The central research question is the extent to which gifted programming affects student academic outcomes of gifted as compared to not-gifted students and how this differs by race/ethnicity and/or poverty status. Since the identification of elementary school students as gifted is not random, propensity score matching is used to remove this bias in the estimates of the effects. A matched sample of North Carolina middle school students based on individual level data of both gifted and not-gifted students of varied racial/ethnic groups and income levels is used for this analysis. This enables a comparison of sixth, seventh, and eighth grade student achievement to determine the extent to which participating in gifted programming differentiates effects by race/ethnicity and poverty status. I show the additional test score gain, if any, from being in gifted programming compared to students not participating in gifted programs. Variations in gifted program effects across race/ethnicity and income are assessed. This research adds empirical evidence to the more qualitatively focused gifted debate by analyzing differences in student outcomes between gifted and not-gifted students in North Carolina. Since black and lower income students are less likely to participate in gifted programs, they disproportionately encounter less experienced teachers, lower expectations, and fewer resources. The extent to which these additional learning supports translate to differences in student outcomes are analyzed.

This study is aimed to unpack the ‘black box’ that connects the grade-3 test-based retention policy with students’ academic outcomes. I theorized that the policy effects on teaching and learning may be modified by instructional capacity, but are unlikely to occur through enhancing teachers’ capability to teach. Analyzing the Early Childhood Longitudinal Study Kindergarten cohort (ECLS-K) dataset, I first explored the relationship between the test-based retention policy and instructional capacity as indicated by teacher expectations of students’ learning capability and then investigated whether and how the expectations moderated the policy effects on instructional time reallocation, student academic performance, and student self-perceived academic competence and interests. To remove the selection bias associated with the non-experimental data, I applied a novel propensity score-based causal inference method, the marginal mean weighting through stratification (MMW-S) method and extended it to a causal analysis that approximates a randomization of schools to the test-based retention policy followed by a randomization of classes to teachers with different levels of expectations. Consistent with my theory, I found that the test-based retention policy had no effects on teacher expectations. Although the policy uniformly increased the time allocated to math instruction, it produced no significant changes in students’ overall performance and overall self-perception in math. In addition, I found that students responded differently to the test-based retention policy depending on the expectations they received from the grade-3 teachers. The results suggested some benefits of positive expectations over negative and indifferent expectations in moderating the policy effects, including more access to advanced content, higher learning gains of average-ability students, and more resilient student learning over a long term. However, the results also showed that having positive expectations alone is not sufficient for academic improvement under the high-stakes policy. If implemented by a positive-expectation teacher, the policy could be detrimental to students’ learning in the nontested subject or to their learning of basic reading/math skills. It would as well place the bottom-ability students at a disadvantage. The findings have significant implications for the ongoing high-stakes testing debate, for school improvement under the current accountability reform, and for research of teacher effectiveness.

This study is aimed to unpack the ‘black box’ that connects the grade-3 test-based retention policy with students’ academic outcomes. I theorized that the policy effects on teaching and learning may be modified by instructional capacity, but are unlikely to occur through enhancing teachers’ capability to teach. Analyzing the Early Childhood Longitudinal Study Kindergarten cohort (ECLS-K) dataset, I first explored the relationship between the test-based retention policy and instructional capacity as indicated by teacher expectations of students’ learning capability and then investigated whether and how the expectations moderated the policy effects on instructional time reallocation, student academic performance, and student self-perceived academic competence and interests. To remove the selection bias associated with the non-experimental data, I applied a novel propensity score-based causal inference method, the marginal mean weighting through stratification (MMW-S) method and extended it to a causal analysis that approximates a randomization of schools to the test-based retention policy followed by a randomization of classes to teachers with different levels of expectations. Consistent with my theory, I found that the test-based retention policy had no effects on teacher expectations. Although the policy uniformly increased the time allocated to math instruction, it produced no significant changes in students’ overall performance and overall self-perception in math. In addition, I found that students responded differently to the test-based retention policy depending on the expectations they received from the grade-3 teachers. The results suggested some benefits of positive expectations over negative and indifferent expectations in moderating the policy effects, including more access to advanced content, higher learning gains of average-ability students, and more resilient student learning over a long term. However, the results also showed that having positive expectations alone is not sufficient for academic improvement under the high-stakes policy. If implemented by a positive-expectation teacher, the policy could be detrimental to students’ learning in the nontested subject or to their learning of basic reading/math skills. It would as well place the bottom-ability students at a disadvantage. The findings have significant implications for the ongoing high-stakes testing debate, for school improvement under the current accountability reform, and for research of teacher effectiveness.

