Can a Broader Education Narrow the Gap? Evidence on Non-Academic Features of Schooling

Empirical studies of education programs and systems, by nature, rely upon use of student outcomes that are measurable. Often, these come in the form of test scores. However, in light of growing evidence about the long-run importance of other student skills and behaviors, the time has come for a broader approach to evaluating education. This dissertation undertakes experimental, quasi-experimental, and descriptive analyses to examine social, behavioral, and health-related mechanisms of the educational process. My overarching research question is simply, which inside- and outside-the-classroom features of schools and educational interventions are most beneficial to students in the long term? Furthermore, how can we apply this evidence toward informing policy that could effectively reduce stark social, educational, and economic inequalities?

The first study of three assesses mechanisms by which the Fast Track project, a randomized intervention in the early 1990s for high-risk children in four communities (Durham, NC; Nashville, TN; rural PA; and Seattle, WA), reduced delinquency, arrests, and health and mental health service utilization in adolescence through young adulthood (ages 12-20). A decomposition of treatment effects indicates that about a third of Fast Track’s impact on later crime outcomes can be accounted for by improvements in social and self-regulation skills during childhood (ages 6-11), such as prosocial behavior, emotion regulation and problem solving. These skills proved less valuable for the prevention of mental and physical health problems.

The second study contributes new evidence on how non-instructional investments – such as increased spending on school social workers, guidance counselors, and health services – affect multiple aspects of student performance and well-being. Merging several administrative data sources spanning the 1996-2013 school years in North Carolina, I use an instrumental variables approach to estimate the extent to which local expenditure shifts affect students’ academic and behavioral outcomes. My findings indicate that exogenous increases in spending on non-instructional services not only reduce student absenteeism and disciplinary problems (important predictors of long-term outcomes) but also significantly raise student achievement, in similar magnitude to corresponding increases in instructional spending. Furthermore, subgroup analyses suggest that investments in student support personnel such as social workers, health services, and guidance counselors, in schools with concentrated low-income student populations could go a long way toward closing socioeconomic achievement gaps.

The third study examines individual pathways that lead to high school graduation or dropout. It employs a variety of machine learning techniques, including decision trees, random forests with bagging and boosting, and support vector machines, to predict student dropout using longitudinal administrative data from North Carolina. I consider a large set of predictor measures from grades three through eight including academic achievement, behavioral indicators, and background characteristics. My findings indicate that the most important predictors include eighth grade absences, math scores, and age-for-grade as well as early reading scores. Support vector classification (with a high cost parameter and low gamma parameter) predicts high school dropout with the highest overall validity in the testing dataset at 90.1 percent followed by decision trees with boosting and interaction terms at 89.5 percent.

Essays on the Role of Government Incentives in the Private Provision of Social Goods

The economic rationale for public intervention into private markets through price mechanisms is twofold: to correct market failures and to redistribute resources. Financial incentives are one such price mechanism. In this dissertation, I specifically address the role of financial incentives in providing social goods in two separate contexts: a redistributive policy that enables low income working families to access affordable childcare in the US and an experimental pay-for-performance intervention to improve population health outcomes in rural India. In the first two papers, I investigate the effects of government incentives for providing grandchild care on grandmothers’ short- and long-term outcomes. In the third paper, coauthored with Manoj Mohanan, Grant Miller, Katherine Donato, and Marcos Vera-Hernandez, we use an experimental framework to consider the the effects of financial incentives in improving maternal and child health outcomes in the Indian state of Karnataka.

Grandmothers provide a significant amount of childcare in the US, but little is known about how this informal, and often uncompensated, time transfer impacts their economic and health outcomes. The first two chapters of this dissertation address the impact of federally funded, state-level means-tested programs that compensate grandparent-provided childcare on the retirement security of older women, an economically vulnerable group of considerable policy interest. I use the variation in the availability and generosity of childcare subsidies to model the effect of government payments for grandchild care on grandmothers’ time use, income, earnings, interfamily transfers, and health outcomes. After establishing that more generous government payments induce grandmothers to provide more hours of childcare, I find that grandmothers adjust their behavior by reducing their formal labor supply and earnings. Grandmothers make up for lost earnings by claiming Social Security earlier, increasing their reliance on Supplemental Security Income (SSI) and reducing financial transfers to their children. While the policy does not appear to negatively impact grandmothers’ immediate economic well-being, there are significant costs to the state, in terms of both up-front costs for care payments and long-term costs as a result of grandmothers’ increased reliance on social insurance.

