Peer rejection and social information-processing factors in the development of aggressive behavior problems in children.

The relation between social rejection and growth in antisocial behavior was investigated. In Study 1,259 boys and girls (34% African American) were followed from Grades 1 to 3 (ages 6-8 years) to Grades 5 to 7 (ages 10-12 years). Early peer rejection predicted growth in aggression. In Study 2,585 boys and girls (16% African American) were followed from kindergarten to Grade 3 (ages 5-8 years), and findings were replicated. Furthermore, early aggression moderated the effect of rejection, such that rejection exacerbated antisocial development only among children initially disposed toward aggression. In Study 3, social information-processing patterns measured in Study 1 were found to mediate partially the effect of early rejection on later aggression. In Study 4, processing patterns measured in Study 2 replicated the mediation effect. Findings are integrated into a recursive model of antisocial development.

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.

Latent Space and Social Psychological Models of Diffusion

The problem of social diffusion has animated sociological thinking on topics ranging from the spread of an idea, an innovation or a disease, to the foundations of collective behavior and political polarization. While network diffusion has been a productive metaphor, the reality of diffusion processes is often muddier. Ideas and innovations diffuse differently from diseases, but, with a few exceptions, the diffusion of ideas and innovations has been modeled under the same assumptions as the diffusion of disease. In this dissertation, I develop two new diffusion models for "socially meaningful" contagions that address two of the most significant problems with current diffusion models: (1) that contagions can only spread along observed ties, and (2) that contagions do not change as they spread between people. I augment insights from these statistical and simulation models with an analysis of an empirical case of diffusion - the use of enterprise collaboration software in a large technology company. I focus the empirical study on when people abandon innovations, a crucial, and understudied aspect of the diffusion of innovations. Using timestamped posts, I analyze when people abandon software to a high degree of detail.

To address the first problem, I suggest a latent space diffusion model. Rather than treating ties as stable conduits for information, the latent space diffusion model treats ties as random draws from an underlying social space, and simulates diffusion over the social space. Theoretically, the social space model integrates both actor ties and attributes simultaneously in a single social plane, while incorporating schemas into diffusion processes gives an explicit form to the reciprocal influences that cognition and social environment have on each other. Practically, the latent space diffusion model produces statistically consistent diffusion estimates where using the network alone does not, and the diffusion with schemas model shows that introducing some cognitive processing into diffusion processes changes the rate and ultimate distribution of the spreading information. To address the second problem, I suggest a diffusion model with schemas. Rather than treating information as though it is spread without changes, the schema diffusion model allows people to modify information they receive to fit an underlying mental model of the information before they pass the information to others. Combining the latent space models with a schema notion for actors improves our models for social diffusion both theoretically and practically.

The empirical case study focuses on how the changing value of an innovation, introduced by the innovations' network externalities, influences when people abandon the innovation. In it, I find that people are least likely to abandon an innovation when other people in their neighborhood currently use the software as well. The effect is particularly pronounced for supervisors' current use and number of supervisory team members who currently use the software. This case study not only points to an important process in the diffusion of innovation, but also suggests a new approach -- computerized collaboration systems -- to collecting and analyzing data on organizational processes.