We study infant industry protection using a dynamic model in which the industry's cost is initially higher than that of foreign competitors. The industry can stochastically lower its cost via learning by doing. Whether the industry has transitioned to low cost is private information. We use a mechanism-design approach to induce the industry to reveal its true cost. We show that (i) the optimal protection, measured by infant industry output, declines over time and is less than that under public information, (ii) the optimal protection policy is time consistent under public information but not under private information, (iii) the optimal protection policy can be implemented with minimal information requirements, and (iv) a government with a limited budget can use a simple approach to choose which industries to protect.
Economists have recently begun using independent online surveys to collect national labor market data. Questions remain over the quality of such data. This paper provides an approach to address these concerns. Our case study is the Real-Time Population Survey (RPS), a novel online survey of the US built around the Current Population Survey (CPS). The RPS replicates core components of the CPS, ensuring comparable measures that allow us to weight and rigorously validate our results using a high-quality benchmark. At the same time, special questions in the RPS yield novel information regarding employer reallocation during the COVID-19 pandemic. We document that 26% of pre-pandemic workers were working for a new employer one year into the COVID-19 outbreak in the US, at least double the rate of any previous episode in the past quarter century. Our discussion contains practical suggestions for the design of novel labor market surveys and highlights other promising applications of our methodology.
In 1936-37, the Federal Reserve doubled member banks' reserve requirements. Friedman and Schwartz (1963) famously argued that the doubling increased reserve demand and forced the money supply to contract, which they argued caused the recession of 1937-38. Using a new database on individual banks, we show that higher reserve requirements did not generally increase banks' reserve demand or contract lending because reserve requirements were not binding for most banks. Aggregate effects on credit supply from reserve requirement increases were therefore economically small and statistically zero.
This note uses the asynchronous cessation of emergency unemployment benefits (EUB) in 2021 to investigate the jobs impact of ending unemployment benefits. While some states stopped providing EUB in September, other states stopped in June and July. Using the cessation month as an instrument, we estimate the causal effect on employment of reducing unemployment rolls. In the first three months following a state’s program termination, for every 100 person reduction in beneficiaries, state employment causally increased by about 35 persons. The effect is statistically different from zero and robust to a wide array of alternative specifications.
In infinite horizon, heterogeneous-agent and incomplete-market models, the existence of an interior Ramsey steady state is often assumed instead of proven. This paper demonstrates the critical importance of proving the existence of the Ramsey steady state when conducting theoretical or numerical analysis on optimal fiscal policies. We use an analytically tractable heterogeneous-agent model to make our point by showing that the conditions for the existence of an interior Ramsey steady state are quite sensitive to structural parameter values. In particular, we show that researchers may draw fundamentally misleading conclusions from their analysis when an interior Ramsey steady state does not exist but is erroneously assumed to exist.
Based on novel survey data, we document a persistent rise in work from home (WFH) over the course of the COVID-19 pandemic. Using theory and direct survey evidence,
we argue that three quarters of this increase reflects adoption of new work arrangements that will likely be permanent for many workers. A quantitative model matched to survey
data predicts that twice as many workers will WFH full-time post-pandemic compared to pre-pandemic, and that one in every five instead of seven workdays will be WFH. These
model predictions are consistent with survey evidence on workers' own expectations about WFH in the future.
The paper examines how two targeted countries strategically deploy their counterterror forces when lobbying defense firms influence counterterror provision. For proactive measures, lobbying activities in a single targeted country lessen underprovision, raise overall counterterrorism, and reduce terrorism. Welfare decreases in the politically influenced country but increases in the other targeted country owing to enhanced free riding. Lobbying influence on the targeted countries’ welfare is tied to terrorists’ targeting preferences and how the lobbied government weighs citizens’ welfare. For key parametric values, lobbying in both targeted countries may result in the first-best equilibrium. With two-country lobbying, international policy coordination by at-risk governments may lead, surprisingly, to less efficient outcomes than the noncooperative equilibrium. Additionally, lobby-influenced defensive countermeasures generally affect efficiency adversely.
