Postdoctoral Publications

Do women apply more for jobs when they know the hiring probability of female job seekers directly from employers? I implemented a randomized control trial and a double-incentivized resume rating to elicit the preferences of employers and job seekers for candidates and vacancies in Iraq. The treatment reveals the job offer rate for women, calculated using the employers’ selection of women divided by the total number of female candidates. After revealing the treatment, the women applied for jobs by three more percentage points than the men in the control group. This paper highlights the value of revealing employers’ preferences to improve the match between female candidates and employers when women underestimate the chances of finding a job. 

We estimate a multi-country multi-sector New Keynesian model to quantify the drivers of domestic inflation during 2020–2023 in several countries, including the United States. The model matches observed inflation together with sector-level prices and wages. We further measure the relative importance of different types of shocks on inflation across countries over time. The key mechanism, the international transmission of demand, supply and energy shocks through global linkages helps us to match the behavior of the USD/Euro exchange rate. The quantification exercise yields four key findings. First, negative supply shocks to factors of production, labor and intermediate inputs, initially sparked inflation in 2020–2021. Global supply chains and complementarities in production played an amplification role in this initial phase. Second, positive aggregate demand shocks, due to stimulative policies, widened demand-supply imbalances, amplifying inflation further during 2021–2022. Third, the reallocation of consumption between goods and service sectors, a relative sector-level demand shock, played a role in transmitting these imbalances across countries through the global trade and production network. Fourth, global energy shocks have differential impacts on the US relative to other countries’ inflation rates. Further, complementarities between energy and other inputs to production play a particularly important role in the quantitative impact of these shocks on inflation.

Did the COVID-19 pandemic create an opportunity to earn population control through illegal violence? We argue that criminal groups in Colombia portray as de facto police by using mass killings to reduce the COVID-19 outbreak. They used massacres as a threat to enforce social distance measures in places they considered worth decreasing mobility. Our results from an Augmented Synthetic Control Method model estimated that commuting to parks fell 20% more in areas with massacres than in places without mass killings. In addition, we do not find a decline in mobility to workplaces and COVID-19 deaths after the first mass killing. These findings are congruent with the hypothesis that illegal armed groups used fear to enforce mobility restrictions without hurting economic activities and their sources of revenue. However, violence slightly impacted the virus’ spread. Treated areas had a decline of 35 cases per 100,000 inhabitants four months after the first massacre.

We introduce incumbency effects into a tractable dynamic model of international trade. The framework nests the canonical Melitz (2003)-Chaney (2008) model as a special case. The key novelty is that fixed costs of market access decrease with tenure. As a consequence, there is less market exit and entry in response to a shock. We derive a gravity equation and show that, ceteris paribus, countries that liberalized their trade relationship earlier trade more today. We provide supporting evidence for the underlying mechanism and derive an augmented ACR formula (Arkolakis et al., 2012) for the gains from trade that accounts for incumbency effects. A quantitative analysis suggests that our mechanism can explain up to 25% of countries’ home shares and that the gains from trade are, on average, 10% larger when accounting for incumbency effects. The analysis further reveals novel distributional effects of trade that benefit real wages but reduce profits.

We build a new longitudinal dataset of job tasks and technologies by transforming the U.S. Dictionary of Occupational Titles (DOT, 1939 -1991) and four books documenting occupational use of tools and technologies in the 1940s, into a database akin to, and comparable with its digital successor, the O*NET (1998 -today). After creating a single occupational classification stretching between 1939 and 2019, we connect all DOT waves and the decennial O*NET databases into a single dataset, and we connect these with the U.S. Decennial Census data at the level of 585 occupational groups. We use the new dataset to study how technology changed the gender pay gap in the United States since the 1940s. We find that computerization had two counteracting effects on the pay gap -it simultaneously reduced it by attracting more women into better-paying occupations, and increased it through higher returns to computer use among men. The first effect closed the pay gap by 3.3 pp, but the second increased it by 5.8 pp, leading to a net widening of the pay gap.

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.

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.

Geo-political tensions and disruptions to global value chains have led policymakers to reevaluate their approach to globalisation. Many countries are considering regionalisation and friend-shoring – trading primarily with countries sharing similar values – as a way of minimising exposure to weaponisation of trade and securing access to critical inputs. If followed through, this process has the potential to reverse global economic integration of recent decades. This paper estimates the economic costs of friend-shoring using a quantitative model incorporating inter-country inter-industry linkages. The results suggest that friend-shoring may lead to real GDP losses of up to 4.6% of global GDP. Thus, although friend-shoring may provide insurance against extreme disruptions and increase the security of supply of vital inputs, it would come at a significant cost.

We analyze the effects of bans on exports at the level of 5,000 products and show how our results can inform economic sanctions against Russia after its invasion of Ukraine. We begin with characterizing export restrictions imposed by the EU and the US until mid May 2022. We then propose a theoretically-grounded criterion for targeting export bans at the 6-digit HS level. Our results show that the cost to Russia are highly convex in the market share of the sanctioning parties, i.e., there are large benefits from coordinating export bans among a broad coalition of countries. Applying our results to Russia, we find that sanctions imposed by the EU and the US are not systematically related to our arguments once we condition on Russia’s total imports of a product from participating countries. Quantitative evaluations of the export bans show (i) that they are very effective with the welfare loss typically ∼100 times larger for Russia than for the sanctioners. (ii) Improved coordination of the sanctions and targeting sanctions based on our criterion allows to increase the costs to Russia by about 60% with little to no extra cost to the sanctioners. (iii) There is scope for increasing the cost to Russia further by expanding the set of sanctioned products.

