Worker Mobility, Inequality, and Credit Scoring: Insights from the Macroeconomic Frontier

By Blair Long, Queen’s University

On Tuesday, May 11, the Queen’s Economics Department (QED) hosted the 12th Frontiers of Macroeconomics workshop (hereafter Frontiers). This conference brings together a diverse collection of contemporary research, exemplifying the frontier of modern macroeconomics. This article summarizes the objectives and findings of some of the conference’s presented work, and provides some commentary on the direction of both theory and methodology in macroeconomics.

What are the Determinants of Worker Job Matches?  

Ilse Lindenlaub’s “Multi-Dimensional Sorting of Workers and Jobs in the Data” (with Fabien Postel-Vinay) explores the mobility of workers and asks which worker skills are relevant in the sorting process. By sorting, economists mean how workers move from job to job, each time finding a “better” match, and ultimately finding one that is stable, in the sense that neither the worker nor the job (the firm) expects to prefer another working arrangement. The key methodological contribution is to develop a relatively simple test of how skills drive sorting behaviour, accounting for the considerable heterogeneity of skills. In doing so, the authors break from a standard simplification found in much economic theory: that worker ability (for instance) is one-dimensional. Of course, in the real world, we can group workers according to a near-infinity of skills, such as interpersonal, cognitive, non-cognitive, routine, manual, or creative.

This test is designed to tease out the effect of individual skills both on the part of workers and on the part of jobs. Applying this test to data from the Survey of Income and Program Participation (SIPP) linked to occupational skills data from ONET, they find that workers who sort tend to have less substitutable skills, such as non-routine cognitive and manual skills, interpersonal skills, and creative skills. Perhaps unsurprisingly, the jobs which these workers sort into tend to involve tasks that require these same skills.

Another finding is the rejection of the notion of a “one-dimensional job ladder”, implying that the rich heterogeneity of workers results in different “ladders” to climb as workers sort into better matches with higher wages. In an application, the authors identify that these multi-dimensional job ladders saw differential degrees of contraction during the Great Recession in that not all career paths/trajectories were affected the same by the slowdown.    

Why Don’t People Just Declare Bankruptcy?

It’s an interesting question. In “A Theory of Credit Scoring and the Competitive Pricing of Default Risk”, Jose-Victor Rios-Rull (with Satyajit Chatterjee, Dean Corbae and Kyle Dempsey) ponder this question and present a theory in which credit scoring solves an inference problem in debt-pricing. On the one hand, filing for bankruptcy is relatively cheap and easy to do. On the other, lower credit scores lead to higher interest rates, less access to credit, and potential issues in renting, employment, and relationships. Still, in light of this cost-benefit analysis, we do observe a considerable amount of unsecured credit (credit that is not collateralized, such as credit cards). The proposed model does match data relatively well in terms of the bankruptcy filing rate and the fraction of households in debt. Even after accounting for the role of reputation in credit markets, this model has considerable difficulty accounting for debt-to-income ratios and interest rates. This line of inquiry highlights a noteworthy gap in our knowledge of how credit markets work, how repayment is enforced, and what drives lending.

Inequality is the Macroeconomic Issue of the Twenty-First Century

The obvious reference to Piketty above is far from an understatement. If you subscribe to working paper updates from the NBER, you routinely observe a flurry of research on inequality of income, wealth, education, and opportunity. Similarly, Frontiers also sees these issues represented prominently. In “Wealth Distribution and Social Mobility in the US: A Quantitative Approach”, Jess Benhabib (with Alberto Bisin and Mi Luo) explores the determinants of the unusual shape of the wealth distribution in the United States. By unusual, I mean that the wealth distribution in the United States has a very long right tail. That is, there are extremely wealthy people in this country.  This shape presents a puzzle in the sense that textbook economic models have difficulty reproducing this pattern. In this paper, the authors focus on three factors, which jointly, match the data well.

The first, is the skewed pattern of earnings we observe. Of course, we would expect that higher income earners are better able to accumulate wealth, but it’s not the entire story. The authors find that this factor alone cannot capture the upper tail of the wealth distribution without incorporating differential savings rates across wealth levels. That is, not only do the rich earn more, but across wealth classes, the wealthier save at higher rates. We also observe this pattern in the data. Finally, another critical factor is the differential return on wealth that we observe in that different people earn different returns on their assets, with higher returns realistically accruing to those with greater wealth to invest. Some financial products and services, for instance, are exclusively available to those with a greater propensity to invest.

More studied than wealth inequality is earnings inequality. However, economists are still refining the rich processes which account for the distribution of earnings in the data. A particularly notable aspect of earnings processes is how they evolve over the life-cycle. In “Nonlinear Household Earnings Dynamics, Self-Insurance, and Welfare”, Giulio Fella (with Mariacristina De Nardi and Gonzalo Paz Pardo) explores the implications of a “rich earnings process”, which incorporates some of the differences in earnings over the life-course. This process leads to a substantially better fit of consumption inequality over the life cycle, as well as that of savings. Despite this latter result, this process by itself cannot explain the aforementioned long right tail of the U.S. wealth distribution.