This paper studies how structural change in labor supply along the development spectrum shapes cross-country differences in hours worked. We emphasize two main forces: sectoral
reallocation from self-employment to wage work, and declining fixed costs of wage work. We show that these forces are crucial for understanding how the extensive margin (the employment rate) and intensive margin (hours per worker) of aggregate hours worked vary with income per capita. To do so we build and estimate a quantitative model of labor supply featuring a traditional self-employment sector and a modern wage-employment sector. When estimated to match cross-country data, the model predicts that sectoral reallocation explains more than half of the total hours decrease at lower levels of development. Declining fixed costs drive the rise in employment rates at higher levels of income per capita, and imply higher hours in the future, in contrast to the lower hours resulting from income effects and expansions in tax-and-transfer systems.
We document two robust features of the cross-sectional distribution of usual weekly hours and hourly wages. First, usual weekly hours are heavily concentrated around 40 hours, while at the same time a substantial share of total hours come from individuals who work more than 50 hours. Second, mean hourly wages are non-monotonic across the usual hours distribution, with a peak at 50 hours. We develop and estimate a model of labor supply to account for these features. The novel feature of our model is that earnings are non-linear in hours, with the extent of nonlinearity varying over the hours distribution. Our estimates imply significant wage penalties for individuals that deviate from 40 hours in either direction, leading to a large mass of individuals that work 40 hours and are not very responsive to shocks. This has important implications for the role of labor supply as a mechanism for self-insurance in a standard heterogeneous agent-incomplete markets model and for empirical strategies designed to estimate labor supply parameters.
This paper studies a dispersed information economy in which agents can exert costly attention to learn about an unknown aggregate state of the economy. Under certain conditions, attention and four measures of uncertainty are countercyclical: Agents pay more attention when they expect the economy to be in a bad state, and their reaction generates higher (i) aggregate output volatility, (ii) cross-sectional output dispersion, (iii) forecast dispersion about aggregate output, and (iv) subjective uncertainty about aggregate output faced by each agent. All these phenomena are prominent features of the U.S. data. When attention cost is calibrated to forecast survey data, the model generates countercyclical fluctuations in attention and uncertainty, consistent with untargeted moments from the data. Fluctuations in attention and uncertainty are higher-order properties of the model. A new method is developed to solve higher-order dynamics of the equilibrium under an infinite regress problem.
We provide evidence on the real-time predictive content of the National Financial Conditions Index (NFCI), for conditional quantiles of U.S. real GDP growth. Our work is distinct from the literature in two specific ways. First, we construct (unofficial) real-time vintages of the NFCI. This allows us to conduct out-of-sample analysis without introducing the kind of look-ahead biases that are naturally introduced when using a single current vintage. We then develop methods for conducting asymptotic inference on tests of equal tick loss between nested quantile regression models when the data are subject to revision. We conclude by evaluating the real-time predictive content of NFCI vintages for quantiles of real GDP growth. While our results largely reinforce the literature, we find gains to using real-time vintages leading up to recessions — precisely when policymakers need such a monitoring device.
We study the determinants of lifetime earnings (LE) inequality in the U.S. by
focusing on job ladder dynamics and on-the-job learning as sources of wage growth.
Using administrative data, we document that i) lower LE workers change jobs
more often, which is mainly driven by nonemployment; ii) average annual earnings
growth for job stayers is similar, around 2% in the bottom two-thirds of the LE
distribution, whereas for job switchers it rises with LE; iii) top LE workers enjoy
around 10% average earnings growth regardless of job switching. We estimate
a job ladder model with on-the-job learning featuring a rich set of worker types
and firm heterogeneity. We find that the vast differences across worker types in
job ladder risk—job loss, job finding, and contact rates—account for 80% of wage
growth differences among workers below median LE. Above the median, almost all
lifetime wage growth differences are a result of Pareto-distributed learning ability.
We conclude that different economic forces are driving the inequality in different
parts of the LE distribution.
Previous studies show the Fed has a forecast advantage over the private sector, either because it devotes more resources to forecasting or because it has an informational advantage in knowing the path of future monetary policy. We evaluate the Fed's forecast advantage to determine how much of it results from the Fed's knowledge of the conditioning path. We develop two tests---an instrumental variable encompassing test and a path-dependent encompassing test---to equalize the Fed's information set with the private sector's. We find that, generally, the Fed does not encompass the private sector when the latter has knowledge of the future of monetary policy. Further, we find that between 20 and 30 percent of the difference between the Fed's average mean squared forecast error and the private sector's can be explained by monetary policy.
