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.
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.
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.
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.
We quantify the effects of the political development cycle – the fluctuations between the left (Maoist) and the right (pragmatist) development policies – on growth and structural transformation of China in 1953-1978. The left policies prioritized structural transformation towards non-agricultural production and consumption at the cost of agricultural development. The right policies prioritized agricultural consumption through slower structural transformation. The imperfect implementation of these policies led to large welfare costs of the political development cycle in a distorted economy undergoing a structural change.
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.