Wall Street Journal: Sanctions Against Russia Could Be Better, These Harvard Economists Say

Video summary: How can sanctions against Russia be more effective?

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).

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.

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.

Based on the criteria of the factor endowment structure of state-owned enterprise (SOE) sectors in China between 1980 and 2018, this paper rationalizes the classified reforming of China's state sectors by constructing a Nash bargaining model to capture the dynamics of ownership restructuring, and the reduction process of policy burden on SOEs. We reveal that the interplay between policy burden bared by SOEs and the ownership restructuring process largely depends upon their factor intensities since the reform period in the 1980s. Our model identifies two Ownership Reform Irrelevance Points (ORIP), which serve as the benchmark for the dynamics of the ownership restructuring process of China's large SOEs, which saw them move from ‘mixed-ownership’ to ‘privatization’. ORIPs demonstrate the need for a reduction in social policy burdens with regards to the state sector's comparative advantage of factor endowment structure through SOE ownership restructuring. This study theoretically analyzes existing literatures on the classified reforms of China's state sectors from 1978 to 2018. This study is the first to base such an analysis on the criteria of factor endowment structure focusing on the connection between the policy burdens bared by SOEs and their ownership restructuring process.

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

Economic complexity offers a potentially powerful paradigm to understand key societal issues and challenges of our time. The underlying idea is that growth, development, technological change, income inequality, spatial disparities, and resilience are the visible outcomes of hidden systemic interactions. The study of economic complexity seeks to understand the structure of these interactions and how they shape various socioeconomic processes. This emerging field relies heavily on big data and machine learning techniques. This brief introduction to economic complexity has three aims. The first is to summarize key theoretical foundations and principles of economic complexity. The second is to briefly review the tools and metrics developed in the economic complexity literature that exploit information encoded in the structure of the economy to find new empirical patterns. The final aim is to highlight the insights from economic complexity to improve prediction and political decision-making. Institutions including the World Bank, the European Commission, the World Economic Forum, the OECD, and a range of national and regional organizations have begun to embrace the principles of economic complexity and its analytical framework. We discuss policy implications of this field, in particular the usefulness of building recommendation systems for major public investment decisions in a complex world.

Estimating the capabilities, or inputs of production, that drive and constrain the economic development of urban areas has remained a challenging goal. We posit that capabilities are instantiated in the complexity and sophistication of urban activities, the know-how of individual workers, and the city-wide collective know-how. We derive a model that indicates how the value of these three quantities can be inferred from the probability that an individual in a city is employed in a given urban activity. We illustrate how to estimate empirically these variables using data on employment across industries and metropolitan statistical areas in the USA. We then show how the functional form of the probability function derived from our theory is statistically superior when compared with competing alternative models, and that it explains well-known results in the urban scaling and economic complexity literature. Finally, we show how the quantities are associated with metrics of economic performance, suggesting our theory can provide testable implications for why some cities are more prosperous than others.

This paper constructs a two-stage sequential game model to shed light on the spillover effect of inward FDI on the efficiency of domestic firms in host countries. Our model shows that, given an optimal joint-venture policy made by foreign firms, the impact of the spillover effect of inward FDI is contingent upon the productivity gap between the domestic firms and foreign ones. In particular, we demonstrate that the spillover effect of inward FDI varies negatively with the productivity gap between domestic low-productivity firms and foreign firms but works in the opposite way for high-productivity firms. This suggests that once the productivity gap widens, the entry of foreign firms will increase the efficiency of high-productivity firms but reduce the efficiency of low-productivity firms. In support of our theoretical model, we provide robust empirical results by using the dataset of annual survey of Chinese industrial enterprises.

As the literature has studied the financing method of Chinese-listed firms for a long time, but with inconclusive indications, this research thus adopts non-financial Chinese-listed firms’ data from 2003 to 2015 to investigate the relationship between long-term debt financing and financing deficit. We pay particular attention to three channels (ownership concentration, market timing, and state ownership) that potentially affect the adoption of long-term debt financing when there is a financing deficit. The empirical analysis documents a positive relationship between financing deficit and changes in the long-term debt ratio in our sampled firms for both static and dynamic panel models. Moreover, among the three channels we show that state ownership has the strongest positive impact on the adoption of long-term debt financing, followed by ownership concentration, while the weakest channel is the market timing’s negative effect. In general, our empirical analysis finds that the important external financing method of long-term debt is most likely to be impacted by the state ownership aspect.

Assignment models in trade predict that countries with higher productivity levels are assortatively matched to industries that make better use of these higher levels. Here, we assume that the driver of productivity differences is the differential distribution of factors among countries. Utilizing such a structure, we define and estimate the average factor level (AFL) for countries and products using only the information about the production patterns. Interestingly, our estimates coincide with the complexity variables of (Hidalgo and Hausmann, 2009), providing an underlying economic rationale. We show that AFL is highly correlated with country-level characteristics and predictive of future economic growth.

Economic growth is associated with the diversification of economic activities, which can be observed via the evolution of product export baskets. Exporting a new product is dependent on having, and acquiring, a specific set of capabilities, making the diversification process path-dependent. Taking an agnostic view on the identity of the capabilities, here we derive a probabilistic model for the directed dynamical process of capability accumulation and product diversification of countries. Using international trade data, we identify the set of pre-existing products, the product Ecosystem, that enables a product to be exported competitively. We construct a directed network of products, the Eco Space, where the edge weight corresponds to capability overlap. We uncover a modular structure, and show that low- and middle-income countries move from product communities dominated by small Ecosystem products to advanced (large Ecosystem) product clusters over time. Finally, we show that our network model is predictive of product appearances.

Listen to the authors discuss their findings in this Growth Lab Podcast.