Browsing by Author "Sickles, Robin C"
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Item Essays in Child Protections and Family Involvement(2020-04-28) Soria, Nigel Devin; Sickles, Robin CThe essays in this dissertation address the central question of whether involving family members in the child welfare decision-making process leads to higher family member engagement—in promoting safety, permanency, and well-being—and better outcomes for children and families. Specifically, these essays look at family group decision making, a child welfare practice in which family and other group members actively participate in developing the case plan that typically follows a report of maltreatment, and its impact on child and family outcomes. In the first essay, I study the impact of family group decision making on the recurrence of child maltreatment using a latent-variable framework. I assume the unobservables in the outcome and selection equations observe a normal factor structure, and I calculate various mean treatment parameters from a common set of structural parameters. In general, I find the effect is positive for both families that select into family group decision making and the entire population, where population is defined as the group of families involved in the child protection process. Also, the results indicate families most likely to participate in family group decision making benefit the most from the program. In the second essay, I study the level of family participation in addressing the outcomes, goals, and tasks listed in the child protection case plan. To address this topic, I exploit a unique family-level data set consisting of over 5,500 families in the United States. For each family member in each of these families, I observe a discrete measure of whether they completed their assigned tasks. Using systems of simultaneous discrete choice models, I estimate each family member's choice of involvement as a static discrete game under complete and incomplete information assumptions. I find that completing one's tasks is the preferred strategy for families in which the mother or father participated in the case planning process. Completing one's tasks also appears to be the preferred strategy for families with very young children, children who were six to 10 years old at the time of the report, and families in which the mother was not the alleged abuser.Item Essays on Treatments of Cross-Section Dependence in Panel Data Models(2016-04-21) Han, Jaepil; Sickles, Robin CThe dissertation consists of three essays on the treatments of cross-sectional dependence in panel data models especially oriented to spatial econometric approaches. The first essay aims to investigate spillover effects of public capital stock in a production function model that accounts for spatial dependencies. In many settings, ignoring spatial dependency yields inefficient, biased and inconsistent estimates in cross country panels. Although there are a number of studies aiming to estimate the output elasticity of public capital stock, many of those fail to reach a consensus on refining the elasticity estimates. We argue that accounting for spillover effects of the public capital stock on the production efficiency and incorporating spatial dependences are crucial. For this purpose, we employ a spatial autoregressive stochastic frontier model based on a number of specifications of the spatial dependency structure. Using the data of 21 OECD countries from 1960 to 2001, we estimate a spatial autoregressive stochastic frontier model and derive the mean indirect marginal effects of public capital stock, which are interpreted as spillover effects. We found that spillover effects can be an important factor explaining variations in technical inefficiency across countries as well as in explaining the discrepancies among various levels of output elasticity of public capital stock in traditional production function approaches. The second essay examines aggregate productivity in the presence of intersectoral linkages. Cross-sectional dependence is inevitable among industries as each sector serves as supplier to other sectors immediately and the chains of the interconnection cause indirect relationship among industries. Spatial analysis is one of the approaches that address cross-sectional dependence using a priori specified spatial weights matrices. We exploit the linkage patterns from the Input-Output table and use the patterns to assign spatial weights that describe the interdependency in economic space. Us- ing the spatial weights matrix, we estimate industry-level production function and productivity of the U.S. for the period from 1947 to 2010. Our main results indicate that the output elasticity estimates are larger when we consider cross-sectional dependence, which is the consequence of indirect effects reflecting interactions among industries. The productivity estimates, however, are found to be comparable across the estimation techniques. The third essay considers a panel data model addressing the issues of endogeneity and cross-sectional dependence together. Unobserved heterogeneity may cause two different results: endogeneity and cross-sectional dependence. In this essay, we model both endogeneity and cross-sectional dependence expanding a spatial error model with a control function on the productivity component. In particular, we found that the two-step estimation procedure for a typical control function approach is not required when it is used with a Spatial Error Model. We estimate a production function and efficiency scores by applying our model to Spanish Dairy farm data in a panel setting for a period of 1999 - 2010. We compare the results from a variety of specifications with and without incorporating endogeneity and cross-sectional dependence. We found that the Spanish Dairy farms shows increasing returns to scale and the yearly average efficiency level decreases with time.Item Examination of the Relationship Between Competition and Innovation: Toward a Robust Approach(2016-04-25) Garcia, Devin Daniel; Sickles, Robin CInterest in the relationship between competition and innovation has seen a resurgence in the last decade. Driven by the theoretical possibility of an inverted-U relationship, current research has focused on non-linear models of competition and innovation. The empirical results that proceed from this research are mixed, including predictions of an inverted-U, a monotonically increasing and a monotonically decreasing relationship. While much attention has been given to the theoretical possibility of a non-linear relationship, relatively little has been given to the subject of measurement. Following Carl Shapiro (2012), I define ``more competitive" as the extent to which a firm stands to lose profitable sales to its rivals should it offer inferior value to consumers. My framework implements this definition in a direct way: two firms must simultaneously choose their innovation strategies under the expectation that, should only one successfully innovate, the unsuccessful firm will have a portion of its sales stolen by its rival. The greater the portion, the more contestable, and therefore competitive, the market. This framework predicts a robust, positive relationship. I apply my model to a sample of U.S. publicly traded manufacturing firms over the period 1962-2009. Innovation is measured via total factor productivity, and competition is measured as the elasticity of firm market value with respect to sales, where sales proxy consumer value. My measure of innovation is consistent with the fact that innovation drives long-run economic growth, and my empirical measure of competition is consistent with ``more competitive" in that it estimates how much market value a firm would have lost if it hypothetically generated less value for consumers. I estimate a dynamic panel model at the firm level with a quadratic specification in competition. My results indicate a positive and monotonically increasing relationship between competition and innovation.