Knowledge that Transforms

A common challenge in estimating the impact of interventions (e.g. job training programmes, educational programmes) is that many outcomes of interest (e.g. lifetime earnings or other labour market outcomes) are observed with a long delay. In biomedical settings, this is often addressed by using short-term outcomes as so-called “surrogates” for the outcome of interest, e.g. tumour size as a surrogate for mortality in cancer studies. We build on this literature by combining multiple, possibly qualitatively distinct, short-term outcomes (e.g. short-run earnings and employment indicators) systematically into a “surrogate index”. Under the Prentice surrogacy assumption, which requires that the primary outcome is independent of the treatment conditional on the surrogates, we show that the average treatment effect on the surrogate index equals the treatment effect on the long-term outcome. We also relate the surrogacy assumption to a set of structural, causal assumptions. We then characterize the bias that arises from violations of each of the key assumptions, and we provide simple methods to validate these assumptions using additional observed outcomes. We apply our method to analyse the long-term impacts of a multi-site job training experiment in California. Rather than waiting a full 9 years to directly observe the long-term impact, we show that it is possible to use short-term (the first six quarters) outcomes as surrogates. Given the surrogacy assumption one could have estimated the programme’s long-term impacts on mean employment rates using the employment rates observed in the first six quarters, with a 35% reduction in standard errors relative to a simple difference in means estimator based on all 9 years of data.

We study the relationship between international trade and development in a model where countries differ in their capability, goods differ in their complexity, and capability growth is a function of a country’s pattern of specialization. Theoretically, we show that it is possible for international trade to increase capability growth in all countries and, in turn, to push all countries up the development ladder. This occurs if (i) shifting employment towards more complex sectors raises capability growth and if (ii) foreign competition is tougher in less complex sectors for all countries. Empirically, we provide causal evidence consistent with (i) using the entry of countries into the World Trade Organization as an instrumental variable for other countries’ patterns of specialization. The opposite of (ii), however, holds in the data. Through the lens of our model, these two empirical observations imply dynamic welfare losses from trade that are pervasive, albeit small for the median country. The same economic forces also suggest that the emergence of China has held back capability growth for a number of African countries who are pushed away from their most-complex sectors, which China exports, and into their least-complex sectors, which China imports.

How do global value chain (GVC) linkages modify countries’ incentives to impose import protection? Are these linkages important determinants of trade policy in practice? We develop a new approach to modelling tariff setting with GVCs, in which optimal policy depends on the nationality of value-added content embedded in home and foreign final goods. Theory predicts that discretionary tariffs will be decreasing in the domestic content of foreign-produced final goods and the foreign content of domestically produced final goods. Using data for 14 countries between 1995 and 2015, we show that governments set lower tariffs and curb their use of temporary trade barriers where GVC linkages are strongest, consistent with theory. Turning to quantitative model counterfactuals, we find that severing GVC linkages would lead to the disappearance of tariff preferences. Further, targeted policies to decouple China from GVCs would increase the optimal tariff set by G7 countries on Chinese exports.

I study racial disparities in the criminal justice system by analysing abnormal bunching in the distribution of crack-cocaine amounts used in federal sentencing. I compare cases sentenced before and after the Fair Sentencing Act, a 2010 law that changed the 10-year mandatory minimum threshold for crack-cocaine from 50 g to 280 g. First, I find that after 2010, there is a sharp increase in the fraction of cases sentenced at 280 g (the point that now triggers a 10-year mandatory minimum), and that this increase is disproportionately large for black and Hispanic offenders. I then explore several possible explanations for the observed racial disparities, including racial discrimination that occurs after entry into the criminal justice system. I analyse data from multiple stages in the criminal justice system and find that the increased bunching for minority offenders is driven by prosecutorial discretion, specifically as used by about 20–30% of prosecutors. Moreover, the fraction of cases at 280 g falls in 2013 when evidentiary standards become stricter. Finally, the racial disparity in the increase cannot be explained by differences in education, sex, age, criminal history, seized drug amount, or other elements of the crime, but it can be largely explained by a measure of state-level racial animus. These results shed light on the role of prosecutorial discretion and racial discrimination as causes of racial disparities in sentencing.

