5. SISTEMA INMUNE DE TELEÓSTEOS
5.1. Sistema inmune innato
5.1.2. Factores humorales
Explanatory variables Whole
sample Child aged 6-14 Child aged 15-18 Informal credit 0.0893 -0.1129 0.2458 (0.38) (0.26) (0.91)
Both sources of credit 0.1084 0.2479 0.1147
(0.43) (0.60) (0.44)
Formal credit -0.3230 0.0149 -0.3755
(1.16) (0.03) (1.21)
Pre-treatment income capita -0.1383 -0.9054 0.3026
in logarithm (0.40) (1.75)+ (0.80)
Pre-treatment asset in -0.0741 -0.0899 -0.0312
logarithm (1.14) (0.84) (0.45)
Highest parental education -0.0812 -0.1620 -0.0484
(years) (2.55)* (2.57)* (1.42)
Household head‟s gender -0.0454 -0.2746 0.1184
(male=1) (0.26) (0.84) (0.60)
Number of children aged 6-18 0.1962 0.2303 0.1853
(2.50)* (1.45) (1.90)+
Labour force 0.0274 0.0196 -0.0024
(0.57) (0.25) (0.05)
Child‟s gender (boy=1) 0.4073 0.3147 0.4156
(2.42)* (1.06) (2.07)*
First born (yes=1) 0.1339 0.1684 0.0046
(0.69) (0.46) (0.02)
Child‟s age 0.3439 0.2679 0.3048
(8.67)** (4.55)** (2.58)*
Distance to the nearest school(a) -0.9786 -1.4678 -1.0661
(2.53)* (2.56)* (2.60)**
Constant -2.8717 5.1939 -6.4822
(0.89) (1.20) (1.47)
H0: informal = formal (P-value) 0.1104 0.7624 0.0520+
Alpha ( 1.3526 (5.73)** 1.5237 (2.13)* 1.1393 (5.31)**
Wald 2 (all coefficients=0) 232.35 67.54 51.07
Prob > 2 0.0000 0.0000 0.0000
Observations 483 286 197
Note: Robust z statistics in parentheses, + significant at 10%; * significant at 5%; **
significant at 1%; Model in column 2, the test of =0 is accepted at the 5% level, either
the Poisson or NB can be applied in this case. The estimated results by the NB and Poisson estimators are similar. All the models were controlled for location dummies.
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Chapter 8:Conclusions
8.1 Conclusions
This thesis has examined how the poor in peri-urban areas of Ho Chi Minh City (HCMC), Vietnam access credit and the impacts of that credit on education and healthcare spending, and child schooling.
Such examination is needed because as shown in the thesis, the role of education in earnings has become much more important during the transition to a market economy in Vietnam. The rate of return to schooling in Vietnam has increased quickly and has reached the global average rate of return of about 10%. The increase in the rate of return to schooling and the demand for skilled workers provides an exit from poverty for the poor in urban and peri-urban areas who rely heavily on labour income. But to use this exit they need to invest more in human capital, such as healthcare and education. One way for the poor to increase their investment in human capital is through microcredit, but the poor in peri-urban areas are highly credit-constrained and rely heavily on informal credit. There is distinction in borrowing behaviours between poor households in rural and urban wards in these peri-urban areas. In urban wards, the poor depend more on limited and subsidized funds from the government and are less likely to borrow from informal credit which provides loans based mainly on interpersonal trust rather than collateral. Households in rural areas who live further away from banks find it easier to borrow, while households in urban areas living near banks have a lower probability of borrowing and are more credit-constrained. There are two main possible reasons: the first is mutual help among people in rural areas where they have better interpersonal trust, while this trust is weak in urban areas; the second is complicated procedures such as lending procedures and property ownership certification blocking the poor from credit resources.89 Apart from loans from government funds, the poor are almost always excluded from other formal credit resources.
Although the poor are highly credit-constrained, a sizable proportion of them have obtained credit, from either government subsidized or informal credit sources. This credit participation has positive and significant effects on household
89 Kim (2004) provides a good example of the complicated procedure required by HCMC
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education and healthcare spending. However, after disaggregating it appears that only formal credit has benefited the poor in terms of increasing education and healthcare spending because loans from informal credit sources, which are often very short-termed and small-sized, are not suitable for human capital investment. Allowing for heterogeous impacts also shows credit has larger impacts for households with initially low levels of healthacre spending. A gender disaggregation of impacts on education shows larger positive effects for girls.
