Today a standard procedure to analyze the impact of environmental factors on productive efficiency of a decision making unit is to use a two stage approach, where first one estimates the efficiency and then uses regression techniques to explain the variation of efficiency between different units. It is argued that the abovementioned method may produce doubtful results which may distort the truth data represents. In order to introduce economic intuition and to mitigate the problem of omitted variables we introduce the matching procedure which is to be used before the efficiency analysis. We believe that by having comparable decision making units we implicitly control for the environmental factors at the same time cleaning the sample of outliers. The main goal of the first part of the thesis is to compare a procedure including matching prior to efficiency analysis with straightforward two stage procedure without matching as well as an alternative of conditional efficiency frontier. We conduct our study using a Monte Carlo study with different model specifications and despite the reduced sample which may create some complications in the computational stage we strongly agree with a notion of economic meaningfulness of the newly obtained results. We also compare the results obtained by the new method with ones previously produced by Demchuk and Zelenyuk (2009) who compare efficiencies of Ukrainian regions and find some differences between the two approaches. Second part deals with an empirical study of electricity generating power plants before and after market reform in Texas. We compare private, public and municipal power generators using the method introduced in part one. We find that municipal power plants operate mostly inefficiently, while private and public are very close in their production patterns. The new method allows us to compare decision making units from different groups, which may have different objective schemes and productive incentives. Despite the fact that at a certain point after the reform private generators opted not to provide their data to the regulator we were able to construct tree different data samples comprising two and three groups of generators and analyze their production/efficiency patterns. In the third chapter we propose a semiparametric approach with shape constrains which is consistent with monotonicity and concavity constraints. Penalized splines are used to maintain the shape constrained via nonlinear transformations of spline basis expansions. The large sample properties, an effective algorithm and method of smoothing parameter selection are presented in the paper. Monte Carlo simulations and empirical examples demonstrate the finite sample performance and the usefulness of the proposed method.

Education, as an indispensable component of human capital, has been acknowledged to play a critical role in economic growth, which is theoretically elaborated by human capital theory and empirically confirmed by evidence from different parts of the world. The educational impact on growth is especially valuable and meaningful when it is for the sake of poverty reduction and pro-poorness of growth. The paper re-explores the precious link between human capital development and poverty reduction by investigating the causal effect of education accumulation on earnings enhancement for anti-poverty and pro-poor growth. The analysis takes the evidence from a well-known conditional cash transfer (CCT) program — Oportunidades in Mexico. Aiming at alleviating poverty and promoting a better future by investing in human capital for children and youth in poverty, this CCT program has been recognized producing significant outcomes. The study investigates a short-term impact of education on earnings of the economically disadvantaged youth, taking the data of both the program’s treated and untreated youth from urban areas in Mexico from 2002 to 2004. Two econometric techniques, i.e. difference-in-differences and difference-in-differences propensity score matching approach are applied for estimation. The empirical analysis first identifies that youth who under the program’s schooling intervention possess an advantage in educational attainment over their non-intervention peers; with this identification of education discrepancy as a prerequisite, further results then present that earnings of the education advantaged youth increase at a higher rate about 20 percent than earnings of their education disadvantaged peers over the two years. This result indicates a confirmation that education accumulation for the economically disadvantaged young has a positive impact on their earnings enhancement and thus inferring a contribution to poverty reduction and pro-poorness of growth.

Much health related research depends heavily on the analysis of a rapidly expanding universe of observational data. A challenge in analysis of such data is the lack of sound statistical methods and tools that can address multiple facets of estimating treatment or exposure effects in observational studies with a large number of covariates. We sought to advance methods to improve analysis of large observational datasets with an end goal of understanding the effect of treatments or exposures on health. First we compared existing methods for propensity score (PS) adjustment, specifically Bayesian propensity scores. This concept had previously been introduced (McCandless et al., 2009) but no rigorous evaluation had been done to evaluate the impact of feedback when fitting the joint likelihood for both the PS and outcome models. We determined that unless specific steps were taken to mitigate the impact of feedback, it has the potential to distort estimates of the treatment effect. Next, we developed a method for accounting for uncertainty in confounding adjustment in the context of multiple exposures. Our method allows us to select confounders based on their association with the joint exposure and the outcome while also accounting for the uncertainty in the confounding adjustment. Finally, we developed two methods to combine het- erogenous sources of data for effect estimation, specifically information coming from a primary data source that provides information for treatments, outcomes, and a limited set of measured confounders on a large number of people and smaller supplementary data sources containing a much richer set of covariates. Our methods avoid the need to specify the full joint distribution of all covariates.

