Defence Spending and Unemployment in the USA: Disaggregated Analysis by Gender and Age Groups

Christos Kollias; Suzanna-Maria Paleologou; Panayiotis Tzeremes

doi:10.1515/peps-2018-0052

Article

Defence Spending and Unemployment in the USA: Disaggregated Analysis by Gender and Age Groups

Christos Kollias , Suzanna-Maria Paleologou and Panayiotis Tzeremes

Published/Copyright: March 13, 2020

Published by

Become an author with De Gruyter Brill

Submit Manuscript Author Information Explore this Subject

From the journal Peace Economics, Peace Science and Public Policy Volume 26 Issue 2

Abstract

The paper examines the effects of military spending using disaggregated unemployment statistics by gender and age group for the period 1948–2017 in the case of the USA. Findings from quantile regression analysis do not seem to point to any robust evidence supporting the thesis that defence spending quashes unemployment levels. This finding appears to be the case across all groups of unemployed persons. In fact, the results suggest a negative effect on unemployment.

Keywords: defence spending; unemployment; quantile regression

JEL Classification: C21; E24; H56

Acknowledgements

The useful comments and constructive suggestions by an anonymous referee that helped improve the paper are gratefully acknowledged. An earlier version of the paper has also greatly benefited from the comments by Paul Dunne and the other participants of the 18th Annual International Conference on Economics & Security, 19–20th June 2014, University of Perugia, Italy. The usual disclaimer applies.

Appendix I: Pairwise correlations

Table 7:

Pairwise correlations 1948–2017.

	D	ALL	WALL	MALL	MALL1619	MALL20	FALL	FALL1619	FALL20	MFALL1619
D	1.00
ALL	0.28	1.00
WALL	0.24	0.99	1.00
MALL	0.36	0.98	0.97	1.00
MALL1619	0.58	0.89	0.86	0.91	1.00
MALL20	0.35	0.95	0.94	0.99	0.88	1.00
FALL	0.06	0.93	0.93	0.85	0.74	0.79	1.00
FALL1619	0.40	0.86	0.84	0.81	0.90	0.74	0.85	1.00
FALL20	0.11	0.89	0.90	0.84	0.69	0.81	0.89	0.70	1.00
MFALL1619	0.51	0.90	0.88	0.89	0.98	0.85	0.81	0.96	0.72	1.00

Appendix II: The methodology

Koenker and Bassett (1978) introduced the quantile regression as a generalization of the sample quantile to the conditional quantile, where the conditional quantile is expressed as a linear function of explanatory variables. This is analogous to the OLS regression where the conditional mean of a random variable is expressed as a linear function of explanatory variables. By enabling the estimation of any conditional quantile, quantile regression allows one to describe the entire conditional distribution of a dependent variable given a set of regressors. The Least Absolute Deviation (LAD) estimator is a special case of quantile regression that expresses the conditional median as linear function of covariates. The key factor that makes quantile regression’s ability to characterize the entire conditional distribution so useful is the presence of heteroskedasticity in the data (Koenker & Bassett, 1982). When the data are homoskedastic, the set of slope parameters of conditional quantile functions at each point of the dependent variable’s distribution will be identical with each other and with the slope parameters of the conditional mean function. In this case, the quantile regression at any point along the distribution of the dependent variable reproduces the OLS slope coefficients, and only the intercepts will differ. However, when the data are heteroskedastic, the set of slope parameters of the conditional quantile functions will differ from each other as well as from the OLS slope parameters. Therefore, estimating conditional quantiles at various points of the distribution of the dependent variable will allow us to trace out different marginal responses of the dependent variable to changes in the explanatory variables at these points.

Two additional features of the quantile regression model are relevant to our application (Buchinsky, 1998). First, the classical properties of efficiency and minimum variance of the least squares estimator are obtained under the restrictive assumption of independently, identically and normally distributed (i.i.d.) errors. When the distribution of errors is non-normal, the quantile regression estimator may be more efficient than the least squares estimator. Second, the quantile regression estimator is “robust” when the dependent variable has outliers or the error distribution is “longtailed.” Since the objective function from which the quantile regression estimator is derived is a weighted sum of absolute deviations, the parameter estimates are less sensitive to a few large or small observations at the tails of the distribution. The distributional statistics reported in Table 8 show that the mean, of the 9 types of the unemployment rate, is consistently higher than the median (50th percentile) unemployment rate. This suggests that the unemployment rates are asymmetrically distributed with some influential observations at the upper tails.

Table 8:

Unemployment rates and their distribution.

