This study estimates the effects of the 1970 Ancash earthquake on human capital accumulation on the affected and subsequent generation, 37 years after the shock, using the Peruvian censuses of 1993 and 2007. The main finding is that males affected by the earthquake in utero completed on average 0.5 years less schooling while females affected by the earthquake completed 0.8 years less schooling. Surprisingly, those whose mothers were affected at birth by the earthquake have 0.4 less years of education, while those whose fathers were affected by the earthquake at birth have no effects on their education. The evaluation of other outcomes also suggests that the level of welfare of the affected individuals has been negatively impacted in the long run. The present investigation supports previous literature on shocks in early childhood, providing evidence of the existence of intergenerational transmission of shocks.

During the past two decades, many United States cities have experienced some form of urban "rebirth". Shops, cafés, restaurants and people are returning to the area in and around the traditional center of the city. A growing body of literature offers various theories and data assisting in the understanding of the types of households now making this center-city residential choice. This dissertation contributes to the literature by comprehensively examining many of the prominent theories of urban "rebirth" using New York City area micro data. Each of the three essays found in this work statistically explores the relationship between the choice of residential neighborhood and the characteristics of the household. In general, educational attainment, income and family structure separate where households reside, but these characteristics are only good predictors of residence when looked at in combination with each other. Any statement regarding the predictive power of a particular household characteristic must include a discussion of other household characteristics as well. The results and implications of this dissertation assist in clarifying the discussions regarding urban "rebirth", in addition to allowing for better forecasts of future residential desires leading to more efficient land use and infrastructure planning.

Background: In the area of artificial learners, not much research on the question of an appropriate description of artificial learners (empirical) performance has been conducted. The optimal solution of describing a learning problem would be a functional dependency between the data, the learning algorithmsinternal specifics and its performance. Unfortunately, a general, restrictions-free theory on performance of arbitrary artificial learners has not been developed yet. Objective: The objective of this paper is to investigate which function is most appropriately describing the learning curve produced by C4.5 algorithm. Methods: The J48 implementation of the C4.5 algorithm was applied to datasets (n=121) from publicly available repositories (e.g. UCI) in step wise k-fold cross-validation. First, four different functions (power, linear, logarithmic, exponential) were fit to the measured error rates. Where the fit was statistically significant (n=86), we measured the average mean squared error rate for each function and its rank. The dependent samples T-test was performed to test whether the differences between mean squared error are significantly different, and Wilcoxons signed rank test was used to test whether the differences between ranks are significant. Results: The decision trees error rate can be successfully modeled by an exponential function. In a total of 86 datasets, exponential function was a better descriptor of error rate function in 64 of 86 cases, power was best in 13, logarithmic in 3, and linear in 6 out of 86 cases. Average mean squared error across all datasets was 0.052954 for exponential function, and was significantly different at P=0.001 frompower and at P=0.000 from linear function. The results also show that exponential functions rank is significantly different at any reasonable threshold (P=0.000) from the rank of any other model. Conclusion: Our findings are consistent with tests performed in the area of human cognitive performance, e.g. with works by Heathcote et al. (2000), who were observing that the exponential function is best describing an individual learner. In our case we did observe an individual learner (C4.5 algorithm) at different tasks. The work can be used to forecast and model the future performance of C4.5 when not all data have been used or there is a need to obtain more data for better accuracy.

