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.
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.
We posit that autocrats introduce local elections when their bureaucratic capacity is low. Local elections exploit citizens' informational advantage in keeping local officials accountable, but they also weaken vertical control. As bureaucratic capacity increases, the autocrat limits the role of elected bodies to regain vertical control. We argue that these insights can explain the introduction of village elections in rural China and the subsequent erosion of village autonomy years later. We construct a novel dataset to document political reforms, policy outcomes, and de facto power for almost four decades. We find that the introduction of elections improves popular policies and weakens unpopular ones. Increases in regional government resources lead to loss of village autonomy, but less so in remote villages. These patterns are consistent with an organizational view of local elections within autocracies.
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.
Virtually all theories of economic growth predict a positive relationship between population size and productivity. In this paper, I study a particular historical episode to provide direct evidence for the empirical relevance of such scale effects. In the aftermath of the Second World War, 8 million ethnic Germans were expelled from their domiciles in Eastern Europe and transferred to West Germany. This inflow increased the German population by almost 20%. Using variation across counties, I show that the settlement of refugees had large and persistent effects on the size of the local population, manufacturing employment, and income per capita. These findings are quantitatively consistent with an idea‐based model of spatial growth if population mobility is subject to frictions and productivity spillovers occur locally. The estimated model implies that the refugee settlement increased aggregate income per capita by about 12% after 25 years and triggered a process of industrialization in rural areas.
The economic impacts of climate change are highly uncertain. Two of the most important uncertainties are the sensitivity of the climate system and the so-called damage functions, which relate climate change to economic costs and benefits. Despite broad awareness of these uncertainties, it is unclear which of them is most important, especially at the regional level. Here we construct regional damage functions, based on two different global damage functions, and apply them to two climate models with vastly different climate sensitivities. We find that uncertainty in both climate sensitivity and aggregate economic damages per degree of warming are of similar importance for the global economic impact of climate change, with the decrease in global economic productivity ranging between 4% and 24% by the end of the century under a high-emission scenario. At the regional level, however, the effects of climate change can vary even more substantially, depending both on a region's initial temperature and the amount of warming it experiences, with some regions gaining in productivity and others losing. The ranges of uncertainty are therefore potentially much larger at a regional level. For example, at the end of the century, under a high-emission scenario, we find that India's productivity decreases between 13% and 57% and Russia's increases between 24% and 74%, while Germany's change in productivity ranges from an increase of 8% to a decrease of 4%. Our findings emphasize the importance of including these uncertainties in estimates of future economic impacts, as they are vital for the resulting impacts and thus policy implications.
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).
In 2018, the United States launched a trade war with China, marking an abrupt departure from its historical leadership in integrating global markets. By late 2019, the United States had imposed tariffs on roughly $350 billion of Chinese imports, and China had retaliated on $100 billion of US exports. Economists have used a diversity of data and methods to assess the impacts of the trade war on the United States, China, and other countries. This article reviews what we have learned to date from this work.
Children's experiences during early childhood are critical for their cognitive and socioemotional development, two key dimensions of human capital. However, children from low-income backgrounds often grow up lacking stimulation and basic investments, which leads to developmental deficits that are difficult, if not impossible, to reverse later in life without intervention. The existence of these deficits is a key driver of inequality and contributes to the intergenerational transmission of poverty. In this article, we discuss the framework used in economics to model parental investments and early childhood development and use it as an organizing tool to review some of the empirical evidence on early childhood research. We then present results from various important early childhoods interventions, with an emphasis on developing countries. Bringing these elements together, we draw conclusions on what we have learned and provide some directions for future research.
Early childhood development is becoming the focus of policy worldwide. However, the evidence on the effectiveness of scalable models is scant, particularly when it comes to infants in developing countries. In this paper, we describe and evaluate with a cluster-Randomized Controlled Trial an intervention designed to improve the quality of child stimulation within the context of an existing parenting program in Colombia, known as FAMI. The intervention improved children’s development by 0.16 of a standard deviation (SD) and children’s nutritional status, as reflected in a reduction of 5.8 percentage points of children whose height-for-age is below -1 SD.
This article examines the role that implementation science can play in evidence-based parenting programs. Although parenting programs can support parents in their caregiving roles, adapting and taking an evidence-based approach from one place to another without attending to implementation factors may contribute to poor impact in a new setting. Implementation science enables researchers to move beyond monitoring and evaluation of outcomes of a parenting program to understanding the process of putting the program into practice. Factors such as whether the program meets the needs of families and communities, how to secure buy-in from key stakeholders, what training and supervision are needed for the workforce, and ways that parenting programs can be integrated in existing infrastructure are all critical to successful implementation. Quality improvement can be built into the implementation process through feedback loops that inform rapid changes and testing cycles over time as a program is implemented. If researchers lead initial implementation of parenting programs, they must determine how the program can continue to work when using community workers and local systems rather than researchers. Open access components are especially important for the implementation of parenting programs in low- and middle-income countries to avoid prohibitive costs of proprietary programs and to benefit from flexibility in adapting components to meet the needs of particular local populations. Parenting programs benefit when policy makers, program leaders, and researchers attend not only to the what but also to the how of implementation.
We deliver one month's average profit to a randomly selected group of female microenterprise owners in Dandora, Kenya, arriving just in advance of an exponential growth in COVID-19 cases. Relative to a control group, firms recoup about one third of their initial decline in profit, and food expenditures increase. Control profit responds to economic conditions and government announcements during our study period, and treatment effects are largest when control profit is at its lowest. PPE spending and precautionary management practices increase to mitigate the health risks of more intensive firm operation, but only among those who perceive COVID-19 as a major risk.
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.