The US-China trade war created net export opportunities rather than simply shifting trade across destinations. Many "bystander" countries grew their exports of taxed products into the rest of the world (excluding the United States and China). Country-specific components of tariff elasticities, rather than specialization patterns, drove large cross-country variation in export growth of tariff-exposed products. The elasticities of exports to US-Chinese tariffs identify whether a country's exports complement or substitute the United States or China and its supply curve's slope. Countries that operate along downward-sloping supplies whose exports substitute (complement) the United States and China are among the larger (smaller) beneficiaries of the trade war.
Building on Pomatto, Strack, and Tamuz (2020), we identify a tight condition for when background risk can induce first-order stochastic dominance. Using this condition, we show that under plausible levels of background risk, no theory of choice under risk can simultaneously satisfy the following three economic postulates: (i) decision-makers are risk averse over small gambles, (ii) their preferences respect stochastic dominance, and (iii) they account for background risk. This impossibility result applies to expected utility theory, prospect theory, rank-dependent utility, and many other models.
There is a large gender wage gap among college graduates. This gender gap could be partially driven by differences in college major and prior skills. We use Swedish register data to study how much of the gender gap can be explained by differences in majors, skills, and skill prices. College majors explain 60 percent of the gender wage gap, but large gaps remain within majors. We find that within-major wage gaps are driven by neither differences in multidimensional skills nor returns to these skills. In fact, women are positively selected in terms of college preparation and skills in almost every major.
Low- and middle-income nations host 76 percent of the world's refugees. This study uses original data to explore within-country spatial variability in refugee-hosting responsibilities. We find that hosting responsibilities for the displaced Rohingya people in Bangladesh are allocated in similarly unequal fashion when analyzed at the national, regional, and microregional levels. Refugee camps are placed in socioeconomically disadvantaged communities relative to both Bangladesh as a whole and surrounding areas. Our findings underscore the importance of considering host communities in the coordination of humanitarian responses to refugee crises to prevent economic hardship and political backlash.
Technological innovations like broadcast television and the internet challenge local newspapers' business model of bundling their local content with third-party content, such as wire national news. We examine how the entry of television affected newspapers and news diets in the United States. We construct a dataset of newspapers' economic performance and content choices from 1944 to 1964 and exploit quasi-random variation in the rollout of television to show its negative impact in the readership and advertising markets. Newspapers responded by reducing content, particularly local news. We tie this change to increased party vote share congruence between congressional and presidential elections.
Firms facing complex objectives often decompose the problems they face, delegating different parts of the decision to distinct subunits. Using comprehensive data and internal models from a large U.S. airline, we establish that airline pricing is not well approximated by a model of the firm as a unitary decision maker. We show that observed prices, however, can be rationalized by accounting for organizational structure and for the decisions by departments that are tasked with supplying inputs to the observed pricing heuristic. Simulating the prices the firm would charge if it were a rational, unitary decision maker results in lower welfare than we estimate under observed practices. Finally, we discuss why counterfactual estimates of welfare and market power may be biased if prices are set through decomposition, but we instead assume that they are set by unitary decision makers.
Does a woman’s take-up of government benefits vary with her perception of how they will be shared within the household? Using randomized assignment to alternative information treatments, we examine this question in the context of Saudi women’s willingness to apply for unemployment assistance (Hafiz). We compare the take-up among women who receive no program information to three groups: those who receive information on program eligibility conditions (Eligibility group) and those who receive additional information that their registration status is broadly confidential (Privacy group) or that they fully control registering and accessing benefits (Agency group). Three months later, the treatments, on average, doubled Hafiz applications, with the treatment impacts largest for the Agency group. Women from poorer households and married women are most responsive to the Agency and Privacy interventions respectively. These findings are consistent with collective household bargaining models where family members’ spending preferences differ; we predict larger treatment impacts when there is more competition for resources.
I study the long-term effects of landing a first job at a large firm versus a small one using Spanish administrative data. Size could be a relevant employer attribute for inexperienced workers since large firms are associated with greater productivity, wages, and training. The key empirical challenge is selection into first jobs based on unobserved worker characteristics. I develop an instrumental variable approach that, keeping business cycle conditions fixed, leverages variation in the composition of labor demand that labor market entrants face. Initially matching with a larger firm persistently improves long-term outcomes, even through subsequent jobs. Mechanisms suggest better skill development at large firms.
This article reviews the literature on automation and its impact on labor markets, wages, factor shares, and productivity. I first introduce the task model and explain why this framework offers a compelling way to think about recent labor market trends and the effects of automation technologies. The task model clarifies that automation technologies operate by substituting capital for labor in a widening range of tasks. This substitution reduces costs, creating a positive productivity effect, but it also reduces employment opportunities for workers displaced from automated tasks, creating a negative displacement effect. I survey the empirical literature and conclude that there is wide qualitative support for the implications of task models and the displacement effects of automation. I conclude by discussing shortcomings of the existing literature and avenues for future research.
This paper considers estimation of short-run dynamics in time series that contain a nonstationary component. We assume that appropriate preliminary methods can be applied to the observed time series to separate short-run elements from long-run slowly evolving secular components, and focus on estimation of the short-run dynamics based on the filtered data. We use a flexible copula-generated Markov model to capture the nonlinear temporal dependence in the short-run component and study estimation of the copula model. Using the rescaled empirical distribution of the filtered data as an estimator of the marginal distribution, Chen et al. (2022) proposed a simple, yet flexible, two-step estimation procedure for the copula model. The two-step estimator works well when the tail dependence is small. However, simulations reveal that the two-step estimator may be biased in finite samples in the presence of tail dependence. To improve the performance of short-term dynamic analysis in the presence of tail dependence, we propose in this paper a pseudo sieve maximum likelihood (PSML) procedure to jointly estimate the residual copula parameter and the invariant density of the filtered residuals. We establish the root-consistency and asymptotic distribution of the PSML estimator of any smooth functional of the residual copula parameter and invariant residual density. We further show that the PSML estimator of the residual copula parameter is asymptotically normal, with the limiting distribution independent of the filtration. Simulations reveal that in the presence of strong tail dependence, compared to the two-step estimates of Chen et al. (2022), the proposed PSML estimates have smaller biases and smaller mean squared errors even in small samples. Applications to nonstationary macro-finance and climate time series are presented.