Bostons population is becoming older than ever before. The oldest Baby Boomer is approaching 70 and reinventing what it means to be a senior citizen. Waves of Boomers will forge a new path into later life, creating a population of seniors that is larger and more long-lived than previous cohorts, and diverse in new ways. In 2010, more than 14% of Bostons residents were 60 years or older, representing 88,000 older people. By 2030, projected increases in the older population will result in as many as 130,000 seniors residing in Boston. How will Boston accommodate its growing older population? What steps may promote livability for Boston residents of all ages; where residents will want to, and can expect to, age in place with security, dignity, and well-being?This report was produced in collaboration between the Boston Commission on Affairs of the Elderly and the Center for Social and Demographic Research on Aging at the Gerontology Institute of the University of Massachusetts Boston. Its aim is to offer a glimpse into important issues relating to aging in Boston. The demographic profilepresented here highlights the citys cultural, racial and ethnic diversity as a central context for the changing needs and concerns of older Bostonians. Potential challengesrelating to health and caregiving, social engagement, income security, and housing are identified; as well, theopportunities presented by an older population are recognized.

Scholars have long been interested in exchange and matching (assortative mating) in romantic partner selection. But many analyses of exchange, particularly those that examine beauty and socioeconomic status, fail to control for partners tendency to match each other on these traits. Because desirable traits in mates are positively correlated between partners and within individuals, ignoring matching may exaggerate evidence of cross-trait beauty-status exchange. Moreover, many prior analyses assume a gendered exchange in which women trade beauty for mens status, without testing whether men might use handsomeness to attract higher-status women. Nor have prior analyses fully investigated how the prevalence of beauty-status exchange varies between different types of couples. I use data from the National Longitudinal Study of Adolescent Health Romantic Pair Sample, a large (N= 1,507), nationally representative probability sample of dating, cohabiting, and married couples, to investigate how often romantic partners exchange physical attractiveness and socioeconomic status, net of matching on these traits. I find that controlling for matching eliminates nearly all evidence of beauty-status exchange. The discussion focuses on the contexts in which beauty-status exchange is most likely and on implications these results have for market-based and sociobiological theories of partner selection.

Scientists, Technology professionals, Engineers, and Mathematicians (STEM workers) are fundamental inputs in scientific innovation and technological adoption, the main drivers of productivity growth in the U.S. In this paper we identify the effect of STEM worker growth on the wages and employment of college and non-college educated native workers in 219 U.S. cities from 1990 to 2010. In order to identify a supply-driven and heterogeneous increase in STEM workers across U.S. cities, we use the distribution of foreign-born STEM workers in 1980 and exploit the introduction and variation of the H-1B visa program granting entry to foreign-born college educated (mainly STEM) workers. We find that H-1B-driven increases in STEM workers in a city were associated with significant increases in wages paid to college educated natives. Wage increases for non-college educated natives are smaller but still significant. We do not find significant effects on employment. We also find that STEM workers increased housing rents for college graduates, which eroded part of their wage gains. Together, these results imply a significant effect of foreign STEM on total factor productivity growth in the average US city between 1990 and 2010.

This paper explores how ones labor supply is affected by their relative wage as compared to people in the same geographical region as them. The study finds the predicted negative relationship between relative wage per hour and hours of work. The paper expands on prior literature because it uses a different data set and examines it through different reference groups (the group in which a person compares themselves to). This finding is important because it may be beneficial for the government to understand people’s labor supply choices when making policies that deal with labor supply. Furthermore, it would be helpful to understand more how people respond to their environment differently.

In the following report, we provide an overview of the state of school funding in Pennsylvania, a review of current conceptions of educational equity, adequacy and equal opportunity, empirical methods for measuring education costs, current policies across states and recent reforms. Our review is organized in three chapters. In the first, we summarize the current status of the school funding system and student outcomes in Pennsylvania. In the second, we outline conceptions of equity, adequacy and equal educational opportunity and provide an overview and critique of methods for measuring educational adequacy and informing state school finance policy. We conclude with an overview of the current landscape of school finance policy, and the intersection between emerging evidence on education costs and state school finance policy design.