Using administrative data from Norway, we first present stylized facts on labor earnings dynamics between 1993 and 2017 and its heterogeneity across narrow population groups. We then investigate the parents' role in children's income dynamics—the intergenerational transmission of income dynamics. We find that children of high-income, high-wealth fathers enjoy steeper income growth over the life cycle and face more volatile but more positively skewed income changes, suggesting that they are more likely to pursue high-return, high-risk careers. Children of poorer fathers also face more volatile incomes, but theirs grow more gradually and are more left skewed. Furthermore, the income dynamics of fathers and children are strongly correlated. In particular, children of fathers with steeper life-cycle income growth, more volatile incomes, or higher downside risk also have income streams of similar properties. We also confirm that fathers' significant role in workers' income dynamics is not simply spurious because of omitted variables, such as workers' own permanent income. These findings shed new light on the determinants of intergenerational mobility.
Over the last few decades, international trade has increased at a rapid pace, altering domestic production and labor demand in different sectors of the economy. A growing literature has studied the heterogeneous effects of trade shocks on workers’ industry and occupation employment and on welfare when reallocation decisions are costly. The estimated effects critically depend on data on workers’ reallocation patterns, which is typically plagued with coding errors. In this paper, I study the consequences of misclassification errors for estimates of the labor market effects of international trade and show that structural parameter values and the estimated effects are biased when the analysis uses uncorrected data. I develop an econometric framework to jointly estimate
misclassification probabilities, corrected mobility matrices, and structural parameters in a unified way. Under different model specifications, I compare how the estimated effects of a trade shock differ on whether the analysis uses correct mobility measures and parameters. The results show that estimated employment and welfare effects of a trade shock are substantially different, raising an important warning for quantitative exercises using mobility data with coding errors.
Are users of a bibliographic database interested in learning about replications? Can they be induced to learn? To answer these questions, we performed an experiment at the online research bibliography, RePEc (Research Papers in Economics). RePEc is the main research bibliography for pre-prints and published papers in economics. Using randomized stratification, we allocated 324 replications and their corresponding original studies to clusters. We then drew from those clusters to construct treatment and control groups. Brightly colored tabs were added to the relevant webpages to alert visitors to the existence of a replication study. We then monitored traffic over three phases lasting several months: a) no treatment, b) treatment on one group, c) treatment on both groups. Our estimates indicate that this intervention generated an average click-through-rate (CTR) of 1.6%, resulting in a 13% increase in the visits to replication webpages, though only the former estimate was statistically significant.
We develop a simple model of relationship lending where lenders have an incentive to evergreen loans by offering better terms to less productive and more indebted firms. We detect such lending distortions using loan-level supervisory data for the United States. Low-capitalized banks systematically distort their risk assessments of firms to window-dress their balance sheets and extend relatively more credit to underreported borrowers. Consistent with our theoretical predictions, these effects are driven by larger outstanding loans and low-productivity firms. We incorporate the theoretical mechanism into a dynamic heterogeneous-firm model to show that evergreening can affect aggregate outcomes, resulting in lower interest rates, higher levels of debt, and lower aggregate productivity.
This article documents the long-term relationship among juvenile conviction, occupation choices, employment, wages, and recidivism. Using data from NLSY97, we document that youths who are convicted at or before age 17 have lower full-time employment rate and lower wage growth rate even after 10 years into the labor market. Merging the NSLY97 with occupational characteristics data from O*NET, we show that youths with a juvenile conviction are less likely to be employed in occupations that have a higher on-the-job (OTJ) training requirement and these high OTJ occupations have higher wage and wage growth. The accumulated occupation-specific work experience, general experience, and education are important to explain the gaps in wage and recidivism between youths with and without a juvenile conviction. Our results highlight the important role of occupation choices as a human capital investment vehicle through which juvenile crimes have a long-term impact on wages and recidivism.
I develop a quantitative multi-country trade model of innovation and technology licensing to study the short- and long-term effects of trade agreements with intellectual property (IP) provisions. A trade agreement involves determining the level of tariffs and IP protection as Nash bargaining between a developed and a developing country. The agreement increases welfare, innovation, and growth in the long-run. However, gains accrue differently across countries along the transition. Developing countries experience short-run losses, as they now pay higher licensing prices. An agreement designed by a politically-motivated government could mitigate these losses, but at the expense of lower growth and welfare.
