Do poor students benefit from China's Merger Program? Transfer path and educational performance

Abstract

Aiming to provide better education facilities and improve the educational attainment of poor rural students, China's government has been merging remote rural primary schools into centralized village, town, or county schools since the late 1990s. To accompany the policy, boarding facilities have been constructed that allow (mandate) primary school-aged children to live at school rather than at home. More generally, there also have been efforts to improve rural schools, especially those in counties and towns. Unfortunately, little empirical work has been available to evaluate the impact of the new merger and investment programmes on the educational performance of students. Drawing on a unique dataset that records both the path by which students navigate their primary school years (i.e., which different types of schools did students attend) as well as math test scores in three poverty-stricken counties, we use descriptive statistics and multivariate analysis (both Ordinary Least Squares (OLS) and covariate matching) to analyse the relationship between different transfer paths and student educational performance. This allows us to examine the costs and benefits of the school merger and investment programmes. The results of the analysis show that students who attend county schools perform systematically better than those who attend village or town schools. However, completing primary school in town schools seems to have no effect on students' academic performance. Surprisingly, starting primary education in a teaching point does not hurt rural students; on the contrary, it increases their test scores in some cases. Finally, in terms of the boarding effect, the neutral estimate in OLS and the negative estimate in covariate matching results confirm that boarding at school does not help the students; in some cases it may even reduce their academic performance.

Keywords::

• Merger Program
• transfer path
• educational performance
• rural China

Notes

^1. In 1994, China's government launched a poverty-reduction initiative under the “8–7 Plan” and designated 592 counties as national designated poor counties. Provinces followed with their own initiatives.

^2. Of course, it is possible that there still is a negative disruption effect, but, that the gain in test scores is due to some sort of selection effect (i.e., better students – who have higher test scores – were the ones who sought to move from poorer schools to better schools) and that this selection effect was high enough to more than offset any disruption effect.

^3. In later analysis, the boarding dummy variable equals one if the student boarded at the school where he/she finished primary education, and it equals zero if the student did not board at the school where he/she finished primary education. In rural areas, some students might rent rooms near school due to the unavailability of dorms in the school, and they are regarded as those who did not board at school. We also tried defining boarding status as the boarding dummy variable equals one if the student ever boarded in primary school and it equals zero if he/she never boarded. The results are more or less the same.

^4. It is possible that some students may have transferred more than once. We try to correct for this possibility by controlling the student, parental and household characteristics that may affect transfer decision. We do not account for it explicitly because any extra transfer as a result of the Merger Program is also part of the transfer effect we intend to estimate.

^5. In the analysis, we are not able to control for the “reasons of moving”. Therefore, the reader needs to be aware of this fact and exercise caution in interpreting the results. In fact, we are aware that this is a problem, because it may be that parents who moved for better educational opportunities are overachievers. We did not control (and it is almost impossible to control for) things such as overachieving. Hence, we admit we can never be sure that we fully controlled for these types of factors. However, this is precisely why we use covariate matching. Under the right circumstances (key unobservables are correlated with observables), our approach will account for factors such as these. This approach would also help us control for the selectivity of differential rates of dropping out of elementary school. Fortunately, for China (and our analysis), few children (elementary school students) in the areas of our study are dropping out.

^6. The joint test of coefficients of village to village school transfer dummy and village to county school transfer dummy shows that the two coefficients are significantly different from each other at 1% level. And, the joint test of the coefficient of village to town school transfer dummy and the coefficient of village to county school transfer dummy shows that the two coefficients are significantly different from each other at 1% level.

^7. The joint test of coefficients of town to town school transfer dummy and town to county school transfer dummy shows that the two coefficients are significantly different from each other at 1% level.

^8. It should be pointed out that the results do not fully hold for students who started primary education in teaching points. Although students transferring from teaching points to county schools score 3.5 points (5.8–2.3 – column 2, rows 3 and 1) and 2.7 points (5.8–3.1 – column 2, rows 3 and 2) higher than students transferring from teaching points to village or town schools, the joint test of the coefficients shows that they are not significantly different from each other. Specifically, the joint test of coefficients of teaching point to village school transfer dummy variable and teaching point to county school transfer dummy variable shows that the two coefficients are not significantly different from each other. And the joint test of coefficients of teaching point to town school transfer dummy variable and teaching point to county school transfer dummy variable also shows that these two coefficients are not significantly different from each other.

^9. The joint test of coefficients of teaching point to village school transfer dummy variable and village to village school transfer dummy variable shows that the two coefficients are significantly different from each other at 10% level.

^10. The joint test of coefficients of teaching point to town school transfer dummy variable and town to town school transfer dummy variable shows that the two coefficients are significantly different from each other at 5% level.

^11. While the negative coefficient on the boarding school variable may suggest that the poor conditions of the boarding school (or emotional stress of living away from home) may lead to poor educational performance, there are other possibilities. In some places in our sample (and elsewhere in China), parents may rent rooms near the school and live with their children instead of putting their children into boarding schools. Sometimes it is because there is not enough room in the boarding schools (though this is becoming less of a problem, at least in our study area); sometimes it is by choice of the parent. If students choose to rent instead of boarding, it is possible that such students may be overachievers, and such selectivity may influence our results. Hence, the negative coefficient may be measuring this additional effect. Unfortunately, we did not ask such questions in our survey so we cannot control for this directly. We do not believe this to be a major part of the effect: in our study area, it is uncommon for parents to rent rooms near the school. However, the possibility does exist.