Nighttime lights data are a measure of economic activity whose error is plausibly independent of the measurement errors of most conventional indicators. Therefore, we can use nighttime lights as an independent benchmark to assess existing measures of economic activity (Pinkovskiy and Sala-i-Martin (2016)). We employ this insight to generate three findings in the study of PPP-adjusted estimates of GDP around the world between 1992 and 2010. First, we find that while market exchange rates described poor economies better than did PPP-adjusted estimates in the late 1990s (Dowrick and Akmal 2008; Almas 2012), this pattern has disappeared by the 2010s. Second, we also find that estimates of PPPs have been steadily improving from one price survey round to the next, including during the controversial 2005 and 2011 rounds. Third, we leverage this fact to assess whether it is optimal to measure relative prices as close as possible to the year of interest or to use the latest available relative price data and discard the rest, and provide a theoretical framework in which the latter may be optimal. Using data from the Penn World Tables, we find that, indeed, it is optimal to only use the latest price data, and hence, to revise existing PPP-adjusted estimates whenever a new price survey is released.
Innovation, Productivity Dispersion, and Productivity Growth -- by Lucia Foster, Cheryl Grim, John C. Haltiwanger, Zoltan Wolf
We examine whether underlying industry innovation dynamics are an important driver of the large dispersion in productivity across firms within narrowly defined sectors. Our hypothesis is that periods of rapid innovation are accompanied by high rates of entry, significant experimentation and, in turn, a high degree of productivity dispersion. Following this experimentation phase, successful innovators and adopters grow while unsuccessful innovators contract and exit yielding productivity growth. We examine the dynamic relationship between entry, productivity dispersion, and productivity growth using a new comprehensive firm-level dataset for the U.S. We find a surge of entry within an industry yields initially an increase in productivity dispersion and then after a significant lag an increase in productivity growth. These patterns are more pronounced for the High Tech sector where we expect there to be more innovative activities. These patterns change over time suggesting other forces are at work during the post-2000 slowdown in aggregate productivity.
We argue theoretically and document empirically that aging leads to greater (industrial) automation, and in particular, to more intensive use and development of robots. Using US data, we document that robots substitute for middle-aged workers (those between the ages of 36 and 55). We then show that demographic change--corresponding to an increasing ratio of older to middle-aged workers--is associated with greater adoption of robots and other automation technologies across countries and with more robotics-related activities across US commuting zones. We also provide evidence of more rapid development of automation technologies in countries undergoing greater demographic change. Our directed technological change model further predicts that the induced adoption of automation technology should be more pronounced in industries that rely more on middle-aged workers and those that present greater opportunities for automation. Both of these predictions receive support from country-industry variation in the adoption of robots. Our model also implies that the productivity implications of aging are ambiguous when technology responds to demographic change, but we should expect productivity to increase and labor share to decline relatively in industries that are most amenable to automation, and this is indeed the pattern we find in the data.
Long-Term Effects of Job-Search Assistance: Experimental Evidence Using Administrative Tax Data -- by Dayanand S. Manoli, Marios Michaelides, Ankur Patel
This paper uses administrative tax data to examine the long-term effects of an experimental job-search assistance program operating in Nevada in 2009. The program required randomly-selected unemployed workers who had just started collecting unemployment insurance (UI) benefits to undergo an eligibility review and receive personalized job-counseling services. The program led to substantial short-term reductions in UI receipt, and to persistent, long-term increases in employment and earnings. The program also affected participants' family outcomes, including total income, tax filing, tax liability, and home ownership. These findings show that job-search assistance programs may produce substantial long-term effects for participants and their families.
