We track the effects of the COVID-19 pandemic on mental health in eight Low and Middle Income Countries (LMICs) in Asia, Africa, and South America utilizing repeated surveys of 21,162 individuals. Many respondents were interviewed over multiple rounds pre- and post-pandemic, allowing us to control for time trends and within-year seasonal variation in mental health. We demonstrate how mental health fluctuates with agricultural crop cycles, deteriorating during pre-harvest “lean” periods. Ignoring this seasonal variation leads to unreliable inferences about the effects of the pandemic. Controlling for seasonality, we document a large, significant, negative impact of the pandemic on mental health, especially during the early months of lockdown. In a random effects aggregation across samples, depression symptoms increased by around 0.3 standard deviations in the four months following the onset of the pandemic. The pandemic could leave a lasting legacy of depression. Absent policy interventions, this could have adverse long-term consequences, particularly in settings with limited mental health support services, which is characteristic of many LMICs.
What do recent advances in economic geography teach us about the spatial distribution of economic activity? We show that the equilibrium distribution of economic activity can be determined simply by the intersection of labor supply and demand curves. We discuss how to estimate these curves and highlight the importance of global geography—the connections between locations through the trading network—in determining how various policy relevant changes to geography shape the spatial economy.
This paper analyzes earnings inequality and earnings dynamics in Sweden over 1985–2016. The deep recession in the early 1990s marks a historic turning point with a massive increase in earnings inequality and earnings volatility, and the impact of the recession and the recovery from it lasted for decades. In the aftermath of the recession, we find steady growth in real earnings across the entire distribution for men and women and decreasing inequality over more than 20 years. Despite the positive trend, large gender differences in earnings dynamics persist. While earnings growth for men is more closely tied to the business cycle, women face much higher volatility overall. Earnings volatility is also substantially higher among foreign-born workers, reflecting weaker labor market attachment and high risk of large negative shocks for low-income immigrants. We document an important role of social benefits usage for the overall trends and for differences across subpopulations. Higher benefits enrollment, especially for women and immigrants, is associated with higher earnings volatility. As the generosity and usage of benefit programs declined over time, we find stronger earnings growth among low-income workers, consistent with higher self-sufficiency.
We apply deep learning to daytime satellite imagery to predict changes in income and population at high spatial resolution in US data. For grid cells with lateral dimensions of 1.2 km and 2.4 km (where the average US county has dimension of 51.9 km), our model predictions achieve R2 values of 0.85 to 0.91 in levels, which far exceed the accuracy of existing models, and 0.32 to 0.46 in decadal changes, which have no counterpart in the literature and are 3–4 times larger than for commonly used nighttime lights. Our network has wide application for analyzing localized shocks.
A Universal Basic Income (UBI) is often seen as an attractive policy option to replace existing targeted transfer and subsidy programs. However, in a budget-neutral switch to a UBI there is a trade-off between the generosity of the universal transfer, and hence its poverty impact, and the implied increase in tax burden. We summarize our results for fourteen low- and middle-income countries. We find that, with the exception of Russia, a poverty reducing, budget-neutral UBI would entail a significant increase in the net tax burden of top deciles. The efficiency cost and political resistance for such a policy would likely be too high.
Background:India's abrupt nationwide Covid-19 lockdown internally displaced millions of migrant workers, who returned to distant rural homes. Documenting their labour market reintegration is a critical aspect of understanding the economic costs of the pandemic for India's poor. In a country marked by low and declining female labour force participation, identifying gender gaps in labour market reintegration – as a marker of both women's vulnerability at times of crisis and setbacks in women's agency – is especially important. Yet most studies of pandemic-displaced internal migrants in India are small, rely on highly selected convenience samples, and lack a gender focus.
Methods: Beginning in April 2020 we enrolled roughly 4,600 displaced migrants who had, during the lockdown, returned to two of India's poorest states into a cohort observational study which tracked enrolees through July 2021. Survey respondents were randomly selected from the states’ official databases of return migrants, with sampling stratified by state and gender. 85% of enrolees (3950) were working prior to the pandemic. Our difference-in-means analysis uses three survey waves conducted in July to August 2020, January to March 2021, and June to July 2021. Our analysis focuses on a balanced panel of 1780 previously working enrolees (the 45% of respondents present in the first wave that also participated in the subsequent two survey rounds). Primary outcomes of interest include labour market re-entry, earnings, and measures of vulnerability by gender.
