Bilingualism is deeply interwoven into the identity of New York City. Known as the world’s most famous immigration hub, New York City represents an unparalleled level of ethnic, cultural, and linguistic diversity that few places in the world have. As Tonnelat and Kornblum (2017) note, one good place to observe dynamic language practices in New York City is the 7 train, which runs through Eastern Queens to West Manhattan. Sitting on a seat with eyes closed, we can listen to a whole range of different languages spoken in the city, from Mandarin and Korean near Flushing, to a range of Spanish dialects near Junction Blvd, a melangé of South Asian languages at Roosevelt Ave, and a group of languages spoken in small communities such as Portuguese, Greek, Arabic, and Japanese at Queensboro Plaza. Finally, additional languages spoken by tourists contribute to this tremendous level of linguistic diversity on the 7 train after the East River into Manhattan.

Accurate forecasts for the utilization rates of electric vehicle charging stations (CSs) are crucial to coordinating the operations of on-site distributed energy resources. In this paper, we propose to forecast the CS utilization rates by considering key explanatory variables such as historical utilization rates, traffic flows, demographic properties, the number of EV registrations, and points of interest. Three machine learning models, namely random forest, feed-forward neural network, and long short-term memory (LSTM) are adopted for the forecasting task. The proposed algorithms are validated using the real-world utilization data collected from around 130 CSs in Contra Costa County, California. The numerical study results show that the LSTM model achieves the best prediction performance. The lagged CS utilization rates and traffic flows are the two most influential features. More interestingly, the traffic flow plays a more important role in predicting the utilization rates of DC Fast CSs than that of the level 1 (L1) and level 2 (L2) CSs.

China’s meteoric rise as a world manufacturing power commenced in the early 1990s and was heralded and amplified by its elevation to Permanent Normal Trading Status with the United States and accession to the World Trade Organization at the start of the new millennium. A rapidly growing body of literature finds that China’s subsequent export surge, which drove the US merchandise trade deficit from 2.7% to 5.7% of GDP between 1998 and 2007, left large and enduring scars on aggregate US manufacturing employment, on workers initially employed in manufacturing establishments competing with China, and on local labor markets specializing in labor-intensive manufacturing, in which China gained comparative advantage (Bernard, Jensen, and Schott 2006; Autor, Dorn, and Hanson 2013; Autor et al. 2014; Ebenstein et al. 2014; Acemoglu et al. 2016; Autor, Dorn, and Hanson 2016; Caliendo, Dvorkin, and Parro 2019).

Mounting evidence suggests that publications and citations of scholars in the STEM fields (Science, Technology, Engineering and Mathematics) suffer from gender biases. In this paper, we study the physics community, a core STEM field in which women are still largely underrepresented and where these gender disparities persist. To reveal such inequalities, we compare the citations received by papers led by men and women that cover the same topics in a comparable way. To do that, we devise a robust statistical measure of similarity between publications that enables us to detect pairs of similar papers. Our findings indicate that although papers written by women tend to have lower visibility in the citation network, pairs of similar papers written by men and women receive comparable attention when corrected for the time of publication. These analyses suggest that gender disparity is closely related to the first-mover and cumulative advantage that men have in physics, and is not an intentional act of discrimination towards women.

We analyse the effects of climate anomalies on women’s fertility in West Africa. We combine socioeconomic and fertility data from 10 rounds of Burkina Faso, Guinea, Nigeria and Mali ’s Demographic and Health Surveys and long-term high resolution spatial temperature and precipitation records to assess how women and households adapt their fertility preferences and births in response to exposure to local temperature and precipitation anomalies. We also analyse the role of physical capital and livelihoods in such response. We find positive associations between higher than normal temperatures and the odds of desiring a larger family and births, while precipitation shocks show weakly robust results. We attribute this upward revision in women’s fertility to higher labour demand or lower costs of childbearing.

Recent studies have found that adoptive parents invest in their children equally or more than biological parents do. Most of these studies observed relationships across families, comparing families with adopted children to those without. In this study, alternatively, we focused on within-family comparisons to more fully isolate the relationship between biological ties and parental investments. Using American Time Use Survey (2007–2018; n = 1,152 children) and American Community Survey (2014–2018; n = 34,673 children) data, we employed within-family fixed effects regression models and focused on both parental time and financial investments, using private school enrollment as a proxy for the latter. Our findings show that parents spent less one-on-one, quality, and total time daily with adopted children compared to biological children. In terms of financial investments, 90% of children in the sample received equal investments, meaning that either all or no siblings within the same family were enrolled in private school. However, among families with enrollment differences between siblings, adopted children were significantly less likely than their non-adopted siblings to be enrolled in private school. These findings show that adopted children within mixed-adoption families may receive equal or fewer investments than their non-adopted siblings. The findings highlight the possibility of selection as an interpretation of the adoptive-child advantage, illustrate the importance of within-family studies on this topic, and point to the complexity of parental investments in adopted children.