Are Resettled Oustees from the Sardar Sarovar Dam Project Better off Today than their Former Neighbors who were not Ousted? -- by Swaminathan S. Anklesaria Aiyar, Neeraj Kaushal
The Sardar Sarovar Dam in Gujarat is arguably the most controversial dam ever built in India, with over a 100,000 displaced people. Most ousted families in Gujarat were resettled in the late 1980s and early 1990s. All oustees were tribals--a term used in India to cover a list of tribes viewed as so backward and historically oppressed that the Indian Constitution in 1950 reserved a quota of seats in education, government jobs, and Parliamentary seats for them. The Gujarat government promised to offer each male adult in the ousted families above the age of 18 five acres of land regardless of their earlier forest holdings. Additional compensation was to be given for loss of houses and livestock. Despite the continuing opposition to the dam from influential NGOs, there is no systematic empirical study of its effects on the long-term wellbeing of the ousted families. Our study investigates: Are resettled oustees from the Sardar Sarovar Dam project better off in 2017, approximately three decades after resettlement, than their former neighbours who were not ousted? We carried out a survey of a randomly selected sample of outsted families (treatment group) and a randomly selected sample of their former neighbors who lived in high areas that would remain above water when the reservoir rises to its maximum height and therefore were allowed to stay (comparison group). We found that, despite implementation glitches, those displaced were far better off than their former forest neighbours in ownership of a range of assets including TVs, cellphones, vehicles, access to schools and hospitals, and agricultural markets. The gap in asset ownership and other outcomes between the treatment and comparison groups were often statistically larger if the heads of the household were illiterate compared to the gap if they were literate. This finding suggests that resettlement helped vulnerable groups more than the less vulnerable and that fears that resettlement will destroy the lives and life-styles of tribals have been grossly exaggerated. We also found that 54% of displaced folk wished to return to their old habitat, showing that nostalgia for ancestral land can matter more than onweship of assets and economic wellbeing. Nearby undisplaced forest dwellers were asked if they would like to be "forcibly" resettled with the full compensation package. Of two forest groups, 31% and 52% said yes. Clearly many, though not all, tribesfolk yearn to leave the forest.
Using US Census data for 1990-2000, we estimate effects of NAFTA on US wages, focusing on differences by gender. We find that NAFTA tariff reductions are associated with substantially reduced wage growth for married blue-collar women, much larger than the effect for other demographic groups. We investigate several possible explanations for this finding. It is not explained by differential sensitivity of female-dominated occupations to trade shocks, or by household bargaining that makes married women workers less able to change their industry of employment than other workers. We find some support for an explanation based on an equilibrium theory of selective non-participation in the labor market, whereby some of the higher-wage married women workers in their industry drop out of the labor market in response to their industry's loss of tariff. However, this does not fully explain the findings so we are left with a puzzle.
We study the effects of local partisanship in a model of electoral competition. Voters care about policy, but they also care about the identity of the party in power. These party preferences vary from person to person, but they are also correlated within each state. As a result, most states are biased toward one party or the other (in popular parlance, most states are either 'red' or 'blue'). We show that, under a large portion of the parameter space, electoral competition leads to maximization of welfare with an extra weight on citizens of the 'swing state:' the one that is not biased toward either party. The theory applies to all areas of policy, but since import tariffs are well-measured they allow a clean test. We show empirically that the US tariff structure is systematically biased toward industries located in swing states, after controlling for other factors. Our best estimate is that the US political process treats a voter living in a non-swing state as being worth 77% as much as a voter in a swing state. This represents a policy bias orders of magnitude greater than the bias found in studies of protection for sale.
