Improving Reproductive Health: Assessing Determinants and Measuring Policy Impacts

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Improving Reproductive Health: Assessing Determinants and Measuring Policy Impacts

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Title: Improving Reproductive Health: Assessing Determinants and Measuring Policy Impacts
Author: Rokicki, Slawa ORCID  0000-0001-9176-373X
Citation: Rokicki, Slawa. 2016. Improving Reproductive Health: Assessing Determinants and Measuring Policy Impacts. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.
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Abstract: In this thesis, I investigate policies and programs to improve reproductive health. My thesis makes a substantive contribution to reproductive health policy and a methodological contribution to quasi-experimental research.

In chapter 1, I evaluate the impact of a mobile phone intervention for adolescent girls. I design and implement a randomized controlled trial in Ghana to test whether sending information via mobile phones is an effective way to improve girls’ knowledge of sexual health and to ultimately reduce teenage pregnancy. I find that mobile phone programs are effective not only in increasing knowledge, but also in decreasing risk of pregnancy among sexually active adolescents. I discuss the results in the context of sexual education policy in Ghana.

In chapter 2, I explore the complex interactions between migration and reproductive health. I reconstruct the complete migration and reproductive health histories of women residing in the urban slums of Accra, Ghana. Using individual fixed effects to reduce selection bias, I find an increased risk of pregnancy, miscarriage, and abortion in the 48 months after migration, with no significant increase in the chance of live birth during this time period. With half of abortions in Ghana classified as unsafe, these results suggest that policies which target the rapidly growing number of urban migrants by providing access to contraception and public hospital services may reduce unsafe abortion and improve maternal health outcomes.

In chapter 3, I investigate the bias of standard errors in difference-in-difference estimation, which typically evaluates the effect of a group-level intervention on individual data. Common modeling adjustments for grouped data, such as cluster-robust standard errors, are biased when the number of clusters is small. I run Monte Carlo simulations to investigate both the coverage and power of a wide variety of modeling solutions from the econometric and biostatistics fields, while varying the balance of cluster sizes, the degree of error correlation, and the proportion of treated clusters. I then apply my results to re-evaluate a recently published study on the effect of emergency contraception on adolescent sexual behavior. I find that the study’s results claiming that emergency contraception increases risky sexual behavior may be spurious once proper adjustments for grouped data are applied.
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