption smoothing, starting a business, switching the main job or becoming a moneylender customer.
Cash loans (PCGs and women’s groups) are associated with stability or expansion of services. These institutions can be associated with growth in assets, in contrast to buffalo or rice banks. Institutions providing extra services such as training, emergency attendance or savings increase the probability of consumption smoothing. Membership of the women’s groups increases the probability of switching jobs, having pledged saving accounts and it also increases job mobility and business start-ups. Finally, institutions overall contribute to lessen the reliance on moneylenders.
Kaboski and Townsend (2009) measure the impact of a program called Million Baht Village which consisted of transfer of one million baht to 77,000 Thai villages. The sudden boost in credit availability is a good opportunity to have pre and post-program information using a panel data approach. They use a 2SLS. The impact found is an increase in borrowing, consumption and investments in agriculture. Income from business and market activities is increased. Finally, the increase in wage rates reveals general equilibrium effects that can be extended to non-borrowers as positive spillovers. Finally, in Chemin (2008) we come back to the first round of the Bangladesh survey (1991-92) to challenge PK outcomes using a Propensity Score Matching approach. The characteristics that a dataset has to comply with in order to be adequate for a PSM study are enumerated in Heckman et al. (1997). Controls and participants should come from the same economic area. All the individuals should respond to the same questionnaire and the set of questions should be rich in order to gather as much information as possible of the relevant variables conditioning participation. The database complies with all of them.
His argument to use this technique is that it does not rely on the eligibility criterion that might bias the calculations in Pitt and Khandker (1998), Morduch (1998) and Madajewicz, (2003). In all of them the underlying assumption is that the eligibility rule is strictly observed when delimiting a group, which is erroneous. It does also neutralize the allocation bias that could have been mishandled in Morduch, (1998), comparing
individuals from poorer program villages to the ones from better-off non program villages. In addition, PSM is a non- parametric approach so it does not assume any underlying structure as linear regression would and will only match comparables. His conclusions agree with PK on the positive effect of microfinance on expenditure but to a lesser extent. The impact is estimated as a 3% increase of consumer expenditures, a higher figure than Morduch (1998) but lower than PK whose calculations, Chemin contends, might be upwardly biased.
Finally, Duvendack and Palmer-Jones (2011) use the same dataset and also PSM and point out some limitations of Chemin’s analysis, mainly regarding its lack of sensitivity analysis of the estimates to a potential bias from unobservables. They find positive impact on some outcome variables but the estimates were quite sensitive to unobservables. From the methodological point of view, they do not report any test for the matching quality of the different algorithms, which might cast some doubts over some of the estimates.
Table 1 below lists the papers reviewed. The range of dependent variables in these studies is vast and in some papers its number is higher than one hundred. Some kind of grouping was needed in order to tabulate the outcomes. We follow the one used in Copestake et al. (2011) who divided the different dependent variables into three groups, economic, social and empowerment indicators, including some of the following example covariates (in the table the group number is used):
1. Economic outcome variables, including business profits and revenues, sales, income/income p.c., consumption/expenditure, assets, employment, savings, debts, poverty indices and others.
2. Social outcome indicators: children’s school enrolment, school attendance, nutritional status, vulnerability to shocks, social capital, contraceptive use and other.
Table 1 Summary table of reviewed papers
R
C
T
s
Study Dependent variable groups Technique Main outcomes
(Banerjee et al., 2009) [India]
1, 2, 3 RCT
Measures the ITT. The likelihood of new businesses is higher and statistically significant in treatment areas. No significance is found with respect to business profits, inputs, revenues and employees. Impact on per-capita overall expenditures and non-durable expenditures is insignificant but it is positive and significant on durables, business related durables. No increase in health and education expenditures is found either.
