This paper documents a new fact, then analyzes its causes and consequences: in most countries, import tariffs and non-tariff barriers are substantially lower on dirty than on clean industries, where an industry’s “dirtiness” is defined as its carbon dioxide (CO2) emissions per dollar of output. This difference in trade policy creates a global implicit subsidy to CO2 emissions in internationally traded goods and so contributes to climate change. This global implicit subsidy to CO2 emissions totals several hundred billion dollars annually. The greater protection of downstream industries, which are relatively clean, substantially accounts for this pattern. The downstream pattern can be explained by theories where industries lobby for low tariffs on their inputs but final consumers are poorly organized. A quantitative general equilibrium model suggests that if countries applied similar trade policies to clean and dirty goods, global CO2 emissions would decrease and global real income would change little.

Well-off Brazilians have brought the coronavirus back home with them from their travels. Many of them also employ domestic workers from the country's favelas - who they're apparently unwilling to protect by telling them to stay home. Brazil's poorest class could make easy quarry for the disease.

We estimate valuations of time using detailed consumer choice data from a large European ride hail platform, where drivers bid on trips and consumers choose between a set of potential rides with different prices and waiting times. We estimate consumer demand as a function of prices and waiting times. While demand is responsive to both, price elasticities are on average four times higher than waiting-time elasticities. We show how these estimates can be mapped into values of time that vary by place, person, and time of day. Regarding variation within a day, the value of time during non-work hours is 16% lower than during work hours. Regarding the spatial dimension, our value of time measures are highly correlated both with real estate prices and urban GPS travel flows. A variance decomposition reveals that most of the substantial heterogeneity in the value of time is explained by individual differences as opposed to place or time of day. In contrast with other studies that focus on long run choices we do not find evidence of spatial sorting. We apply our measures to quantify the opportunity cost of traffic congestion in Prague, which we estimate at $483,000 per day.

We study the trade-offs faced by Internet Service Providers (ISPs) that serve as platforms through which consumers access both television and internet services. As online streaming video improves, these providers may respond by attempting to steer consumers away from streaming video toward their own TV services, or by attempting to capture surplus from this improved internet content. We augment the standard mixed bundling model to demonstrate the trade-offs the ISP faces when dealing with streaming video, and we show how these trade-offs change with the pricing options available to the ISP. Next, we use unique household-level panel data and the introduction of usage-based pricing (UBP) in a subset of markets to measure consumers' responses and to evaluate quantitatively the ISP's trade-offs. We find that the introduction of UBP led consumers to upgrade their internet service plans and lower overall internet usage. Our findings suggest that while steering consumers towards TV services is possible, it is likely costly for the ISP and therefore unlikely to be profitable. This is especially true if the ISP can offer rich pricing menus that allow it to capture some of the surplus generated by a better internet service. The results suggest that policies like UBP can increase ISPs' incentive to maintain open access to new internet content.

Dominican Yankees and Mets stars are working with Pedro Martinez to respond to the coronavirus pandemic in their homeland.

Levine is making the rounds to make the case for baseball in the time of the coronavirus pandemic.

This paper investigates the effect of a large negative agricultural shock, the boll weevil, on black-white inequality in the first half of the twentieth century. To do this we use complete count census data to generate a linked sample of fathers and their sons. We find that the boll weevil induced enormous labor market and social disruption as more than half of black and white fathers moved to other counties following the arrival of the weevil. The shock impacted black and white sons differently. We compare sons whose fathers initially resided in the same county and find that white sons born after the boll weevil had similar wages and schooling outcomes to white sons born prior to its arrival. In contrast, black sons born after the boll weevil had significantly higher wages and years of schooling, narrowing the black-white wage and schooling gaps. This decrease appears to have been driven by relative improvements in early life conditions and access to schooling both for sons of black fathers that migrated out of the South and sons of black fathers that stayed in the South.

This paper proposes a new methodology for estimating teacher value-added. Rather than imposing a normality assumption on unobserved teacher quality (as in the standard empirical Bayes approach), our nonparametric estimator permits the underlying distribution to be estimated directly and in a computationally feasible way. The resulting estimates fit the unobserved distribution very well regardless of the form it takes, as we show in Monte Carlo simulations. Implementing the nonparametric approach in practice using two separate large-scale administrative data sets, we find the estimated teacher value-added distributions depart from normality and differ from each other. To draw out the policy implications of our method, we first consider a widely-discussed policy to release teachers at the bottom of the value-added distribution, comparing predicted test score gains under our nonparametric approach with those using parametric empirical Bayes. Here the parametric method predicts similar policy gains in one data set while overestimating those in the other by a substantial margin. We also show the predicted gains from teacher retention policies can be underestimated significantly based on the parametric method. In general, the results highlight the benefit of our nonparametric empirical Bayes approach, given that the unobserved distribution of value-added is likely to be context-specific.