Muslim-Americans are often at the center of conversations in the U.S. political and socioeconomic sphere. They are also one of the fastest-growing demographics in the U.S., with around 1.1% of the U.S. population belonging to the Muslim faith. The 2018 Pew survey projections say that Muslim-Americans are going to be the second largest faith in the U.S. by 2040. Muslim-Americans are also one of the most racially diverse groups in the U.S., comprising Arabs, Asians, African-Americans, and Caucasians among others. Latinos are currently one of the fastest-growing Muslim-Americans demographics as well. However, despite their importance, there is limited data-driven research on Muslim giving. There is extraordinarily little data and information available on Muslim philanthropic practices. Given the centrality of giving among Muslim communities and the vital role religious giving plays in philanthropy more broadly, it is worth taking a deeper look at how and why American Muslims give. Thousands of organizations nationwide support Muslim communities across a broad spectrum of needs. While some funding for these pursuits flows from outside Muslim communities (both from individuals and foundations), they are limited; most financing of organizations that support Muslim communities’ unique needs still comes from Muslims themselves. Thus, a deep dive into the philanthropic practices of American Muslims is necessary as we consider the development and strengthening of the communities and the institutions that serve them. At the same time, it is also essential to understand the motivations behind Muslim giving, mainly the reasons that may motivate them to giving toward specific causes. To this end, the Muslim Philanthropy Initiative at the Indiana University Lilly Family School of Philanthropy survey in collaboration with Islamic Relief USA included a broad range of studies for uncovering American Muslims’ philanthropic practices and motivations. Moreover, this survey is also unique because earlier surveys only looked at whether Muslims practiced philanthropy and gave to specific causes. This study is the first survey to attempt to understand Muslim charities’ overall extent and scope and assess monetary support toward various causes. This report presents the data and findings from the section on Muslim philanthropy in the Muslim American Philanthropy Poll. The study was a self-administered web survey conducted by the Indiana University Lilly Family School of Philanthropy. The study investigated the opinions of Muslims and the general population regarding faith customs, donation practices and attitudes, volunteer work, COVID-19, uncertainty intolerance, financial well-being, and discrimination. The survey also looked at how Muslims made decisions about donations. SSRS surveyed from March 17 through April 7, 2021. Overall, the team surveyed 2,005 respondents, including 1,003 Muslim respondents and 1,002 general population adult respondents. Overall, the survey finds that Muslims give more toward both faith- and non-faith-based causes than nonMuslims. Overall, if we extrapolate the overall giving to 3.45 million Muslims (based on Pew Survey), we find that Muslims gave a guestimate of 4.3 billion USD. Similarly, Muslims gave USD 1,810 to faith-based causes compared to USD 1,138 in the general population. Again, if we extrapolate the average giving of Muslims to 3.45 million Muslims, we find that Muslims gave USD 2.4 billion toward faith-based giving. Similarly, an average Muslim gave USD 1,400 to non-faith-based causes compared to USD 767 in the general population. If we extrapolate it to 3.45 million, Muslims gave an estimated USD 1.9 billion for non-faith-based causes. Overall, the report finds that American Muslims give their money and time generously in the U.S., much more than the average population. Their philanthropic patterns and preferences are well-assimilated with the American landscape and, for the most part, aligned remarkably well with the general public. There are, however, some distinct differences. For instance, Muslims generally spend a smaller proportion of their charity toward houses of worship or mosques (27.45%) compared to the average population (51.28%). Similarly, Muslims give a more significant proportion of their donations toward overseas relief and civil rights causes than the average population.