(Dupas and Robinson,
2009)[Kenya] 1, 2 RCT
Measures ITT, the effect of having assigned to the treatment and of using the account. The main findings are a positive and significant impact on investment in business for women and food and private
expenditures. Non-significant impact is found on labour supply, overall expenditures and male investment and expenditures. No crowding out effect with respect to ROSCA is found
(Karlan and Zinman,
2007) [South Africa] 1, 2 RCT
Profits are increased when owners are males, but not for female-owned. Male owners also increase the school enrolment of children and are more likely to be employed at the family business. Increase of stress is also significant for males. No impact is found on fixed assets, income and expenditures. Formal credit seems to complement rather than substitute informal.
(Karlan and Zinman, 2009)
[Philippines] 1, 2 RCT
They create some indexes. Economic self-sufficiency index, including current employment status and income experience a positive and significant increase for borrowers. The impact on index including decision power and optimism is positive and significant but not so the impact on “investment and durables” index. Borrowers also increase their stress and their consumption. Customers selected randomly did not fall into a debt trap
(De Mel et al., 2008) [Sri Lanka]
1 RCT
Grants increase profits by 5% per month or 60% per year. Marginal returns highest for more able entrepreneurs and businesses with fewer workers. Impact is higher for male owned businesses and non- significant for female-owned. Grants are also associated with an increase in capital stock and hours worked by the owner. They find also negative spillovers in the economy in the neighbourhood of the granted businesses.
(De Mel et al., 2009)
[Sri Lanka] 1, 2 RCT They research further the differences between male and female-owned businesses. They do not findsignificant differences in investment on education, groceries or health. Neither they do on ability, risk aversion or the capacity to increase their hours worked. Male-owners tend to make profitable investment in their enterprises while females did not generate, on average, a sustained source of income from grants.
Q ua si ex pe ri m en ta lA pp ro ac he s
(Pitt and Khandker,
1998)[Bangladesh] 1, 2, 3 Regression discontinuity design Female borrowers increase expenditures by 18 takas per 100 takas borrowed, males by 11 takas only. Theyalso found an increase in school enrolment and health indicators in borrowing households. (Pitt, 1999)[Bangladesh] 1, 2 Regression discontinuity design Questions Morduch 1999 approach. Recalculates impact with additional land specifications and finds PK
outcomes quite robust to these changes. (Morduch, 1998) [
Bangladesh] 1, 2 DID
Criticised PK approach and did not find any significant impact on expenditures as claimed in PK. Microcredit is found to smooth consumption and labour income.
(Khandker,
2005)[Bangladesh] 1, 2 Panel Fixed Effects The annual impact of female borrowing on expenditures is 21 takas per 100 extra takas borrowed. Impactof past borrowing is higher than present borrowing. Moderate poverty is reduced by 1.6% per annum and extreme poverty by 2.2% among participants. Attributes 40% of the village-level poverty reduction to microfinance.
(Roodman and Morduch,
2009)[Bangladesh] 1, 2 Regression discontinuitydesign, Panel Fixed Effects Replicate PK, Morduch 1999 and Khandker, 2005. Finds evidence neither of impact on consumption norof consumption smoothing. Khander’s 2005 approach is criticised for its weaknesses in its statistical approach and therefore its outcomes are questioned.
(Copestake et al.,
(Bruhn and Love,
2009)[Mexico] 1 Panel FE
Impact studied at municipality level. Opening of Azteca branches increased the fraction of informal business owner, male in particular. It also increases the fraction of female wage-earners but not males and impact on income is significant after controlling for time trends. No significant impact is found with respect to the share of people above the minimum wage.
(Barnes et al., 2001)[Zimbabwe]
1,2,3 ANCOVA
Compares continuing clients with new clients and non-clients. Impact is positive and significant on the number of household durable assets. Departing and continuing clients experience a rise in the education of boys between 6-16 years old, but not for girls. Consumption smoothing effect also observed on departing clients. Limited impact on monthly revenue and assets of enterprises and none on employment. Also training was associated to improvements on management and participation in MFIs increases confidence. (Chen and Snodgrass,
2001)[India] 1,2,3 ANCOVA
It compares borrowers vs. only savers vs. non-clients. Overall, borrowers and savers are better off than non-clients. Borrowers show higher income in both periods and savers the highest rate of growth. Borrowers increase their poverty rate in the second period, not the rest of groups. Repeating borrowers have greater income and food expenditures.