The Retirement-Consumption Puzzle: New Evidence from Personal Finances -- by Arna Olafsson, Michaela Pagel
This paper uses a detailed panel of individual spending, income, account balances, and credit limits from a personal finance management software provider to investigate how expenditures, liquid savings, and consumer debt change around retirement. The longitudinal nature of our data allows us to estimate individual fixed-effects regressions and thereby control for all selection on time-invariant (un)observables. We provide new evidence on the retirement-consumption puzzle and on whether individuals save adequately for retirement. We find that, upon retirement, individuals reduce their spending in both work-related and leisure categories. However, we feel that it is difficult to tell conclusively whether expenses are work related or not, even with the best data. We thus look at household finances and find that individuals delever upon retirement by reducing consumer debt and increasing liquid savings. We argue that these findings are difficult to rationalize via, for example, work-related expenses. A rational agent would save before retirement because of the expected fall in income, and dissave after retirement, rather than the exact opposite
Financial Centrality and Liquidity Provision -- by Arun G. Chandrasekhar, Robert Townsend, Juan Pablo Xandri
We study an endowment economy in which agents face income risk, as if uncertain returns on a portfolio, and agents can only make transfers in states when they are actively participating in the market. Besides income risk, agents also have limited and stochastic market access, with a probability distribution governed by an underlying social network. While network connections may serve to dissipate shocks, they may also provide obstacles to the sharing of risk, as when participation frictions are generated through the network. We identify and quantify the value of key players in terms of whether they are likely to be able to smooth the resulting market participation risk and how valuable that smoothing would be when they are there. We define financial centrality in economic terms, given the model, as the ex ante marginal social value of injecting an infinitesimal amount of liquidity to the agent. We show that the most financially central agents are not only those who trade often - as in standard network models - but are more likely to trade when there are few traders, when income risk is high, when income shocks are positively correlated, when attitudes toward risk are more sensitive in the aggregate, when there are distressed institutions, and when there are tail risks. We extend our framework to allow for endogenous market participation. Observational evidence from village risk sharing network data is consistent with our model.
The US Gains from Trade: Valuation Using the Demand for Foreign Factor Services -- by Arnaud Costinot, Andres Rodriguez-Clare
About 8 cents out of every dollar spent in the United States is spent on imports. What if, because of a wall or some other extreme policy intervention, imports were to remain on the other side of the US border? How much would US consumers be willing to pay to prevent this hypothetical policy change from taking place? The answer to this question represents the welfare cost from autarky or, equivalently, the welfare gains from trade. In this article, we discuss how to evaluate these gains using the demand for foreign factor services. The estimates of gains from trade for the US economy that we review range from 2 to 8 percent of GDP.
Bartik Instruments: What, When, Why, and How -- by Paul Goldsmith-Pinkham, Isaac Sorkin, Henry Swift
The Bartik instrument is formed by interacting local industry shares and national industry growth rates. We show that the Bartik instrument is numerically equivalent to using local industry shares as instruments. Hence, the identifying assumption is best stated in terms of these shares, with the national industry growth rates only affecting instrument relevance. We then show how to decompose the Bartik instrument into the weighted sum of the just-identified instrumental variables estimators, where the weights sum to one, can be negative and are easy to compute. These weights measure how sensitive the parameter estimate is to each instrument. We illustrate our results through three applications: estimating the inverse elasticity of labor supply, estimating local labor market effects of Chinese imports, and using simulated instruments to study the effects of Medicaid expansions.
What is the Impact of Successful Cyberattacks on Target Firms? -- by Shinichi Kamiya, Jun-Koo Kang, Jungmin Kim, Andreas Milidonis, Rene M. Stulz
We examine which firms are targets of successful cyberattacks and how they are affected. We find that cyberattacks are more likely to occur at larger and more visible firms, more highly valued firms, firms with more intangible assets, and firms with less board attention to risk management. These attacks affect firms adversely when consumer financial information is appropriated, but seem to have little impact otherwise. Attacks where consumer financial information is appropriated are associated with a significant negative stock market reaction, an increase in leverage following greater debt issuance, a deterioration in credit ratings, and an increase in cash flow volatility. These attacks also affect sales growth adversely for large firms and firms in retail industries, and there is evidence that they decrease investment in the short run. Affected firms respond to such attacks by cutting the CEO's bonus as a fraction of total compensation, by reducing the risk-taking incentives of management, and by taking actions to strengthen their risk management. The evidence is consistent with cyberattacks increasing boards' assessment of target firm risk exposures and decreasing their risk appetite.