	Mean	50% percentile
ALL	5.8	5.93
WALL	5.1	5.20
MALL	5.7	5.84
MALL1619	17.00	17.86
MALL20	4.9	5.04
FALL	6.1	6.05
FALL1619	15.4	15.95
FALL20	5.4	53.22
MFALL1619	16.2	16.96

The quantile regression model for equation (1) can be written as:

(2)[Ut=xt′βθ+εθt,Qθ=(Ut|xt)=xt′βθt=1,...,N,]

where x_t denotes a (K + 1) × 1 vector of the explanatory variables, β_θ is the corresponding vector of coefficients, and Q_θ = (U_t|x_t) denotes the θ^th conditional quantile of U_t, (0 < θ < 1). Quantile regressions categorize the data sample into high-down to low quantiles of the outcome variable in a form that differs from simple categorization. In a quantile regression, on the other hand, the classification of (U) is conditional on the regressor (X) (Koenker 2004). From equation (2), the quantile regression estimator of β_θ is obtained by solving (3):

(3)minβθ⁡1N{∑t:Ut≥xt′βθθ|Ut−xt′βθ|+∑t:Ut<xt′βθ(1−θ)|Ut−xt′βθ|}

When K = 0 and x_t is a (1 × 1) vector that includes only the intercept for all t, this minimization problem reduces to an estimator of the sample θ-quantile. The minimization problem in equation (3) has a linear programming representation, which is guaranteed to have a solution in a finite number of simplex iterations (Buchinsky, 1998). Several estimators for the asymptotic covariance matrix for β^θ obtained from the above minimization are available, but for obvious reasons, those that rely on the assumption of i.i.d. errors are of limited value. Buchinsky (1995) has shown that the design matrix bootstrap estimator provides a consistent estimator for the covariance matrix under very general conditions. In the design matrix bootstrap, the quantile regression is estimated on a sample of n observations drawn randomly with replacement from the original sample. The process is repeated B times to obtain bootstrap estimates, β^∗θb, b = 1, …, B. The covariance matrix of β^θ is then obtained as the covariance of β^∗θcomputed from the B bootstrap estimates with β^θ as the pivotal value. The minimum-distance method can be used to test for the equality of slope coefficients of a given dependent variable across all estimated quantiles (Buchinsky, 1998).

The quantile regression parameter estimates are obtained by estimating a separate equation for each quantile of each type of unemployment rate. The variance-covariance matrix of the estimates can be obtained by bootstrapping each of these equations separately. However, to carry out tests of the equality of slope coefficients for a given dependent variable across the p estimated quantiles and to obtain the restricted parameter estimates and their standard errors, it is necessary to have the variance-covariance matrix of the stacked vector of parameter estimates at the p quantiles. This can be obtained by simultaneously estimating quantile regressions at the p quantiles for each bootstrap sample. Hence, the following procedure was used for the estimation and testing of the quantile regressions for each type of the unemployment rate. First, the coefficient estimates for the unemployment rates are obtained by running quantile regressions separately at the p desired quantiles. Second, a bootstrap sample is drawn for that unemployment rate and the bootstrap estimates for the p quantiles are obtained by running quantile regressions separately at the p quantiles for the sample. Finally, after repeating the bootstrap procedure B times, the variance-covariance matrix of the stacked vector of parameter estimates at the p quantiles is calculated to obtain the standard errors of the coefficient estimates and to conduct the equality tests. This estimation process is carried out in Stata 13 using the sqreg procedure. Additional details regarding the estimation of the quantile regression model and the asymptotic covariance matrix of the parameters are discussed in Buchinsky’s Buchinsky (1998) methodological survey.

Appendix III: The disaggregated unemployment categories 1948–2017.

References

Abbott, A., & Jones, P. (2014). Leaning against an open door’: Ideology and the cyclicality of public expenditure. Journal of Policy Modeling, 36, 957–969.10.1016/j.jpolmod.2014.09.003Search in Google Scholar

Abell, J. D. (1990). Defence spending and unemployment rates: An empirical analysis disaggregated by race. Cambridge Journal of Economics, 14, 405–419.10.1093/oxfordjournals.cje.a035143Search in Google Scholar

Abell, J. D. (1992). Defence spending and unemployment rates: An empirical analysis disaggregated by race and gender. American Journal of Economics and Sociology, 51(1), 27–42.10.1093/oxfordjournals.cje.a035143Search in Google Scholar

Aksoy, Y., & Melina, G. (2012). An empirical investigation of US fiscal expenditures and macroeconomic outcomes. Economics Letters, 114, 64–68.10.1016/j.econlet.2011.09.017Search in Google Scholar

