Introduction: This report examines demographic trends in educational attainment and employment among Puerto Ricans living in New York City between 1990 and 2019. The report also observes the relationship between race and gender with employment and education trends. Methods: This report uses the American Community Survey PUMS (Public Use Microdata Series) data for all years released by the Census Bureau and reorganized for public use by the Minnesota Population Center, University of Minnesota, IPUMSusa, (https://usa.ipums.org/usa/index.shtml). See Public Use Microdata Series Steven Ruggles, J. Trent Alexander, Katie Genadek, Ronald Goeken, Matthew B. Schroeder, and Matthew Sobek. Integrated Public Use Microdata Series: Version 5.0 [Machine-readable database]. Minneapolis: University of Minnesota, 2020. Census tract data depicted in maps were derived from Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 15.0 [dataset]. Minneapolis, MN: IPUMS. Discussion: There have been major improvements in educational attainment among Puerto Ricans. Since 1990, more Puerto Ricans in NYC are remaining in school during young adulthood, indicating that Puerto Ricans are continuing education almost immediately after graduating high school. As the report shows, the number of Puerto Ricans enrolled in school increased from under 45% in 1990, which grew to 48.8% by 2010 and reached 54.4% in 2019. The report also explores the relationship between gender and educational attainment and employment trends. Across all educational attainment categories, females are earning degrees in larger proportions compared to males. In 1990, the share of Puerto Ricans with a bachelor’s degree or greater was at a rate of 57.4% among females and 42.6% among males. By 2019 these figures increased to 62.6% among the former but decreased to 37.4% among the latter. Another important finding featured in this report demonstrates the impact of race on educational attainment, employment trends and median household income. Although there were no wide gaps in occupation groups among Puerto Ricans by race, there were substantial differences in earnings. White Puerto Ricans reported higher median incomes than Black Puerto Ricans and Puerto Ricans of other races over the past four decades. This gap shows that despite increased rates of education attainment among Puerto Ricans of all race groups and working in similar fields, there remained a large difference in earnings, where white Puerto Ricans earned more compared to Black Puerto Ricans and Puerto Ricans of other races.

As postponement of first births continues in the United States, women and couples will likely continue to turn to assisted reproductive technologies (ART) to overcome biological barriers to childbearing. This paper uses stochastic projections to estimate the potential impacts of ART on the US total fertility rate (TFR) overall and across sociodemographic groups using publicly available data. Assuming the trends in ART continue and the TFR remains at the mean estimate, the projection shows the ART TFR will rise from 0.023 accounting for 1.29% of the mean projected TFR in 2020 to 0.048 or 2.64% of the TFR by 2040. However, for the TFR of women over 30, this percentage is estimated at 2.68% in 2020 and 5.60% by 2040. Group-level projections quantify stratification by parity, race, and education assuming trends across these groups continue. Overall, the results show that if current trends continue, growth in demand for ART will likely increase, especially at older maternal ages, even as inequalities by race and social class remain. These projections provide a picture of ART births if inequality in access and outcomes is not addressed and highlight the need for attention to policies that address these disparities.

This article examines schooling in the western American state of Colorado during the early twentieth century. Conditions of youth and education are examined at the Walsen coal-mining camp near the town of Walsenburg in the southern region of the state. The experiences of children in the camp are compared to with those living in the town, with a focus on educational opportunities. While provision of elementary schooling in both settings was generally similar, important differences distinguished opportunities for secondary education. Variation in youth experiences in these settings is examined through census data, camp records and other sources. Ethnic and social class differences were linked to significant disparities in the ability of local youth to take advantage of schooling options that otherwise may have been available to them. Even in the West, background characteristics such as these played major roles in the educational experiences of area children and youth.

Population contact patterns fundamentally determine the spread of directly transmitted airborne pathogens such as SARS-CoV-2 and influenza. Reliable quantitative estimates of contact patterns are therefore critical to modeling and reducing the spread of directly-transmitted infectious diseases and to assessing the effectiveness of interventions intended to limit risky contacts. While many countries have used surveys and contact diaries to collect national-level contact data, local-level estimates of age-specific contact patterns remain rare. Yet, these local-level data are critical since disease dynamics and public health policy typically vary by geography. To overcome this challenge, we introduce a flexible model that can estimate age-specific contact patterns at the subnational level by combining national-level interpersonal contact data with other locality-specific data sources. We estimate daily contact matrices for all 50 US states and Washington DC from April 2020 to May 2021 using national contact data from the US. Our results reveal important state-level heterogeneities in levels and trends of contacts across the US over the course of the COVID-19 pandemic, with implications for the spread of respiratory diseases.

