An Econometric Approach for Modeling Population Change in Doña Ana County, New Mexico

Thomas M. Fullerton Jr.^1, , Adam G. Walke¹ and Diana Villavicencio²

¹Department of Economics and Finance, University of Texas at El Paso, El Paso, USA

²Economic and Market Analysis Division, United States Department of Housing and Urban Development, Chicago, USA

Cite this paper:
Thomas M. Fullerton Jr., Adam G. Walke and Diana Villavicencio. An Econometric Approach for Modeling Population Change in Doña Ana County, New Mexico. Journal of Finance and Economics. 2015; 3(1):20-28. doi: 10.12691/jfe-3-1-4

Abstract

An econometric model using time series analysis techniques is employed to model and forecast population changes in Doña Ana County, New Mexico. The model focuses on the interplay between economic and demographic variables. Individual, cointegrated equations are generated to account for the components of population change - births, deaths, net domestic and net international migration. Birth and death equations prove easier to model because of stable changes from period to period in relation to income levels and national demographic trends. Net migration equations were more difficult to model as economic conditions, specifically labor market conditions, influence changes over time. Predefined exogenous variables are used to generate out-of-sample simulations for the individual components of population change. Using those results, total population projections are estimated until the year 2018. Doña Ana County is projected to witness a slowdown in population growth, primarily as a consequence of increased domestic out-migration.

Keywords:
population economics regional economics applied econometrics migration forecasting

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References:

