The aging of populations has become a global trend, which brings great challenges to the quality of life of the elderly, and improving the quality of life of the elderly has gradually become the focus of attention. Tourism is seen as a useful activity to improve the quality of life and enhance the happiness of the elderly. Most scholars believe that retired the elderly, with their abundant income and leisure time, have a high desire to travel and are the most eligible group for travel. At the global scale, the elderly increasingly have the ability to spend on tourism and are important participants in tourism. The silver-haired tourism market has broad prospects for development, and this field has always been an essential topic in academic research. Tourism is a typical leisure activity, tourism decision-making is significantly constrained by individual leisure time, and individuals with more leisure time have the basic conditions for tourism. It can be seen that tourism decision-making involves time allocation behavior. Previous applications of Becker’s time allocation theory have focused on issues related to labor market time allocation, caregiving and intra-household time allocation, and there is a lack of systematic research related to the allocation of travel time. Many studies have confirmed that income and leisure time are the most important tourism constraints; however, few studies have investigated the impact of income and leisure time on tourism for the elderly from a time allocation perspective. Cross-country differences prevail in time allocation for elderly groups, and culture is a vital factor in this difference, but there has been no detailed discussion or research work on how culture affects the differences in time allocation for elderly groups among countries, nor has there been any systematic research on how cultural differences affect tourism for the elderly. This paper fills these two research gaps to understand better the similarities and differences in the allocation of travel time for the elderly under different cultural characteristics. Based on the multinational time use study (MTUS) data, this study adopts Becker's time allocation theory and Hofstede's cultural dimension to examine the elderly (≥65 years old) in Western countries. Moreover, it employs regression analysis and the instrumental variable method to investigate the influence of income and leisure time on travel time allocation for the elderly and verifies the moderating effect of national cultural differences. The study results revealed that income and leisure time have a significant impact on travel time allocation for the elderly; when compared with income, leisure time has a greater impact on travel time allocation for the elderly. National cultural differences play a moderating role in the relationship between income and leisure time and travel time allocation for the elderly, thereby confirming that there are differences in travel time distribution among the elderly in different national cultures. Thus, these results will aid tourism managers to deepen their understanding of the characteristics of tourism behaviors of the elderly in different countries and help develop and formulate the marketing strategies of the tourism market for the elderly.

This paper studies how demand for labor reacts to financial technology (fintech) shocks based on comprehensive databases of fintech patents and firm job postings in the U.S. during the past decade. We first develop a measure of fintech exposure at the occupation level by intersecting the textual information in job task descriptions and fintech patents. We then document a significant decline of job postings in the most exposed occupations, and an increase in industry as well as geographical concentration of these occupations. Firms resort to an upskilling strategy in face of the fintech disruption, requiring “combo” (finance and software) skills, higher education attainments, and longer work experiences in the hiring of fintech-exposed jobs. Financial firms and those with high innovation outputs are able to offset the disruptive effect from the fintech shock. Among innovating firms, however, only inventors (but not acquisition-driven innovators) experience growth in hiring, sales, investment, and enjoy better returns on assets.

Since 1970, the share of Black individuals living in suburbs of larger cities has risen from 16 to 36 percent. We present three facts illustrating how this suburbanization has changed spatial inequality. First, suburbanization entirely accounts for Black households’ relative improvements in several key neighborhood characteristics, while Black city dwellers saw declines. Second, suburbanization accounts for over half of the increase in within-Black income segregation. Selective Black migration and muted suburban “White flight” both contribute to these patterns. Third, total Black population in central cities has plummeted since 2000, driven by young people and declines in high-poverty, majority-Black neighborhoods.

Multiple organizations would benefit from collaborative learning models trained over aggregated datasets from various human activity recognition applications without privacy leakages. Two of the prevailing privacy-preserving protocols, secure multi-party computation and differential privacy, however, are still confronted with serious privacy leakages: lack of provision for privacy guarantee about individual data and insufficient protection against inference attacks on the resultant models. To mitigate the aforementioned shortfalls, we propose privacy-preserving architecture to explore the potential of secure multi-party computation and differential privacy. We utilize the inherent prospects of output perturbation and gradient perturbation in our differential privacy method, and progress with an innovation for both techniques in the distributed learning domain. Data owners collaboratively aggregate the locally trained models inside a secure multi-party computation domain in the output perturbation algorithm, and later inject appreciable statistical noise before exposing the classifier. We inject noise during every iterative update to collaboratively train a global model in our gradient perturbation algorithm. The utility guarantee of our gradient perturbation method is determined by an expected curvature relative to the minimum curvature. With the application of expected curvature, we theoretically justify the advantage of gradient perturbation in our proposed algorithm, therefore closing existing gap between practice and theory. Validation of our algorithm on real-world human recognition activity datasets establishes that our protocol incurs minimal computational overhead, provides substantial utility gains for typical security and privacy guarantees.