Findings: Before the March 2020 national lockdown, 98% (95% CI [97,99]) of workers were employed in the non-agricultural sector. In July 2020, one month after the end of the lockdown, incomes plummet, with both genders earning roughly 17% of their pre-pandemic incomes. 47% (95% CI [45,49]) were employed in agriculture and 37% (95% CI [35,39]) were unemployed. Remigration is critical to regaining income – by January 2021, male re-migrants report earnings on par with their pre-pandemic incomes, while men remaining in rural areas earn only 23% (95% CI [19,27]) of their pre-pandemic income. Remigration benefits women to a lesser extent – female re-migrants regain no more than 65% (95% CI [57,73]) of their pre-pandemic income at any point. Yet men and women struggle to remigrate throughout – by July 2021, no more than 63% (95% CI [60,66]) of men and 55% (95% CI [51,59]) of women had left their home villages since returning. Gender gaps in income recovery largely reflect higher rates of unemployment among women, both among those remaining in rural areas (9 percentage points (95% CI [6,13]) higher than men across waves) and among those who remigrate (13 percentage points (95% CI [9,17]) higher than men across waves). As a result, we observe gender gaps in well-being: relative to male counterparts, women across waves were 7 percentage points (95% CI [4,10]) more likely to report reduced consumption of essential goods and fared 6 percentage points (95% CI [4,7]) worse on a food insecurity index.
Interpretation: Displaced migrants of both genders experienced persistent hardships for over a year after the initial pandemic lockdown. Women fare worse, driven by both lower rates of remigration and lower rates of labour market re-entry both inside and outside home villages. Some women drop out of the labour force entirely, but most unemployed report seeking or being available to work. In short, pandemic-induced labour market displacement has far-reaching, long-term consequences for migrant workers, especially women.
Funding: Survey costs were funded by research grants from IZA/FCDO Gender, Growth, and Labour Markets in Low Income Countries Programme, J-PAL Jobs and Opportunity Initiative, and the Evidence-based Measures of Empowerment for Research on Gender Equality (EMERGE) program at University of California San Diego.
We examine the employment effects of 3G mobile internet expansion in developing countries. We find that 3G significantly increases the labor force participation rate of women and the employment rates of both men and women. Our results suggest that 3G affects the type of jobs and there is a distinct gender dimension to these effects. Men transition away from unpaid agricultural work into operating small agricultural enterprises, while women take more unpaid jobs, especially in agriculture, and operate more small businesses in all sectors. Both men and women are more likely to work in wage jobs in the service sector.
We examine election voting and legislators’ roll-call votes in the United States over a twenty-five year period. Voters in areas more exposed to trade liberalization with China in 2000 subsequently shift their support toward Democrats, relative to the 1990s, though this boost for Democrats wanes after the rise of the Tea Party in 2010. House members’ votes in Congress rationalize these trends, with Democratic representatives disproportionately supporting protection during the early 2000s. Together, these results imply that voters in areas subject to higher import competition shifted votes toward the party more likely to restrict trade.
We provide evidence of the role of community networks in emergence of Indian entrepreneurship in early stages of cotton and jute textile industries in the late 19th and early 20th century respectively, overcoming lack of market institutions and government support. From business registers, we construct a yearly panel dataset of entrepreneurs in these two industries. We find no evidence that entry was related to prior upstream trading experience or price shocks. Firm directors exhibited a high degree of clustering of entrepreneurs by community. Consistent with a model of network-based dynamics, the stock of incumbent entrepreneurs of different communities diverged non-linearly, controlling for year and community fixed effects.
