Research 

Abstract: 

This paper estimates the economic impacts of broadband deployment in rural areas in the United States under the Connect America Fund (CAF) Phase II Price Cap program – the largest component of CAF, a federally funded initiative designed to subsidize service provision in high-cost, underserved or unserved areas. Using a panel dataset of census tracts from the years 2010–2023, we exploit the staggered implementation of the program as a quasi-experimental setting and estimate its effects. Our analysis focuses on impacts on employment, self-employment, and household income. Results indicate that broadband deployment had a positive impact on both employment and income in treated areas, with effects strengthening over time. However, we find no evidence of impacts on self-employment. This study contributes to a growing literature on the economic effects of broadband infrastructure by evaluating one of the most extensive telecommunications interventions in US history.

Keywords: Connect America fund; Broadband infrastructure; Employment; Digital divide; Economic development; Rural areas

Abstract: 

Discourse surrounding the future of work often treats technological substitution of workers as a cause for concern, but complementarity as a good. However, while automation and artificial intelligence may improve productivity or wages for those who remain employed, they may also have mixed or negative impacts on worker well-being. This study considers five hypothetical channels through which automation may impact worker well-being: influencing worker freedom, sense of meaning, cognitive load, external monitoring, and insecurity. We apply a measure of automation risk to a set of 402 occupations to assess whether automation predicts impacts on worker well-being along the dimensions of job satisfaction, stress, health, and insecurity. Findings based on a 2002–2018 dataset from the General Social Survey reveal that workers facing automation risk appear to experience less stress, but also worse health, and minimal or negative impacts on job satisfaction. These impacts are more concentrated on workers facing the highest levels of automation risk. This article encourages new research directions by revealing important heterogeneous effects of technological complementarity. We recommend that firms, policymakers, and researchers not conceive of technological complementarity as a uniform good, and instead direct more attention to mixed well-being impacts of automation and artificial intelligence on workers.

[Replication files] 

Abstract:

As artificial intelligence (AI) technologies expand rapidly, public debate has focused on their societal impacts and the need for regulatory oversight. This report offers the first overview of the “what, where, and when” of AI-related legislation introduced in U.S. state legislatures between 2019 and 2024, analyzing key policy trends, priorities, and equity considerations. Although relatively few bills have been enacted, legislative activity has accelerated, reflecting growing political attention to the promises and risks of AI. Most approved bills focus on regulating public or private sector uses of AI, establishing commissions or study groups, and updating education and workforce development programs. While equity is not always central in bill titles or summaries, it surfaces in provisions related to fairness, non-discrimination, transparency, risk assessments, and protections for vulnerable communities, especially in health, employment, and education.

Keywords: Artificial Intelligence, AI Policy, AI Regulation

Abstract:

The COVID−19 pandemic brought the digital divide to center stage. This article investigates whether the crisis disrupted mobile broadband infrastructure, taking Georgia as a case study. We hypothesize that the pandemic could have slowed down ongoing infrastructure provision initiatives, as in other segments of the economy, or spurred them by bringing renewed attention and resources to overcoming the digital divide. We find that the per capita antenna gap between rural and micropolitan areas as compared to metropolitan has drastically reduced during the pandemic. Long−Term Evolution expansion was positively associated with the presence of vulnerable populations with variation across areas.

Keywords: Broadband, urban, rural, digital equity, COVID−19

Note: This project was part of the Public Interest Technology collaboration between Georgia State University and the Georgia Institute of Technology, connecting scholars from Policy and Computer Sciences.

Abstract:

Ridesharing has expanded globally. While pioneering studies often focus on high-income countries, it remains unclear how findings translate to other contexts. This study develops expectations about ridesharing’s impacts in middle-income countries and examines them using Uber’s staggered entry in Brazil. Findings reveal that ridesharing increasingly becomes a primary job, countering assumptions of easy entry and part-time schedules. As the job becomes popular, drivers experience declines in earnings and reduced social security contribution rates (a proxy for access to labor protections). These patterns are not observed for other groups of workers. However, there is also suggestive evidence that ridesharing may represent the best available option, particularly relative to unemployment or informal work. These results support expectations and underscore a twofold policy challenge: safeguarding workers’ protections while preserving the model’s benefits.