This study revisits classic questions of political economy through an interdisciplinary lens, wedding the insights of modern social theory with heterodox political economy. The first chapter synthesizes the economic and sociological literature on wage determination, developing a conceptual apparatus, which is better situated than previous approaches, to understand the factors animating stagnating wages in the United States since the emergence of Neoliberalism. The second chapter applies reflexivity, a method in the sociology of science, to the Phillips curve. In doing so, the implicit biases within both theoretical and empirical approaches to the Phillips curve are elucidated, recognizing the limitations of traditional labor underutilization measures. Alternative labor underutilization measures are constructed using labor market transition rates, which are then used to estimate alternative econometric specifications of the Phillips curve. The results of these estimations are consistent with a flattening of the Phillips curve, expected as a result of declining institutional bargaining power of workers. The chapter finishes highlighting the limitations of the models estimated, commentating on how the literature should approach the Phillips curve going forward. The final chapter, uses path-dependency as a conceptual entry point to iv problematize the instrumental-ceremonial dichotomy, arguing that ceremonial institutions (culture) must be comprehensively considered in theorizing progressive institutional change, moving beyond an understanding of them as purely “imbecile”. A theory of political mobilization for progressive institutional change is laid out, one which systematically accounts for ceremonial institutions. By using rhetoric as a tool, we can play into ceremonial habits of thought, weaving progressive policy through the ceremonial net to implementation, where its instrumentality can be revealed, and a lock-in can form as constituents become accustomed to the material benefits provided. It is here where a progressive pathdependency is formed.

Race in America is defined through the duality of Black and White, but there are subgroups of Black people demanding attention in microeconomic study. Black Hispanics, also known as Afro-Latinxs, are a Black minority group in the United States which also share a similar experience as Black Non-Hispanics or Afro-Americans. Afro-Latinxs exist within an intersectionality that combines two minority identities: Black and Hispanic. Economic literature has shown that race has created wage disparities among people based on varying forms of discrimination. Although there is substantial evidence revealing wage disparities among difference races and ethnicities, there is little known about wage disparities in groups of a specific combination of race and ethnicity such as Black Hispanics. In my thesis, I will research labor markets to explore wage disparities that may exists among Black and Non-Black Americans and Black and Non-Black Hispanics to reveal any racial discrimination.

In contrast to expectations, remittances to Central America and the Caribbean (CAC) surprised positively during 2020 and 2021. This study revisits the key macro indicators driving remittances, looks at the heterogeneous impacts of the global financial crisis (GFC) and COVID shocks, then uses micro data from the U.S. Current Population Census to examine individual features of immigrant households and how this might affect the “propensity to remit”. The paper finds that remittance flows are responsive to both sending and receiving country economic conditions and that labor market conditions are particularly important determinants of remittance flows, explaining the unexpected jump in remittance flows in 2020-2021 and providing stronger predictive power when combined with income variables. Analysis of the micro data reinforces these findings, reflecting the existence of a family resource sharing model at play.

Background: Emergency employment programmes during the 1930s and 1940s invested income, infrastructure and social services into communities affected by the Great Depression. We estimate the long-term associations of growing up in an area exposed to New Deal emergency employment in 1940 with cognitive functioning in later life. Methods: Members of the Health and Retirement Study cohort (N=5095; mean age 66.3 at baseline) who were age 0–17 in 1940 were linked to their census record from that year, providing prospective information about childhood contextual and family circumstances. We estimated the association between subcounty-level emergency employment participation in 1940 and baseline cognition and rate of cognitive decline between 1998 and 2016. Results: Compared with those living in the lowest emergency employment quintile in 1940, those who were exposed to moderate levels of emergency employment (third quintile) had better cognitive functioning in 1998 (b=0.092 SD, 95% CI 0.011 to 0.173), conditional on sociodemographic factors. This effect was modestly attenuated after adjusting for respondents’ adult education, finances and health factors. There were no significant effects of area-level emergency employment on rate of cognitive decline. Conclusions: Exposure to New Deal employment policies during childhood is associated with long-term cognitive health benefits. This is partially explained by increases in educational attainment among those with greater levels of emergency employment activity in the place where they were raised. Future research should investigate which types of New Deal investments may most be related to long-term cognitive health, or if the associations we observe are due to co-occurring programmes.

Machine learning is the ability of machines to learn from past experiences using historical data (supervised and semi-supervised cases) to solve a given problem. Machines make decisions using Artificial Intelligence (past experiences). Companies adequately understand that increasing business efficiency and employee productivity are of supreme importance to thriving in a highly competitive digital environment. Any process can be automated as long as there is a clear operating procedure available. In today's time, almost every sector is automating things to minimise cost and improve reliability. So, we need something that will resolve this. In this paper, we will study the many good and bad impacts of artificial intelligence, robotics, and automation on employment. We will also discuss some ideas to minimise the bad impact and maximise the good impact in the related fields.