We build a quantitative spatial model in which some workers can substitute on-site effort with work done from home. Ability and propensity to telecommute vary by education and industry. We quantify our framework to match the distribution of jobs and residents across 4,502 U.S. locations. Then, we simulate permanent increases in the attractiveness and productivity of telework that lead to greater adoption of hybrid and fully remote work. To validate our model, we show that our results are positively correlated with local changes in residents, jobs, and housing costs since 2019. The rise of telework results in a rich non-monotonic pattern of reallocations of residents and jobs within and across cities. Workers who can telecommute experience welfare gains, and those who cannot suffer losses. Broader access to jobs reduces wage inequality across residential locations, and heralds a partial reversal in the spatial concentration of talent and spending power known as the “Great Divergence”.

In a data economy, transactions of goods and services generate data, which is stored, traded and depreciates. How are the economics of this economy different from traditional production economies? How do these differences matter for measurement of GDP, firm values, depreciation rates, welfare and externalities? We incorporate active experimentation and data as an intangible asset to devise a tractable recursive representation of the data economy. The model rationalizes why apps are often “free” and why even non-digital economic activity might be greater than GDP suggests. Calibrating the model using a combination of macroeconomic and financial moments suggests that the mis-measurement in U.S. GDP due to missing value of data has been as high as 6% in 2018.

This paper develops a theory of competitive equilibrium with indivisible goods based entirely on economic conditions on demand. The key idea is to analyze complementarity and substitutability between bundles of goods, rather than merely between goods themselves. This approach allows us to formulate sufficient and essentially necessary conditions for equilibrium existence—which unify settings with complements and settings with substitutes. Our analysis has implications for auction design.

We study the aggregate and redistributive effects of currency devaluations in a small open economy model with leverage-constrained banks and heterogeneous households. Our framework captures three stylized facts about financial dollarization in emerging economies: (1) a sizable share of domestic deposits is denominated in foreign currency; (2) these deposits represent significant foreign currency liabilities for local banks; and (3) dollar deposits are mainly held by wealthier households. A devaluation increases the real burden of foreign currency debt, causing an erosion of banks’ net worth, which depresses credit supply and economic activity. While richer households are partially insulated through their dollar deposits, poorer households cut consumption sharply in response to rising borrowing costs and falling real labor income. In our model deposit, dollarization amplifies the contractionary effects of a devaluation on output, investment, and consumption, in line with new empirical evidence for emerging economies. To achieve this result, both constrained intermediaries and heterogeneous households are crucial. In our framework, regulating dollarization can result in widespread welfare gains, especially for poorer households.

We define the (physical) opportunity cost of a choice x as the alternative that would be chosen if x were not available, and the opportunity cost of any unchosen alternative as x itself. The agent has preferences over pairs consisting of alternatives and their opportunity costs. Because costs affect choice and vice-versa, choice results from an intrapersonal equilibrium rather than from simple maximisation. In spite of significant rationality assumptions, the resulting behaviour can be highly non-standard, allowing intransitive choices. Rational utility maximisation is ensured by an additional new consistency condition on preferences. However, we argue that the maximised utility cannot be straightforwardly interpreted as a welfare relevant “revealed preference”. A generalisation of our model accommodates additional departures from standard rationality in the form of menu effects.

A salient trend in crisis intervention has emerged in recent decades: government and central banks have offered funding directly to nonfinancial firms, bypassing banks and other credit intermediaries. We analyse the long-term consequences of such policies by focussing on firm quality dynamics. In a laissez-faire economy, firms with high productivity are more likely to survive crises than those with low productivity. The government funding support saves more firms but cannot be customized based on firm productivity, dampening the cleansing effect of crises. The policy distortion is self-perpetuating: a downward bias in the firm quality distribution necessitates larger interventions in future crises. Our mechanism is quantitatively important: we show that if policymakers ignore such distortionary effects on firm quality dynamics, the resultant credit intervention would almost double the optimal amount.