1,721,073 research outputs found
An analysis of the crime as work model - Evidence from the 1958 Philadelphia birth cohort study
No full textWilliams, Jenny ; Sickles, Robin C
Productivity and efficiency analysis software: A survey of the options
No full textThis contribution surveys the currently available options to estimate a variety of frontier methodologies using either general or dedicated software. We also offer an information-theoretic perspective on the creation and systematic maintenance of a database and repository of this type of information. After having surveyed the available software, the objective of this study is to propose a model for the creation and systematic maintenance of a repository of this type of information
Essays on Productivity Analysis
No full textIn Chapter One, to measure the efficiency changes in the U.S. banking industry after structural changes since the late 1970s, we utilize a set of panel data stochastic frontier models of varying parametric assumptions and function specifications. Our estimates support the opinion of improving efficiency in the banking industry in the period from 1984 to early 1990s. The first chapter raises two research questions. First, the comparison of different estimates shows that the choice of methodologies has significant impacts on the levels and dynamics of estimation results. How should we consider a more general approach to incorporate modeling uncertainty? Second, to fit in a broader picture, how can we extend our tools of estimating industry-level efficiencies to measure efficiency changes of countries and regions? These two questions motivated us to conduct researches which are in the second and third chapters. In Chapter Two, we propose the construction of a consensus estimate to extract information from all involved studies. Insights from different fields of economics supporting aggregating estimators are provided. We discuss three methodologies in detail: model averaging, combining forecast and rule-based methods using Meta-Regression Analysis. Two Monte Carlo experiments are conducted to examine the finite-sample performance of the combined estimators. In Chapter Three, we accommodate the models discussed in Chapter One to measure the Total Factor Productivity (TFP) changes. Discussions of various theories explaining economic growth and productivity measurements are provided. We decompose the change of TFP into technical efficiency change and innovational change. Estimations are also combined according to principles in Chapter Two. Two studies utilizing the World Productivity Database from the UNIDO are conducted. In the first study, we find out that from 1972 to 2000 the Asian region had the highest Total Factor Productivity growth, which was mainly contributed to innovation progress instead of efficiency catch-up. In the second study, we find out that between 1970 and 2000, Asia Four Tigers and new tiger countries (China, India, Indonesia, Malaysia, and Thailand) had substantial TFP advancements, mainly due to innovations. The other four groups of countries including developed and developing countries had downward trends in TFP growth
Essays on Treatments of Cross-Section Dependence in Panel Data Models
No full textThe 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
Examination of the Relationship Between Competition and Innovation: Toward a Robust Approach
No full textInterest 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
Efficiency and Productivity Analysis of Multidivisional Firms
No full textMultidivisional firms are those who have footprints in multiple segments and hence using multiple technologies to convert inputs to outputs, which makes it difficult to estimate the resource allocations, aggregated production functions, and technical efficiencies of this type of companies. This dissertation aims to explore and reveal such unobserved information by several parametric and semiparametric stochastic frontier analyses and some other structural models. In the empirical study, this dissertation analyzes the productivity and efficiency for firms in the global oilfield market
Airline Travel Demand, the Derived Demand for Aircraft Fuel, and Fuel Utilization Forecasts Using Structural and Atheoretical Approaches
No full textIn the first chapter, we develop a dynamic model of collusion in city-pair routes for selected US airlines and specify the first order conditions using a state-space representation that is estimated by Kalman-filtering techniques using the Databank 1A (DB1A) Department of Transportation (DOT) data during the period 1979I-1988IV. We consider two airlines, American (AA) and United (UA) and four city pairs. Our measure of market power is based on the shadow value of long-run profits in a two person strategic dynamic game and we find evidence of relative market power of UA in three of the four city pairs we analyze. The second chapter explores three models of forecasting airline energy demand: Trend line, ARIMA and Structural Model based on results from Chapter 1 and find that none of them is a dominant winner in American (AA) and United (UA) between Chicago and Salt Lake City. In the third chapter, we use Model Averaging and Forecast Combination Techniques to provide a decisive conclusion focusing on discussing Equal Weighted Averaging, Mean Square Weighted Averaging and Optimized Weighted Averaging on UA and AA in City-Pairs Chicago -Seattle and Chicago-San Diego
Essays in Child Protections and Family Involvement
No full textThe 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
Essays on Causal Inference and Treatment Effects in Productivity and Finance: Double Robust Machine Learning with Deep Neural Networks and Random Forests
No full textIn this dissertation, I use novel methodologies that incorporate machine learning into causal policy evaluation such as double robust machine learning to study some key issues in Productivity and Finance. In the first chapter, I evaluate the impacts of European public subsidies on innovation. I use double machine learning with deep neural networks to explore the effects of public subsidies on firms’ R&D input and output. I find that public subsidies increase both R&D intensity and R&D output and these results remain economically and statistically significant even after accounting for treatment endogeneity. In the second chapter, I evaluate the effects of public subsidies and collaboration agreements on innovation output. Many public schemes related to R&D have pushed towards collaborative agreements between firms/organizations and this chapter studies whether subsidies not promoting collaboration perform as well in terms of stimulating R&D output. Results show that subsidized noncollaborative firms would have gained in terms of R&D output had they collaborated. I also find that collaboration alone seems to generate significantly higher (double) R&D output compared to subsidies alone. In the third chapter, I analyze the impacts of offering non-core and non-financial ("plus") services in addition to core financial services on Microfinance Institutions' (MFIs) performance using a double machine learning model with random forests. The results indicate no differences in the performance of MFIs offering core financial and microfinance plus services, however, MFIs that offer non-core financial services together with non-financial services are serving less poor clients, suggesting a rather surprising "mission drift". In the fourth chapter, I analyze the impacts of regulation on MFIs' performance. I provide evidence of the impact of regulation on the double bottom line of the microfinance industry using double machine learning with neural networks. Results show that regulation does not affect financial results but affects the outreach of savings-and-loan MFIs. Regulation increases the depth of outreach of this group, indicating fewer poor clients, and suggesting a mission drift. In the fifth chapter, I investigate the link between the term structure of sovereign credit default swaps and the market efficiency of carry trades. I use Kneip et al. (2012) factor model to deal with large dimensions and unknown forms of unobservable heterogeneous effects. I document a divergent pattern of carry trade risk for developed and developing countries. In the sixth chapter, I use recurrent neural networks and feed forward deep networks, to predict NYSE, NASDAQ and AMEX stock prices from historical data. I experiment with different architectures and compare data normalization techniques. Then, I leverage those findings to question the efficient-market hypothesis through a formal statistical test and I find evidence of an inefficient stock market.
Each of these studies requires the implementation of new methods of estimation and inference that have not been utilized to examine these important economic policy issues. My research points to many advantages of the approaches that I introduce in my dissertation. Robustness of inferences is a crucial dimension to acceptable policy recommendations and my development of semi/nonparametric estimators and their applications to crucial evaluations of public policy and regulatory oversight provides evidence that they are well-motivated theoretically, that they can be feasibly implemented in empirical applications, and they are in many cases, a dominant strategy in regard to model specification and estimation
Room to move: Why some industries drive the trade-specialization nexus and others do not
No full textWe investigate which industries drive the trade-specialization nexus in the european union over the 1997–2006 period. We study the impact of the reallocation of resources within industries. We find that the true drivers of the trade-specialization nexus are productive firms, who benefit from the increase in trade openness by appropriating resources from less productive firms, coinciding with the expansion of the industry in which they operate, at the expense of other industries, in which there is no room to make such moves.keywordstrade barrierslatent class modelgravity model
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