Alexander, R. (2015). The Keynesian IS-MR model and military spending. Defence and Peace Economics, 26(2), 213–221.10.1080/10242694.2013.857449Search in Google Scholar

Alptekin, A., & Levine, P. (2012). Military expenditure and economic growth: A meta-analysis. European Journal of Political Economy, 28, 636–650.10.1016/j.ejpoleco.2012.07.002Search in Google Scholar

Aschauer, D. A. (1989). Does public capital crowd out private capital? Journal of Monetary Economics, 24(2), 171–188.10.1016/0304-3932(89)90002-0Search in Google Scholar

Atesoglu, S. (2002). Defense spending promotes aggregate output in the US. Defense and Peace Economics, 13(1), 55–60.10.1080/10242690210963Search in Google Scholar

Atesoglu, S. (2004). Defense spending and investment in the United States. Journal of Post Keynesian Economics, 27(1), 163–170.Search in Google Scholar

Baran, P., & Sweezy, P. (1966). Monopoly capital. New York: Monthly Review Press.Search in Google Scholar

Barro, R., & Redlick, C. (2011). Macroeconomic effects from Government purchases and taxes. Quarterly Journal of Economics, 126(1), 51–102.10.3386/w15369Search in Google Scholar

Barro, R., & de Rugy, V. (2013). Defense spending and the economy. Arlington, VA: Mercatus Center, George Mason University. https://www.mercatus.org/system/files/Barro_DefenseSpending_v2.pdf.Search in Google Scholar

Bergh, A., & Henrekson, M. (2011). Government size and growth: A survey of interpretation of the evidence. Journal of Economic Surveys, 25, 872–897.10.1111/j.1467-6419.2011.00697.xSearch in Google Scholar

Blanchard, O., & Perotti, R. (2002). An empirical characterization of the dynamic effects of changes in government spending and taxes on output. Quarterly Journal of Economics, 107(4), 1329–1368.10.3386/w7269Search in Google Scholar

Blien, U. (1993). Consequences of the disarmament process for the labour market in Germany. Labour, 7(2), 3–23.10.1111/j.1467-9914.1993.tb00194.xSearch in Google Scholar

Brock, W. A., Dechert, W. D., & Scheinkman, J. A. (1987). A test for independence based on the correlation dimension. Social Systems Research Institute, University of Wisconsin-Madison. Working Paper 8702.10.1080/07474939608800353Search in Google Scholar

Buchinsky, M. (1995). Estimating the asymptotic covariance matrix for Quantile Regression models: A Monte Carlo Study. Journal of Econometrics, 68, 303–338.10.1016/0304-4076(94)01652-GSearch in Google Scholar

Buchinsky, M. (1998). Recent advances in Quantile Regression Models: A practical guideline for empirical research. The Journal of Human Resources, 33, 88–126.10.2307/146316Search in Google Scholar

Cevik, S., & Ricco, J. (2018). No buck for the bang: revisiting the military-growth nexus. Empirica, 45(4), 639–653.10.1007/s10663-017-9380-8Search in Google Scholar

Chester, E. (1978). Military spending and capitalist stability. Cambridge Journal of Economics, 2(3), 293–298.Search in Google Scholar

Chletsos, M., & Roupakias, S. (2017). Defense spending and unemployment. Evidence from Southern European countries. Peace Economics, Peace Science and Public Policy, 23(1), 1-36.Search in Google Scholar

Chodorow-Reich, G., Feiveson, L., Liscow, Z., & Woolston, W. (2012). Does state fiscal relief during recessions increase employment? Evidence from the American Recovery and Reinvestment Act. American Economic Journal: Economic Policy, 4(3), 118–145.10.1257/pol.4.3.118Search in Google Scholar

Churchill, S., & Yew, S. (2018). The effect of military expenditure on growth: An empirical synthesis. Empirical Economics, 55(3), 1357–1387.10.1007/s00181-017-1300-zSearch in Google Scholar

Cypher, J. (1987). Military production and capital accumulation: A comment. Journal of Post Keynesian Economics, 10(2), 304–309.10.1080/01603477.1987.11489679Search in Google Scholar