Each year in the US, hundreds of billions of dollars are spent on transportation infrastructure and billions of hours are lost in traffic. We develop a quantitative general equilibrium spatial framework featuring endogenous transportation costs and traffic congestion and apply it to evaluate the welfare impact of transportation infrastructure improvements. Our approach yields analytical expressions for transportation costs between any two locations, the traffic along each link of the transportation network, and the equilibrium distribution of economic activity across the economy, each as a function of the underlying quality of infrastructure and the strength of traffic congestion. We characterize the properties of such an equilibrium and show how the framework can be combined with traffic data to evaluate the impact of improving any segment of the infrastructure network. Applying our framework to both the US highway network and the Seattle road network, we find highly variable returns to investment across different links in the respective transportation networks, highlighting the importance of well-targeted infrastructure investment.
This paper studies the role of private sector companies in the development of local amenities. We use evidence from one of the largest multinationals of the 20th century: the United Fruit Company (UFCo). The firm was given a large land concession in Costa Rica—one of the so‐called “Banana Republics”—from 1899 to 1984. Using administrative census data with census‐block geo‐references from 1973 to 2011, we implement a geographic regression discontinuity design that exploits a land assignment that is orthogonal to our outcomes of interest. We find that the firm had a positive and persistent effect on living standards. Company documents explain that a key concern at the time was to attract and maintain a sizable workforce, which induced the firm to invest heavily in local amenities—like the development of education and health infrastructure—that can account for our result. Consistent with this mechanism, we show, empirically and through a proposed model, that the firm's investment efforts increase with worker mobility.
We model the world economy as one system of endogenous input-output relationships subject to frictions and study how the world's input-output structure and world's GDP change due to changes in frictions. We derive a sufficient statistic to identify frictions from the observed world input-output matrix, which we fully match for the year 2011. We show how changes in internal frictions impact the whole structure of the world's economy and that they have a much larger effect on world's GDP than external frictions. We also use our approach to study the role of internal frictions during the Great Recession of 2007–2009.
Many children in developing countries grow up in environments that lack stimulation, leading to deficiencies in early years of development. Several efficacy trials of early childhood care and education (ECCE) programmes have demonstrated potential to improve child development; evidence on whether these effects can be sustained once programmes are scaled is much more mixed. This study evaluates whether an ECCE programme shown to be effective in an efficacy trial maintains effectiveness when taken to scale by the Government of Ghana (GoG). The findings will provide critical evidence to the GoG on effectiveness of a programme it is investing in, as well as a blueprint for design and scale-up of ECCE programmes in other developing countries, which are expanding their investment in ECCE programmes.
The use of air quality monitoring networks to inform urban policies is critical especially where urban populations are exposed to unprecedented levels of air pollution. High costs, however, limit city governments’ ability to deploy reference grade air quality monitors at scale; for instance, only 33 reference grade monitors are available for the entire territory of Delhi, India, spanning 1500 sq km with 15 million residents. In this paper, we describe a high-precision spatio-temporal prediction model that can be used to derive fine-grained pollution maps. We utilize two years of data from a low-cost monitoring network of 28 custom-designed low-cost portable air quality sensors covering a dense region of Delhi. The model uses a combination of message-passing recurrent neural networks combined with conventional spatio-temporal geostatistics models to achieve high predictive accuracy in the face of high data variability and intermittent data availability from low-cost sensors (due to sensor faults, network, and power issues). Using data from reference grade monitors for validation, our spatio-temporal pollution model can make predictions within 1-hour time-windows at 9.4, 10.5, and 9.6% Mean Absolute Percentage Error (MAPE) over our low-cost monitors, reference grade monitors, and the combined monitoring network respectively. These accurate fine-grained pollution sensing maps provide a way forward to build citizen-driven low-cost monitoring systems that detect hazardous urban air quality at fine-grained granularities.
United States households’ consumption expenditures and car purchases collapsed during the Great Recession and more so than income changes would have predicted. Using CEX data, we show that both the extensive and the intensive car spending margins contracted sharply in the Great Recession. We also document significant crosscohort differences in the impact of the Great Recession including a stronger reduction in car spending by younger cohorts. We draw inference on the sources of the Great Recession by investigating which shocks can explain household choices in a 60 period life-cycle model with idiosyncratic and aggregate shocks fitted to aggregate and lifecycle moments. We find that the Great Recession was caused by a combination of large aggregate income and wealth shocks, while cross-cohort adjustment patterns imply a role for life-cycle income profile shocks. We also find a role for car loan premia shocks in accounting for car spending and car loans.