This report provides an estimation of the total economic contribution that Northwestern Michigan College (NMC) makes on its surrounding region. Of course, the full comprehensive impact of the college on the regions social and cultural environment is much larger than its economic influence. Since its founding in 1951, the college has changed the social environment of the greater Grand Traverse region. This change has occurred informally by the simple presence of more young adults staying in the community to pursue their education, and formally through activities such as the Dennos Museum Center, WNMC 90.7 FM, and the Rogers Observatory which have increased both the cultural offerings and cultural expectations of the region. The purpose of this report is to document the colleges economic contributions to the region which, while an important measure, reflects only a portion of the colleges total impact.

We investigated urban development trend in contiguous United States based on metropolitan areas (MAs), using two scaled measurements for 1990, 2000 and 2010. Linear population density (LPD) was used to compare the same MA over time. Population area growth index (PAGI) was adopted to compare different MAs at the same time. We found that (1) DMSP-OLS nighttime stable lights data are effective for delineating extent of developed areas; (2) both measurements show the growth of developed land have slowed from 1990 to 2010; (3) both measurements show clear regional pattern in urban development in contiguous United States.

In crowdsourcing database, human operators are embedded into the database engine and collaborate with other conventional database operators to process the queries. Each human operator publishes small HITs (Human Intelligent Task) to the crowdsourcing platform, which consists of a set of database records and corresponding questions for human workers. The human workers complete the HITs and return the results to the crowdsourcing database for further processing. In practice, published records in HITs may contain sensitive attributes, probably causing privacy leakage so that malicious workers could link them with other public databases to reveal individual private information. Conventional privacy protection techniques, such as K-Anonymity, can be applied to partially solve the problem. However, after generalizing the data, the result of standard K-Anonymity algorithms may render uncontrollable information loss and affects the accuracy of crowdsourcing. In this paper, we first study the tradeoff between the privacy and accuracy for the human operator within data anonymization process. A probability model is proposed to estimate the lower bound and upper bound of the accuracy for general K-Anonymity approaches. We show that searching the optimal anonymity approach is NP-Hard and only heuristic approach is available. The second contribution of the paper is a general feedback-based K-Anonymity scheme. In our scheme, synthetic samples are published to the human workers, the results of which are used to guide the selection on anonymity strategies. We apply the scheme on Mondrian algorithm by adaptively cutting the dimensions based on our feedback results on the synthetic samples. We evaluate the performance of the feedback-based approach on U.S. census dataset, and show that given a predefined K, our proposal outperforms standard K-Anonymity approaches on retaining the effectiveness of crowdsourcing.

This dissertation consists of three independent chapters. Chapter 1 examines the determinants and welfare implications of the increased geographic of workers by skill from 1980 to 2000. I estimate a structural spatial equilibrium model of local labor demand, housing supply, labor supply, and amenity levels. The estimates indicate that cross-city changes in Örmsídemands for high and low skill labor were the underlying forces driving the increase in geographic skill sorting. I Önd that the combined e§ects of changes in citiesíwages, rents, and endogenous amenities increased well-being in- equality between high school and college graduates by a signiÖcantly larger amount than would be suggested by the increase in the college wage gap alone. Chapter 2 examines the abilities of state and local governments to extract rent from private sector workers by charging high tax rates and paying government workers high wages. Using a spatial equilibrium model where private sector workers are free to migrate across government jurisdictions, I show that variation in areasíhousing supply elasticities di§erentially restrains governmentsíabilities to extract rent from private sector workers. Governments in less housing elastic areas can charge higher taxes without worry of shrinking their tax bases. I test the modelís predictions by using worker wage data from the CPS-MORG. I Önd the public-private sector wage gap is higher in areas with less elastic housing supplies. Chapter 3 studies the standard practice in regression analyses to allow for clustering in the error covariance matrix when an explanatory variable varies at a more aggregate level than the units of observation. However, the structure of the error covariance matrix may be more complex, with correlations not vanishing for units in di§erent clusters. I show that with equal-sized clusters, if the covariate of interest is randomly assigned at the cluster level, only accounting for non-zero covariances at the cluster level, and ignoring correlations between clusters as well as di§erences in within-cluster correlations, leads to valid conÖdence intervals. However, in the absence of random assignment of the covariates, ignoring general correlation structures may lead to biases in standard errors.

