Paywizard.org

Some interesting earnings data is being collected by the the Labour and Worklife Program at Harvard Law School - through their Paywizard.org website. The website allows US individuls to compare their earnings against what other people get for the same job in the same state with a free 'Salary Calculator'. In return, individuals are requested to keep the Calculator updated, by sharing their salary and job information. For a salary check on a 'postsecondary economics teacher' in New York, the following data is provided:

Total number of employed people within this occupation within this state 690.00

Mean hourly wage in Dollars: *
Annual mean wage in Dollars: 86130.00
Lowest hourly wage in occupation, 10% percentile: *
Highest hourly wage in occupation, 90% percentile: *
Lowest annual wage in occupation, 10% percentile: 46250.00
Highest annual wage in occupation, 90% percentile: 133520.00

Source: Bureau of Labor Statistics (May 2007) State Occupational Employment and Wage Estimates.

* = indicates that a wage estimate is not available
** = indicates that an employment estimate is not available
# = indicates a wage greater than $70.00 per hour or $145,600 per year

In addition, Paywizard.org is connected to the WageIndicator Network, a 75 country research program designed to enhance labor market transparency and to aid data-collection for social science research. For anyone interested in earnings data, this is an interesting project. There is no partner in Ireland yet, but in the UK data is being collected by the Trade Union Congress (TUC), Incomes Data Services (IDS) and the Mayor of London.

Does Skills Mismatch Lower Returns to Education?

A new IZA discussion paper by Nordin, Persson and Rooth (Education-Occupation Mismatch: Is There an Income Penalty?) "adds to the small literature on the consequences of education-occupation mismatches." The consequences of skills-matching and the measurement of skills-matching are issues that have been discussed on this blog a fair bit recently.

Nordin, Persson and Rooth use microdata from Statistics Sweden, but are forced to drop 36 percent of their sample due to restrictions on fields of education to well-defined categories. The authors state that this approach is necessary because some fields of education (e.g. in the humanities and languages) are either vague or cannot easily be matched with any specific occupation. Also, the authors exclude a further 11 percent of their sample because of missing occupation data. One way around these problems might be to use self-rated measures of skill-matching, from which wage penalties might be more accurately estimated.

Nordin, Persson and Rooth focus on the income penalty for field of education-occupation mismatches; they find that the penalty for such mismatches is large for both men and women. They also find that it is substantially larger than has been found for the US. Interestingly, the authors also control for cognitive ability and find that the "income penalty is not caused by ability, at least for Swedish men." The income penalty for men decreases with work experience which the authors suggest is an indication that education-specific skills and work experience are substitutes to some extent. "There is no evidence, though, that the mismatched individuals move to a matching occupation over time. Thus, for some, the income penalty seems to be permanent."