Primary commodities are used as inputs into all production processes, yet they account for approximately 16 percent of world trade. Despite their share in trade, we show that the aggregate gains from trade are largely understated if we ignore key features of commodities: low price elasticities of demand (difficulty in finding substitutes), low price elasticities of supply, and high dispersion of natural resources across countries. We develop a general-equilibrium model of consumption, production, and input-output linkages that explicitly accounts for these features. Our simulations confirm that the gains from trade are significantly larger, especially when considering large trade cost changes.
Commodity Trade Matters -- by Thibault Fally, James Sayre
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Machine Learning for Regularized Survey Forecast Combination: Partially-Egalitarian Lasso and its Derivatives -- by Francis X. Diebold, Minchul Shin
Despite the clear success of forecast combination in many economic environments, several important issues remain incompletely resolved. The issues relate to selection of the set of forecasts to combine, and whether some form of additional regularization (e.g., shrinkage) is desirable. Against this background, and also considering the frequently-found good performance of simple-average combinations, we propose a LASSO-based procedure that sets some combining weights to zero and shrinks the survivors toward equality ("partially-egalitarian LASSO"). Ex-post analysis reveals that the optimal solution has a very simple form: The vast majority of forecasters should be discarded, and the remainder should be averaged. We therefore propose and explore direct subset-averaging procedures motivated by the structure of partially-egalitarian LASSO and the lessons learned, which, unlike LASSO, do not require choice of a tuning parameter. Intriguingly, in an application to the European Central Bank Survey of Professional Forecasters, our procedures outperform simple average and median forecasts - indeed they perform approximately as well as the ex-post best forecaster.
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The Impact of Chief Diversity Officers on Diverse Faculty Hiring -- by Steven W. Bradley, James R. Garven, Wilson W. Law, James E. West
As the American college student population has become more diverse, the goal of hiring a more diverse faculty has received increased attention in higher education. A signal of institutional commitment to faculty diversity often includes the hiring of an executive level chief diversity officer (CDO). To examine the effects of a CDO in a broad panel data context, we combine unique data on the initial hiring of a CDO with publicly available faculty and administrator hiring data by race and ethnicity from 2001 to 2016 for four-year or higher U.S. universities categorized as Carnegie R1, R2, or M1 institutions with student populations of 4,000 or more. We are unable to find significant statistical evidence that preexisting growth in diversity for underrepresented racial/ethnic minority groups is affected by the hiring of an executive level diversity officer for new tenure and non-tenure track hires, faculty hired with tenure, or for university administrator hires.
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Target setting and Allocative Inefficiency in Lending: Evidence from Two Chinese Banks -- by Yiming Cao, Raymond Fisman, Hui Lin, Yongxiang Wang
We study the consequences of month-end lending incentives for Chinese bank managers. Using data from two banks, one state-owned and the other partially privatized, we show a clear increase in lending in the final days of each month, a result of both more loan issuance and higher value per loan. We estimate that daily end-of-month lending is 95 percent higher in the last 5 days of each month as a result of loan targets, with only a small amount plausibly attributable to shifting loans forward from the following month. End-of-month loans are 2.1 percentage points (more than 16 percent) more likely to be classified as bad in the years following issuance; a back-of-the-envelope calculation suggests that the incremental loans made in order to hit targets are 26 percent more likely to eventually turn bad. Our work highlights the distortionary effects of target-setting on capital allocation, in a context in which such concerns have risen to particular prominence in recent years.
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Market Effects of Adverse Regulatory Events: Evidence from Drug Relabeling -- by Matthew J. Higgins, Xin Yan, Chirantan Chatterjee
The FDA maintains post-approval safety surveillance programs to monitor the safety of drugs. As adverse events are reported, the FDA may choose to intervene and change the safety labeling associated with a drug. We provide causal evidence of the impact that these regulatory interventions have on aggregate demand for pharmaceuticals. We find that aggregate demand declines by 16.9 percent within two years of a relabeling event. After accounting for substitution patterns by physicians along with competitor actions, aggregate demand declines by 5.1 percent. Critically, this decline represents consumers that leave the market. The overall effect appears to be driven by 'high-intensity' markets or those with significant relabeling activity. Results control for the level of advertising and are robust to variation across types of relabeling, market sizes, levels of competition and degrees of cross-molecular substitution.