Historically, the majority of immigrants to the United States came from European countries. The 1965 Immigration Reform Act (IRA) fundamentally changed the configuration of immigrants to the United States. The largest immigrant communities now consist of Latin Americans, Asians, and Africans. Another important development in immigrant communities in the past few decades has been the growth of a disproportionately large upper class in non-Hispanic immigrant communities. In this study, I will investigate the determinants of upper class status with a focus on non- Hispanic immigrants in the United States. ABSTRACT I have used a theoretical framework consisting of a wide spectrum of social theories. The theoretical framework encompasses Demographic and Structural Factors, Assimilation Theory, Weberian Theory, Elite Theories, and Marxian Theory. From this wide spectrum of theories, I have developed a set of fifteen exogenous variables including key socio-demographic variables, metropolitan area, region, the number of hours worked, immigration from an English speaking country, citizenship status, the length of stay in the United States, occupational status, occupational prestige, educational attainment, private schooling, and employment status. Consequently, I have analyzed the influence of these variables on upper class status attainment among non- Hispanic immigrants in the United States. The current study has utilized secondary data analysis from the 2010 American Community Survey (ACS). Bivariate and multivariate regression analyses were used to examine the relationship among study variables. More specifically, by conducting block analysis, the result of the regression analysis spelled out the degree of influence of each theoretical perspective on the dependent variable, upper class status attainment. The results of this study have revealed that Structural and Demographic factors make up the largest share of influence on upper class status attainment among non- Hispanic immigrants in the United States. The most influential variables emerged as “hours worked” (β = 0.211) from the Demographic and Structural Factors block, “educational attainment” (β = 0.174) from elite theory, and “Marital Status” (β = 0.119) from the Demographic and Structural Factors block respectively.

In a Bewley model with endogenous price volatility, home ownership and mobility across locations and jobs, we assess the contribution of financial constraints, housing illiquidities and house price risk to home ownership over the life cycle. The model can explain the rise in home ownership and fall in mobility over the life cycle. While some households rent due to borrowing constraints in the mortgage market, factors that affect propensities to save and move, such as risky house values and transactions costs, are equally important determinants of the ownership rate.

The number of black immigrants living the US has increased 13-fold from 1970 to 2010, increasing their share of the black population from 1% to 10%. Black immigrants' labor market outcomes surpass those of native blacks. The first chapter determines in how far the relative success of black immigrants is passed on to the second generation. If second generation males work, they earn a stunning 29% more than the first generation. But 28% of the black second generation do not work and do not attend school. The joblessness and year-round idleness experienced by many young black men in the US is spilling over to second generation blacks, and blacks who immigrate young. The upward convergence in idleness between black immigrants and black natives should not be thought of as a fixed parameter. Education is a dividing mark: For immigrants without a college degree the convergence is strong, for college-educated immigrants it is weak. Location-specific characteristics play an important role: Counties with high levels of racial segregation experience quicker convergence, highly educated counties experience slower convergence. Both discrimination and assimilation play an important role for the convergence between black immigrants and black natives.Controlling for one mechanism cuts the effect of the other in half or more as assimilation and discrimination coexist in many counties. Besides a massive increase in black immigration, the 1980-2010 period also experienced a substantial widening of the black-white wage and employment gap. The second chapter determines in how far increased selective immigration masks an even greater deterioration in the economic condition of native blacks. In 2011, excluding black immigrants increases the white-black wage gap by 4% for men and 9% for women. It increases the employment gap by 13% and 19% for men and women respectively.

Building on the idea that religious communities provide mutual insurance against some idiosyncratic risks, we argue that religious membership is more valuable in societies exposed to greater common risk. In our empirical analysis we exploit rainfall risk as a source of common economic risk in the nineteenth-century United States and show that religious communities were larger in counties where they faced greater rainfall risk. The link between rainfall risk and the size of religious communities is stronger in counties that were more agricultural, that had lower population densities, or that were exposed to greater rainfall risk during the growing season.

