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.

Essays in Empirical Operations Management: Bayesian Learning of Service Quality and Structural Estimation of Complementary Product Pricing and Inventory Management

This dissertation contributes to the rapidly growing empirical research area in the field of operations management. It contains two essays, tackling two different sets of operations management questions which are motivated by and built on field data sets from two very different industries --- air cargo logistics and retailing.

The first essay, based on the data set obtained from a world leading third-party logistics company, develops a novel and general Bayesian hierarchical learning framework for estimating customers' spillover learning, that is, customers' learning about the quality of a service (or product) from their previous experiences with similar yet not identical services. We then apply our model to the data set to study how customers' experiences from shipping on a particular route affect their future decisions about shipping not only on that route, but also on other routes serviced by the same logistics company. We find that customers indeed borrow experiences from similar but different services to update their quality beliefs that determine future purchase decisions. Also, service quality beliefs have a significant impact on their future purchasing decisions. Moreover, customers are risk averse; they are averse to not only experience variability but also belief uncertainty (i.e., customer's uncertainty about their beliefs). Finally, belief uncertainty affects customers' utilities more compared to experience variability.

The second essay is based on a data set obtained from a large Chinese supermarket chain, which contains sales as well as both wholesale and retail prices of un-packaged perishable vegetables. Recognizing the special characteristics of this particularly product category, we develop a structural estimation model in a discrete-continuous choice model framework. Building on this framework, we then study an optimization model for joint pricing and inventory management strategies of multiple products, which aims at improving the company's profit from direct sales and at the same time reducing food waste and thus improving social welfare.

Collectively, the studies in this dissertation provide useful modeling ideas, decision tools, insights, and guidance for firms to utilize vast sales and operations data to devise more effective business strategies.

Potassium Measures and Their Associations with Glucose and Diabetes Risk: The Multi-Ethnic Study of Atherosclerosis (MESA).

BACKGROUND: Recent studies have found low-normal potassium (K) to be associated with increased diabetes risk. We sought to verify these associations in a multi-ethnic US cohort; and to determine if these associations extend to US Hispanics and Asian-Americans. METHODS: We analyzed data from Multi-Ethnic Study of Atherosclerosis (MESA) participants who were free-of-diabetes at baseline. We examined cross-sectional associations between measures of K-serum, dietary, and urine-with fasting glucose and HOMA-IR. We examined longitudinal associations between K and diabetes risk over 8 years. FINDINGS: In multivariable models, compared to those with higher serum K (≥4.5mmol/L), those with lower serum K (<4.0mmol/L) had significantly higher fasting glucose [1.3 mg/dL (95%CI 0.2, 2.4), P-value = 0.03]. Incident diabetes developed in 1281 of 5415 at-risk participants. In minimally-adjusted models, we found inverse associations between serum and dietary K and diabetes risk. Compared to those with higher serum K, those with lower serum K had an HR (95% CI) of incident diabetes of 1.23 (1.04, 1.47), P-value = 0.02. However, these associations were attenuated in fully-adjusted models. We found no significant interaction between potassium and ethnicity. CONCLUSIONS: In this multi-ethnic cohort, we found a significant inverse association between serum K and fasting glucose but no significant association with longer-term diabetes risk. This inverse association between potassium and glucose must be studied further to understand the physiology and its potential impact on chronic health.

Using Bird Distributions to Assess Extinction Risk and Identify Conservation Priorities in Biodiversity Hotspots

Habitat loss, fragmentation, and degradation threaten the World’s ecosystems and species. These, and other threats, will likely be exacerbated by climate change. Due to a limited budget for conservation, we are forced to prioritize a few areas over others. These places are selected based on their uniqueness and vulnerability. One of the most famous examples is the biodiversity hotspots: areas where large quantities of endemic species meet alarming rates of habitat loss. Most of these places are in the tropics, where species have smaller ranges, diversity is higher, and ecosystems are most threatened.

Species distributions are useful to understand ecological theory and evaluate extinction risk. Small-ranged species, or those endemic to one place, are more vulnerable to extinction than widely distributed species. However, current range maps often overestimate the distribution of species, including areas that are not within the suitable elevation or habitat for a species. Consequently, assessment of extinction risk using these maps could underestimate vulnerability.

In order to be effective in our quest to conserve the World’s most important places we must: 1) Translate global and national priorities into practical local actions, 2) Find synergies between biodiversity conservation and human welfare, 3) Evaluate the different dimensions of threats, in order to design effective conservation measures and prepare for future threats, and 4) Improve the methods used to evaluate species’ extinction risk and prioritize areas for conservation. The purpose of this dissertation is to address these points in Colombia and other global biodiversity hotspots.

In Chapter 2, I identified the global, strategic conservation priorities and then downscaled to practical local actions within the selected priorities in Colombia. I used existing range maps of 171 bird species to identify priority conservation areas that would protect the greatest number of species at risk in Colombia (endemic and small-ranged species). The Western Andes had the highest concentrations of such species—100 in total—but the lowest densities of national parks. I then adjusted the priorities for this region by refining these species ranges by selecting only areas of suitable elevation and remaining habitat. The estimated ranges of these species shrank by 18–100% after accounting for habitat and suitable elevation. Setting conservation priorities on the basis of currently available range maps excluded priority areas in the Western Andes and, by extension, likely elsewhere and for other taxa. By incorporating detailed maps of remaining natural habitats, I made practical recommendations for conservation actions. One recommendation was to restore forest connections to a patch of cloud forest about to become isolated from the main Andes.

