Abstract Children’s indirect exposure to the justice system through biological parents or coresident adults is both a marker of their own vulnerability and a measure of the justice system’s expansive reach in society. Estimating the size of this population for the United States has historically been hampered by inadequate data resources, including the inability to observe nonincarceration events, follow children throughout their childhood, and measure adult nonbiological parent cohabitants. To overcome these challenges, we leverage billions of restricted administrative and survey records linked with Criminal Justice Administrative Records System data and find substantially larger exposure rates than previously reported: prison, 9% of children born between 1999–2005; felony conviction, 18%; and any criminal charge, 39%. Charge exposure rates exceed 60% for Black, American Indian, and low-income children. While broader definitions reach a more expansive population, strong and consistently negative correlations with childhood well-being suggest that these remain valuable predictors of vulnerability. Finally, we document substantial geographic variation in exposure, which we leverage in a movers design to estimate the effect of living in a high-exposure county during childhood. We find that children moving into high-exposure counties are more likely to experience postmove exposure events and exhibit significantly worse outcomes by age 26 on multiple dimensions (earnings, criminal activity, teen parenthood, mortality); effects are strongest for those who moved at earlier ages.
more »
« less
Individualistic culture increases economic mobility in the United States
Where an individual grows up has large implications for their long-term economic outcomes, including earnings and intergenerational mobility. Even within the United States, the “causal effect of place” varies greatly and cannot be fully explained by socioeconomic conditions. Across different nations, variation in growth and mobility have been linked to more individualistic cultures. We assess how variation of historically driven individualism within the United States affects mobility. Areas in the United States that were isolated on the frontier for longer periods of time during the 19th century have a stronger culture of “rugged individualism” [S. Bazzi, M. Fiszbein, M. Gebresilasse, Econometrica 88, 2329–2368 (2020)]. We combine county-level measures of frontier experience with modern measures of the causal effect of place on mobility—the predicted percentage change in an individual’s earnings at age 26 y associated with “growing up” in a particular county [R. Chetty, N. Hendren, Q. J. Econ. 133, 1163–1228 (2018)]. Using commuting zone fixed effects and a suite of county-level controls to absorb regional variation in frontier experience and modern economic conditions, we find an additional decade of frontier experience results in 25% greater modern-day income mobility for children of parents in the 25th percentile of income and 14% for those born to parents in the 75th percentile. We use mediation analysis to present suggestive evidence that informal manifestations of “rugged individualism”—those embodied by the individuals themselves—are more strongly associated with upward mobility than formal policy or selective migration.
more »
« less
- Award ID(s):
- 2117660
- NSF-PAR ID:
- 10350787
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 118
- Issue:
- 37
- ISSN:
- 0027-8424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Social capital—the strength of an individual’s social network and community—has been identified as a potential determinant of outcomes ranging from education to health 1–8 . However, efforts to understand what types of social capital matter for these outcomes have been hindered by a lack of social network data. Here, in the first of a pair of papers 9 , we use data on 21 billion friendships from Facebook to study social capital. We measure and analyse three types of social capital by ZIP (postal) code in the United States: (1) connectedness between different types of people, such as those with low versus high socioeconomic status (SES); (2) social cohesion, such as the extent of cliques in friendship networks; and (3) civic engagement, such as rates of volunteering. These measures vary substantially across areas, but are not highly correlated with each other. We demonstrate the importance of distinguishing these forms of social capital by analysing their associations with economic mobility across areas. The share of high-SES friends among individuals with low SES—which we term economic connectedness—is among the strongest predictors of upward income mobility identified to date 10,11 . Other social capital measures are not strongly associated with economic mobility. If children with low-SES parents were to grow up in counties with economic connectedness comparable to that of the average child with high-SES parents, their incomes in adulthood would increase by 20% on average. Differences in economic connectedness can explain well-known relationships between upward income mobility and racial segregation, poverty rates, and inequality 12–14 . To support further research and policy interventions, we publicly release privacy-protected statistics on social capital by ZIP code at https://www.socialcapital.org .more » « less
-
Abstract This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions. The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. Emerging viral pandemics can place extraordinary and sustained demands on public health and health systems and on providers of essential community services. Modeling the Covid-19 pandemic spread is challenging. But there are data that can be used to project resource demands. Estimates of the reproductive number (R) of SARS-CoV-2 show that at the beginning of the epidemic, each infected person spreads the virus to at least two others, on average (Emanuel et al. in N Engl J Med. 2020, Livingston and Bucher in JAMA 323(14):1335, 2020). A conservatively low estimate is that 5 % of the population could become infected within 3 months. Preliminary data from China and Italy regarding the distribution of case severity and fatality vary widely (Wu and McGoogan in JAMA 323(13):1239–42, 2020). A recent large-scale analysis from