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We show that the neighborhoods in which children grow up shape their earnings, college attendance rates, and fertility and marriage patterns by studying more than 7 million families who move across commuting zones and counties in the United States. Exploiting variation in the age of children when families move, we find that neighborhoods have significant childhood exposure effects: the outcomes of children whose families move to a better neighborhood—as measured by the outcomes of children already living there—improve linearly in proportion to the amount of time they spend growing up in that area, at a rate of approximately 4% per year of exposure. We distinguish the causal effects of neighborhoods from confounding factors by comparing the outcomes of siblings within families, studying moves triggered by displacement shocks, and exploiting sharp variation in predicted place effects across birth cohorts, genders, and quantiles to implement overidentification tests. The findings show that neighborhoods affect intergenerational mobility primarily through childhood exposure, helping reconcile conflicting results in the prior literature.

This paper studies causal inference in randomized experiments under network interference. Commonly used models of interference posit that treatments assigned to alters beyond a certain network distance from the ego have no effect on the ego’s response. However, this assumption is violated in common models of social interactions. We propose a substantially weaker model of “approximate neighborhood interference” (ANI) under which treatments assigned to alters further from the ego have a smaller, but potentially nonzero, effect on the ego’s response. We formally verify that ANI holds for well-known models of social interactions. Under ANI, restrictions on the network topology, and asymptotics under which the network size increases, we prove that standard inverse-probability weighting estimators consistently estimate useful exposure effects and are approximately normal. For inference, we consider a network HAC variance estimator. Under a finite population model, we show that the estimator is biased but that the bias can be interpreted as the variance of unit-level exposure effects. This generalizes Neyman’s well-known result on conservative variance estimation to settings with interference.

We estimate the causal effect of each county in the United States on children’s incomes in adulthood. We first estimate a fixed effects model that is identified by analyzing families who move across counties with children of different ages. We then use these fixed effect estimates to (i) quantify how much places matter for intergenerational mobility, (ii) construct forecasts of the causal effect of growing up in each county that can be used to guide families seeking to move to opportunity, and (iii) characterize which types of areas produce better outcomes. For children growing up in low-income families, each year of childhood exposure to a one standard deviation (std. dev.) better county increases income in adulthood by 0.5%. There is substantial variation in counties’ causal effects even within metro areas. Counties with less concentrated poverty, less income inequality, better schools, a larger share of two-parent families, and lower crime rates tend to produce better outcomes for children in poor families. Boys’ outcomes vary more across areas than girls’ outcomes, and boys have especially negative outcomes in highly segregated areas. Areas that generate better outcomes have higher house prices on average, but our approach uncovers many “opportunity bargains”—places that generate good outcomes but are not very expensive.

How does one's place of residence affect individual behavior and long-run outcomes? Understanding neighborhood and place effects has been a leading question for social scientists during the past half-century. Recent empirical studies using experimental and quasi-experimental research designs have generated new insights on the importance of residential neighborhoods in childhood and adulthood. This paper summarizes the recent neighborhood effects literature and interprets the findings. Childhood neighborhoods affect long-run economic and educational outcomes in a manner consistent with exposure models of neighborhood effects. For adults, neighborhood environments matter for their health and well-being but have more ambiguous impacts on labor market outcomes. We discuss the evidence on the mechanisms behind the observed patterns and conclude by highlighting directions for future research.

This study uses a boundary design and propensity score methods to study the effects of the 1930s-era Home Owners Loan Corporation (HOLC) “redlining” maps on the long-run trajectories of urban neighborhoods. The maps led to reduced home ownership rates, house values, and rents and increased racial segregation in later decades. A comparison on either side of a city-level population cutoff that determined whether maps were drawn finds broadly similar conclusions. These results suggest the HOLC maps had meaningful and lasting effects on the development of urban neighborhoods through reduced credit access and subsequent disinvestment.

Historical anecdotes abound of new investors being drawn into a booming asset market, only to suffer when the market turns. While the role of investor contagion in asset bubbles has been explored extensively in the theoretical literature, causal empirical evidence on the topic is much rarer. This paper studies the recent boom and bust in the US housing market and establishes that many novice investors entered the market as a direct result of observing investing activity of multiple forms in their own neighborhoods and that “infected” investors performed poorly relative to other investors along several dimensions.

Recent research on location-routing problems has been focusing on locating facilities as the starting and end point of routes. In this paper, we investigate a new type of location-routing problem. In the location-routing problem with intra-route facilities, the location of depots is known, whereas the location of facilities for intermediate stops has to be determined to keep vehicles operational. We present an adaptive large neighborhood search which is enhanced by local search and dynamic programming components, and derive new penalty functions for time-efficient neighborhood evaluation. We show that this algorithm is suitable for solving various problems with intra-route facilities by deriving new best known solutions for the recently published electric location-routing problem with time windows and partial recharging, as well as for the battery swap station electric vehicle location-routing problem. Additionally, we create new real-world benchmark instances and show results as well. Furthermore, we assess the competitiveness of our algorithm on the electric vehicle routing problem with time windows for full and partial recharging, and derive new best known solutions for both problem variants. The online appendix is available at https://doi.org/10.1287/trsc.2017.0746 .

Variable neighborhood search (VNS) is a framework for building heuristics, based upon systematic changes of neighborhoods both in a descent phase, to find a local minimum, and in a perturbation phase to escape from the corresponding valley. In this paper, we present some of VNS basic schemes as well as several VNS variants deduced from these basic schemes. In addition, the paper includes parallel implementations and hybrids with other metaheuristics.

We find that a firm's investment is highly sensitive to the investments of other firms headquartered nearby, even those in very different industries. A firm's investment also responds to fluctuations in the cash flows and stock prices (q) of local firms outside its sector. These patterns do not appear to reflect exogenous area shocks such as local shocks to labor or real estate values, but rather suggest that local agglomeration economies are important determinants of firm investment and growth.

We use unique administrative data and a quasi-field experiment of exogenous allocation in Sweden to estimate medium-and longer-run effects of peoples’exposure to financially literate neighbors on their financial behavior. We contribute evidence of (1) a causal impact of exposure and of a social multiplier of financial knowledge and (2) unfavorable distributional aspects of externalities. Exposure promotes saving in private retirement accounts and stockholding, especially when neighbors have economics or business education, but only for educated households and for substantial interaction possibilities. Findings point to a transfer of knowledge rather than mere imitation or effects through labor, education, or mobility channels.