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.
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.
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.
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.
To counteract the adverse effects of shocks, such as the global pandemic, on the economy, governments have discussed policies to improve the resilience of supply chains by reducing dependence on foreign suppliers. In this paper, we develop and quantify an adaptive production network model to study network resilience and the consequences of reshoring of supply chains. In our model, firms exit due to exogenous shocks or the propagation of shocks through the network, while firms can replace suppliers they have lost due to exit subject to switching costs and search frictions. Applying our model to a large international firm-level production network dataset, we find that restricting buyer–supplier links via reshoring policies reduces output and increases volatility and that volatility can be amplified through network adaptivity.
The COVID-19 pandemic has upended health and living standards around the world. This article provides an interim overview of these effects, with a particular focus on low- and middle-income countries (LMICs). Economists have explained how the pandemic is likely to have different consequences for LMICs and demands distinct policy responses compared to those of rich countries. We survey the rapidly expanding body of empirical research that documents the pandemic's many adverse economic and noneconomic effects in terms of living standards, education, health, and gender equality, which appear to be unprecedented in scope and scale. We also review research on successful and failed policy responses, including the failure to ensure widespread vaccine coverage in many LMICs, which is needed to end the pandemic. We close with a discussion of implications for public policy in LMICs and for the institutions of international governance, given the likelihood of future pandemics and other major shocks (e.g., climate).
Hasan Mahmud Reza and colleagues argue that access to vaccines enabled by predictable supply of vaccine doses and delivery to remote areas are critical for vaccine uptake in low and middle income countries.
We construct an endogenous growth model with random interactions where firms are subject to distortions. The TFP distribution evolves endogenously as firms seek to upgrade their technology over time either by innovating or by imitating other firms. We use the model to quantify the effects of misallocation on TFP growth in emerging economies. We structurally estimate the stationary state of the dynamic model targeting moments of the empirical distribution of R&D and TFP growth in China during the period 2007–2012. The estimated model fits the Chinese data well. We compare the estimates with those obtained using data for Taiwan and perform counterfactuals to study the effect of alternative policies. R&D misallocation has a large effect on TFP growth.
A data intermediary acquires signals from individual consumers regarding their preferences. The intermediary resells the information in a product market wherein firms and consumers tailor their choices to the demand data. The social dimension of the individual data—whereby a consumer’s data are predictive of others’ behavior—generates a data externality that can reduce the intermediary’s cost of acquiring the information. The intermediary optimally preserves the privacy of consumers’ identities if and only if doing so increases social surplus. This policy enables the intermediary to capture the total value of the information as the number of consumers becomes large.
Financial, informational and other constraints lower the adoption of welfare-improving technologies amongst people living in poverty. Field trials have identified effective strategies to facilitate behaviour change. Researchers and policymakers need to apply this knowledge, and form institutional partnerships to implement solutions at scale.
Vaccines are changing the course of the COVID-19 pandemic, but in grossly uneven ways. Low- and middle-income countries (LMICs) face considerable obstacles in both receiving and distributing doses. To limit virus transmission, its devastating impacts, and opportunities for further mutations, this must change. Until it does, nonpharmaceutical interventions such as masking must remain a priority. Science invited global experts to highlight research and innovations aimed at quickening the end of COVID-19 in LMICs.
We show that labor market transaction costs explain why the smallest farms are more efficient than slightly larger farms in most low-income countries and that increases in machine capacity with operational scale result in the globally observed rising upper tail of productivity. We find evidence consistent with these mechanisms using Indian data, and we show that if all Indian farms were at the minimum scale required to maximize the return on land, the number of farms would be reduced by 82% and income per farm worker would rise by 68%.
We show using a theoretical framework that embeds a voting model in a general-equilibrium model of a rural economy with two interest groups defined by land ownership that the effects of democratization – a shift from control of public resources by the landed elite to a democratic regime with universal suffrage – on the portfolio of public goods is heterogeneous, depending the population landless. In accord with the model and empirical findings from micro data on the differing material interests of the two land classes, we find, based on 30-year panel data describing the democratization of Indian villages, that democratization in villages with a larger landless population share shifted resources away from public irrigation, secondary schools, and electrification and towards programs that increase employment. When the landed farmers have a large population share, public resources were shifted towards irrigation, secondary schools and electrification and away from employment programs.
We document that an experimental intervention offering transport subsidies for poor rural households to migrate seasonally in Bangladesh improved risk sharing. A theoretical model of endogenous migration and risk sharing shows that the effect of subsidizing migration depends on the underlying economic environment. If migration is risky, a temporary subsidy can induce an improvement in risk sharing and enable profitable migration. We estimate the model and find that the migration experiment increased welfare by 12.9%. Counterfactual analysis suggests that a permanent, rather than temporary, decline in migration costs in the same environment would result in a reduction in risk sharing.