What is the developmental trajectory of the skills that underlie emergent literacy during the preschool years? Are there individual characteristics which predict whether a child will be at-risk for difficulties in acquiring literacy skills? Does a child’s experience in a high-quality early care and education environment enhance the development of his or her emergent literacy? The present study is an investigation of the individual and environmental factors relevant to children’s emergent literacy skills as they unfold in time. Using a combination of principal components analysis, growth modeling with a multi-level approach, and propensity score analysis, the trajectories of growth in emergent literacy were examined. In addition to child characteristics, the effects of early child environments on emergent literacy were also examined. The effects of home literacy environment and of high-quality early care and education environments were investigated using propensity score matching techniques. The growth in emergent literacy was examined using a nationally representative dataset, the Early Childhood Longitudinal Study – Birth cohort (ECLS-B). Child characteristics, such as primary home language and poverty, were associated with lower initial abilities and suppressed growth in emergent literacy. A high-quality home literacy environment had a strong effect on the growth of children’s emergent abilities, even after controlling for child characteristics. High-quality early care and education environments, as defined by structural attributes of the program such as class size, had a modest impact on the growth of emergent literacy skills for some but not all children. When high-quality early education was defined in terms of teacher interaction, children who are exposed to such care experienced an increase in growth of their emergent literacy abilities. This study provides an examination of individual and group paths toward literacy as an element of school readiness, including the role of environment in the development of literacy skills. These findings have implications for early education policy, especially relevant to state-funded preschool programs and Early Head Start, to provide insight into contexts in which policy and the investment of resources can contribute most effectively to early literacy development.

When researchers are unable to randomly assign students to treatment conditions, selection bias is introduced into the estimates of treatment effects. Random assignment to treatment conditions, which has historically been the scientific benchmark for causal inference, is often impossible or unethical to implement in educational systems. For example, researchers cannot deny services to those who stand to gain from participation in an academic program. Additionally, students select into a particular treatment group through processes that are impossible to control, such as those that result in a child dropping-out of high school or attending a resource-starved school. Propensity score methods provide valuable tools for removing the selection bias from quasi-experimental research designs and observational studies through modeling the treatment assignment mechanism. The utility of propensity scores has been validated for the purposes of removing selection bias when the observations are assumed to be independent; however, the ability of propensity scores to remove selection bias in a multilevel context, in which group membership plays a role in the treatment assignment, is relatively unknown. A central purpose of the current study was to begin filling in the gaps in knowledge regarding the performance of propensity scores for removing selection bias, as defined by covariate balance, in multilevel settings using a Monte Carlo simulation study. The performance of propensity scores were also examined using a large-scale national dataset. Results from this study provide support for the conclusion that multilevel characteristics of a sample have a bearing upon the performance of propensity scores to balance covariates between treatment and control groups. Findings suggest that propensity score estimation models should take into account the cluster-level effects when working with multilevel data; however, the numbers of treatment and control group individuals within each cluster must be sufficiently large to allow estimation of those effects. Propensity scores that take into account the cluster-level effects can have the added benefit of balancing covariates within each cluster as well as across the sample as a whole.

Att vara ideellt engagerad ses av många som en faktor som leder till positiva effekter för individer. Problemet med detta resonemang är att de flesta som engagerar sig aktivt även ärde som redan har dessa fördelaktigaegenskaper.Frågan är därför om det är en kausal effekt eller en stark korrelation som lett till bilden av att ideellt engagemang är positivt för individers livsstandard.Syftet med denna uppsats är att undersöka om ett aktivt engagemang inom ideella organisationer i sig gereffekter hos individerna. Studiengörs med utgång från hypotesen att socialt kapital skapas inom grupper av aktivt ideellt engagerade medlemmar och att detta i sin tur leder till positiva yttringarhos individen.Undersökningen sker genomen kvantitativ longitudinell studie som använder sig av propensity score matchingför att uppskatta den kausala effekten aktivt engagemang kan ha.Studiens resultat indikerar att ingen statistisk signifikant skillnad existerar mellan en person som varitaktiv i en ideell organisationoch en person som ej varit det, vilket tyder på att det aktiva engagemanget inom en ideell organisation i sig inte ger någon effekt. Dessa resultat förkastar därmed hypotesen att aktivt engagemang inom ideella organisationer i sig leder till positiva effekter genom det sociala kapital som skaffas inom denna form av nätverk.