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Frictional Intermediation in Over-the-counter Markets -- by Julien Hugonnier, Benjamin Lester, Pierre-Olivier Weill
We extend Duffie, Garleanu, and Pedersen's (2005) search-theoretic model of over-the-counter asset markets, allowing for a decentralized inter-dealer market with arbitrary heterogeneity in dealers' valuations or inventory costs. We develop a solution technique that makes the model fully tractable and allows us to derive, in closed form, theoretical formulas for key statistics analyzed in empirical studies of the intermediation process in OTC market. A calibration to the market for municipal securities reveals that the model can generate trading patterns and prices that are quantitatively consistent with the data. We use the calibrated model to compare the gains from trade that are realized in this frictional market with those from a hypothetical, frictionless environment, and to distinguish between the quantitative implications of various types of heterogeneity across dealers.
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Mismatch and Assimilation -- by Ping Wang, Tsz-Nga Wong, Chong K. Yip
Income disparity across countries has been large and widening over time. We develop a tractable model where factor requirements in production technology do not necessarily match a country's factor input profile. Appropriate assimilation of frontier technologies balances such multi-dimensional factor input-technology mismatch, thus mitigating the efficiency loss. This yields a new measure for endogenous TFP, entailing a novel trade-off between a country's income level and income growth that depends critically on the assimilation ability and the factor input mismatch. Our baseline model accounts for 80%-92% of the global income variation over the past 50 years. The widening of mismatch and heterogeneity in the assimilation ability account for 41% and 20% of the global growth variation, whereas physical capital accounts for about one third with human capital largely inconsequential. In particular, about 30% of the output growth in miracle Asian economies comes from narrowing the gap arisen from mismatch, and 94% of the growth stagnation in trapped African economies due to the widening mismatch. A country may fall into a middle-income trap after a factor advantage reversal that changes the pattern of mismatch.
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Schooling, Wealth, Risky Sexual Behavior, and HIV/AIDS in Sub-Saharan Africa -- by Adrienne M. Lucas, Nicholas L. Wilson
Economic growth and development have improved human health in many regions, while sub-Saharan Africa continues to lag behind. Economic theory and the existing empirical evidence suggest that development may not generate large reductions in the leading cause of adult mortality in the region, HIV/AIDS, and may increase risky sexual behavior. We examine the association between schooling/material standard of living and HIV risk using data from more than 500,000 respondents in 32 sub-Saharan African countries. The results of our descriptive analysis suggest that the rapid increase in primary school completion without improvements in living standards or secondary school completion might not mitigate HIV transmission.
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The Limits of Simple Implementation Intentions: Evidence from a Field Experiment on Making Plans to Exercise -- by Mariana Carrera, Heather Royer, Mark F. Stehr, Justin R. Sydnor, Dmitry Taubinsky
Recent large-scale randomized experiments find that helping people form implementation intentions by asking when and where they plan to act increases one-time actions, such as vaccinations, preventative screenings and voting. We investigate the effect of a simple scalable planning intervention on a repeated behavior using a randomized design involving 877 subjects at a private gym. Subjects were randomized into i) a treatment group who selected the days and times they intended to attend the gym over the next two weeks or ii) a control group who instead recorded their days of exercise in the prior two weeks. In contrast to recent studies, we find that the planning intervention did not have a positive effect on behavior and observe a tightly estimated null effect. This lack of effect is despite the fact that the majority of subjects believe that planning is helpful and despite clear evidence that they engaged with the planning process.
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Top of the Class: The Importance of Ordinal Rank -- by Richard Murphy, Felix Weinhardt
This paper establishes a new fact about educational production: ordinal academic rank during primary school has long-run impacts that are independent from underlying ability. Using data on the universe of English school students, we exploit naturally occurring differences in achievement distributions across primary school classes to estimate the impact of class rank conditional on relative achievement. We find large effects on test scores, confidence and subject choice during secondary school, where students have a new set of peers and teachers who are unaware of the students' prior ranking. The effects are especially large for boys, contributing to an observed gender gap in end-of-high school STEM subject choices. Using a basic model of student effort allocation across subjects, we derive and test a hypothesis to distinguish between learning and non-cognitive skills mechanisms and find support for the latter.