In his April 2021 address to Congress, President Biden announced plans to provide a national paid medical and family leave program. The United States is currently the only Organization for Economic Cooperation and Development (OECD) country without a national paid leave policy. However, momentum for paid leave has surged in recent years. There are currently nine states (and Washington, DC) with paid family and medical leave (PFML) programs, and there is growing bipartisan support for a national paid leave program. To ensure that paid leave programs are equitable across population groups, policymakers need research that examines potential disparate impacts of proposed PFML policies. Our research addresses three questions: (1) What percentage of US workers would be eligible for paid leave under potential leave policies? (2) How would eligibility rates under these policies vary for different population groups (i.e., by gender, race, age, education, total family income, urban/rural status, industry, occupation, and parental status)? And (3) What would workers’ average weekly benefits be under these policies and how would they vary for different groups? We compared the average benefit amount for the FAMILY Act, a proposed national program sponsored by Representative Rosa DeLauro and Senator Kirsten Gillibrand, and the three longest-running state PFML policies. We found that the FAMILY Act would cover the most workers of any PFML policy. We attribute this to its low earnings requirement ($1 in the last year) and its inclusion of workers from all sectors and industries. We found that among state paid leave programs, New Jersey has the most generous average weekly benefits, which we attribute to its high reimbursement rate. We end our report with recommendations for developing eligibility and wage replacement structures for future PFML programs that would ensure greater equity of paid leave programs as well as suggestions for further research. Findings and recommendations from this report are even more salient given recently released proposals on PFML within the Biden-Harris administration’s American Families Plan and the U.S. House Ways and Means Committee’s Building an Economy for Families Act.1,2 While this analysis was conducted prior to the release of these proposals, the findings provide important lessons for structuring paid leave programs to maximize access and equity.

Background: Sleep disorders are among important challenges for health policymakers due to its high prevalence and very high economic burden. Effect of air and noise pollutions on sleep quality and disorders has been proposed. Objectives: In the present study, the geographical distribution of people diagnosed as insomnia in Sleep Disorders Research Centers (SDRC) in the western Iran was investigated. Methods: All patients with primary insomnia who diagnosed by polysomnography (PSG) in SDRC between 2011 to 2019 were recruited. The postal addresses of participants specified by a geographical coordinate represented longitude, latitude, and vertical position. These geographical coordinates converted to Geographic Information System (GIS) and addresses were located on the map. Then participants were divided into noisy and quiet environment groups based on their distance from highways, boulevards, and streets. Subjective and objective sleep characteristics were compared between these two groups by independent sample t-test or U Mann Whitney test using the Statistical Package for the Social Sciences (SPSS) version.22. Results: Of 78 participants, 47 were from Kermanshah city, 19 were from counties of Kermanshah province, and 14 participants were from Kurdistan, Ilam, Hamedan, Lorestan, Khuzestan and Tehran provinces. The location of all participants was presented on GIS maps. Result didn’t indicate any significant differences in sleep characteristics between the two noisy and quiet environment groups. Conclusions: Most insomniac patients investigated in the SDRC come from Kermanshah city. Patient with insomnia may reach a level of the pathophysiological condition in sleep-wake regulation systems that lead to problematic sleep characteristics independently from environmental noise.

The quality of life literature revealed the role that natural resources and local consumption amenities play in household preferences for cities and their growing importance in determining the success of cities. As women's control over household resources has increased, a growing body of consumer behavior literature has been documenting gender differences in preferences for consumer goods. Yet, the quality of life literature does not seem to make any distinction along gender lines in determining preferences for local consumption amenities. We estimate the quality of life across U.S. cities separately for unmarried men and women, and using a rich set of location-specific amenities, examine which amenities cause preferences to differ between men and women. Our results suggest that men and women's valuations of cities are correlated, suggesting commonality in preferences for many location-based amenities, most notably natural resources, but there are also important deviations in valuations especially for local public goods and gender role attitudes.