Conventional charcoal and firewood are the main source of energy in Haiti. They provide up to 90% of the country’s energy for domestic and industrial use, resulting in severe environmental and health issues. The present study is initiated to better understand the reasons why two promising alternative technologies (improved cookstoves and alternative charcoal briquettes) have experienced low adoption in Haiti. The research was carried out in two districts in southern Haiti where the improved stoves and briquettes production units exist and where households benefited from a program distributing the improved stoves. This project contributes to the literature by gauging interest in the improved stove and briquettes, as well as their specific characteristics. It helps understand factors that affect the adoption and dis-adoption of the technologies. Additionally, the research measures tangible benefits for households that adopted the improved stoves. The study reveals that the use of the improved stoves lowers fuel expenditures by 14.6 cents/day to 23.6 cents/day. Haitian consumers are interested in both the stove and briquettes, but their willingness-to-pay depends on their personal characteristics such as location and income. The study has revealed two surprising results as well: Unnecessary dis-adoption of the stoves occurs because the two technologies were needlessly marketed together. Despite the target audience, which is poor and rural consumers, the improved stove is perceived as a rich, urban user’s technology.

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觀察性研究資料中，透過傾向分數的使用，可以使基準變數在實驗與對照兩組間達到某種程度的平衡，並可視同為一隨機試驗，進而進行有效的統計推論。文獻中有關平衡與否的診斷，大多聚焦於平均數與變異數的比較。本文中我們提出使用KLIC(Kullback-Leibler Information Criterion)及KS(Kolmogorov and Simonov)兩種比較分配函數差異的統計量，作為另一種平衡診斷工具的構想，並針對其可行性進行探討與評比。此外，數據顯示KLIC及KS與透過傾向分數配對的成功比例呈現負相關。由於配對成功比例過低將導致後續統計推論結果的侷限性，因此本文也就KLIC及KS作為是否進行配對的一個先行指標之可行性作探討。模擬結果顯示，二者的答案均是肯定的。 / In observational studies, propensity scores are frequently used as tools to balance the distribution of baseline covariates between treated and untreated groups to some extent so that the data could be treated as if they were from a randomized controlled trial (RCT) and causal inferences could thus be made. In the past, balance or not was usually diagnosed in terms of the means and/or the variances. In this study, we proposed using either Kullback-Leibler Information Criterion (KLIC) or Kolmogorov and Simonov (KS) statistic as a diagnostic measure, and evaluated its feasibility. In addition, since low propensity score matching rate decreases the power of the statistical inference and a pilot study showed that the matching rate was negatively correlated with KLIC and KS; thus, we also discussed the possibilities of using KLIC and KS to be pre-indices before implementing propensity score matching. Both considerations appear to be positive through our simulation study.

O objetivo dessa dissertação é avaliar o impacto de um programa de treinamento voltado para trabalhadores, utilizando o propensity score matching, mas com dois tipos de abordagem, uma não-paramétrica e a outra semi-paramétrica. Para estimação não paramétrica foi utilizado um método proposto por Li, Racine e Wooldridge (2009) e para estimação semi-paramétrica, o modelo utilizado foi o Generalized Additive Model proposto por Hastie e Tibshirani (1990). Os resultados obtidos indicam que os dois métodos utilizados apresentam estimativas tão boas ou melhores do que quando estimadas paramétricamente. / The goal of this thesis is to evaluate the impact of a job training program using propensity score matching methods with two types of approaches: a nonparametric e another semiparametric. For non-parametric estimation was used a method proposed by Li, Racine and Wooldridge (2009) and for the semiparametric model the Generalized Additive Model proposed by Hastie and Tibshirani (1990). The results indicate that both methods provide estimates as good or better than when parametrically estimated.