Baby Boomers’ Impact on Work Force and Tax Issues in the Great Plains

  • Richard Rathge
  • Justin Garosi
  • Karen Olson
Part of the Understanding Population Trends and Processes book series (UPTA, volume 7)


This study examines the economic consequences of the aging of the baby boom generation, specifically focusing on the Great Plains. We illustrate the region’s stark differences in labor availability between metropolitan and nonmetropolitan areas. Using North Dakota as a case study, we examine the predicted change in available labor and income generation from 2000 to 2020 through an economic simulation model. Our findings indicate that the aging of the baby boom into retirement translates into a significant loss in wage income. Surprisingly, however, in spite of lost wage earnings in the short term, North Dakota’s tax collections will increase due to the aging of the baby boomers into a higher earnings age group who spend more of their disposable income. We conclude by addressing several policy concerns that arise from our analysis.


Great Plain Baby Boom American Community Survey Rural County Income Earner 
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  1. Belt, B. D. (Ed.). (1999). The twenty-first century retirement security plan: The National Commission on Retirement Policy final report. Washington, DC: Center for Strategic and International Studies.Google Scholar
  2. Cahill, K. E., Giandrea, M. D., & Quinn, J. F. (2006). Are traditional retirements a thing of the past? New evidence on retirement patterns and bridge jobs. Business Perspectives, 18(2), 26–37.Google Scholar
  3. Dohm, A. (2000). Gauging the labor force effects of retiring baby-boomers. Monthly Labor Review, 123(7), 17–25.Google Scholar
  4. Fullerton, H. N. J., & Toossi, M. (2001). Labor force projections to 2010: Steady growth and changing composition. Monthly Labor Review, 124(11), 21–38.Google Scholar
  5. Gibbs, R., & Cromartie, J. B. (2000). Low-wage counties face locational disadvantages. Rural Conditions and Trends, 11(2), 18–26.Google Scholar
  6. Goins, R. T., & Krout, J. A. (Eds.). (2006). Service delivery to rural older adults: Research, policy and practice. New York: Springer.Google Scholar
  7. Greengard, S. (1998). Economic forces are squeezing growth potential—But HR can unlock a prosperous future. Workforce, 77(3), 44–54.Google Scholar
  8. Hamermesh, D. S. (2001, March 23). Older workers in the coming labor “shortage”: Implications of labor demand. Paper presented at the Roundtable on the Demand for Older Workers sponsored by the Retirement Research Consortium. The Brookings Institution, Washington, DC.Google Scholar
  9. Herman, R. E., Olivo, T. G., & Gioia, J. L. (2003). Impending crisis: Too many jobs, too few people. Winchester: Oakhill Press.Google Scholar
  10. Johnson, K. M., & Rathge, R. W. (2006). Agricultural dependence and changing population in the great plains. In W. A. Kandel & D. L. Brown (Eds.), Population change and rural society (pp. 197–217). Dordrecht: Springer.CrossRefGoogle Scholar
  11. Little, J. S., & Triest, R. K. (2002). The impact of demographic change on U.S. labor markets. New England Economic Review, 1, 47–68.Google Scholar
  12. Maestas, N. (2004). Back to work: Expectations and realizations of work after retirement (Working PAPER WP No. 2004–085). Ann Arbor: Michigan Retirement Research Center, University of Michigan.Google Scholar
  13. Nyce, S. A. (2007). The aging workforce: Is demography destiny? Generations, 31(1), 9–15.Google Scholar
  14. Nyce, S. A., & Schieber, S. J. (2002). The decade of the employee: The workforce environment in the coming decade. Benefits Quarterly, 18(1), 60–79.Google Scholar
  15. Penner, R. G., Perun, P., & Steuerle, C. E. (2002). Legal and institutional impediments to partial retirement and part-time work by older workers. Washington, DC: The Urban Institute.Google Scholar
  16. Rathge, R. W. (2005). The changing population profile of the great plains. Great Plains Sociologist, 17(2), 82–99.Google Scholar
  17. Rathge, R. W. (2008). Future population shifts in the great plains and their implications. Great Plains Sociologist, 19, 109–126.Google Scholar
  18. Rathge, R. W., Clemenson, M., & Danielson, R. (2002). North Dakota population projections: 2005 to 2020. Fargo: North Dakota State Data Center, North Dakota State University.Google Scholar
  19. Reeder, R. J., & Calhoun, S. D. (2002). Federal funding in nonmetro elderly counties. Rural America, 17(3), 20–27.Google Scholar
  20. Rogerson, P. A., & Kim, D. (2005). Population distribution and redistribution of the baby-boom cohort in the United States: Recent trends and implications. Proceedings of the National Academy of Sciences of the United States of America, 102(43), 15319–15324. doi: 10.1073/pnas.0507318102.CrossRefGoogle Scholar
  21. Sjoquist, D. L., Wallace, S., & Winters, J. (2007). Selected fiscal and economic implications of aging. Atlanta: Fiscal Research Center and the Department of Economics, Andrew Young School of Policy Studies, Georgia State University.Google Scholar
  22. Stum, M., Bechman, J., & Knight, S. E. (2002). Later life and financial security: What’s beyond 60?. Minneapolis–St Paul: Financial security in later life National Initiative, University of Minnesota. Accessed 2008.
  23. Toder, E., & Solanki, S. (1999). Effects of demographic trends on labor supply and living standards: The retirement project (Occasional Paper 2). Washington, DC: The Urban Institute.Google Scholar
  24. US Bureau of Labor Statistics. (2000). 2000 Consumer expenditure survey. Washington, DC: US Bureau of Labor Statistics.Google Scholar
  25. US Census Bureau. (2004–2006). American community survey, public use microdata sample. Washington, DC: US Census Bureau.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht. 2013

Authors and Affiliations

  1. 1.Department of Sociology and Anthropology, and North Dakota State Data CenterNorth Dakota State UniversityFargoUSA
  2. 2.California Legislative Analyst’s OfficeDaVinci SacramentoUSA
  3. 3.North Dakota State Data CenterNorth Dakota State UniversityFargoUSA

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