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Estimating the Elasticity of Intertemporal Substitution Using Mortgage Notches -- by Michael Carlos Best, James Cloyne, Ethan Ilzetzki, Henrik Kleven
Using a novel source of quasi-experimental variation in interest rates, we develop a new approach to estimating the Elasticity of Intertemporal Substitution (EIS). In the UK, the mortgage interest rate features discrete jumps - notches - at thresholds for the loan-to-value (LTV) ratio. These notches generate large bunching below the critical LTV thresholds and missing mass above them. We develop a dynamic model that links these empirical moments to the underlying structural EIS. The average EIS is small, around 0.1, and quite homogeneous in the population. This finding is robust to structural assumptions and can allow for uncertainty, a wide range of risk preferences, portfolio reallocation, liquidity constraints, present bias, and optimization frictions. Our findings have implications for the numerous calibration studies that rely on larger values of the EIS.
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Measuring Bias in Consumer Lending -- by Will Dobbie, Andres Liberman, Daniel Paravisini, Vikram Pathania
This paper tests for bias in consumer lending decisions using administrative data from a high-cost lender in the United Kingdom. We motivate our analysis using a simple model of bias in lending, which predicts that profits should be identical for loan applicants from different groups at the margin if loan examiners are unbiased. We identify the profitability of marginal loan applicants by exploiting variation from the quasi-random assignment of loan examiners. We find significant bias against both immigrant and older loan applicants when using the firm's preferred measure of long-run profits. In contrast, there is no evidence of bias when using a short-run measure used to evaluate examiner performance, suggesting that the bias in our setting is due to the misalignment of firm and examiner incentives. We conclude by showing that a decision rule based on machine learning predictions of long-run profitability can simultaneously increase profits and eliminate bias.
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Discouraging Deviant Behavior in Monetary Economics -- by Lawrence Christiano, Yuta Takahashi
We consider a model in which monetary policy is governed by a Taylor rule. The model has a unique equilibrium near the steady state, but also has other equilibria. The introduction of a particular escape clause into monetary policy works like the Taylor principle to exclude the other equilibria. We reconcile our finding about the escape clause with the sharply different conclusion reached in Cochrane (2011). Atkeson et al. (2010) study a different version of the escape clause policy, but that version is fragile in that it lacks a crucial robustness property.
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Measuring Gentrification: Using Yelp Data to Quantify Neighborhood Change -- by Edward L. Glaeser, Hyunjin Kim, Michael Luca
We demonstrate that data from digital platforms such as Yelp have the potential to improve our understanding of gentrification, both by providing data in close to real time (i.e. nowcasting and forecasting) and by providing additional context about how the local economy is changing. Combining Yelp and Census data, we find that gentrification, as measured by changes in the educational, age, and racial composition within a ZIP code, is strongly associated with increases in the numbers of grocery stores, cafes, restaurants, and bars, with little evidence of crowd-out of other categories of businesses. We also find that changes in the local business landscape is a leading indicator of housing price changes, and that the entry of Starbucks (and coffee shops more generally) into a neighborhood predicts gentrification. Each additional Starbucks that enters a zip code is associated with a 0.5% increase in housing prices.
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Occupational Classifications: A Machine Learning Approach -- by Akina Ikudo, Julia Lane, Joseph Staudt, Bruce Weinberg
Characterizing the work that people do on their jobs is a longstanding and core issue in labor economics. Traditionally, classification has been done manually. If it were possible to combine new computational tools and administrative wage records to generate an automated crosswalk between job titles and occupations, millions of dollars could be saved in labor costs, data processing could be sped up, data could become more consistent, and it might be possible to generate, without a lag, current information about the changing occupational composition of the labor market. This paper examines the potential to assign occupations to job titles contained in administrative data using automated, machine-learning approaches. We use a new extraordinarily rich and detailed set of data on transactional HR records of large firms (universities) in a relatively narrowly defined industry (public institutions of higher education) to identify the potential for machine-learning approaches to classify occupations.
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Death, Trauma and God: The Effect of Military Deployments on Religiosity -- by Resul Cesur, Travis Freidman, Joseph J. Sabia
Learning to cope with man's mortality is central to the teachings of the world's major religions. However, very little is known about the impact of life-and-death trauma on religiosity. This study exploits a natural experiment in military deployments to estimate the causal effect of traumatic shocks on religiosity. We find that combat assignment is associated with a substantial increase in the probability that a serviceman subsequently attends religious services regularly and engages in private prayer. Combat-induced increases in religiosity are largest for enlisted servicemen, those under age 25, and servicemen wounded in combat. The physical and psychological burdens of war, as well as the presence of military chaplains in combat zones, emerge as possible mechanisms.
