The Impact of a Rise in Expected Income on Child Labor: Evidence from Coca Production in Colombia

Can households’ beliefs about future income shocks affect child labor? This paper examines whether the three-year gap between the announcement (in 2014) and the start (in 2017) of the Illicit Crop Substitution Program (ICSP) increased child labor in Colombia. The ICSP provides farmers with financial support for not planting and harvesting coca leaves – the key input of cocaine. My results from a difference-in-differences model using differences in historical coca production show that due to the ICSP announcement, children became four percentage points more likely to work in municipalities with historical coca production than in non–coca-growing areas. Although the likelihood of working increased in coca–growing areas, the hours worked per child declined modestly after the ICSP announcement. The expansion of the children working in coca fields but the decline in working hours per child produce null effects of the announcement on education outcomes. The rise in the expected income affects the time allocation decision within households in rural areas.

The impact of return migration on employment and wages in Mexican cities

How does return migration from the US to Mexico affect local workers? Return migrants increase the local labor supply, potentially hurting local workers. However, having been exposed to a more advanced U.S. economy, they may also carry human capital that benefits non-migrants. Using an instrument based on involuntary return migration, we find that, whereas workers who share returnees’ occupations experience a fall in wages, workers in other occupations see their wages rise. These effects are, however, transitory and restricted to the city-industry receiving the returnees. In contrast, returnees permanently alter a city’s long-run industrial composition, by raising employment levels in the local industries that hire them.

Evaluating the Principle of Relatedness: Estimation, Drivers and Implications for Policy

A growing body of research documents that the size and growth of an industry in a place depends on how much related activity is found there. This fact is commonly referred to as the “principle of relatedness.” However, there is no consensus on why we observe the principle of relatedness, how best to determine which industries are related or how this empirical regularity can help inform local industrial policy. We perform a structured search over tens of thousands of specifications to identify robust – in terms of out-of-sample predictions – ways to determine how well industries fit the local economies of US cities. To do so, we use data that allow us to derive relatedness from observing which industries co-occur in the portfolios of establishments, firms, cities and countries. Different portfolios yield different relatedness matrices, each of which help predict the size and growth of local industries. However, our specification search not only identifes ways to improve the performance of such predictions, but also reveals new facts about the principle of relatedness and important trade-offs between predictive performance and interpretability of relatedness patterns. We use these insights to deepen our theoretical understanding of what underlies path-dependent development in cities and expand existing policy frameworks that rely on inter-industry relatedness analysis.

Birthplace diversity and economic complexity: Cross-country evidence

We empirically investigate the relationship between a country’s economic complexity and the diversity in the birthplaces of its immigrants. Our cross-country analysis suggests that countries with higher birthplace diversity by one standard deviation are more economically complex by 0.1 to 0.18 standard deviations above the mean. This holds particularly for diversity among highly educated migrants and for countries at intermediate levels of economic complexity. We address endogeneity concerns by instrumenting diversity through predicted stocks from a pseudo-gravity model as well as from a standard shift-share approach. Finally, we provide evidence suggesting that birthplace diversity boosts economic complexity by increasing the diversification of the host country’s export basket.

The Value of Early-Career Skills

We develop novel measures of early-career skills that are more detailed, comprehensive, and labor-market-relevant than existing skill proxies. We exploit that skill requirements of apprenticeships in Germany are codified in state-approved, nationally standardized apprenticeship plans. These plans provide more than 13,000 different skills and the exact duration of learning each skill. Following workers over their careers in administrative data, we find that cognitive, social, and digital skills acquired during apprenticeship are highly – yet differently – rewarded. We also document rising returns to digital and social skills since the 1990s, with a more moderate increase in returns to cognitive skills.

What Can the Millions of Random Treatments in Nonexperimental Data Reveal About Causes?

