Knowledge that Transforms

Developing countries face massive urbanization and slum upgrading is a popular policy to improve shelter for many. Yet, preserving slums at the expense of formal developments can raise concerns of misallocation of land. We estimate causal, long-term impacts of the 1969–84 KIP programme, which provided basic upgrades to 5 million residents covering 25% of land in Jakarta, Indonesia. We assemble high-resolution data on programme boundaries and 2015 outcomes and address programme selection bias through localized comparisons. On average, KIP areas today have lower land values, shorter buildings, and are more informal, per a photographs-based slum index. The negative effects are concentrated within 5 km of the CBD. We develop a spatial equilibrium model to characterize the welfare implications of KIP. Counterfactuals suggest that 79% of the welfare effects stem from removing KIP in the centre and highlight how to mitigate losses to displaced residents.

Do periods of persistently loose monetary policy increase financial fragility and the likelihood of a financial crisis? This is a central question for policymakers, yet the literature does not provide systematic empirical evidence about this link at the aggregate level. In this article, we fill this gap by analysing long-run historical data. We find that when the stance of monetary policy is accommodative over an extended period, the likelihood of financial turmoil in the medium term increases considerably. We investigate the causal pathways that lead to this result and argue that credit creation and asset price overheating are important intermediating channels.

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.