Formal credit has brought beneficial effects to children‟s education (both expenditure and child schooling outcomes), whereas informal credit has failed to do so. Consequently, to improve child schooling in the long run requires easing access to formal credit for the poor; otherwise, the poor will rely upon informal credit and will end up in debt and may then pull their children out of school. As a result, informal credit may exacerbate poverty in the long term rather than help the poor out of poverty.
8.2 Avenue for future research
Future research should take into account some of the limitations in this thesis as indicated below.
First, one may think about possible endogeneity of education due to unobservable individual ability and the methods which fail to control for schooling quality that could lead to bias in the estimated returns to schooling. The fixed effect models with panel data would overcome the bias by estimating returns to changes in schooling over time (two points of time). Alternatively, future research using data on siblings‟ education or IQ index with an instrumental variable model can be feasible approaches; siblings‟ education may be highly feasible in the context of available data in Vietnam to overcome the endogeneity problem. Another approach, though it may be harder to find data for, is to use parents‟ education to predict wage-earner‟s education; this approach could be applied to younger groups of the sample, but for the older groups, their parents‟ education data would be unavailable in Vietnam.
Second, credit participation and constraints can also be determined by unobservable attributes, such as households‟ entrepreneurial ability, attitude to risks and access to social networks. In the current questionnaire used for collecting data for this thesis, I designed some questions (in Section 6 of the questionnaire attached) to capture the access to social networks, but the “yes/no”
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questions did not reflect fully the access to social networks. Future studies should design questions to represent levels of households‟ actual contribution or involvement in the social networks, and to measure entrepreneurial ability and attitude to risk, which will enable researchers to control for influences by these variables and confirm the current thesis findings.
Third, the PSM estimates are considerably lower than those of the OLS because PSM compares borrowers with similar non-borrowers, though still based on observable characteristics. Controlling for the pre-treatment income and assets, which are more likely to be associated with some main unobservable attributes such as motivation, entrepreneurial ability and skills, would reduce the bias. However, the pre-treatment variables may not capture all the unobserved bias, thus future research should develop some indicators to represent these attributes and control for the indicators when estimating propensity scores. For example, likert-scale questions such as indicators for parents‟ attitude to children‟s education, indicators for individual attitude to risk or entrepreneurial ability, or indicators representing a household‟s social network should be used to confirm and consolidate my findings.
Future research may also use the instrumental variable method to overcome the endogeneity problem. Distance to the bank is commonly used as IV in the literature, but it does not work in peri-urban areas because distance affects both credit participation and the outcomes (since banks and services are clustered together). Therefore, future research should look for appropriate IVs for peri- urban areas, for example, access to credit information could be a better IV than the distance to the nearest bank.
Fourth, the conclusion of heterogeneous impacts in Chapter 6 relied on the assumption that the sample population is homogeneous, hence there are no sub- groups who would have the LATE (and for whom a particular instrumental variable might bind, while it does not bind for others), and that the heterogeneity in the outcomes comes from the random errors. Since I assume it is unobservables rather than local treatment effects causing the heterogeneity, I do not necessarily need an instrumental variable estimator. However, the assumption that the selection into the treatment is based on observables, may not be true; in future research one should look for good instruments for peri-urban areas and the QTE with instrumental variables (IQTE) may be more precise than the conventional IV
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estimator at the median (Abadie, Angrist, & Imbens, 2002) and can address the potential bias.
Fifth, future studies also need to investigate impacts on child academic performance, higher education and other health indicators such as height for ages, BMI, malnutrition rate, etc. to confirm the effect findings.
Finally, the sample used in this thesis is likely to be representative of the poor group whose initial income per capita is below the poverty line at the survey time in this particular district but not necessarily for Ho Chi Minh City nor for Vietnam. In future if resources suffice, one should enlarge the sample size and cover of survey areas to some other peri-urban districts of HCMC, then the result will be representative of both the poor in HCMC and Vietnam as a whole because HCMC is the fastest urbanizing and biggest city in Vietnam.
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