China suggests that 80 % of those infected either are asymptomatic or have mild symptoms; a finding that implies that demand for advanced medical services might apply to only 20 % of the total infected. Of patients infected with Covid-19, about 15 % have severe illness and 5 % have critical illness (Emanuel et al. in N Engl J Med. 2020). Overall, mortality ranges from 0.25 % to as high as 3.0 % (Emanuel et al. in N Engl J Med. 2020, Wilson et al. in Emerg Infect Dis 26(6):1339, 2020). Case fatality rates are much higher for vulnerable populations, such as persons over the age of 80 years (> 14 %) and those with coexisting conditions (10 % for those with cardiovascular disease and 7 % for those with diabetes) (Emanuel et al. in N Engl J Med. 2020). Overall, Covid-19 is substantially deadlier than seasonal influenza, which has a mortality of roughly 0.1 %. Public health efforts depend heavily on predicting how diseases such as those caused by Covid-19 spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness. Modern computational power allows these models to quickly incorporate multiple inputs, such as a given disease’s ability to pass from person to person and the movement patterns of potentially infected people traveling by air and land. This process sometimes involves making assumptions about unknown factors, such as an individual’s exact travel pattern. By plugging in different possible versions of each input, however, researchers can update the models as new information becomes available and compare their results to observed patterns for the illness. In this paper we describe the development a model of Corona spread by using innovative big data analytics techniques and tools. We leveraged our experience from research in modeling Ebola spread (Shaw et al. Modeling Ebola Spread and Using HPCC/KEL System. In: Big Data Technologies and Applications 2016 (pp. 347-385). Springer, Cham) to successfully model Corona spread, we will obtain new results, and help in reducing the number of Corona patients. We closely collaborated with LexisNexis, which is a leading US data analytics company and a member of our NSF I/UCRC for Advanced Knowledge Enablement. The lack of a comprehensive view and informative analysis of the status of the pandemic can also cause panic and instability within society. Our work proposes the HPCC Systems Covid-19 tracker, which provides a multi-level view of the pandemic with the informative virus spreading indicators in a timely manner. The system embeds a classical epidemiological model known as SIR and spreading indicators based on causal model. The data solution of the tracker is built on top of the Big Data processing platform HPCC Systems, from ingesting and tracking of various data sources to fast delivery of the data to the public. The HPCC Systems Covid-19 tracker presents the Covid-19 data on a daily, weekly, and cumulative basis up to global-level and down to the county-level. It also provides statistical analysis for each level such as new cases per 100,000 population. The primary analysis such as Contagion Risk and Infection State is based on causal model with a seven-day sliding window. Our work has been released as a publicly available website to the world and attracted a great volume of traffic. The project is open-sourced and available on GitHub. The system was developed on the LexisNexis HPCC Systems, which is briefly described in the paper.more » « less
-
more » « less
Abstract We document that children growing up in places left behind by today’s economy experience lower levels of social mobility as adults. Using a longitudinal database that tracks over 20,000 places in the USA from 1980 to 2018, we identify two kinds of left behind places: the ‘long-term left behind’ that have struggled over long periods of history; and ‘recently left-behind’ places where conditions have deteriorated. Compared to children of similar baseline household income levels, we find that exposure to left behind places is associated with a 4-percentile reduction in adult income rank. Children fare considerably better when exposed to places where conditions are improving. These outcomes vary across prominent social and spatial categories and are compounded when nearby places are also experiencing hardship. Based on these findings, we argue that left behind places are having ‘scarring effects’ on children that could manifest long into the future, exacerbating the intergenerational challenges faced by low-income households and communities. Improvements in local economic conditions and outmigration to more prosperous places are, therefore, unlikely to be full remedies for the problems created by left behind places.
-
Children’s beliefs about the contribution of effort and ability to success and failure shape their decisions to persist or give up on challenging tasks, with consequences for their academic success. But how do children learn about the concept of “challenge”? Prior work has shown that parents’ verbal responses to success and failure shape children’s motivational beliefs. In this study, we explore another type of talk - parent and child talk about difficulty - which could contribute to children’s motivational beliefs. We performed secondary analyses of two observational studies of parent-child interactions in the United States (Boston and Philadelphia) from age 3 to 4th grade (Study 1, 51% girls, 65.5% White, at least 43.2% below Federal poverty line) and at 1st grade (Study 2, 54% girls, 72% White, family income-to-needs ratio M(SD) = 4.41(2.95)) to identify talk about difficulty, characterize the content of those statements, and assess whether task context, child and parent gender, child age, and other parent motivational talk were associated with quantity of child and parent difficulty talk. We found that many families did discuss difficulty, with variation among families. Parents and children tended to use general statements to talk about difficulty (e.g., “That was hard!”), and task context affected child and parent difficulty talk. In the NICHD-SECCYD dataset, mothers’ highlighting how task features contributed to task difficulty was positively correlated with their process praise, suggesting that this talk could be motivationally relevant.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1073/pnas.2107273118