Estimating the returns to education remains an active area of research amongst applied economists. Most studies that estimate the causal return to education exploit changes in schooling and/or labor laws to generate exogenous differences in education. An implicit assumption is that more time in school may translate into greater earnings potential. None of these studies, however, explicitly consider the quality of schooling to which impacted students are exposed. To extend this literature, we examine the interaction between school quality and policy-induced returns to schooling, using temporally-available school quality measures from Card and Krueger (1992). We find that additional compulsory schooling, via either schooling or labor laws, increases earnings only if educational inputs are of sufficiently high quality. In particular, we find a consistent role for teacher quality, as measured by relative teacher pay across states, in generating consistently positive returns to compulsory schooling.

Traveling beyond the immediate surroundings of one’s residence can lead to greater exposure to new ideas and information, jobs, and greater transmission of disease. In this paper, we document the geographic mobility of individuals in the U.S., and how this mobility varies across U.S. cities, regions, and income classes. Using geolocation data for ~1.7 million smartphone users over a 10-month period, we compute different measures of mobility, including the total distance traveled, the median daily distance traveled, the maximum distance traveled from one’s home, and the number of unique haunts visited. We find large differences across cities and income groups. For example, people in New York travel 38% fewer total kilometers and visit 14% fewer block-sized areas than people in Atlanta. And, individuals in the bottom income quartile travel 12% less overall and visit 13% fewer total locations than the top income quartile.

Most jobs require teamwork. Are some people good team players? In this paper we design and test a new method for identifying individual contributions to group performance. We randomly assign people to multiple teams and predict team performance based on previously assessed individual skills. Some people consistently cause their group to exceed its predicted performance. We call these individuals “team players”. Team players score significantly higher on a well-established measure of social intelligence, but do not differ across a variety of other dimensions, including IQ, personality, education and gender. Social skills – defined as a single latent factor that combines social intelligence scores with the team player effect – improve group performance about as much as IQ. We find suggestive evidence that team players increase effort among teammates.

Recent research has highlighted unequal treatment for women in academic economics along several different dimensions, including promotion, hiring, credit for co-authorship, and standards for publication in professional journals. Can the source of these differences lie in biases against women that are pervasive in the discipline, even among students in the earliest stages of their training? In this paper, we provide evidence on the importance of explicit and implicit biases against women among students in economics relative to other fields. We conducted a large scale survey among undergraduate students in Chilean universities, among both entering first-year students and students in years 2 and above. On a wide battery of measures, economics students are more biased than students in other fields. Economics students are somewhat more biased already upon entry, before exposure to any economics classes. The gap is more pronounced among students in years 2 and above, in particular for male students. We also find an increase in bias in a sample of students that we follow longitudinally. Differences in political ideology explain essentially all the gap at entry, but none of the increase in the gap with exposure. Exposure to female students and faculty attenuates some of the bias.

China is the largest creditor in the world, funding infrastructure projects in the developing world in exchange for access to raw materials. A new study shows that the risk of a new debt crisis is significant.

Vitor Constâncio spent eight years as the vice president of the European Central Bank. In an interview, he explains why not him or outgoing ECB head Mario Draghi are to blame for negative interest rates in the eurozone.

Scientists believe designer viruses created in the laboratory can help the agricultural industry deal with pathogens and extreme weather. A vast experiment is currently being planned. But can the viruses be controlled?

Employees at the Swedish unit of the German travel conglomerate TUI are volunteering to have a microchip implanted in their hands. The technology literally opens doors, but also raises numerous ethical questions.

Greed, envy, poor leadership and a poisonous internal culture: New York Times journalist David Enrich has written a book about Deutsche Bank that also sheds light on the financial institution's relationship with U.S. President Donald Trump.

Just as Volkswagen was undergoing a radical restructuring to focus on e-mobility and driverless cars, the company has announced it is shutting down factories across Europe due to the coronavirus. There is hope in China, however.

The coronavirus pandemic has inflicted massive damage on the aviation industry. But Lufthansa CEO Carsten Spohr says that the German flag carrier is in a good position to survive, even if it will take several years for the industry to recover.