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Children's education and economic opportunities differ substantially across US neighborhoods. This paper develops and estimates a spatial equilibrium model that links children's education outcomes to their childhood location. Two endogenous factors determine education choices in each location: local education quality and local labor market access. This paper estimates the model with US county-level data and studies the effects of a school funding equalization on education outcomes and social mobility. The reform's direct effects improve education outcomes among children from low-skill families. However, the effects are weaker in spatial general equilibrium because average returns to education decline and residential and educational choices of low-skill families shift them toward locations with lower education quality.

Access to Education --- Economic Geography --- Economics of Education --- Education --- Education For All --- Education Quality --- Education Reform --- Education Reform and Management --- Effective Schools and Teachers --- Equality of Opportunity --- Intergenerational Mobility --- Labor Markets --- Macroeconomics and Economic Growth --- Rural Development --- Rural Labor Markets --- Spatial Economics

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Social welfare functions that assign weights to individuals based on their income levels can be used to document the relative importance of growth and inequality changes for changes in social welfare. In a large panel of industrial and developing countries over the past 40 years, most of the cross-country and over-time variation in changes in social welfare is due to changes in average incomes. In contrast, the changes in inequality observed during this period are on average much smaller than changes in average incomes, are uncorrelated with changes in average incomes, and have contributed relatively little to changes in social welfare.

Economic Conditions and Volatility --- Economic Theory & Research --- Equity and Development --- Growth --- Inequality --- Macroeconomics and Economic Growth --- Poverty Reduction

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Incomes in the poorest two quintiles on average increase at the same rate as overall average incomes. This is because, in a global dataset spanning 118 countries over the past four decades, changes in the share of income of the poorest quintiles are generally small and uncorrelated with changes in average income. The variation in changes in quintile shares is also small relative to the variation in growth in average incomes, implying that the latter accounts for most of the variation in income growth in the poorest quintiles. These findings hold across most regions and time periods and when conditioning on a variety of country-level factors that may matter for growth and inequality changes. This evidence confirms the central importance of economic growth for poverty reduction and illustrates the difficulty of identifying specific macroeconomic policies that are significantly associated with the relative growth rates of those in the poorest quintiles.

Achieving Shared Growth --- Economic Growth --- Economic Theory & Research --- Emerging Markets --- Growth --- Inequality --- Macroeconomics and Economic Growth

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We benchmark seven global optimization algorithms by comparing their performance on challenging multidimensional test functions as well as a method of simulated moments estimation of a panel data model of earnings dynamics. Five of the algorithms are taken from the popular NLopt open-source library: (i) Controlled Random Search with local mutation (CRS), (ii) Improved Stochastic Ranking Evolution Strategy (ISRES), (iii) Multi-Level Single-Linkage (MLSL) algorithm, (iv) Stochastic Global Optimization (StoGo), and (v) Evolutionary Strategy with Cauchy distribution (ESCH). The other two algorithms are versions of TikTak, which is a multistart global optimization algorithm used in some recent economic applications. For completeness, we add three popular local algorithms to the comparison--the Nelder-Mead downhill simplex algorithm, the Derivative-Free Non-linear Least Squares (DFNLS) algorithm, and a popular variant of the Davidon-Fletcher-Powell (DFPMIN) algorithm. To give a detailed comparison of algorithms, we use a set of benchmarking tools recently developed in the applied mathematics literature. We find that the success rate of many optimizers vary dramatically with the characteristics of each problem and the computational budget that is available. Overall, TikTak is the strongest performer on both the math test functions and the economic application. The next-best performing optimizers are StoGo and CRS for the test functions and MLSL for the economic application.