人口移動は，出生・死亡と比べ明確な定義が難しく，調査の仕方により多くの指標があるため国 際比較が難しい.一方，日本における人口移動の指標は，悉皆調査である国勢調査，住民基本台帳 人口移動報告に付け加え，国立社会保障・人口問題研究所が行っている標本調査である人口移動調 査より得ることができ，人口移動調査の個票を用いると，国際的に用いられている人口移動指標の すべての値を算出することが可能である.本稿では世界各国のセンサスや標本調査から，人口移動 に関するデータを収集し，合計92カ国，12種類の移動指標からなるデータベースを構築した.各国 の異なった指標を比較するために，それぞれの指標の該当する日本の値に対する比を求め，その加 重平均をその国の「移動性向指標」とし，92カ国の移動性向を比較した.その結果，移動性向はオー ストラリア，スイス，韓国，北欧諸国の順で高く，経済水準と強い正の相関，若年人口割合と弱い 負の相関があり，中国，ロシア，ヴェトナムといった旧共産主義国，社会主義国では経済水準に比 して移動性向が低い，といった結果が得られた.この移動性向指標の算出は，ある一国または地域 の人口にはあるレベルの移動性向があり，それに応じて移動各指標は一様に上下する，という仮定 に基づいており，日本の12種類の移動指標群をモデル人口移動性向としたものである.日本のデー タを使って，地域別に移動各指標が同様に変化するかどうかを検証したところ， 1年・ 5年・10年 といった期間別移動率には強い一定の関係があり，生涯移動率，行政区分による移動率はそれより も弱い関係があることが認められた.

Differential privacy has recently emerged in private statistical data release as one of the strongest privacy guarantees. Releasing synthetic data that mimic original data with differential privacy provides a promising way for privacy preserving data sharing and analytics while providing a rigorous guarantee. However, to this date there is no open-source tools that allow users to generate differentially private synthetic data, in particular, for high dimensional and large domain data. Most of the existing techniques that generate differentially private histograms or synthetic only work well for single dimensional or low-dimensional histograms. They become problematic for high dimensional and computation complexity. We propose DPSynthesizer, a toolkit for differentially private data synthesization. The core of DPSynthesizer is DPCopula designed for high-dimensional and large-domain data. DPCopula computes a differentially private copula function from which synthetic data can be sampled. Copula functions are used to describe the dependence between multivariate joint distribution using one-dimensional marginal distribution. DPSynthesizer also implements a set of state-of-the-art methods for building differentially private histograms, suitable for low-dimensional data, from which synthetic data can be generated. We will demonstrate the system using DPCopula as well as other methods with various data sets and show the feasibility, utility, and efficiency of various methods.

The number of black immigrants living in the US has increased 13-fold from 1970 to 2010, increasing their share of the black population from 1% to 10%. Black immigrants labor market outcomes surpass those of native blacks. This paper determines in how far the relative success of black immigrants is passed on to the second generation. If second generation males work, they earn a stunning 29% more than the first generation. But 28% of the black second generation do not work and do not attend school. The joblessness and year-round idleness experienced by many young black men in the US is spilling over to second generation blacks, and blacks who immigrate young. The upward convergence in idleness between black immigrants and black natives should not be thought of as a fixed parameter. Education is a dividing mark: For immigrants without a college degree the convergence is strong, for college-educated immigrants it is weak. Location-specific characteristics play an important role: Counties with high levels of racial segregation experience quicker convergence, highly educated counties experience slower convergence. Both discrimination and assimilation play an important role for the convergence between black immigrants and black natives. Controlling for one mechanism cuts the effect of the other in half as assimilation and discrimination coexist in many counties.