Keywords: platform work, ridesharing, Brazil, Uber, gig work, middle-income countries

Abstract:

Ridesharing gained popularity as a flexible and readily available job option. However, limited research has focused on the gendered impacts of this new model on a large scale. In theory, ridesharing could disproportionately attract women, as they often take flexible work to balance family responsibilities. However, the driver occupation is historically male-dominated, and women need to overcome cultural barriers to join ridesharing. This article leverages the staggered entry of Uber in Brazilian cities to examine gender variations in responses to ridesharing. Overall, the results identify important gender nuances. There was a noticeable increase in the number of female drivers, although men still dominate the occupation. Gender differences in hours of work and earnings have decreased, mainly due to reductions experienced by men. The presence of children in the household is a key determinant of which women become drivers but has less impact on men. For instance, mothers of young children (aged 0–6) were not significantly attracted to ridesharing, but mothers of older children and childless women were. Safety concerns remain a significant barrier to women, and the level of gender violence in cities is negatively associated with women's probability of being a driver.

Abstract

We examine how the expansion of social protection policies during Covid-19 affected workers’ job outcomes (employment probability and income changes) and access to the safety net. Leveraging a panel of 173 US workers across three arrangements—platform-based gig workers, creative freelancers, and low-wage W-2 employees—tracked from 2020–2024, we find that gig workers initially faced unemployment insurance access barriers, resulting in a 50%-point higher likelihood of receiving benefits later in the crisis than low-wage W-2 workers. Creative freelancers were more likely to remain employed and experienced fewer income losses. This paper shows that flexibilizing regulation may counter global labor precarity trends.

Abstract:

The U.S. nonstandard workforce remained at around 10% of the total employed population for the past decades, although the subnational levels reveal variation. Insufficient scholarly attention has been devoted to understanding its spatial distribution and associated causes. This paper addresses this gap by analyzing the contextual factors that help explain the geographic unevenness of the nonstandard workforce across U.S. metropolitan areas from 1997 to 2017. We find evidence that the urban context matters, but unevenly across arrangements and time. Three out of four of the nonstandard arrangements studied are more prevalent in metropolitan areas, while on-call workers are typically rural. Independent contractors are more concentrated in cities with higher fissuring, contrary to temporary and contracted out workers. Higher unemployment rates seem to push workers toward on-call arrangements, and inequality to temporary jobs. While the city effects change substantially over time, individual determinants are consistent.

Keywords: Alternative work, nonstandard work, metropolitan areas, workforce development

Abstract:

This article explores the implications of nonstandard employment for types of workers and their change over time. Using data from 1995, 2005, and 2017, we trace the evolving forms of nonstandard employment over the last decade and the associated job-quality patterns for workers with different skills, measured by education levels and occupation tasks. We find that nonstandard employment reduces earnings and weekly work schedule but does not affect the likelihood of feeling insecure about job continuity for workers in general. However, a closer examination reveals considerable variation along these three dimensions: highly educated nonstandard workers have lower earnings and fewer working hours than traditional workers over time and nonstandard routine occupation workers tend to feel greater job insecurity. Variations across gender and race-ethnicity are also discussed.

Keywords: nonstandard work arrangements, job quality, skills

Abstract:

The policy responses to the COVID-19 pandemic varied widely between countries. Understanding how effective these responses were is important to improve preparedness for future crises. This paper investigates how one of largest scale conditional cash transfer COVID relief policies in the world—the Brazilian Emergency Aid (EA)—impacted poverty, inequality, and the labor market amidst the public health crisis. We use fixed-effects estimators to analyze the impact of the EA on labor force participation, unemployment, poverty, and income at the household level. We find that inequality, measured by per capita household income, reduced to a historical low and was accompanied by substantial poverty declines—even as compared to pre-pandemic levels. Furthermore, our results suggest that the policy has effectively targeted those in most need—temporarily reducing historical racial inequalities—while not incentivizing reductions in labor force participation. Absent the policy, adverse shocks would have been significant and are likely to occur once the transfer is interrupted. We also observe that the policy was not enough to curb the spread of the virus, suggesting that cash transfers alone are insufficient to protect citizens.

Abstract:

To deal with the problem of underestimation of top incomes in household surveys, we propose a methodology to combine the income distributions of the Brazilian 2010 Census (survey) and of 2010 DIRPF (personal income tax reports). The method consists in estimating a system of non-response weights that uses as frame the tax register and is applied to the top of the distribution. After applying this calibration methodology, we decompose inequality income sources. Correcting survey distributions with tax data increases the contribution of non-labor income to inequality, as the case of the Brazilian Census shows. Changes in the methodology do not affect the results substantially.