The importance of education for the unemployed: based on a working paper.

• Author: Zimmer, Timothy

To answer these questions, this analysis uses unique employment and unemployment claims data and a simple model. The model attempts to determine which factors impact the relative wage of the person emerging from unemployment and the duration of unemployment. The results emphasize the relative short-term importance of education on the ability of an unemployed individual to successfully navigate the re-employment market. Higher levels of education increase the chance an unemployed person will emerge with a comparable wage and reduce the time required to find new employment.

Unemployment can have a devastating impact both on a household and the general economy. The loss of income has an immediate effect in the reduction of consumer spending. However, the increase in uncertainty for the household can have a multiplier effect on the reduction of consumer spending. A household that endures unemployment is likely to significantly cut spending, often in excess of the loss of income due to the uncertainty, and the resumption of spending can lag after the return of income. The psychological impact of unemployment on a household can have a significant impact on the broader economy. For this reason, economists have long sought better information on the dynamic influences of the re-employment market. It is in society's best interest for the newly unemployed to quickly navigate the re-employment market and re-emerge with the best wage outcome possible. The study examines factors, within the constraints of data availability, to determine which influences impact both the wage that someone will receive and the duration of unemployment. In particular, this article will examine the impact of education on the unemployed.

Researching the possible link between wage achievement in the labor market and education levels is well established in academic literature. The link is built on the commonly accepted idea of imperfect substitution between the work and the availability of skills in the labor market. The labor market maintains a positive wage bias in favor of skills and increased human capital. There is a large and consistent body of literature establishing a connection between wages and years of schooling as overviewed by Card (1999).

It is also argued from a dynamic perspective that wage inequality should decrease with increasing levels of education (Tilak, 1989). In the short term, higher wages are afforded positions requiring more skill. As more people pursue these positions and educational levels increase, the supply of higher skilled workers increase. The increase in supply puts downward price pressure on high-skilled jobs, which lowers wages. At the same time, fewer people pursuing low-skilled jobs push wages higher. From this dynamic perspective, education will cause wages to converge. This view is summarized and empirically shown in data prior to 1970 between white-and blue-collar employees by Goldin and Margo (1992).

Teulings (1995, 2005) attempts to bridge the gap between short-term and long-term dynamic trends by explaining that highly educated people are more skilled in complex jobs and, thus, demand higher salaries. In the longer term, the increased supply of highly educated people puts pressure on wages of the complex jobs or pushes the highly educated into jobs of lower wages with fewer skill requirements. Thus, the effect of education on income is positive as a first-order condition, but negative as a second-order condition.

Others disagree with the notion of long-term dynamic wage convergence and decreased wage disparity. Acemoglu (2002) argues that diminishing returns to education are not likely to exist. The increase in human capital due to education will induce greater levels of investment in technology, which promotes innovation. Innovation is a positive externality derived from education, which reduces the potential for diminishing returns to education. This argument is consistent with research using data after 1970 that indicates increasing wage inequality in the labor market due to skill requirement differentiation (Blackburn, 1990; Bound & Johnson, 1992; Karoly, 1992; Katz & Murphy, 1992; Kosters, 1991). This also includes general equilibrium models linking education and human capital development to increasing disparity (Mehta, 2000).

Another possible explanation for the observed increase in income inequality after 1970 is job mix. Thurow (1987) and Revenga (1992) suggest that high-wage job creation (such as manufacturing jobs) is in decline, while low-wage job creation (such as service-based jobs) is increasing. This change in the mix of job creation suppresses low-skilled wages and maintains a high-wage disparity. This view of job creation and wage growth is not universally accepted (Dickens & Lang, 1985, 1987).

This article adds to the existing literature by examining whether the link between wages and education extends into the re-employment market. Once factors of influence are identified, better policies can be initiated that can expedite the ability of the unemployed to attach with a job from the re-employment market. The twin goals of the unemployed person are to find a new position quickly and receive an adequate wage. This study helps to identify the influences that achieve these outcomes.

Methodology

This study uses a unique longitudinal data set that includes de-identified Indiana unemployment claimant data that have been linked to Indiana wage reporting records and public university education records. A random identifier is applied and the researcher never has access to identified data, insuring record anonymity. Only aggregate results are provided with the study. Unfortunately, many of the potential records are incomplete and do not contain complete information on the variables of interest. These incomplete records were discarded. Over the six years of collection, 342,890 records contain complete information on the desired variables. The first year had the most observations at approximately 30 percent, while the remaining years each represented about 15 percent of observations. The number of records collected from each of the years is provided in the summary statistics in Table 1.

The study uses data for individuals applying for Indiana unemployment insurance (UI) benefits from 2004 through 2009 that had been successfully matched with corresponding wage and education records. Individuals with wage data before (starting first quarter of 2004) and after UI claims (ending fourth quarter of 2009) are included.

Individuals without wage matches are excluded from the study since the study is focused on reintegration. This undoubtedly includes those unable to find work in addition to those moving for work outside Indiana for which wage records are unattainable. These individuals did not have available data and are beyond the reach of this...