Determining Wages: The Changing Role of Education Professor David L. Schaffer and Jacob P. Raleigh, Economics Department We gratefully acknowledge generous funding support from the UWEC Office of Research and Sponsored Programs Introduction In this study, we take a detailed look at the determinants of wages in the U.S. and how they have changed over the last 40 years. Although we consider many different factors, we focus our attention especially on education level, gender, and race. We work with an enormous data set from the U.S. Census Bureau, consisting of 40 years of answers to a detailed survey about demographics and work. The sample size is greater than one million persons. We apply standard economics tools to estimate wage equations. We use a familiar form with the log of the hourly wage rate on the left-hand side of the equation and various independent variables on the other. Because our data set is so large, we are able to allow non-linear effects between each of the independent variables and the log of the wage. We present our results by estimating and presenting the real hourly wage rate for each combination of characteristics. These calculations are based on the results of all of our regressions. Our findings confirm many results by other economists but some unexpected ones as well. Potential Work Experience The CPS does not include survey questions about actual work experience. This is a serious weakness. Instead we use a common proxy for it. For each person surveyed we calculate their current age minus their age when they finished school. This number is referred to as "potential work experience." Labor economists have always predicted that most people with jobs would see their real wage gradually climb until they are in their 40s or 50s. Then it would level off for a while and probably dip down a little at the end. Looking at the figure labeled "Potentital Experience and Estimated Real Hourly Wage" we can see that the pattern is exactly as predicted. However, also note that the estimated wages for white males are consistently higher than for the other three groups with no overlap. Among the other three groups black males have the highest estimated wages while white females and black females follow Description of Data The basic data for this project come from 40 years of March Current Population Surveys (CPS) collected by the U.S. Census Bureau. The surveys used are the Annual Demographic Surveys from March of 1971 through March of The CPS is a household sample survey conducted monthly by the U.S. Census Bureau. It is designed to provide estimates of the population as well as estimates of employment, unemployment, earnings and other economic characteristics of the population. The universe for the surveys is the entire population of the United States living in households, from which a national probability sample of about 120,000 persons is selected. The March survey, often referred to as the Annual Demographic Survey, includes all of the usual monthly economic data, plus detailed supplementary data on every adult’s work experiences during the previous calendar year. The data available for the earlier years are much sparser than the data available for recent years. The earlier year samples are smaller - usually about 60,000 persons. The number of questions asked is much smaller - perhaps only 1/3 as many as in recent years. In the March 1971 through March 1975 data sets, many of the quantity variables are “coded” into broad categories rather than listed directly. For example, “usual hours worked per week last year,” is coded as a 0, 1, or 2, indicating whether the person typically worked 0 hours, 1 to 34 hours, or more than 35 hours per week last year. There are also some serious problems with consistency over time. One of the biggest problems is that the occupation classification system used by the Census Bureau changed significantly between March 1982 and March 1983, and again between March 2002 and March Previous work has allowed us to translate the 1970s occupations codes into the post-1983 occupation codes. However, it is not yet possible to translate the pre-2003 codes into the post-2002 codes. Another consistency problem has to do with people's reported education level. Throughout the first 11 years of this period, people were asked how many years of education they had completed. The numbers ranged from 0 to a maximum of 18. However, in 1982 the education questions were changed and now ask about degrees obtained beyond high school. A third consistency problem has to do with the definition of race used in the surveys. Up through the March 2002 data, there were only five categories of race to choose from. Since March 2003, there are now 21 categories. ∫Education Figs 1-4. Shows the changing effects of education level on predicted wages over the period The vertical line at 1991 represents a change in the definitions used by the Census Bureau, as described in Table 1. Fig 1 focuses on white males. Over the whole time frame, the gap between education categories has increased dramatically. At the beginning of the time period, the spread between a high school diploma and a bachelor’s degree was about $2. By 2010, that gap had grown to about $7, or an increase of 350%. However, the growth in this gap wasn’t fueled by large average wage increases for bachelor’s degree holders. Over the whole time period, the estimated real hourly wage for those with a bachelor’s degree has only gone up $1. On the other hand, estimated real hourly wages for those holding high school diplomas has actually decreased over the time period, by roughly $3. Because of this, the rate of return for a bachelor’s degree is actually increasing despite the stagnant wage rates. Other education categories have also experienced dramatic change over this time period. Every education level under an Associate’s degree has seen a steady decrease, while every category above a Bachelor’s degree has seen an increase. The estimated real wage for graduate/professional degrees is an especially interesting category. In 1971, the estimated real hourly wage for people with these degrees was about $22. By the mid- 2000s, this wage had increased to above $36. Figs 2-4 show the education graphs for white females, black males, and black females respectively. The same general pattern that was described for white males can be seen for all of these graphs as well. All degree levels over Bachelor’s degrees have seen increases in wages, while Associate’s degrees and under have seen wage decreases. Comparing these three graphs to the graph for white males yields some interesting results. Every one of these categories earn wages that are well below the wages earned by white males. In 2010, a white male with a Bachelor’s degree earned an estimated real wage of $23 an hour. Comparatively, white females earned $16 an hour, black males earned $19 an hour, and black females earned $15 an hour. Because these graphs are controlled for all other variables, this result suggests both a gender and a race gap are still present in today’s society. Fig 5 examines the gender pay gap in more detail. This graph compares white males to white females directly. At the beginning of the time period, every category for white females is below even the lowest white male category. That means a woman with a PhD earned an estimated hourly wage below a white male with less than a high school education. Over time, this gap has narrowed slightly. In 2010, a woman with a bachelor’s degree earned as much as a white man with only a high school diploma. White women with PhDs now earn as much as men with bachelor’s degrees. While this gap has narrowed, it is still present. The difference between men and women with bachelor’s degrees in 2010 was still $7 an hour, which is roughly a $14000 difference. Average Occupation Education How do you measure and compare the skills and abilities required for different occupations? One way is to calculate the average level of education among everyone currently employed in that occupation. Within our data set we have information on 500 occupations. For each year and each occupation we calculate the "average occupation education" and include it in all our wage regressions. Figure 12 shows some results for the most recent eight years. The results are dramatic. For any individual in any of our four demographic groups, regardless of their own level of education, they are likely to earn a substantially higher real wage if they work in an occupation with a high level of average occupation education. For white males, their estimated real wage increases from $14 to more than $31 as they move from an occupation with a very low level of average occupation education to one in the highest level group. For the other three demographic groups the gains are somewhat smaller than those for white males, but still significant. Overall, this supports the view that real hourly wages are linked not just to an individual's education level, but also to the education characteristics of their occupation. Fraction Female One aspect of wage discrimination reveals itself through what economists call "gender segregation." Within our data set we have information on 500 occupations. For each year and each occupation we calculate the fraction of workers that are female. We call this "fraction female" and include it in all our wage regressions. Figure 13 shows some results for the most recent eight years. The results are dramatic. For any individual in any of our four demographic groups, they are likely to earn a substantially lower real wage if they work in an occupation with a high level of fraction female. For white males, their estimated real wage drops from $22 to less than $17 as they move from a zero-percent female occupation to a mostly female occupation. Even for white females, their estimated real wage drops from just above $18 to about $ Overall, this supports the view that gender segregation is alive and well.