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Thesis (Ph. D.)--Florida State University, 2004. / Advisor: Dr. Jill Quadagno, Florida State University, College of Social Sciences, Dept. of Sociology. Title and description from dissertation home page (viewed Sept. 24, 2004). Includes bibliographical references.

Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains ix, 86 p.; also includes graphics. Includes bibliographical references (p. 81-86). Available online via OhioLINK's ETD Center

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We consider a longitudinal system in which transitions between the states are governed by a discrete time finite state space stochastic process X. Our aim, using aggregated sample survey data of the form typically collected by official statistical agencies, is to undertake model based inference for the underlying process X. We will develop inferential techniques for continuing sample surveys of two distinct types. First, longitudinal surveys in which the same individuals are sampled in each cycle of the survey. Second, cross-sectional surveys which sample the same population in successive cycles but with no attempt to track particular individuals from one cycle to the next. Some of the basic results have appeared in Davis et al (2001) and Davis et al (2002).¶ Longitudinal surveys provide data in the form of transition frequencies between the states of X. In Chapter Two we develop a method for modelling and estimating the one-step transition probabilities in the case where X is a non-homogeneous Markov chain and transition frequencies are observed at unit time intervals. However, due to their expense, longitudinal surveys are typically conducted at widely, and sometimes irregularly, spaced time points. That is, the observable frequencies pertain to multi-step transitions. Continuing to assume the Markov property for X, in Chapter Three, we show that these multi-step transition frequencies can be stochastically interpolated to provide accurate estimates of the one-step transition probabilities of the underlying process. These estimates for a unit time increment can be used to calculate estimates of expected future occupation time, conditional on an individual’s state at initial point of observation, in the different states of X.¶ For reasons of cost, most statistical collections run by official agencies are cross-sectional sample surveys. The data observed from an on-going survey of this type are marginal frequencies in the states of X at a sequence of time points. In Chapter Four we develop a model based technique for estimating the marginal probabilities of X using data of this form. Note that, in contrast to the longitudinal case, the Markov assumption does not simplify inference based on marginal frequencies. The marginal probability estimates enable estimation of future occupation times (in each of the states of X) for an individual of unspecified initial state. However, in the applications of the technique that we discuss (see Sections 4.4 and 4.5) the estimated occupation times will be conditional on both gender and initial age of individuals.¶ The longitudinal data envisaged in Chapter Two is that obtained from the surveillance of the same sample in each cycle of an on-going survey. In practice, to preserve data quality it is necessary to control respondent burden using sample rotation. This is usually achieved using a mechanism known as rotation group sampling. In Chapter Five we consider the particular form of rotation group sampling used by the Australian Bureau of Statistics in their Monthly Labour Force Survey (from which official estimates of labour force participation rates are produced). We show that our approach to estimating the one-step transition probabilities of X from transition frequencies observed at incremental time intervals, developed in Chapter Two, can be modified to deal with data collected under this sample rotation scheme. Furthermore, we show that valid inference is possible even when the Markov property does not hold for the underlying process.

This dissertation addresses three separate questions in human capital and development economics. In the first chapter, I study how college admission concerns drive students' field choices in a field-specific college admission system. To study this question, I leverage a college admission policy reform in Ethiopia that sharply increased the proportion of college seats in public universities allocated to college STEM fields. The reform significantly decreased the admission selectivity of STEM fields in the short run. Using cohort analysis and a regression discontinuity design, I show that students are significantly more likely to choose the STEM fields after the reform. More importantly, I find significant heterogeneity in the field choice response, with the marginal students responding more strongly compared to the infra-marginal students. In addition, using a complier characteristics analysis, I show that the reform led to a significant sorting on field-specific skills. In particular, those induced to choose the STEM fields have a comparative advantage in skills valued more in the STEM fields. These overall and field-specific sorting significantly changed the peer quality in STEM and non-STEM fields. These findings imply that admission concerns play a significant role in students' field choices. However, students do not naively sort into less selective college fields. Instead, their choices are consistent with their relative position in the distribution of multi-dimensional skills. The latter suggests that students make more informed and rational choices than the existing literature suggests. In the second chapter, I investigate the effect of the fast expansion of the Second Generation (2G) and Third Generation (3G) mobile technologies in Sub-Saharan Africa (SSA) on health literacy in the region. Using Demographic and Health Surveys data from 25 countries in SSA and a historical mobile network coverage map, I estimate an Instrumental Variable (IV) specification. I show that the widespread use of these technologies has significantly improved health literacy in the region. Specifically, access to either technology significantly decreases misconceptions and wrong beliefs about diseases and health. The benefits are substantial in regions where the technologies have been available longer. Consistent with the range of services it provides, 3G technology results in a larger gain in health literacy. These findings imply that mobile technologies have considerable potential to improve health and quality of life in many developing countries. In the final chapter, I study the relationship between birth spacing and children's outcomes, focusing on the mechanisms that underlie the relationship. Using linked mother-child data from the National Longitudinal Survey of Youth (NLSY), I explore two mechanisms: the maternal health and physiology channel and the material and parental time input channel. The result shows that a short pre-birth inter-pregnancy interval negatively affects the birth endowments of children, highlighting the importance of the birth spacing effect that works through the maternal health and physiology channel. The study also provides evidence in support of the material and parental time input channel. In particular, I show that closely spaced siblings score significantly lower on the standardized Peabody International Achievement Test (PIAT), a wide-range measure of academic achievement for children aged five and above. On the other hand, I find limited evidence of the birth spacing effect on long-term outcomes such as schooling and labor market outcomes. These findings have implications for parental leave and other labor market policies affecting the birth spacing choices of parents.

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