Bike enthusiasts argue that bikesharing programs can be an important element of sustainable mobility planning in the urban cores of large metropolitan areas. However, the objective long-term impact of bikesharing on reducing auto-dependence is not well-examined, as prior studies have tended to rely on self-reported subjective mode substitution effects. We use a unique longitudinal dataset containing millions of geo-referenced vehicle registrations and odometer readings in Massachusetts over a six-year period - the Massachusetts Vehicle Census - to examine the causal impact of bikesharing on various metrics of auto-dependence in the inner core of Metro Boston. The difference-in-differences (DiD) framework is extended to accommodate spatial spillover effects with the inclusion of a spatial autoregressive lag leading to the spatial DiD (SpDiD) model. We also account for seasonal variation in bikeshare operations, where several stations are shut down for the winter months, by setting up a dynamic treatment definition. We find that a new bikeshare station reduces vehicle ownership per household by 2.2%, vehicle miles traveled per person by 3.3%, and per-capita vehicular GHG emissions by 2.9%. We also find strong evidence to support the use of bikesharing as a first/last-mile connector to mass transit. Auto-dependence reductions are around 10% (more than thrice as high as average) where bikeshare connections to transit stations are less than one kilometer long. Finally, we find that vehicle ownership reductions are almost immediate and last up to a year, while vehicle use and emission reductions are lagged over 1.5 years. These sizeable and measurable auto-substitution effects do support some of the claims of bikesharing advocates. These findings are especially important in the post-COVID-19 era, as cities strive to counter the pandemic-inspired safety skepticism about non-car travel.

The Institute for Research on Race and Public Policy has as a core part of our mission to increase society’s understanding of the root causes of racial and ethnic inequality. To this end, over the last four years we have published a series of reports on The State of Racial Justice in Chicago. In our reports, we document the experiences and conditions of life in the city for different groups and ask of ourselves and others, Chicago for whom? Across the reports, we make clear that racial and ethnic inequities in Chicago remain pervasive, persistent, and consequential: they affect the lives of Chicagoans in every neighborhood; they have not just spatial roots but also deep historical ones and are embedded in our social institutions; and they have powerful effects on the lived experiences and opportunities of all Chicagoans. Because there was such a vast trove of existing data on Chicago, in our first reports we drew exclusively on this available data and focused our efforts on bringing all the information together in a way that was accessible. As we did so, however, we also started an internal list of topics where we felt like we needed to know more. At the top of that list were questions about local patterns in racial gaps in wealth and how those gaps shape the lives of Chicagoans. To answer these questions we initiated a project to talk to middle class families in the Chicagoland area about their experiences and life trajectories. This report captures their stories. We are deeply grateful to everyone who spent time with us during this process and were willing to share their family’s joys and struggles. As we write about in what follows, there is much work to be done to support families who are contending today not just with the legacies of past inequities but with the ongoing failures of public policy to address basic needs. A Chicago that works for all is possible only if we intentionally create policies and practices that address racial inequities at all levels of our society.

We exploit the 2008 Oregon ban on non-compete agreements (NCAs) for hourly-paid workers to provide the first evidence on the impact of NCAs on low-wage workers. We find that banning NCAs for hourly workers increased hourly wages by 2-3% on average. Since only a subset of workers sign NCAs, scaling this estimate by the prevalence of NCA use in the hourly-paid population suggests that the effect on employees actually bound by NCAs may be as great as 14-21%, though the true effect is likely lower due to labor market spillovers onto those not bound by NCAs. While the positive wage effects are found across the age, education and wage distributions, they are stronger for female workers and in occupations where NCAs are more common. The Oregon low-wage NCA ban also improved average occupational status in Oregon, raised job-to-job mobility, and increased the proportion of salaried workers without affecting hours worked.