Cypher, J. (2015). The origins and evolution of military Keynesianism in the United States. Journal of Post Keynesian Economics, 38(3), 449–476.10.1080/01603477.2015.1076704Search in Google Scholar

d’Agostino, G., Dunne, P., & Pieroni, L. (2011). Optimal military spending in the US: A time series analysis. Economic Modelling, 28, 1068–1077.10.1016/j.econmod.2010.11.021Search in Google Scholar

d’Agostino G, Dunne, P., & Pieroni, L. (2017). Does military spending matter for long-run growth? Defence and Peace Economics, 28, 429–436.10.1080/10242694.2017.1324723Search in Google Scholar

Dunne, P. (2013). Military keynesianism: An assessment. Contributions to Conflict Management, Peace Economics and Development, 20, 117–129.10.1108/S1572-8323(2013)00020.2011Search in Google Scholar

Dunne, J. P., & Smith, R. (1990). Military expenditure and employment in the OECD. Defence Economics, 1, 57–73.10.1080/10430719008404650Search in Google Scholar

Dunne, J. P., & Watson, D. (2000). Military expenditure and employment in South Africa. Defence and Peace Economics, 11, 587–596.10.1080/10430710008404968Search in Google Scholar

Dunne P., & Braddon, D. (2008). Economic impact of military R&D. Brussels: Flemish Peace Institute.Search in Google Scholar

Dunne, P., & Tian, N. (2013). Military expenditure and economic growth: A survey. Economics of Peace and Security Journal, 8, 5–11.10.15355/epsj.8.1.5Search in Google Scholar

Dunne, P., & Tian, N. (2016). Military expenditure and economic growth, 1960–2014. Economics of Peace and Security Journal, 11, 50–56.10.15355/epsj.11.2.50Search in Google Scholar

Emmanouilidis, K., & Karpetis, C. (2020). The defense–growth nexus: A review of time series methods and empirical results. Defence and Peace Economics, 31(1), 86–104.10.1080/10242694.2018.1428261Search in Google Scholar

Feldstein, M. (2011). The effect on the U.S. Economy of changes in defense spending. Testimony to the Committee on Armed Services of the U.S. House of Representatives, October 26. http://www.nber.org/feldstein/Armed%20Services.pdf.Search in Google Scholar

Griffin, L., Devine, J., & Wallace, M. (1982). Monopoly capital, organized labor, and military expenditures in the United States, 1949–1976. American Journal of Sociology, 88, S113–S153.10.1086/649254Search in Google Scholar

Hooker, M., & Knetter, M. (1997). The effects of military spending on economic activity: Evidence from State procurement spending. Journal of Money, Credit and Banking, 29(3), 400–421.10.2307/2953702Search in Google Scholar

Huang, J.-T., & Kao, A.-P. (2005). Does defence spending matter to employment in Taiwan? Defence and Peace Economics, 16, 101–115.10.1080/10242690500070094Search in Google Scholar

Irmen, A., & Kuehnel, J. (2009). Productive government expenditure and economic growth. Journal of Economic Surveys, 23, 692–733.10.1111/j.1467-6419.2009.00576.xSearch in Google Scholar

Johnston, J., & Di Nardo, J. (1997). Econometric Methods. (4th ed.). New York: McGraw Hill.Search in Google Scholar

Koenker, R. (2004). Quantile regression for longitudinal data. Journal of Multivariate Analysis, 91(1), 74–89.10.1016/j.jmva.2004.05.006Search in Google Scholar

Koenker, R. (2005). Quantile regression. Cambridge: Cambridge University Press.10.1017/CBO9780511754098Search in Google Scholar

Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46, 33–50.10.2307/1913643Search in Google Scholar

Koenker, R., & Bassett, G. (1982). Robust tests for heteroscedasticity based on regression quantiles. Econometrica, 50, 43–62.10.2307/1912528Search in Google Scholar

Koenker, R., & Machado, J. A. F. (1999). Goodness of fit and related inference processes for quantile regression. Journal of the American Statistical Association, 94(448), 1296–1310.10.1080/01621459.1999.10473882Search in Google Scholar

Kollias, C., & Paleologou, S. M. (2013). Guns, highways and economic growth in the United States. Economic Modelling, 30, 449–455.10.1016/j.econmod.2012.09.048Search in Google Scholar

Kollias, C., & Paleologou, S. M. (2015). Defence and non-defence spending in the USA: Stimuli to economic growth? Comparative findings from a semiparmetric approach. Bulletin of Economic Research, 67(4), 359–370.10.1111/boer.12011Search in Google Scholar

Korb, L., Conley, L., Duggan, S., & Merighi, M. (2009). Military spending can grow the Nation’s economy. Center for American Progress Action Fund. https://cdn.americanprogress.org/wp-content/uploads/issues/2009/02/pdf/military_spending_memo.pdf.Search in Google Scholar