This thesis consists of four self-contained papers related to energydemand and household cooking energy.Paper [I] examine the impact of price, income and non-economicfactors on gasoline demand using a structural time series model. Theresults indicated that non-economic factors did have an impact ongasoline demand and also one of the largest contributors to changes ingasoline demand in both countries, especially after the 1990s. Theresults from the time varying parameter model (TVP) indicated thatboth price and income elasticities were varying over time, but thevariations were insignificant for both Sweden and the UK. Theestimated gasoline trend also showed a similar pattern for the twocountries, increasing continuously up to 1990 and taking a downturnthereafter.Paper [II] studies whether the commonly used linear parametricmodel for estimating aggregate energy demand is the correctfunctional specification for the data generating process. Parametricand nonparametric econometric approaches to analyzing aggregateenergy demand data for 17 OECD countries are used. The resultsfrom the nonparametric correct model specification test for theparametric model rejects the linear, log-linear and translogspecifications. The nonparametric results indicate that the effect of theincome variable is nonlinear, while that of the price variable is linearbut not constant. The nonparametric estimates for the price variable isrelatively low, approximately −0.2.Paper [III] relaxed the weak separability assumption betweengasoline demand and labor supply by examining the effect of laborsupply, measured by male and female working hours on gasolinedemand. I used a flexible semiparametric model that allowed fordifferences in response to income, age and labor supply, respectively.Using Swedish household survey data, the results indicated that therelationship between gasoline demand and income, age and laborsupply were non-linear. The formal separability test rejects the null ofseparability between gasoline demand and labor supply. Furthermore,there was evidence indicating small bias in the estimates when oneignored labor supply in the model.Paper [IV] investigated the key factors influencing the choice ofcooking fuels in Ghana. Results from the study indicated thateducation, income, urban location and access to infrastructure werethe key factors influencing household’s choice of the main cookingfuels (fuelwood, charcoal and liquefied petroleum gas). The study alsofound that, in addition to household demographics and urbanization,the supply (availability) of the fuels influenced household choice forthe various fuels. Increase in household income was likely to increasethe probability of choosing modern fuel (liquefied petroleum gas andelectricity) relative to solid (crop residue and fuelwood) and transitionfuel (kerosene and charcoal).

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.

Personlighetsfaktorerna har betydelse för hur mycket risker individer tar. Syftet med denna studie är att undersöka om det finns ett samband mellan personlighetsfaktorerna enligt femfaktormodellen och graden av generell riskbenägenhet. Mycket av tidigare forskning har kopplat personlighet till domänspecifikt risktagande som hälsa, ekonomi och arbete, medan denna studie undersöker kopplingen till generell riskbenägenhet i vardagen vilket kan vara att välja ett okänt resmål eller att söka till en ny universitetsutbildning. 189 undersökningsdeltagare i olika åldrar och från olika yrkesgrupper besvarade en enkät bestående av två olika tester, den svenska versionen av the Big Five Inventory (BFI) och en svensk översättning av the Risk Propensity Scale (RPS). Resultatet visar på ett signifikant samband mellan fyra av de fem personlighetsfaktorerna enligt femfaktormodellen och en ökad riskbenägenhet, nämligen Öppenhet, Samvetsgrannhet, Utåtriktning och Vänlighet. Däremot fanns ingen signifikant korrelation mellan personlighetsfaktorn Neuroticism och riskbenägenhet.