This paper examines local-level variation in the primary disabling conditions of new awardees for Social Security Disability Insurance (SSDI) from 2005 through 2018. It uses data from the Social Security Administration’s Disability Analysis File data linked to other publicly available information from the American Community Survey and Area Health Resource File. The analysis is at the level of U.S. Census Bureau Public Use Microdata Areas (PUMAs). The paper documents the share of awards in each PUMA and year in one of five impairment group categories, selected to align with areas of strong policy interest. In each impairment group, the paper identifies “hot spots” as PUMAs in which the share of awards for that condition is in the top 10 percent relative to other PUMAs in the same year. The paper documents the geographic variation in award shares and hot spots using maps and uses regression analysis to explore relationships between SSDI award shares by impairment group and a range of PUMA-level factors. The findings are descriptive and should not be interpreted causally. We find that: • SSDI awards by impairment groups have geographic patterns that show important variation across state lines as well as within state borders. The share of awards for musculoskeletal disorders is higher in PUMAs in Appalachia and into the southeast, while award shares for mental disorders are highest in New England – persistently so in in Vermont, and New Hampshire. Award shares for circulatory and respiratory disorders are higher in the Mississippi Delta region and northward along the Mississippi River to and through Illinois and Indiana. There are no obvious patterns for the neoplasms, infectious diseases, injuries category nor the systems diseases category. • The general geographic patterns that show cross- and within-state differences are stable over time, but PUMA “hot spots” are not always the same in each year. In most cases, this simply reflects PUMAs that are just above the 90th percentile in some years and just below in others, rather than large swings in award shares within PUMAs over time. Nonetheless, areas with high award shares in particular impairment groups do persist over time. • Regression analysis shows that demographic and socioeconomic factors explain part of the observed variation in award shares, but the estimated effects are small, suggesting that other factors may be critical determinants of local-level variation in award shares. The policy implications of the findings are: • When considering future caseload patterns, it may be important to consider local-level predictors of new awards, overall and by impairment type. Some of the factors that might affect award shares for certain disabling conditions include environmental exposures and features of the disability determination process (such as distance to a field office and examiner stringency). Because of concerns about multicollinearity, it is difficult to incorporate the full range of observable factors that might affect awards by impairment category. Additionally, other factors such as the efficacy of legal representation when filing claims or social networks and attitudes toward benefit receipt may be important determinants of SSDI award patterns but more difficult to measure in a way that would allow for them to be incorporated into a regression model. • Qualitative research on geographic patterns in new benefit awards may help SSA better understand program trends, as well as the supports that new beneficiaries might need if they want to consider leaving the program rolls.

The post-Covid shutdown U.S. labor market is characterized by many open jobs and substantial unemployment. Despite numerous hypotheses regarding this paradoxical situation, existing models in labor economics lack the features necessary to adequately evaluate the potential policy remedies or causes. I construct a search model to reproduce the prevailing dynamics, which I then use to evaluate the employment impact and effect on the welfare of low-income workers from large-scale transfers, unemployment benefits, and an earnings subsidy. One-off transfers hardly affect employment in a downturn. Raising unemployment benefits by $300 per month leads to a 0.6 point increase in the unemployment rate. Adding an earnings subsidy to unemployment benefits raises employment by 7 percentage points at a 23% lower level of inequality, yet increases costs by only 16-30%. In contrast to a compensating differentials framework, the model shows that lower earners tend to be more at risk for layoffs, and that this effect is stronger following a negative aggregate shock. Following a simulated shock, mean consumption falls by 5% yet consumption at the 10th percentile drops by 16%. Although the risk borne by lower earners is often greater, their savings are often lower, because they have less consumption to seek to smooth and less opportunity to accumulate savings.

Background Thirty per cent of all women experience intimate partner violence (IPV) in their lifetime. The aim of this study was to examine the association between the WHO’s novel R.E.S.P.E.C.T framework and IPV among women in Kenya. Methods We used the 2014 Kenya Demographic and Health Survey (KDHS). Only women selected for the domestic violence module and who were married/living with their partner were eligible for this study (n=3737). We created a summary score for the strategies denoted by R.E.S.P.T based on availability of questions addressing these strategies in the KDHS, and a total score that summed responses across all strategies. Each letter was assessed with Cronbach’s alpha. Multiple logistic regression models were used to investigate the relationship between R.E.S.P.T scores and IPV. Results All strategies except for E lowered the odds of IPV. Decision-making (R) was negatively associated with experiencing IPV (OR=0.62 (0.53 to 0.72)). Land and property ownership (E) were positively associated with experiencing IPV (OR=1.25 (1.08 to 1.43)). Access to healthcare (S) was negatively associated with experiencing IPV (OR=0.55 (0.48 to 0.63)). Higher levels of wealth (P) were negatively associated with experiencing IPV (OR=0.47 (0.37 to 0.62)). Not justifying wife-beating in any scenario (T) was negatively associated with experiencing IPV (OR=0.39 (0.29 to 0.53)). After adjusting for demographics, a 1-unit increase in total R.E.S.P.T score was negatively associated with experiencing IPV (AOR=0.63 (0.57 to 0.70)) with a similar finding for IPV in the past 12 months (AOR=0.59 (0.53 to 0.66)). Younger women, higher education and Muslim religion were associated with decreased odds of experiencing IPV while living in a rural location and working were associated with increased odds of experiencing IPV. Conclusions Our study provides initial evidence that by using the multistrategy R.E.S.P.E.C.T framework, countries can dramatically lower the odds of women experiencing IPV. IPV prevention strategies must have a wide approach. The DHS can be used as a tool to monitor implementation and efficacy of this novel strategy.