Machine learning applications in the clinical domain are speedily changing the health care sector with refinement in cost and quality of service. Still, clinical data privacy concerns a lot, limiting the revolution in the clinical care industry. Patient’s clinical information is exceptionally subtle and personally identifiable, like medical history, ongoing conditions, payment and credit card information; due to these private data, regulations like Health Insurance Portability and Accountability heavily protect patient’s medical data. Here synthetic patient data artificially generated can be the best solution to tackle the challenges of health care transformation. Researchers, medical institutes, and companies building artificial intelligence solutions always need clinical data to work upon so synthetic data can help them eliminate the above challenges. In this proposal, weighted bayesian association rules concepts are applied to generate realistic synthetic data while preserving the relationships among the data and validating the synthetic information using the multivariate relationships method and predictive techniques. University of California Irvine datasets of heart disease, breast cancer, and diabetes are used to generate synthetic datasets to build weighted Bayesian belief networks with promising results. The proposed idea will allow healthcare organizations to efficiently distribute synthetic clinical data to researchers, develop inferences, and create analytical tools without compromising data privacy.

This paper develops early estimates of how public pensions affect retirement timing by examining the Railroad Retirement Act of 1937, which replaced private railroad pensions with a national program similar to Social Security. Leveraging various samples of linked Decennial Census records between 1910-1940, the analysis compares male labor force nonparticipation by previous industry, year, and age. Higher benefits led to earlier retirement, largely driven by exit at age 65. Exploiting newly progressive benefits, the elasticity of nonparticipation at ages 65-69 is 0.55, which is large relative to findings in modern settings but consistent with contemporary elderly transfer programs.

An “urban” subfield exists in virtually every social science discipline, but these subfields seldom engage one another. We asked scholars from five urban subfields to respond to questions about the state of urban research within their discipline. This article consists of their consequent essays and reflections on their responses. The questions posed included the discipline’s conception of “urban,” the main concerns motivating the subfield, the primary methodologies pursued, the extent to which their subfield interacted with or was informed by research in other urban subfields, and the main concepts or approaches it had to offer to other subfields or might take away from them. In our reflections, we particularly note the intellectual and institutional difficulties in creating a broader field of urban research or of engaging in truly inter-disciplinary research. We also highlight the desirability of greater engagement across these subfields through encouraging a “republic of conversation” among them.

The true cost of child care in the title of this study refers to the actual monetary costs of providing the services of quality child care, as contrasted to the price that is paid for child care (the market rate), and contrasted to the subsidy, the public assistance that means-tested families receive for child care. Public subsidies, the primary form of public support for child care in the U.S., are determined as a percentile of market rates. More fundamentally, however, the true costs of the existing child care system in Erie County, in New York and around the country, are much larger. They are the social and economic costs of persistent racial, gender and economic inequality; lack of access to child care services for many families; an entire sector of low-wage work; forfeiture of workforce participation among parents; and loss of the human potential that derives from quality early childhood development and education. These costs may be harder to measure—some are immeasurable—but they inform the growing consensus that America’s child care system is not working for the families, workers, and providers directly involved, nor is it working for society and the economy as a whole. This Phase Two of a collaborative action research report illuminates both kinds of costs: current operational monetary costs per child at the enterprise or institutional level, and indicators of broader social costs of the existing child care system itself in Erie County and New York State. Undertaken by Cornell ILR Buffalo Co-Lab and Erie County’s Live Well Erie Emergency Child Care Task Force, the year-long project has provided vital empirical information for advocates, care providers, child care service organizations, and local government agencies, as well as to New York State elected officials through the 2022 budget debates and decisions. The immediate goals of this project were: • to determine the actual true cost of quality child care by modality in Erie County, • to compare those actual costs with New York State Office of Child and Family Services (OCSF) market rate price of care, • to compare with the NYS/Erie County Department of Social Services (DSS) subsidy rates, • further, to compare what the actual true costs would be if all child care workers earned a living wage, • and finally, to provide the data and analysis to County and State officials for consideration in public policy and budgets negotiations in 2022. In both purpose and methodology, collaboration was central to this action research project. The widely representative and agile Live Well Erie Emergency Child Care Task Force and applied researchers of Cornell University ILR Buffalo Co-Lab worked as a team to compile reliable and valid information and to share the findings with public officials and stakeholders in a timely manner.