(Dunn and Arbuckle,
2001)[Peru] 1,2,3 ANCOVA
Great impact at enterprise level. Shows a positive and significant impact on net revenues, enterprises fixed assets, employment, sources of input supplies. At household level, income is increased for treated and education expenditures are decreased for new entrant households. At individual level, the feel of being more prepared to face the future is increased among participants.
(Coleman,
1999)[Thailand] 1, 2, 3 OLS
Finds no significant impact on physical assets, savings, production, sales, productive expenses, labour time, and most measures of expenditure
on health care and education. The impact is positive and significant for women’s high interest debt, women’s lending out with interest and negative and significant on men’s health care.
(Coleman,
2006)[Thailand] 1, 2, 3 OLS
No remarkable impact on rank-and-file members. Committee members experience positive and significant impact on household wealth: women’s wealth, nonland assets and consumer durables. It is also positive on savings, women’s self-employment sales and expenses and educational expenses for boys at committee member households.
(Kondo et al., 2008)[Philippines]
1,2 OLS
Impact of the presence of microfinance is positive and mildly significant on income, total expenditures and food expenditures, all in per capita terms. It becomes insignificant and even negative when households are poorer. Also increases also savings accounts and amounts in those accounts. Also increases program client’s activities and their number of employees. No significant impact was found on household assets, health or education.
(Tedeschi, 2008)[Peru] 1 OLS (pooled) & panel FE Challenges Dunn & Arbuckle, 2001. Increase in enterprises net revenues is still positive and significant but much lower than reported in the former.
(Kaboski and Townsend, 2005)[Thailand] 1, 2
General Equilibrium Models, 2 Stage Least Squares (2SLS) & Simultaneous Equation, Maximum Likelihood
Production Credit Groups (PCGs similar to credit institutions) and women’s groups can be associated with a positive impact on asset growth. Women’s group membership increases also consumption smoothing, job mobility and moneylender reliance. PCGs, on the contrary, decreases job mobility, the likelihood of starting a business and is not significant with respect to moneylender reliance.
(Kaboski and Townsend,
2009)[Thailand] 1,2 2SLS
Million Baht program: boost the availability of credit without crowding out other sources. There is an increase in consumption levels and income growth but the impact on asset growth is negative. No differences between female and male headed households with respect to credit or agricultural income but female-headed show higher business income and lower probability of education expenditures.
(Chemin,
2008)[Bangladesh] 1, 2 Propensity Score Matching
Estimates impact on expenditures is 3%. Consumption smoothing is not found significant. (Duvendack and Palmer-
Jones, 2011)
[Bangladesh] 1,2 Propensity Score Matching
They replicate Chemin (2008) and add some additional treated and control groups. Their conclusion is that the estimates cannot be trusted as they are extremely sensitive to potential unobservables. They do not test for matching quality and their sensitivity analysis could have been applied to the kernel instead to the nearest neighbour.
Conclusion
This review discusses many of the main references regarding microfinance impact
effect. Random studies are a promising alternative or complement to quasi
experimental approaches. Some issues have been raised regarding their validity and the randomization processes that have to be considered before taking for granted the randomized experiment assumptions. They are however at a quite early stage with regard to social sciences but their repetition will allow us to test whether the reservations put forward by some top academics (Rodrik, 2008; Deaton, 2010) are confirmed or not. Quasi-experimental studies have a richer background of techniques that try to improve in the quest for unbiased estimates although they need more complex assumptions. However, this is not enough reason to state that they are inferior as impact evaluation techniques.