When the first COVID-19 vaccine was approved for emergency use in the US, many breathed a sigh of relief. Finally a light of hope was shining through the fog of uncertainty that had surrounded us since the beginning of the pandemic. But it’s now March 2021 and as the pandemic stretches towards the one-year mark, vaccines are not getting to the communities who need them most.

Earnings are riskier and more unequal for households born in the 1960s and 1980s than for those born in the 1940s. Despite the improvements in financial conditions, younger generations are less likely to be living in their own homes than older generations at the same age. By using a life-cycle model with housing and portfolio choice that includes flexible earnings risk and aggregate asset price risk, I show that changes in earnings dynamics account for a large part of the reduction in homeownership across generations. Lower-income households find it harder to buy housing, and as a result accumulate less wealth.

In an editorial for this journal a decade ago, then-Editor-in-Chief Fred Phillips asserted that social change was proceeding at hyper-speed and, moreover, that it had consequently come to outpace technological change. This paper submits these claims to empirical assay. In so doing, we address the myriad problems attendant upon determining and interpreting the sort of data that might support us in our cause. Notwithstanding the innumerable caveats that this necessarily entails, and restricting ourselves to considering US data, we conclude that a wide range of indicators suggest that millennial Americans are not living in a time of particularly rapid social change, at least not when compared to the period 1900–1950. Furthermore, our analysis suggests that the data that we have considered does not easily support a contention that significant variation in social change occurs in long wave-like cycles. The evidence is more supportive of a punctuated equilibrium model of change.

One of the many interests of the late Dietrich Stauffer was the modelling of mortality. Here we review the features of mortality data for various biological species. Age specific mortality (death rate) leads to a discussion of possible models of the death rate, including that of Gompertz, raising the interesting question: is our lifetime finite or could we contemplate living for ever? The answer judging from many different data sources is that without radical changes in our biology it seems death above age 120 is extremely unlikely. We then show how a toy model, linking mortality to the immune system, can predict the general variation of the death rate with time, spanning both infant and adult phases. The outcome provides underpinning support for the many nutritionists and medical experts who increasingly advocate the benefits to mortality of a healthy lifestyle. Age specific mortality within social networks is also shown to be significantly affected by both psychological and physical shocks. The review concludes with the description of novel experiments using soap films for the study of failure. These allow for a reproduction of high infant mortality, the so-called bath-tub curve of mortality, and the Gompertz law.

Estimated labor force participation rates among free women in the pre-Civil War period were exceedingly low. This is due, in part, to cultural or societal expectations of the role of women and the lack of thorough enumeration by Census takers. This paper develops an augmented labor force participation rate for free women in 1860 and compares it with the augmented rate for 1920 and today. Our methodology identifies women who are likely providing informal and unenumerated labor for market production in support of a family business, that is, unreported family workers. These individuals are not coded in the original data as formally working, but are likely to be engaged in the labor force on the basis of the self-employment of other relatives in their household. Unreported family workers are classified into four categories: farm, merchant, craft, and boardinghouse keepers. Using microdata, the inclusion of these workers more than triples the free female labor force participation rate in the 1860 Census from 16 percent to 57 percent, more than doubles the participation rate in the 1920 Census from 24 percent to 50 percent, and has a small effect on the currently measured rate of 56 percent (2015-2019 American Community Survey). This suggests that rather than a steep rise from a very low level in the female labor force participation rate since 1860, it has in fact always been high and fairly stable over time. In contrast, the effect of including unreported family workers in the male augmented labor force participation rate is relatively small.

This study explores the association between sanctuary policies and the high school completion and college enrollment of Hispanic undocumented youth. Sanctuary policies, which city, county, and/or state governments implement, prohibit local political leaders and police officials from cooperating with federal immigration enforcement officers regarding the questioning, detention, and deportation of undocumented immigrants. This study uses data from the American Community Survey and applies a difference-in-differences design. On average, my preferred specification detected no association with high school completion or college enrollment. These findings suggest that although these policies may help counteract immigration enforcement, they may not reduce uncertainty enough to have a significant relationship with educational outcomes.

The Diversity Commission held its first meeting on November 10, 2021 with First Selectman Sandra Pierog presiding. Also, in attendance were: Interim Administrative Office James Rupert, Amanda Gordon, Cheryl Udin, Kawan Gordon, Jennifer Cyr, John Toomey, Rhea Klein and Paula Meyers.