We propose a new method to estimate causal effects from nonexperimental data. Each pair of sample units is first associated with a stochastic ‘treatment’—differences in factors between units—and an effect—a resultant outcome difference. It is then proposed that all pairs can be combined to provide more accurate estimates of causal effects in nonexperimental data, provided a statistical model relating combinatorial properties of treatments to the accuracy and unbiasedness of their effects. The article introduces one such model and a Bayesian approach to combine the O(n2) pairwise observations typically available in nonexperimental data. This also leads to an interpretation of nonexperimental datasets as incomplete, or noisy, versions of ideal factorial experimental designs. This approach to causal effect estimation has several advantages: (1) it expands the number of observations, converting thousands of individuals into millions of observational treatments; (2) starting with treatments closest to the experimental ideal, it identifies noncausal variables that can be ignored in the future, making estimation easier in each subsequent iteration while departing minimally from experiment-like conditions; (3) it recovers individual causal effects in heterogeneous populations. We evaluate the method in simulations and the National Supported Work (NSW) program, an intensively studied program whose effects are known from randomized field experiments. We demonstrate that the proposed approach recovers causal effects in common NSW samples, as well as in arbitrary subpopulations and an order-of-magnitude larger supersample with the entire national program data, outperforming Statistical, Econometrics and Machine Learning estimators in all cases. As a tool, the approach also allows researchers to represent and visualize possible causes, and heterogeneous subpopulations, in their samples.

A Measure of Countries’ Distance to Frontier Based on Comparative Advantage

This paper presents a structural ranking of countries by their distance to frontier. The ranking is based on comparative advantage. Hence, it reveals information on the productive capabilities of countries that is fundamentally different from GDP per capita. The ranking is centered on the assumption that countries’ capabilities across products are similar to those of other countries with comparable distance to frontier. It can be micro-founded using standard trade models. The estimation strategy provides a general, non-parametric approach to uncovering a log-supermodular structure from the data, and I use it to also derive a structural ranking of products by their complexity. The underlying theory provides a flexible micro-foundation for the Economic Complexity Index (Hidalgo and Hausmann, 2009).

A Simple Theory of Economic Development at the Extensive Industry Margin

We revisit the well-known fact that richer countries tend to produce a larger variety of goods and analyze economic development through (export) diversifcation. We show that countries are more likely to enter ‘nearby’ industries, i.e., industries that require fewer new occupations. To rationalize this finding, we develop a small open economy (SOE) model of economic development at the extensive industry margin. In our model, industries differ in their input requirements of non-tradeable occupations or tasks. The SOE grows if profit maximizing frms decide to enter new, more advanced industries, which requires training workers in all occupations that are new to the economy. As a consequence, the SOE is more likely to enter nearby industries in line with our motivating fact. We provide indirect evidence in support of our main mechanism and then discuss implications: We show that there may be multiple equilibria along the development path, with some equilibria leading on a pathway to prosperity while others resulting in an income trap, and discuss implications for industrial policy. We finally show that the rise of China has a non-monotonic effect on the growth prospects of other developing countries, and provide suggestive evidence for this theoretical prediction.

Global Supply Chain Pressures, International Trade, and Inflation

We study the impact of the Covid-19 pandemic on Euro Area inflation and how it compares to the experiences of other countries, such as the United States, over the two-year period 2020-21. Our model-based calibration exercises deliver four key results: 1) Compositional effects – the switch from services to goods consumption – are amplified through global input-output linkages, affecting both trade and inflation. 2) Inflation can be higher under sector-specific labor shortages relative to a scenario with no such supply shocks. 3) Foreign shocks and global supply chain bottlenecks played an outsized role relative to domestic aggregate demand shocks in explaining Euro Area inflation over 2020-21. 4) International trade did not respond to changes in GDP as strongly as it did during the 2008-09 crisis despite strong demand for goods. These lower trade elasticities in part reflect supply chain bottlenecks. These four results imply that policies aimed at stimulating aggregate demand would not have produced as high an inflation as the one observed in the data without the negative sectoral supply shocks.

How production networks amplify economic growth

Technological improvement is the most important cause of long-term economic growth. In standard growth models, technology is treated in the aggregate, but an economy can also be viewed as a network in which producers buy goods, convert them to new goods, and sell the production to households or other producers. We develop predictions for how this network amplifies the effects of technological improvements as they propagate along chains of production, showing that longer production chains for an industry bias it toward faster price reduction and that longer production chains for a country bias it toward faster growth. These predictions are in good agreement with data from the World Input Output Database and improve with the passage of time. The results show that production chains play a major role in shaping the long-term evolution of prices, output growth, and structural change.

Media release: New study finds economic progress is aided by longer supply chains and deeper networks