Outcomes show that overall microfinance makes a difference in some variables and not in others. Although they can be contradictory for the same variable in different studies, the fact that it shows significant in many of them should encourage further study regarding the extent of these effects. The pointed concerns with respect to internal validity of all these studies in Copestake et al. (2011) also have to be taken into account. Better surveying techniques are needed to confirm this optimistic view of microfinance, as in their opinion almost none of the studies so far can support the argument of a positive impact. Another question would be the effect of microfinance at different strata of the targeted population. This has been pointed out by Coleman (2006), Copestake et al. (2005), Hulme and Mosley (1996) and Kondo et al. (2008) and will also be addressed in one of the studies of the present set of papers.
The following sections comprise a brief description of the theoretical background of the techniques used in the present set of studies to find the impact of microfinance. In the first section Propensity Score Matching is described and some differences with respect to OLS are discussed. PSM is applied to the second round of the Bangladeshi dataset in 1998-99 . In the second empirical chapter OLS and DID or panel Fixed Effects are used as in Tedeschi (2008). This will be the first chapter dealing with the Andhra Pradesh dataset. In addition, the second empirical chapter on Andhra Pradesh dataset tries to describe the quantile regression approach. In particular it will be found the distributional
impact of microfinance basing on the analysis done in (Abrevaya and Dahl, 2008). The latter, in turn, is based, on Chamberlain (1982) and Chamberlain (1984).
Part II: Techniques of impact evaluation
There are several methods to estimate the impact and each method tackle the problem of the missing counterfactual in a different way. Regarding Blundell and Costa Dias (2000), the appropriate methodology depends on three factors: the information available, the model and the parameter of interest. Khandker et al. (2010) enumerate and explain in detail seven different approaches in their book:
Randomized evaluations
matching methods
double difference methods
instrumental variable methods
regression discontinuity methods
distributional impacts
structural and other modelling approaches.
In our work some of these are used to some extent and a description of the essential theoretical background of the most relevant techniques used in the empirical chapter is included below.
Matching estimators and Propensity Score5.
Assumptions
As already seen, in the absence of random experiments, researchers have to turn to quasi-experimental methods to solve the problem of selection bias. Within these, matching is one of the most popular and Propensity Score Matching in particular has been widely used in the last few years. In essence, it tries to resemble a random experiment. Basically the method assumes that, once observables have been controlled for, the differences between the treated and the control group is just participation. Thus, differences in the dependent variable (income, expenditures or any other) between the treated and the control group can be attributed to intervention.
Both OLS and matching methods and PSM in particular rely on this Conditional Independence Assumption (CI) in the sense that they assume that bias is avoided by just
5This section relies heavily on Caliendo, M. 2006.Microeconometric Evaluation Labour Market Policies.
controlling for observables. But they also have differences, the main being that OLS assume an underlying linear functional form and that PSM is a non-parametric method. This CI assumption could be described more formally saying that outcomes values are independent of the participation, given a set X of covariates:
ܻ,ଵ,ܻ,⊥ܦ|ܺ (1.8)
where means “statistically independent of”. Thus:
ܧ൫ܻ,หܺ,ܦ= 1൯= ൫ܻ,หܺ,ܦ= 0൯ (1.9)
and therefore the selection bias is not present any more. Apart from Conditional
Independence this assumption has been named in literature as Ignorability,
UnconfoundednessorSelection on Observables.
However, a great size ofܺ(number of covariates) might bring about difficulties in the
matching process. Rosenbaum and Rubin (1983a) showed that matching can be done
more easily conditioning onܲ(ܺ) = Pr(ܦ|ܺ). In order to do this, they establish a
second condition that has been called Overlapping Assumption or Common Support6
(CS) condition:
0 <ܲ(ܦ= 1|ܺ) < 1 (1.10) This second condition entails that all the individuals have a positive probability of belonging to both the treatment and the control group. Rosenbaum and Rubin (1983a) name these two conditions, unconfoundedness and common support, as the “strong ignorability” condition.
When only the ATT is of interest, these conditions can adopt a laxer form. In the case of the unconfoundedness assumption, the following will be enough:
ܻ,⊥ܦ|ܺ (1.11)
also called “unconfoundedness for controls”.
6Support is a statistical concept that includes the values where the density function is found to be