In the present study possible associations between driver characteristics and aggressive driving were examined. 210 participants responded to a questionnaire consisting of self-report measures of emotion regulation ability, personality traits, and attitudes towards traffic behaviours in a Swedish translation of the Propensity for Angry Driving Scale (PADS). The main results showed that females, older age, agreeableness, openness, and social desirability were negatively correlated with angry driving behaviour as measured by the PADS. Impulsivity, attention seeking, trait irritability, verbal trait aggression, positive attitude towards speeding, and a high self-reported car manoeuvring ability were positively correlated with angry driving. Partial correlations showed that social desirability, trait irritability, and a positive attitude towards speeding explained most of the unique variance. Multiple regression analysis showed that trait irritability, positive attitude towards speeding, and a high self-reported car manoeuvring ability were the most important predictors of angry driving.

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.

It has been widely accepted that people do not always behave rationally when making decisions. However, cognitive biases are still of interest to a relatively small group (mostly working in the area of psychology) even though they have been introduced to a wider audience by Tversky and Kahneman&rsquo / s article in Science in 1974. It has already been shown that behavioral biases affect most decisions of people / therefore, they have an important role in a wide range of fields, from financial marketing to gambling. The purpose of this study is to investigate some cognitive biases (anchoring, reference point, probability judgment and risk propensity) in Turkey. In brief, anchoring bias is the fallacious effect of anchor values on decision making process, the presence of reference point bias proves that people are excessively affected by comparisons, probability judgment bias is the erroneous evaluation of probabilities, and risk propensity bias is the fallacious effect of the risk propensity levels on decision making processes. The relationships of these biases with individual cognitive ability levels and socioeconomic variables are also inspected. The data are collected by using a survey that is composed of the related measures which are taken from previous surveys in the literature. The sample is composed of a large number of participants (1575) from a wide range of socioeconomic statuses, from students to working professionals to retired individuals. The results lend support to the presence of a reference point bias, and an effect of risk propensity levels on decisions. However, an evidence which supports anchoring and probability judgment biases are failed to be found at a significant level. A significant relationship between cognitive ability level and risk propensity level is found. Moreover, demographic variables are also found to have an effect on the selected biases and cognitive ability.

Propensity score analysis has been used to minimize the selection bias in observational studies to identify causal relationships. A propensity score is an estimate of an individual's probability of being placed in a treatment group given a set of covariates. Propensity score analysis aims to use the estimate to create balanced groups, akin to a randomized experiment. This study used Monte Carlo methods to examine the appropriateness of using propensity score methods to achieve balance between groups on observed covariates and reproduce treatment effect estimates in multilevel studies. Specifically, this study examined the extent to which four different propensity score estimation models and three different propensity score conditioning methods produced balanced samples and reproduced the treatment effects with clustered data. One single-level logistic model and three multilevel models were investigated. Conditioning methods included: (a) covariance adjustment, (b) matching, and (c) stratification. Design factors investigated included: (a) level-1sample size, (b) level-2 sample size, (c) level-1 covariate relationship to treatment, (d) level-2 covariate relationship to treatment, (e) level-1 covariate relationship to outcome, (f) level-2 covariate relationship to outcome, and (g) population effect size. The results of this study suggest the degree to which propensity score analyses are able to create balanced groups and reproduce treatment effect estimates with clustered data is largely dependent upon the propensity score estimation model and conditioning method selected. Overall, the single-level logistic and random intercepts models fared slightly better than the more complex multilevel models while covariance adjustment and matching methods tended to be more stable in terms of balancing groups than stratification. Additionally, the results indicate propensity score analysis should not be conducted with small samples. Finally, this study did not identify an estimation model or conditioning method that was consistently able to create adequately balanced groups and reproduce treatment effect estimates.