The potential tradeoff between health outcomes and economic impact has been a major challenge in the policy making process during the COVID-19 pandemic. Epidemic-economic models designed to address this issue are either too aggregate to consider heterogeneous outcomes across socio-economic groups, or, when sufficiently fine-grained, not well grounded by empirical data. To fill this gap, we introduce a datadriven, granular, agent-based model that simulates epidemic and economic outcomes across industries, occupations, and income levels with geographic realism. The key mechanism coupling the epidemic and economic modules is the reduction in consumption demand due to fear of infection. We calibrate the model to the first wave of COVID-19 in the New York metropolitan area, showing that it reproduces key epidemic and economic statistics, and then examine counterfactual scenarios. We find that: (a) both high fear of infection and strict restrictions similarly harm the economy but reduce infections; (b) low-income workers bear the brunt of both the economic and epidemic harm; (c) closing non-customer-facing industries such as manufacturing and construction only marginally reduces the death toll while considerably increasing unemployment; and (d) delaying the start of protective measures does little to help the economy and worsens epidemic outcomes in all scenarios. We anticipate that our model will help designing effective and equitable non-pharmaceutical interventions that minimize disruptions in the face of a novel pandemic.

How have the longer journeys to work faced by Black commuters evolved in the United States over the last four decades? Black commuters spent 50.3 more minutes commuting per week in 1980 than White commuters; this difference declined to 22.4 minutes per week in 2019. Two factors account for the majority of the difference: Black workers are more likely to commute by transit, and Black workers make up a larger share of the population in cities with long average commutes. Increases in car commuting by Black workers account for nearly one quarter of the decline in the racialized difference in commute times between 1980 and 2019. Today, commute times have mostly converged (conditional on observables) for car commuters in small-and mid-sized cities. In contrast, persistent differences in commute times today arise in large, segregated, congested, and-especially-expensive cities, revealing the limits of cars in overcoming entrenched racialization of other factors of commuting.

An extensive sociological literature maintains that cultural capital plays a pivotal role in perpetuating social inequalities. However, empirical tests of cultural capital theory focus on how culture influences educational outcomes, not earnings, and they mainly look for cultural differences across social classes within societies. We propose a direct test of economic returns to cultural capital by capitalizing instead on differences in national cultures across countries. Using the American Community Survey and the National Survey of College Graduates, we analyze the relationship between immigrants’ lack of U.S.-specific cultural capital, proxied by cultural distance between the origin country and the U.S., and their earnings. Findings consistently indicate that origin–U.S. cultural distance is linked to immigrants’ lower earnings after controlling for various other factors, supporting cultural capital theory. The earnings penalties vary systematically across cultural distances, and such systematic variations are more pronounced for immigrants with at least a bachelor’s degree, those who arrive in adulthood, and with foreign highest degrees. Moreover, county-level analysis reveals more sizeable cultural distance penalties in more competitive and unequal labor markets, highlighting how subnational receiving contexts shape disparities in immigrants’ economic incorporation at their destinations.

Manufacturing has historically played a significant role in productivity and R&D. Jorgenson (2001) suggests that advances in microprocessors alone were associated with 50 percent of total factor productivity growth in the US and worldwide in the 1990s. This outsized role in R&D and productivity appears to continue today, even with significant changes across the sector in technology and globalization. US manufacturing is a disproportionate source of private R&D spending relative to its share of employment and global value added (GVA) and has higher than average labor productivity relative to other sectors.

Perspectives like Rhone’s are pessimistic, ignoring the fact that people make transportation choices in response to urban design. When biking is safe, more people do it.

The Growth Management Act in Washington allows subarea plans as an optional element, where the subarea plan is consistent with the Lewis County Comprehensive Plan (RCW 36.70A.085). A starting point for any comprehensive planning effort is to establish the factual basis from which the plan is developed. This existing conditions report forms the factual basis for the Packwood Subarea Plan.

Given that the U.S. military uses science, technology, engineering, and math (STEM) exposure as a key recruitment tool, one should expect that military service is associated with STEM outcomes. While research demonstrates this pattern for women veterans, we know little about racialized and intersectional patterns. This article uses the American Community Survey data (2014–2018) to examine the association between military service, race/ethnicity, and gender to STEM degrees earned. We find that military service operates contingently: White men’s plus white, Hispanic, and multiracial/other women’s predicted probability of earning a STEM degree increases with military service. In contrast, for other minority groups, military service is not associated with a higher predicted probability of earning a STEM degree. Indeed, for groups typically overrepresented in STEM fields (i.e., Asian veterans), a negative association exists. These findings inform extant research on the long-term impact of military service on civilian reintegration, including educational and occupational outcomes.