This paper reviews recent studies on the impact of financial frictions on international trade. We first present evidence on the relation between measures of access to external finance and export decisions. We then present an analytical framework to analyze the impact of financial frictions on firms' export decisions. Finally, we review recent applications of this framework to investigate the impact of financial frictions on international trade dynamics across firms, industries, and in the aggregate. We discuss related empirical, theoretical, and quantitative studies throughout.
A two-stage game investigates how counterterrorism measures affect within-country competition between two rival terrorist groups. Although such competition is commonplace (e.g., al-Nusra Front and Free Syria Army; Revolutionary Armed Forces of Colombia and the National Liberation Army; and al-Fatah and Hamas), there is no theoretical treatment of how proactive and defensive measures influence this interaction. Previous studies on rival terrorist groups are solely empirical concerning group survival, outbidding, and terrorism level, while ignoring the role that government countermeasures exert on the rival groups’ terrorism. In a theoretical framework, alternative counterterrorism actions have diverse impacts on the level of terrorism depending on relative group sizes and government-targeting decisions. In the two-stage game, optimal counterterrorism policy rules are displayed in terms of how governments target symmetric and asymmetric terrorist groups. Comparative statics show how parameter changes affect Nash or subgame perfect equilibrium outcomes.
We develop a quantitative framework to assess the cross-state implications of a U.S. trade policy change: a unilateral increase in the import tariff from 2 to 25 across all goods-producing sectors. Although the U.S. gains overall from the tariff increase, we find the impact differs starkly across locations. Changes in real consumption (welfare) range from as high as 3.8% in Wyoming to $-0.3% in Florida, depending mainly on how exposed states are to differentially-impacted sectors. As a result, the "preferred'' tariff rate varies greatly across states. Foreign retaliation in trade policy substantially reduces the welfare gains across states, while perpetuating the cross-state variation in those gains. The presence of internal trade frictions amplifies the welfare impacts of changes in trade policy.
This paper presents a method to decompose the causal effect of government defense spending into: (i) a local (or direct) effect, and (ii) a spillover (or indirect) effect. Each effect is measured as a multiplier: the unit change in output of a one unit change in government spending. We apply this method to study the effect of U.S. defense spending on output using regional panel data. We estimate a positive local multiplier and a negative spillover multiplier. By construction, the sum of the local and spillover multipliers provides an estimate of the aggregate multiplier. The aggregate multiplier is close to zero and precisely estimated. We show that enlisting disaggregate data improves the precision of aggregate effect estimates, relative to using aggregate time series alone. Our paper provides a template for researchers to conduct inference about local, spillover and aggregate causal effects in a unified framework.
This paper empirically investigates the causal linkages between COVID-19 spread, government health containment and economic support policies, and economic activity in the U.S. up to the introduction of vaccines in early 2021. We model their joint dynamics as generated by a structural vector autoregression and estimate it using U.S. state-level data. We identify structural shocks to the variables by making assumptions on their short-run relation consistent with salient epidemiological and economic features of COVID-19. We isolate the direct impact of COVID-19 spread and policy responses on economic activity by controlling for demand fluctuations using disaggregate exports data. We find that health containment and economic support policies are highly effective at curbing the spread of COVID-19 without leading to a long-term contraction of economic activity.
We quantify the barriers to the economic integration of immigrants using an occupational choice model with natives and immigrants of multiple types subject to wedges that distort their allocations. We show that key parameters, including wedges, can be estimated to match the distribution of employment and earnings across individuals and occupations. We find sizable output gains from removing immigrant wedges in the U.S., accounting for 7 percent of immigrants' overall economic contribution. These gains arise from increased labor force participation and from reallocation from manual toward cognitive jobs. We show that the model-implied elasticities are consistent with empirical estimates and that immigrant wedges affect the impact of alternative immigration policies. Finally, we use harmonized microdata across 19 economies and find substantial cross-country differences in the estimated immigrant wedges. Differences in immigrant labor force participation and the correlation between wedges and productivities account for the heterogeneous gains from removing the wedges.
Using a large panel dataset of US workers, we calibrate a search-theoretic model of the
labor market, where workers are heterogeneous with respect to the parameters governing
their employment transitions. We first approximate heterogeneity with a discrete number
of latent types, and then calibrate type-specific parameters by matching type-specific
moments. Heterogeneity is well approximated by 3 types: αs, βs and γs. Workers of type
α find employment quickly because they have large gains from trade, and stick to their
jobs because their productivity is similar across jobs. Workers of type γ find employment
slowly because they have small gains from trade, and are unlikely to stick to their job
because they keep searching for jobs in the right tail of the productivity distribution.