Objective The authors analyzed variation by education and type of day in the “time availability” association between U.S. mothers' paid work hours and housework and child care, types of work that vary by their urgency, affect, and symbolic meaning. Background Research shows a stronger negative association of women's work hours with housework than child care, and interprets this as evidence to show mothers prioritize child care over housework. The authors extend this work by determining if associations of work hours with partnered mothers' housework and child care differ by college education and type of day. Method The authors used ordinary least squares regression on weekend and weekday time diaries of partnered mothers aged 18–65 (N = 22,816) from the 2003–2018 American Time Use Survey ( https://timeuse.ipums.org/). Results Authors found negative associations of mothers' work hours with weekday housework and child care. They found a negative association of college degrees with weekday housework but a positive association with child care that attenuates at longer work hours. The negative work hour association, and the education gap in predicted child care time, persisted on weekends. Work hour and education associations with weekend housework were positive, and the education gap widened at longer work hours. Conclusion The “time availability” constraint of employment hours applies to child care and housework, even among mothers with college degrees. Education differences in unpaid work, particularly child care, are most evident on weekends.

To what extent have immigrants contributed to the growth of the United States creative arts economy? In this paper, we explore the impact of immigration during the Age of Mass Migration on the development of the arts in the U.S. over the past century. In the short run, our results suggest that immigration helped produce greater numbers of native artists. Over a century later, counties with greater historical immigration house more arts businesses and nonprofit organizations that generate more revenue, employ a larger proportion of the community, and have earned more federal arts grants. When evaluating potential mechanisms, we see that arts developmenwas not solely attributable to immigrant artists or artists from immigrant families. Our analysis instead suggests that broader interactions between the general immigrant population and natives contributed to this growth. Altogether, our results highlight the important role that immigrants played in the early development of the creative arts in America.

We study the relationship between internal migration and industrialization in the United States between 1850 and 1880. We use the Linked Representative Samples from IPUMS and find significant amounts of rural-urban and urban-urban migration in New England. Rural-urban migration was mainly driven by agricultural workers shifting to manufacturing occupations. Urban-urban migration was driven by foreign-born workers in manufacturing. We argue that rural-urban migration was a significant factor in US economic development and the structural transformation from agriculture to manufacturing.

Underrepresented minority (URM) college students have been steadily earning degrees in relatively less-lucrative fields of study since the mid-1990s. A decomposition reveals that this widening gap is principally explained by rising stratification at public research universities, many of which increasingly enforce GPA restriction policies that prohibit students with poor introductory grades from declaring popular majors. We investigate these GPA restrictions by constructing a novel 50-year dataset covering four public research universities' student transcripts and employing a staggered difference-indifference design around the implementation of 29 restrictions. Restricted majors' average URM enrollment share falls by 20 percent, which matches observational patterns and can be explained by URM students' poorer average pre-college academic preparation. Using first-term course enrollments to identify students who intend to earn restricted majors, we find that major restrictions disproportionately lead URM students from their intended major toward less-lucrative fields, driving within-institution ethnic stratification and likely exacerbating labor market disparities. Abstract Underrepresented minority (URM) college students have been steadily earning degrees in relatively less-lucrative fields of study since the mid-1990s. A decomposition reveals that this widening gap is principally explained by rising stratification at public research universities, many of which increasingly enforce GPA restriction policies that prohibit students with poor introductory grades from declaring popular majors. We investigate these GPA restrictions by constructing a novel 50-year dataset covering four public research universities' student transcripts and employing a staggered difference-indifference design around the implementation of 29 restrictions. Restricted majors' average URM enrollment share falls by 20 percent, which matches observational patterns and can be explained by URM students' poorer average pre-college academic preparation. Using first-term course enrollments to identify students who intend to earn restricted majors, we find that major restrictions disproportionately lead URM students from their intended major toward less-lucrative fields, driving within-institution ethnic stratification and likely exacerbating labor market disparities.

With the advent of the Covid-19 pandemic, labour-force survey non-response rates have surged in many countries. We show that in the case of Canada, the bulk of this increase can be explained by the suspension of in-person interviews following the adoption of telework in Federal agencies, including Statistics Canada. Individuals with vulnerabilities to the Covid-19 economic shock-i.e., the young, low-educated, lowsalary, low job-tenure individuals, and those working in occupations with low telework potential-have been harder to reach and have been gradually less and less represented in the Canadian Labour Force Survey (LFS) during the pandemic. Using exogenous variation in the assignment of individuals to the different LFS rotations, we present evidence suggesting that the decline in employment and labour-force participation have been underestimated over the March-July 2020 period. We believe, however, that these non-response biases have been moderate when contrasted with the unprecedented severity of the Covid-19 disruption. Furthermore, since attrition only represents a minor part of the non-response increase, we argue that one should not expect additional difficulties when using panels as compared to cross-sectional samples, and when using public-use LFS files instead of restricted-access files. All in all, the LFS remains a reliable data source for analyzing the economic impact of Covid-19 in a timely manner.

