Geographic Differences in the Earnings of Economics Majors

Abstract

Economics has been shown to be a relatively high-earning college major, but geographic differences in earnings have been largely overlooked. The authors of this article use the American Community Survey to examine geographic differences in both absolute earnings and relative earnings for economics majors. They find that there are substantial geographic differences in both the absolute and relative earnings of economics majors, even when controlling for individual characteristics such as age, education, occupation, and industry. They argue that mean earnings in specific labor markets are a better measure of the benefits of majoring in economics than simply looking at national averages.

Keywords:

• college education
• earnings differentials
• economics major local labor markets

JEL codes:

• I23
• J24
• J31
• R23

Notes

¹In addition to higher future earnings, other benefits of a college education include better future health (Eide and Showalter 2011), opportunities to meet higher-ability potential spouses (Becker 1973; Lefgren and McIntyre 2006), and the consumption value of education itself (Alstadsæter 2011).

²There is a separate literature that investigates geographic differences in earnings more generally (e.g., DuMond, Hirsch, and Macpherson 1999; Glaeser and Maré 2001; Yankow 2006; Winters 2009), but that literature has not examined geographic differences for specific college majors.

³In results not shown, we also explored the effects of restricting the sample to persons with only a bachelor's degree. Doing so does not appreciably alter the qualitative results in this study. These results are available by request.

⁴A full list of the 147 majors and mean earnings for each are available online in an earlier working paper version (Winters and Xu 2013).

⁵Our state-level analysis also includes the District of Columbia, and for convenience, we hereafter refer to it is as a state giving us a total of 51 states.

⁶We refer to MSAs by their primary core city, although the official names often include other smaller cities in the metropolitan area.

⁷A considerably more complicated alternative to examining earnings relative to non-economics majors is to construct a cost-of-living index for each geographic area and assess the value of each area's location-specific amenities. One could then compute a “real wage” for economics majors in each area. Winters (2009) reported a cost-of-living index available for use, but valuing location-specific amenities is considerably more subjective.

⁸To some extent, the local mix of occupations and industries is part of what an area offers to its residents, and controlling for these may net out some of the wage premium specific to an area. An earlier working paper version (Winters and Xu 2013), available online as IZA Discussion Paper No. 7584, reports results without controls for occupation and industry. These results are qualitatively similar for most areas but differ slightly for a few areas. Additionally, there is likely some interest in what an economics major would earn in a specific job (e.g., financial analyst) in a specific area. However, economics majors are employed in a wide variety of jobs, and the number of economics majors in the dataset in a given job and area becomes quite small. Reliable estimates of area and occupation-specific earnings for economics majors are only possible for the very largest area and occupation combinations. As such, economics majors very interested in a particular occupation in a given area should also consider the mean earnings of that occupation in the area for all college majors.

⁹The IPUMS groups individual occupation and industry codes at multiple levels. We adopted a grouping scheme that includes 83 dummies for occupation and 15 dummies for industry. The specific codes for occupation are 3/22 = 1; 23/37 = 2; 43 = 3; 44/59 = 4; 64/68 = 5; 69/83 = 6; 84/89 = 7; 95/97 = 8; 98/106 = 9; 113/154 = 10; 155/163 = 11; 164/165 = 12; 166/173 = 13; 174/176 = 14; 178/179 = 15; 183/199 = 16; 203/208 = 17; 213/225 = 18; 226/227 = 19; 228 = 20; 229/233 = 21; 234 = 22; 243 = 23; 253/256 = 24; 258/274 = 25; 275 = 26; 276 = 27; 277 = 25; 283 = 28; 303 = 29; 308 = 30; 313/315 = 31; 316/323 = 32; 326/336 = 33; 337/344 = 34; 345/347 = 33; 348/349 = 35; 354/355 = 36; 356/357 = 37; 359/373 = 38; 375/378 = 39; 379/389 = 40; 405/407 = 41; 415 = 42; 417 = 43; 418/423 = 44; 425/427 = 45; 434/435 = 46; 436/444 = 47; 445/447 = 48; 448/455 = 49; 456/469 = 50; 473/476 = 51; 479 = 52; 485/489 = 53; 496 = 54; 498 = 55; 503 = 56; 505/519 = 57; 523/534 = 58; 535/549 = 59; 558 = 60; 563/599 = 61; 614/617 = 62; 628 = 63; 634/653 = 64; 657/659 = 65; 666/674 = 66; 675/684 = 67; 686/688 = 68; 694/699 = 69; 703/717 = 70; 719/724 = 71; 726/733 = 72; 734/736 = 73; 738/749 = 74; 753/779 = 75; 783/789 = 76; 799 = 77; 803/813 = 78; 812/834 = 79; 844/859 = 80; 865/874 = 81; 875/889 = 82; 905 = 83; 991/999 = 84. The specific codes for industry are 170/290 = 1; 370/490 = 2; 770 = 3; 1070/3990 = 4; 4070/4590 = 5; 4670/5790 = 6; 6070/6390 = 7; 570/690 = 8; 6470/6780 = 9; 6870/7190 = 10; 7270/7790 = 11; 7860/8470 = 12; 8560/8690 = 13; 8770/9290 = 14; 9370/9590 = 15; 9670/9870 = 16. Occupation titles are available at https://usa.ipums.org/usa-action/variables/OCC1990#codes_section. Industry titles are available at https://usa.ipums.org/usa/volii/08indus.shtml. Reasonable alternative grouping schemes provided similar results.

¹⁰Making these the omitted base groups also produces much more precise estimates than would result if the omitted groups were areas with relatively few economics majors.

¹¹A few smaller states have relatively large coefficients but have small samples of economics majors and are not precisely estimated. For example, Wyoming is the least populous state in the United States and has only seven observations in our sample who are economics majors, causing its coefficient estimate to be very imprecisely estimated.

¹²The high relative earnings for the Northeastern mid-size metropolitan group may partially result from proximity to New York. The Northeastern mid-size metropolitan group includes the following metropolitan areas: Allentown, PA; Harrisburg, PA; Monmouth, NJ; New Haven, CT; Portland, ME; Providence, RI; Scranton, PA; Springfield, MA; Stamford, CT; Syracuse, NY; and Trenton, NJ.