During the Great Recession, the magnitude and persistence of aggregate unemployment
is caused by γs, who are vulnerable to shocks and, once displaced, they cycle through
multiple unemployment spells before finding stable employment.
The role of unconditional lump-sum transfers in improving social welfare in heterogenous agent models has not been thoroughly understood in the literature. We adopt an analytically tractable Aiyagari-type model to study the distinctive role of unconditional lump-sum transfers in reducing consumption inequality due to ex-post uninsurable income risk under borrowing constraints. Our results show that in the presence of ex-post heterogeneity and in the absence of wealth inequality, unconditional lump-sum transfers are not a desirable tool for reducing consumption inequality---the Ramsey planner opts to rely solely on public debt and a linear labor tax (in the absence of a lump-sum tax) to mitigate income risk without the need for lump-sum transfers, in contrast to the result obtained by Werning (2007), Azzimonti and Yared (2017), and Bhandari, Evans, Golosov, and Sargent (2017) in models with ex-ante heterogeneity.
Quantitative macroeconomics is often portrayed as a science—because of its intensive use of high-powered mathematics—with the possible limitation of being unable to conduct controlled experiments. To qualify as a science, however, theories in that discipline must meet a minimum number of criteria: (i) It has explanatory power to explain phenomena; (ii) it has predictive power to yield quantifiable and falsifiable statements about new phenomenon; and (iii) it has operational power to change the world.
A scientific theory consists of axioms and working hypotheses that facilitate the derivation of contestable statements from the axioms.2 Hence, simply laying out a list of contradictions between a theory’s implications and the data is often insufficient to disqualify a theory as science; it may have just challenged its working hypotheses, not its axioms. But, challenging a theory’s working hypotheses is a crucial step to improve or falsify a theory. This is why Isaac Newton spent so much effort in his Principia Mathematica to deal with the law of motion under air friction.
This article discusses one of the working hypotheses of the Arrow-Debreu paradigm and its dynamic stochastic general equilibrium reincarnation in quantitative macroeconomics—the supply curve and its embodiment in the neoclassical production function. The supply curve is a much stronger pillar than the demand curve in holding up the Arrow-Debreu paradigm, but we argue in this article that the neoclassical production function embodying the supply curve is full of cracks.
More specifically, we show that the neoclassical production function is not quantifiable as a working hypothesis to support the Arrow-Debreu DSGE model, unlike the chemical reaction equations based on Lavoisier’s oxygen theory of combustion. The neoclassical production function relies on the unobservable and unmeasurable Solow residual to explain the quantity of output produced at the firm, industry, or national level, and the hypothetical factors of production (capital and labor) are much like “fire, air, water, and earth” in the ancient Greek theory of the universe. Because the working hypotheses of quantitative macroeconomics are not themselves quantifiable, the neoclassical theory is not yet a science. And this explains the lack of power for DSGE models to predict the 2008 Financial Crisis and the inability of economic theory to change the world by engineering or recreating economic prosperity in developing countries.
While aggregate shocks account for most business cycle fluctuations, sectoral shocks have become relatively more important since the 1980s. Previous studies show that sectoral shocks propagate through industry supply chains. Typically, sectors are defined by similarities in function and/or market. While some industries have supply chains within their own sector (vertical), others have supply chains across a number of sectors (horizontal). Similarity in these supply chain characteristics appear to be a determining factor in how industries comove. Using industrial production data of 82 four-digit NAICS industries over the period 1972 to 2019, this comovement is analyzed in a panel Markov-switching model incorporating a number of features relevant for sub-national analysis: (i)industry-specific trends that differentiate cyclical downturns from secular declines; (ii) a national-level business cycle; and (iii) factors that represent industrial comovement. While national-level shocks are typically still the most important driver of cyclical fluctuations, endogenously clustering by industry comovement highlights the role of sectoral shocks.
Cohen, Diether, and Malloy (Journal of Finance, 2007), find that shifts in the demand curve predict negative stock returns. We use their approach to examine changes in supply and demand at the time of FOMC announcements. We show that shifts in the demand for borrowing Treasuries and agencies predict quantitative easing. A reduction in the quantity demanded at all points along the demand curve predicts expansionary quantitative easing announcements.