Using a structural approach, I quantify the eect of land-use regulations on dierent age and education groups. Building on the seminal work of Roback, 1982, I estimate a dynamic spatial structural equilibrium model of household location choice, local housing supply, and amenity supply. I show that in the long-run, removing land-use restrictions benefits all household groups and increases aggregate consumption by 7.1%. These consumption gains vary across households, less educated and younger households see increases in consumption about twice as large as more educated or older households. In contrast, in the short-run, removing landuse regulations reduces the consumption of older-richer homeowners while increasing the consumption of younger renters. In a counterfactual 1990-2019 transition, abolishing landuse regulations reduces the consumption of households born before the mid-1960s, while increasing consumption of more recent generations. Given the diffculty in reforming landuse regulations, I explore whether a shift to remote working or creating new urban areas leads to similar consumption gains compared with removing land-use restrictions. Qualitatively, I find the gains are similar, but quantitatively are only about 20% as large as abolishing land-use regulations from existing urban areas.

Do male-biased labour demand shocks affect women's labour market outcomes? To study this question, we examine large oil field discoveries in the southern USA from 1900 to 1940. We find that oil wealth has an overall positive effect on female labour force participation that is driven by single women. While oil discoveries increase demand for male labour and raise male wages, they do not drive women out of the tradable goods sector or the labour force. Our findings suggest that the absence of any crowding out effects of oil wealth can be explained by compensating forces such as demand effects within the tradable sector, or by income effects that lead to growth in the non-tradable sector.

This dissertation centers on research at the intersection of labor, public, and urban economics. Chapter 1 details the role, process, and history of census tract delineation prior to each Decennial Census, and investigates short- and long-run implications of neighborhoods that receive further delineation, or become “split”. Using a difference-in-differences empirical design, I exploit Decennial Censuses from 1980 to 2010 to find that “split” census tracts increase in their proportion of Black residents and these effects persist decades. Further evidence suggests that the Low-Income Housing Tax Credit program may play a role in concentrating residents in areas with greater census tract delineation. These results suggest that census tract delineation may play an important role in shaping neighborhood dynamics. Chapter 2 focuses on the role urban racial inequality has in contributing to economic inequality for Black residents. Using variation in census tract boundaries to measure levels of urban racial separation, I find that tract-induced racial separation negatively impacts Black individuals across both income and skill distributions. Contributing factors include fewer local job opportunities in predominantly Black neighborhoods, and increased commuting costs for those with jobs. Further evidence suggests these increases in separation, in already predominantly Black neighborhoods, reduce economic and geographic mobilities into adulthood. These results have important implications for fostering equal economic opportunity in areas of high racial separation. Chapter 3 documents the changing landscape of enrollment and graduation in higher education during the rapid expansion of the for-profit sector in the 2000s. I construct a measure of institutional quality and show that institutions of the lowest quality, primarily for-profit institutions, experienced the largest increases in enrollment as well as large decreases in graduation rates. Decomposing these changes in the lowest quality institutions suggests that aggregate graduation rates decreased primarily as a result of the changing enrollment landscape across higher education, while growth in aggregate rates of the highest quality institutions comes from within institution changes. These results suggest that as enrollment in higher education expands, schools become more selective in who they enroll, and may have consequences for students attending lower quality institutions.

We analyze sectoral labor reallocation and the reversal of urbanization in the U.S. during the Great Depression. The widespread movement to farms, which serves as a form of migratory insurance during the crisis, is largely towards farms with low levels of mechanization. In contrast, the mechanized agricultural sector sheds workers, many of whom reallocate into low-productivity or subsistence farming. The crisis perverts the normal process of structural change—in which workers displaced by farm equipment are released into more productive occupations—suggesting that macroeconomic fluctuations are an important factor determining the labor market consequences of technological change.