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Measuring the Gig Economy: Current Knowledge and Open Issues -- by Katharine G. Abraham, John C. Haltiwanger, Kristin Sandusky, James R. Spletzer
The rise of the "gig economy" has attracted wide attention from both scholars and the popular media. Much of this attention has been devoted to jobs mediated through various online platforms. While non-traditional work arrangements have been a perennial subject of debate and study, the perception that new technology is producing an accelerated pace of change in the organization of work has fueled a resurgence of interest in how such changes may be affecting both workers and firms. This paper provides a typology of work arrangements and reviews how different arrangements, and especially gig activity, are captured in existing data. A challenge for understanding recent trends is that household survey and administrative data paint a different picture, with the former showing little evidence of the growth in self-employment that would be implied by a surge in gig activity and the latter providing evidence of considerable recent growth. An examination of matched individual-level survey and administrative records shows that a large and growing fraction of those with self-employment activity in administrative data have no such activity recorded in household survey data. The share of those with self-employment activity in household survey data but not administrative data is smaller and has not grown. Promising avenues for improving the measurement of self-employment activity include the addition of more probing questions to household survey questionnaires and the development of integrated data sets that combine survey, administrative and, potentially, private data.
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Understanding Physician Decision Making: The Case of Depression -- by Janet M. Currie, W. Bentley MacLeod
Faulty physician decision making has been blamed for everything from medical errors to excessive procedure use and wasteful spending. Yet medical treatment is often complex, requiring a sequence of decisions that may involve trade offs between selecting the choice with the highest expected value or selecting a choice with higher possible payoffs. We show that the best choice depends on a physician's diagnostic skill so that the optimal treatment can vary even for identical patients. Bringing the model to patient claims data for depression, we show that doctors who experiment more with drug choice achieve better patient outcomes, except when physician decisions violate professional guidelines for drug choice.
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The Problem of Data Quality in Analyses of Opioid Regulation: The Case of Prescription Drug Monitoring Programs -- by Jill Horwitz, Corey S. Davis, Lynn S. McClelland, Rebecca S. Fordon, Ellen Meara
States, which have the primary legal role in regulating the prescribing and dispensing of prescription medications, have created Prescription Drug Monitoring Programs (PDMP) to try to reduce inappropriate prescribing, dispensing, and related harm. Research assessing whether these interventions are effective has produced inconclusive and contradictory results. Here we examine whether different data sources may have contributed to the varying results. Specifically, we: 1) identify the decisions inherent in creating such a dataset; 2) discuss the public data sources used by researchers in previous work; 3) develop and apply a detailed research protocol to create a novel PDMP law dataset; and 4) to illustrate potential consequences of data choice, apply various data sources to analyze the relationship between PDMP laws and prescribing and dispensing of opioids among disabled Medicare beneficiaries. We find that our dates differ from those in existing datasets, sometimes by many years. The regression analyses generated a twofold difference in point estimates, as well as different signed estimates, depending on the data used. We conclude that the lack of transparency about data assembly in existing datasets, differences among dates by source, and the regression results raise concerns for PDMP researchers and policymakers.
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Why Have Negative Nominal Interest Rates Had Such a Small Effect on Bank Performance? Cross Country Evidence -- by Jose A. Lopez, Andrew K. Rose, Mark M. Spiegel
We examine the effect of negative nominal interest rates on bank profitability and behavior using a cross-country panel of over 5,100 banks in 27 countries. Our data set includes annual observations for Japanese and European banks between 2010 and 2016, which covers all advanced economies that have experienced negative nominal rates, including currency union members as well as both fixed and floating exchange rates countries. When we compare negative nominal interest rates with low positive rates, banks experience losses in interest income that are almost exactly offset by savings on deposit expenses and gains in non-interest income, including capital gains on securities and fees. We find heterogeneous effects of negative rates: banks from regimes with floating exchange rates, small banks, and banks with low deposit ratios drive most of our results. Low-deposit banks have enjoyed particularly striking gains in non-interest income, likely from capital gains on securities. There have only been modest differences between high and low deposit-ratio banks' changes in interest expenses; high deposit banks do not seem disproportionately vulnerable to negative rates. Banks also responded to negative rates by increasing lending activity, and raising the share of deposit funding. Overall, our results indicate surprisingly benign implications of negative rates for commercial banks thus far.
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