[1]	Anderson, J.B. and Gerber, J. (2008). Fifty years of change on the U.S.-Mexico border: Growth, development, and quality of life. Austin, TX: University of Texas Press.
[2]	Armstrong, J.S. (2001). Extrapolation for time-series and cross-sectional data. Principles of forecasting: A handbook for researchers and practitioners. Norwell, MA: Kluwer Academic Publishers.
[3]	Asteriou, D. and Hall, S.G. (2011). Applied econometrics. New York, NY: Palgrave Macmillan.
[4]	BEA (2014a). Local area personal income and employment. Washington, DC: U.S. Bureau of Economic Analysis.
[5]	BEA (2014b). National income and product accounts tables. Washington, DC: U.S. Bureau of Economic Analysis.
[6]	Berg, A., Meyer, R., and Yu, J. (2004). Deviation information criterion for comparing stochastic volatility models. Journal of Business and Economic Statistics, 22(1), 107-120.
[7]	Booth, H. (2006). Demographic forecasting: 1980 to 2005 in review. International Journal of Forecasting, 22(3), 547-581.
[8]	Becker, G. S. (1960). An economic analysis of fertility. Demographic and economic change in developed countries, a conference of the Universities-National Bureau Committee for Economic Research. New York, NY: National Bureau of Economic Research, distributed by Columbia University Press.
[9]	Bolton, R. (1985). Regional econometric models. Journal of Regional Science, 25(4), 495-520.
[10]	Breusch, T.S. and Godfrey, L. (1981). A review of recent work on testing for autocorrelation in dynamic simultaneous models. In D. Currie, R. Nobay and D. Peel (Eds.), Macroeconomicanalysis: Essays in macroeconomics and econometrics(pp. 63-105). London, UK: Croom Helm.
[11]	CDC (2014). Vital statistics data. Available at: http://www.cdc.gov/nchs/data_access/Vitalstatsonline.htm
[12]	Cebula, R.J. and Alexander, G.M. (2006). Determinants of net interstate migration, 2000-2004. Journal of Regional Analysis and Policy, 36(2), 116-123.
[13]	Cebula, R.J. and Clark, J.R. (2011). Migration, economic freedom, and personal freedom: An empirical analysis. Journal of Private Enterprise, 27(1), 43-62.
[14]	Cebula, R.J. and Clark, J.R. (2013). An extension of the Tiebouthypothesis of voting with one’s feet: The Medicaid magnet hypothesis. Applied Economics, 45(31), 4575-4583.
[15]	Cushing, B. and Poot, J. (2004). Crossing boundaries and borders: Regional science advances in migration modeling. Papers in Regional Science, 83(1), 317-338.
[16]	Davis, H.C. (1995). Demographic projection techniques for regions and smaller areas, a primer. Vancouver, Canada: UBC Press.
[17]	Djafar, F. and Hassan, M.K. (2012). Dynamics of push and pull factors of migrant workers in developing countries: The case of Indonesian workers in Malaysia. Journal of Economics and Behavioral Studies, 4(12), 703-711.
[18]	Engle, R.F. and Granger, C.W.J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2), 251-276.
[19]	Estrella Valenzuela, G. (1992). The floating population of the border. In J.R. Weeks & R. Ham-Chande (Eds.), Demographic dynamics of the U.S.-Mexico border (pp. 187-200).El Paso, TX: Texas Western Press.
[20]	Fukuchi, T. and Yamaguchi, M. (1997).An econometric analysis of a suburban city. Studies in Regional Science, 27(2), 1-31.
[21]	Fullerton, T.M., Jr. (2001). Specification of a Borderplex econometric forecasting model. International Regional Science Review, 24(2), 245-260.
[22]	Fullerton, T.M., Jr. and Barraza de Anda, M.P. (2008). Borderplex population modeling. Migraciones Internacionales, 4(3), 91-104.
[23]	Fullerton, T.M., Jr. Ramirez, D.A. and Walke, A.G. (2014). An econometric analysis of population change in Arkansas. Oxford Journal, 9(1), 28-40.
[24]	Handler, D. and Behravesh, N. (2014).A note on economic data versus economic vitality.US Economic Outlook, (July), 5-18, Lexington, MA: IHS Global Insight.
[25]	Harris, J.R. and Todaro, M.P. (1970). Migration, unemployment, and development: A two-sector analysis. American Economic Review, 60(1), 126-142.
[26]	Hernández-Murillo, R., Ott, L.S., Owyang, M.T. and Whalen, D. (2011). Patterns of interstate migration in the United States from the survey of income and program participation. Federal Reserve Bank of St. Louis Review, 93(3), 169-185.
[27]	Hoff, J.C. (1983). A practical guide to Box-Jenkins forecasting. Belmont, CA: Lifetime Learning Publications.
[28]	Isen, A. and Stevenson, B. (2010).Women’s education and family behavior: Trends in marriage, divorce, and fertility (No. w15725). Cambridge, MA: National Bureau of Economic Research.
[29]	Kazlauskienė, A. andRinkevičius, L. (2006). Lithuanian “brain drain” causes: Push and pull factors. Engineering Economics, 46(1), 27-37.
[30]	Lee, E.S. (1966). A theory of migration. Demography, 3(1), 47-57.
[31]	Lim, J. (2011). Does wage differential driven migration continue to exist? Tests on the role of regional economic structure in wage differential driven migration. Annals of Regional Science, 47(1), 213-233.
[32]	Massey, D.S., Arango, J., Hugo, G., Kouaouci, A., Pellegrino, A. and Taylor, J.E. (1994). An evaluation of international migration theory: The North American case. Population and Development Review, 20(4), 699-751.
[33]	Mayda, A.M. (2010). International migration: A panel data analysis of the determinants of bilateral flows. Journal of Population Economics, 23(4), 1249-1274.
[34]	NMDH. (2014). New Mexico’s indicator-based information system (NM-IBIS).Santa Fe, NM: New Mexico Department of Health.
[35]	Peach, J. and Williams, J.D. (1994).Demographicchange inthe El Paso-Juárez-Las Cruces region. Estudios Fronterizos, 34 (Julio-Diciembre), 117-138.
[36]	Peach, J. and Williams, J.D. (2000). Population and economic dynamics on the U.S.-Mexican border: Past, present, and future. Chapter 4 in P. Ganster (Ed.), The U.S. -Mexican border environment: A road map to a sustainable 2020. San Diego, CA: San Diego State University Press.
[37]	Pindyck, R.S. andRubinfeld, D.L. (1976). Econometric models and economic forecasts. New York, NY: McGraw Hill.
[38]	Plaut, T.R. (1981). An econometric model for forecasting regional population growth. International Regional Science Review, 6(1), 53-70.
[39]	PRS Group. (2013). Mexico country report. East Syracuse, NY: The PRS Group, Inc.
[40]	Ruyssen, I., Evaert, G. and Rayp, G. (2014).Determinants and dynamics of migration to OECD countries in a three-dimensional panel framework.Empirical Economics 46(1), 175-197.
[41]	Shryock, H.S., Siegel, J.S. and Associates (1980).The methods and materials of demography.Washington, DC: United States Government Printing Office.
[42]	Smith, S.K. (1984). Population projections: What do we really know? BEBRMonographs, 1.Gainesville, FL: Bureau of Economic and Business Research University of Florida.
[43]	Smith, S.K. (1997). Further thoughts on simplicity and complexity in population projection models. International Journal of Forecasting, 13(4), 557-565.
[44]	Shultz, T.P. (2005). Fertility and income (Discussion paper no. 925). Yale University: Economic Growth Center.
[45]	Spiegelhalter, D.J., Best, N.G., Carlin, B.P. and van der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(4), 583-639.
[46]	Stark, O. andTaylor, J.E. (1991). Migration incentives, migration types: The role of relative deprivation. Economic Journal, 101(408), 1163-1178.
[47]	Tayman, J. and Swanson, D.A. (1996). On the utility of population forecasts. Demography, 33(4), 523-528.
[48]	Todaro, M.P. and Smith, S.C. (2011). Economic development (11thed.). Boston, MA: Addison-Wesley.
[49]	Tuiran, R.A. (1992). Households and emigration in the northern border: The case of Reynosa. In J.R. Weeks & R. Ham-Chande (Eds.), Demographic dynamics of the U.S. -Mexico border (pp. 165-186).El Paso, TX: Texas Western Press.
[50]	USCB. (2012). Projected population by single year of age, sex, race, and Hispanic origin for the United States: 2012 to 2060.Washington, DC: United States Census Bureau.
[51]	Vogler, M. and Rotte, R. (2000) The effects of development on migration: Theoretical issues and new empirical evidence. Journal of Population Economics, 13(3), 485-508.
[52]	WB (2014). World development indicators database: Mexico. Washington, DC: World Bank.
[53]	White, D. (2014). Las Cruces. Moody’s Analytics Précis U.S. Metro. West Chester, PA: Moody’s Economic & Consumer Credit Analytics.
[54]	Woodward, M.C. (2013). The U.S. economy to 2022: Settling into a new normal. Washington, D.C.: U.S. Bureau of Labor Statistics.
[55]	Wooldridge, J.M. (2013). Introductory econometrics: A modern approach (5thed.). Mason, OH: South-Western, Cengage Learning.
[56]	Xiao, N., Zarnikau, J. and Damien, P. (2007). Testing functional forms in energy modeling: An application of the Bayesian approach to U.S. electricity demand. Energy Economics 29(2), 158-166.