When comparing high- to low-immigrant locations, a large literature documents small effects of immigration on labor market outcomes over 10-year horizons. The literature also documents short-run negative effects of immigrant-driven labor supply shocks, at least for some groups of native workers. Taken together, these results suggest that there are mechanisms in place that help local economies recover from the short-run effects of immigrant shocks. This paper introduces a small-open-city spatial equilibrium model that allows, with simple reduced-form estimates of the effects of immigrant shocks on the outcomes of interest, the local adjustment to be decomposed through various channels.

Objective: We analyze fertility preferences among women at risk of pregnancy with children ages five or younger as a function of two food security metrics: perceptions of household hunger and child stunting (height for age z scores ≤ −2.0) in order to convey a robust picture of food insecurity. Methods: We use data from the 2016 Tanzania Demographic and Health Surveys to analyze this research question. Multinomial generalized logit models with cluster-adjusted standard errors are used to determine the association between different dimensions of food insecurity and individual-level fertility preferences. Results: On average, women who experience household hunger are 19% less likely to want more children compared to women who do not experience household hunger (AOR: 0.81, p = 0.02) when controlling for education, residence, maternal age, number of living children, and survey month. Adjusting for the same covariates, having at least one child ≤ 5 years old who is stunted is associated with 13% reduced odds of wanting more children compared to having no children stunted (AOR: 0.87, p = 0.06). Conclusions for Practice: In the context of a divided literature base, this research aligns with the previous work identifying a preference among women to delay or avoid pregnancy during times of food insecurity. The similarity in magnitude and direction of the association between food insecurity and fertility preferences across the two measures of food insecurity suggest a potential association between lived or perceived resource insecurity and fertility aspirations. Further research is needed in order to establish a mechanism through which food insecurity affects fertility preferences. Significance Statement: Individual fertility preferences are sensitive to dynamic multi-level factors in a woman’s life. While qualitative research has explored the effect that food insecurity and associated resource constraints have on fertility preferences, results are conflicting. Here, we quantitatively examine how individual woman’s fertility preferences associate with two measures of food insecurity and qualitatively compare the associations across food insecurity measures. We establish that two food insecurity measures- household hunger and child stunting- capture similar populations and have similar associations with fertility preferences. This is a critical step forward in understanding the dynamic relationship between resource availability, child well-being, and fertility preferences.

Residential segregation by income and race is a salient feature of most US cities. An important determinant of residential location choice is access to desirable urban amenities via affordable travel modes. The first chapter of the dissertation studies residential and travel mode choices of commuters in US cities to estimate the heteregenous demand for access to neighborhoods offering faster commutes and to characterize what that means for how the gains from mass transit improvements are distributed among rich and poor commuters. I show that cities where transit improvements would be most effective at generating new transit ridership and overall welfare gains are ones where the gains accrue more to higher income commuters. Within cities, who gentrify transit-accessible neighborhoods and ride mass transit depends on the type (e.g. bus versus rail) and location of the transit improvements. The second chapter of this dissertation models household choices of where to live and how to travel in a stylized city with a competitive housing market. I characterize when and where marginal improvements in transit access reduce residential segregation by income instead of exacerbating it, and I show that an urban planners trying to maximize transit ridership is often incentivized to expand the transit network where it increases income segregation. Residential segregation has important implications for inequality. The third chapter of the dissertation studies how racially segregated housing markets have historically exacerbated racial inequality in US cities. The Great Migration of black families from the rural South to northern cities in the 1930s saw a growing number of segregated city blocks transition racially. Over a single decade, while rental prices soared on city blocks that transitioned from all white to majority black and pioneering black families paid large premiums to buy homes on majority white blocks, such homes quickly lost value on blocks that transitioned from majority white to majority black. These findings suggest that segregated housing markets eroded much of the gains for black families moving out of ghettos.

The Earned Income Tax Credit (EITC) targets refundable tax credits to low-income workers, incentivizing labor supply and raising the incomes of tens of millions of Americans. One possible consequence of subsidizing low-wage work, however, is to reduce wage growth. A monopsony model of the EITC is developed in order to analyze its impacts on labor market outcomes, which are identified by exploiting variation in state EITC supplements. A first set of results focused on the food service industry find that the EITC increases employment and reduces turnover among young women. Further results suggest that the EITC reduces wages for workers without college degrees. These findings prompt a reconsideration of the redistributive effects of the EITC, particularly for groups like older low-wage workers who face slower wage growth as a result of the policy but do not receive the same level of benefits on average.