Kraay, A. (2012). How large is the government spending multiplier? Evidence from World Bank lending. Quarterly Journal of Economics, 127(2), 829–887.10.1596/1813-9450-5500Search in Google Scholar

Malizard, J. (2014). Defense spending and unemployment in France. Defence and Peace Economics, 23(6), 635–642.10.1080/10242694.2013.857450Search in Google Scholar

Mertens, A., Gash, V., & McGinnity, F. (2007). The cost of flexibility at the margin. Comparing the wage penalty for fixed-term contracts in Germany and Spain using quantile regression. Labour, 21(4–5), 637–666.10.1111/j.1467-9914.2007.00396.xSearch in Google Scholar

Newey, W. K., & Powell, J. L. (1987). Asymmetric least squares estimation. Econometrica, 55(4), 819–847.10.2307/1911031Search in Google Scholar

Ohinata, A., & Ours, J. V. (2016). Quantile peer effects of immigrant children at primary schools. Labour, 30(2), 135–157.10.1111/labr.12076Search in Google Scholar

Paul, S. (1996). Defence spending and unemployment rates: An empirical analysis for the OECD. Journal of Economic Studies, 23, 44–54.10.1108/01443589610109667Search in Google Scholar

Pieroni, L. (2009). Does defence expenditure affect private consumption? Evidence from the United States. Economic Modelling, 26(6), 1300–1309.10.1016/j.econmod.2009.06.004Search in Google Scholar

Pieroni, L., d’Agostino, G., & Lorusso, M. (2008). Can we declare military Keynesianism dead? Journal of Policy Modeling, 30(5), 675–691.10.1016/j.jpolmod.2008.02.005Search in Google Scholar

Pivetti, M. (1992). Military spending as a burden on growth: An ‘underconsumptionist’ critique. Cambridge Journal of Economics, 16(4), 373–384.Search in Google Scholar

Pivetti, M. (1994). Effective demand, ‘Marxo-marginalism’ and the economics of military spending: A rejoinder. Cambridge Journal of Economics, 18(5), 523–527.10.1093/oxfordjournals.cje.a035288Search in Google Scholar

Pollin, R., & Garrett-Peltier, H. (2011). The U.S. employment effects of military and domestic spending priorities: 2011 update. Political Economy Research Institute (PERI), Amherst, MA: University of Massachusetts.Search in Google Scholar

Sempere, C-M. (2018). What is known about defence research and development spill-overs? Defence and Peace Economics, 29(3), 225–246.10.1080/10242694.2016.1239364Search in Google Scholar

Smith, R. (1977). Military expenditure and capitalism. Cambridge Journal of Economics, 1(1), 61–76.10.1093/oxfordjournals.cje.a035351Search in Google Scholar

Smith, R., & Dunne, P. (1994). Is military spending a burden? A ‘Marxo-marginalist’ response to Pivetti. Cambridge Journal of Economics, 18(5), 515–521.10.1093/oxfordjournals.cje.a035287Search in Google Scholar

Sweezy, P. (1942). The theory of capitalist development. New York: Monthly Review Press.Search in Google Scholar

Tang, J-H., Lai, C-C., & Lin, E. (2009). Military expenditure and unemployment rates: Granger causality tests using global panel data. Defence and Peace Economics, 20(4), 253–267.10.1080/10242690903105257Search in Google Scholar

Toporowski, J. (2016). Multilateralism and military Keynesianism: Completing the analysis. Journal of Post Keynesian Economics, 39(4), 437–443.10.1080/01603477.2016.1240589Search in Google Scholar

Wilson, D. (2012). Fiscal spending jobs multipliers: Evidence from the 2009 American Recovery and Reinvestment Act. American Economic Journal: Economic Policy, 4(3), 251–282.10.1257/pol.4.3.251Search in Google Scholar

Yildirim, J., & Sezgin, S. (2003). Military expenditure and employment in Turkey. Defence and Peace Economics, 14(2), 129–139.10.1080/10242690302919Search in Google Scholar

Zhong, M., Chang, T., Tang, D. P., & Wolde-Rufael, Y. (2015). The causal nexus between military spending and unemployment in the G7: a bootstrap panel causality test. Defence and Peace Economics, 26(6), 609–622.10.1080/10242694.2014.994835Search in Google Scholar

Published Online: 2020-03-13

You are currently not able to access this content.

Articles in the same Issue

https://doi.org/10.1515/peps-2018-0052

Keywords for this article

defence spending; unemployment; quantile regression