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Determinants of Educational Attainment in Rural Thailand: A Life Course Approach

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Abstract

Using data from rural Thailand, we examine the determinants of educational attainment of school-age children. We organize our analysis around three central principles of the life course perspective: the embeddedness of lives in historical time and place, linked or interconnected lives, and the timing of lives. We examine these principles using comparisons within cohorts and between cohorts. We find that educational attainment is related to a combination of factors related to each of these principles. Specifically, despite a serious economic downturn occurring midway through our study, we nonetheless found that educational levels were higher following the downturn than preceding it. Interconnections to parents and siblings also affected educational outcomes, as did the timing of life events such as migration, marriage, and childbearing. For the latter, mother’s education and migration both positively affected educational attainment. For the former, having more siblings, both those in the household and those migrating, reduced education.

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Notes

  1. We note that our empirical models contain information on the characteristics of members of the sample cohort, their parents, and in some cases their children. To help distinguish these generations of family members, hereafter we refer to the focal individuals (i.e., those for whom we use measures of educational attainment as the dependent variable in our analysis) as the ‘G2’ generation, which makes their parents the ‘G1’ generation, and their children the ‘G3’ generation.

  2. See the project website (http://www.cpc.unc.edu/projects/nangrong/) for more details on the data.

  3. In the Nang Rong data set, all individuals listed on the previous panel and any new individuals in the panel were listed on the household roster. Those not listed on the roster include individuals who were not listed on a prior data panel, either because they were longtime migrants (i.e., migrated before the previous panel), had been dead for a long time, or simply had unknown whereabouts. For example, if we were interested in describing the characteristics of a child’s father using the 1994 data, we could do so only if the father’s record could be linked to the previous panel (i.e., 1984). However, if the father died, migrated, or was otherwise missing prior to 1984, he would not be included in the 1994 data collection, and his child’s record would be dropped from our analysis.

  4. In 2000, we had some additional information on the parents’ whereabouts even if they were not listed on the household roster. We found the parents’ status differed for mothers and fathers. For the former, most (around 62 %) were still in the household, but presumably their husband was absent. About a quarter were living outside the village, and the rest were either: dead (almost 6 %), living in another household in the village (around 4 %) or missing (close to 4 %). For the latter, over half were living outside the village, another fifth were living in the household, around 17 % were dead, and the remainder was either in another household in the village or missing.

  5. Non-formal education includes such things as adult-, Chinese-, Islamic-, or Indian-education. Because we did not have data on the number of years of schooling completed for these cases, we had to exclude them from our sample.

  6. As a sensitivity analysis for the 2000 wave of the within-cohort design, we compared our estimates to results of another equation that used an inverse probability weighting (IPW) procedure described by Fitzgerald et al. (1998) that adjusts for attrition on observed characteristics. The procedure involves estimating a pair of probit regressions, which differ only in their inclusion of ‘auxiliary’ variables, which are variables that are significantly associated with attrition and to the outcome of interest (in our analysis, only the variable ‘age’ met these criteria). The ratio of the predicted probabilities from these regressions is used to reweight observations in such a way that cases with characteristics more similar to the sample before attrition are given more weight. Comparing the results of the 2000 equation to those which used IPW, only two variables had different effects. Specifically, the effect of own migration and land became significant. Otherwise, results were similar in terms of the pattern of statistical significance, magnitude, and direction. Again, we do not suspect that attrition bias is unduly influencing our results.

  7. It is also possible that children’s education could suffer as a result of parental divorce. However, in the study population, such a phenomenon is rather rare. Using the within-cohort design, we determined that less than 2 % of the sample experienced a parental divorce across survey waves. Given its low frequency of occurrence and lack of variation on parental marital status, we did not include such measures in our analysis.

  8. We conducted a sensitivity analysis by interacting parents’ education with the measure of whether the parent was dead to examine the robustness of our findings to our imputation. In the between-cohort design, we found that the slope for father’s education was slightly higher for fathers who were dead, compared to those who were not. In the within-cohort design, the slope of mother’s education was also slightly higher. This suggests that our imputation may somewhat exaggerate the effect of parents education, or alternatively, that parents who died had slightly higher levels of education.

  9. Divorce, separation, and widowhood were relatively uncommon for cases in our analysis so this contrast largely examines the difference between being never married and being currently married.

  10. For example, households having no children age 10–15 in the between-cohort design were excluded.

  11. In our study setting, for example, the number of sample villages split from 51 in 1984, to 76 in 1994, to 92 in 2000.

  12. We note that coefficients obtained from the simultaneous estimation are the same as those from separate estimations, and we only employ this technique for the purpose of conducting these tests. Statistically significant differences for the within-cohort design are designated in the tables using a dagger (i.e., †) while differences in the between-cohort design are designated with a double dagger (i.e., ‡).

  13. It is possible that we are picking up effects of prior remittances sent before the year for which we have remittance data.

  14. We also estimated equations in which we included an interaction term between siblings’ relative birth order and sex measures and a measure of the focal individual’s sex. We found very few significant effects, although we did find that males with an older sister had lower educational attainment levels, on average, compared to males without an older sister.

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Acknowledgments

This research is supported by Grasmick Summer Research Award, offered by the Department of Sociology at the University of Oklahoma, awarded to both authors.

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  1. Department of Sociology, University of Oklahoma, 780 Van Fleet Oval, KH 331, Norman, OK, 73019, USA

    Martin Piotrowski & Yok-Fong Paat

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  1. Martin Piotrowski
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Correspondence to Martin Piotrowski.

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This paper is a joint work of both authors. Both authors contributed equally to this manuscript.

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Piotrowski, M., Paat, YF. Determinants of Educational Attainment in Rural Thailand: A Life Course Approach. Popul Res Policy Rev 31, 907–934 (2012). https://doi.org/10.1007/s11113-012-9242-9

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