For Chapter 3, I identified areas where bird conservation met ecosystem service protection in the Central Andes of Colombia. Inspired by the November 11th (2011) landslide event near Manizales, and the current poor results of Colombia’s Article 111 of Law 99 of 1993 as a conservation measure in this country, I set out to prioritize conservation and restoration areas where landslide prevention would complement bird conservation in the Central Andes. This area is one of the most biodiverse places on Earth, but also one of the most threatened. Using the case of the Rio Blanco Reserve, near Manizales, I identified areas for conservation where endemic and small-range bird diversity was high, and where landslide risk was also high. I further prioritized restoration areas by overlapping these conservation priorities with a forest cover map. Restoring forests in bare areas of high landslide risk and important bird diversity yields benefits for both biodiversity and people. I developed a simple landslide susceptibility model using slope, forest cover, aspect, and stream proximity. Using publicly available bird range maps, refined by elevation, I mapped concentrations of endemic and small-range bird species. I identified 1.54 km2 of potential restoration areas in the Rio Blanco Reserve, and 886 km2 in the Central Andes region. By prioritizing these areas, I facilitate the application of Article 111 which requires local and regional governments to invest in land purchases for the conservation of watersheds.

Chapter 4 dealt with elevational ranges of montane birds and the impact of lowland deforestation on their ranges in the Western Andes of Colombia, an important biodiversity hotspot. Using point counts and mist-nets, I surveyed six altitudinal transects spanning 2200 to 2800m. Three transects were forested from 2200 to 2800m, and three were partially deforested with forest cover only above 2400m. I compared abundance-weighted mean elevation, minimum elevation, and elevational range width. In addition to analyzing the effect of deforestation on 134 species, I tested its impact within trophic guilds and habitat preference groups. Abundance-weighted mean and minimum elevations were not significantly different between forested and partially deforested transects. Range width was marginally different: as expected, ranges were larger in forested transects. Species in different trophic guilds and habitat preference categories showed different trends. These results suggest that deforestation may affect species’ elevational ranges, even within the forest that remains. Climate change will likely exacerbate harmful impacts of deforestation on species’ elevational distributions. Future conservation strategies need to account for this by protecting connected forest tracts across a wide range of elevations.

In Chapter 5, I refine the ranges of 726 species from six biodiversity hotspots by suitable elevation and habitat. This set of 172 bird species for the Atlantic Forest, 138 for Central America, 100 for the Western Andes of Colombia, 57 for Madagascar, 102 for Sumatra, and 157 for Southeast Asia met the criteria for range size, endemism, threat, and forest use. Of these 586 species, the Red List deems 108 to be threatened: 15 critically endangered, 29 endangered, and 64 vulnerable. When ranges are refined by elevational limits and remaining forest cover, 10 of those critically endangered species have ranges < 100km2, but then so do 2 endangered species, seven vulnerable, and eight non-threatened ones. Similarly, 4 critically endangered species, 20 endangered, and 12 vulnerable species have refined ranges < 5000km2, but so do 66 non-threatened species. A striking 89% of these species I have classified in higher threat categories have <50% of their refined ranges inside protected areas. I find that for 43% of the species I assessed, refined range sizes fall within thresholds that typically have higher threat categories than their current assignments. I recommend these species for closer inspection by those who assess risk. These assessments are not only important on a species-by-species basis, but by combining distributions of threatened species, I create maps of conservation priorities. They differ significantly from those created from unrefined ranges.

Neural Mechanisms of Young Adult Sexual Decision-Making and Risk Behavior

Sexual risk behavior among young adults is a serious public health concern; 50% will contract a sexually transmitted infection (STI) before the age of 25. The current study collected self-report personality and sexual history data, as well as neuroimaging, experimental behavioral (e.g., real-time hypothetical sexual decision making data), and self-report sexual arousal data from 120 heterosexual young adults ages 18-26. In addition, longitudinal changes in self-reported sexual behavior were collected from a subset (n = 70) of the participants. The primary aims of the study were (1) to predict differences in self-report sexual behavior and hypothetical sexual decision-making (in response to sexually explicit audio-visual cues) as a function of ventral striatum (VS) and amygdala activity, (2) test whether the association between sexual behavior/decision-making and brain function is moderated by gender, self-reported sexual arousal, and/or trait-level personality factors (i.e., self-control, impulsivity, and sensation seeking) and (3) to examine how the main effects of neural function and interaction effects predict sexual risk behavior over time. Our hypotheses were mostly supported across the sexual behavior and decision-making outcome variables, such that neural risk phenotypes (heightened reward-related ventral striatum activity coupled with decreased threat-related amygdala activity) were associated with greater lifetime sexual partners at baseline measured and over time (longitudinal analyses). Impulsivity moderated the relationship between neural function and self-reported number of sexual partners at baseline and follow up measures, as well as experimental condom use decision-making. Sexual arousal and sensation seeking moderated the relationship between neural function and baseline and follow up self-reports of number of sexual partners. Finally, unique gender differences were observed in the relationship between threat and reward-related neural reactivity and self-reported sexual risk behavior. The results of this study provide initial evidence for the potential role for neurobiological approaches to understanding sexual decision-making and risk behavior. With continued research, establishing biomarkers for sexual risk behavior could help inform the development of novel and more effective individually tailored sexual health prevention and intervention efforts.