Henry E. Siu’s research while affiliated with University of British Columbia and other places

We document that, since 1980, higher paying occupations in the US have experienced increases in the importance of tasks requiring social skills compared to lower paying ones. Economic theory indicates that the occupational sorting of workers depends on their comparative advantage in performing occupational tasks. Hence, changes in the relative importance of tasks across occupations change sorting. We document that the increasing relative importance of social tasks in high-paying occupations can account for an important fraction of the increased sorting of women relative to men towards these occupations over recent decades.

The decline in middle-wage occupations and rise in automation over the last decades is at the center of policy discussions. We develop an empirically relevant general equilibrium model that features endogenous labor force participation, occupational choice, and automation capital. We use the model to consider two types of policies: the retraining of workers who were adversely affected by automation, and redistribution policies that transfer resources to these workers. Our framework emphasizes general equilibrium effects such as displacement effects of retraining programs, complementarities between the factors of production, and the effects of distortionary taxation that is required to fund these programs.

We document two COVID‐19–related risks, viral risk and employment risk, and their distributions across the Canadian population. The measurement of viral risk is based on the VSE COVID‐19 Risk/Reward Assessment Tool, created to assist policy‐makers in determining the impacts of pandemic‐related economic shutdowns and re‐openings. Women are more concentrated in high‐viral‐transmission‐risk occupations, which is the source of their greater employment loss over the first part of the pandemic. They were also less likely to maintain contact with their former employers, reducing employment recovery rates. Low‐educated workers face the same viral risk rates as high‐educated workers but much higher employment losses. This is largely due to their lower likelihood of switching to working from home. For both women and the low‐educated, existing inequities in their occupational distributions and living situations have resulted in them bearing a disproportionate amount of the risk emerging from the pandemic. Assortative matching in couples has tended to exacerbate risk inequities.

The extent to which elementary and secondary (K–12) schools should remain open is at the forefront of discussions on long-term pandemic management. In this context, little mention has been made of the immediate importance of K–12 schooling for the rest of the economy. Eliminating in-person schooling reduces the amount of time parents of school-aged children have available to work and therefore reduces income to those workers and the economy as a whole. We discuss two measures of economic importance and how they can be modified to better reflect the vital role played by K–12 education. The first is its size, as captured by the fraction of gross domestic product produced by that sector. The second is its centrality, reflecting how essential the sector is to the network of economic activity. Using data from Canada’s Census of Population and Symmetric Input–Output Tables, we show how accounting for this role dramatically increases the importance of K–12 schooling.

We use matched individual-level CPS data to study the decline in middle-wage routine occupations during the last 40 years, and determine how the associated labor market flows have evolved. The decline in employment in these occupations can be primarily accounted for by changes in transition rates from non-participation and unemployment to routine employment. We study how these transition rates have changed since the mid-1970s, and find that changes are primarily due to the propensity of individuals to make such transitions, whereas relatively little is due to demographic changes. We also find that changes in the propensity to transition into routine occupations account for a substantial proportion of the rise in non-participation observed in the U.S. in recent decades.

The U.S. economy has experienced a significant drop in the fraction of the population employed in middle wage, “routine task-intensive” occupations. Applying machine learning techniques, we identify characteristics of those who used to be employed in such occupations and show they are now less likely to work in routine occupations. Instead, they are either non-participants in the labor force or working at occupations that tend to occupy the bottom of the wage distribution. We then develop a quantitative, heterogeneous agent, general equilibrium model of labor force participation, occupational choice, and capital investment. This allows us to quantify the role of advancement in automation technology in accounting for these labor market changes. We then use this framework as a laboratory to evaluate various public policies aimed at addressing the disappearance of routine employment and its consequent impacts on inequality.

In the last four decades, the US and other industrialized economies have experienced a pronounced drop in the fraction of the population working in middle-waged jobs. Since employment growth has been weighted toward the upper- and lower-tails of the wage distribution, this phenomenon has become known as job polarization. An important literature demonstrates that this change has meant the loss of job opportunities in certain types of occupations—those that are routine in nature, for which the tasks performed on the job follow a well-defined linear structure or procedural routine. The fact that such occupational tasks are easily automated has led researchers to study the role of recent advances in “automation technologies” in this disappearance of middle-skilled jobs. In this paper, we review the literature regarding polarization and the changing nature of work in the US economy, and discuss its implications for the middle-class.

We construct longitudinal data from U.S. census records to study the economics of the Dust Bowl migration of the 1930s. Most of our findings contradict long-standing perceptions. While migration rates were high relative to elsewhere in the United States, they were similar to migration rates from the region in the 1920s. Relative to other occupations, farmers were the least likely to move. Furthermore, migrants from the Dust Bowl were not exceptionally likely to move to California. Finally, there was negligible migrant selectivity, and migration was not associated with long-lasting negative labor market outcomes; indeed, for farmers, the gains from migration were positive.

We document a new finding regarding changes in labor market outcomes for high-skilled men and women in the US. Since 1980, conditional on being a college-educated man, the probability of working in a cognitive/high-wage occupation has fallen. This contrasts starkly with the experience for college-educated women: their probability of working in these occupations rose, despite a much larger increase in the supply of educated women relative to men. We show that one key channel capable of rationalizing these findings is a greater increase in the demand for female-oriented skills in cognitive/high-wage occupations relative to other occupations. Using occupation-level data, we find evidence that this relative increase in the demand for female skills is due to an increasing importance of social skills within such occupations. Evidence from both male and female wages is also indicative of an increase in the demand for social skills. Finally, we document how these patterns change across the early and latter portions of the period.