The final paper, The Role of Non-Cognitive Traits in Response to Financial Incentives: Evidence from a Randomized Control Trial of Obstetrics Care Providers in India, is coauthored with Manoj Mohanan, Grant Miller, Katherine Donato and Marcos Vera-Hernandez. We report the results from “Improving Maternal and Child Health in India: Evaluating Demand and Supply Side Strategies” (IMACHINE), a randomized controlled experiment designed to test the effectiveness of supply-side incentives for private obstetrics care providers in rural Karnataka, India. In particular, the experimental design compares two different types of incentives: (1) those based on the quality of inputs providers offer their patients (inputs contracts) and (2) those based on the reduction of incidence of four adverse maternal and neonatal health outcomes (outcomes contracts). Along with studying the relative effectiveness of the different financial incentives, we also investigate the role of provider characteristics, preferences, expectations and non-cognitive traits in mitigating the effects of incentive contracts.

We find that both contract types input incentive contracts reduce rates of post-partum hemorrhage, the leading cause of maternal mortality in India by about 20%. We also find some evidence of multitasking as output incentive contract providers reduce the level of postnatal newborn care received by their patients. We find that patient health improvements in response to both contract types are concentrated among higher trained providers. We find improvements in patient care to be concentrated among the lower trained providers. Contrary to our expectations, we also find improvements in patient health to be concentrated among the most risk averse providers, while more patient providers respond relatively little to the incentives, and these difference are most evident in the outputs contract arm. The results are opposite for patient care outcomes; risk averse providers have significantly lower rates of patient care and more patient providers provide higher quality care in response to the outputs contract. We find evidence that overconfidence among providers about their expectations about possible improvements reduces the effectiveness of both types of incentive contracts for improving both patient outcomes and patient care. Finally, we find no heterogeneous response based on non-cognitive traits.

Passive Acoustics: A Multifaceted Tool for Marine Mammal Conservation

Marine mammals exploit the efficiency of sound propagation in the marine environment for essential activities like communication and navigation. For this reason, passive acoustics has particularly high potential for marine mammal studies, especially those aimed at population management and conservation. Despite the rapid realization of this potential through a growing number of studies, much crucial information remains unknown or poorly understood. This research attempts to address two key knowledge gaps, using the well-studied bottlenose dolphin (Tursiops truncatus) as a model species, and underwater acoustic recordings collected on four fixed autonomous sensors deployed at multiple locations in Sarasota Bay, Florida, between September 2012 and August 2013. Underwater noise can hinder dolphin communication. The ability of these animals to overcome this obstacle was examined using recorded noise and dolphin whistles. I found that bottlenose dolphins are able to compensate for increased noise in their environment using a wide range of strategies employed in a singular fashion or in various combinations, depending on the frequency content of the noise, noise source, and time of day. These strategies include modifying whistle frequency characteristics, increasing whistle duration, and increasing whistle redundancy. Recordings were also used to evaluate the performance of six recently developed passive acoustic abundance estimation methods, by comparing their results to the true abundance of animals, obtained via a census conducted within the same area and time period. The methods employed were broadly divided into two categories – those involving direct counts of animals, and those involving counts of cues (signature whistles). The animal-based methods were traditional capture-recapture, spatially explicit capture-recapture (SECR), and an approach that blends the “snapshot” method and mark-recapture distance sampling, referred to here as (SMRDS). The cue-based methods were conventional distance sampling (CDS), an acoustic modeling approach involving the use of the passive sonar equation, and SECR. In the latter approach, detection probability was modelled as a function of sound transmission loss, rather than the Euclidean distance typically used. Of these methods, while SMRDS produced the most accurate estimate, SECR demonstrated the greatest potential for broad applicability to other species and locations, with minimal to no auxiliary data, such as distance from sound source to detector(s), which is often difficult to obtain. This was especially true when this method was compared to traditional capture-recapture results, which greatly underestimated abundance, despite attempts to account for major unmodelled heterogeneity. Furthermore, the incorporation of non-Euclidean distance significantly improved model accuracy. The acoustic modelling approach performed similarly to CDS, but both methods also strongly underestimated abundance. In particular, CDS proved to be inefficient. This approach requires at least 3 sensors for localization at a single point. It was also difficult to obtain accurate distances, and the sample size was greatly reduced by the failure to detect some whistles on all three recorders. As a result, this approach is not recommended for marine mammal abundance estimation when few recorders are available, or in high sound attenuation environments with relatively low sample sizes. It is hoped that these results lead to more informed management decisions, and therefore, more effective species conservation.