The analysis of synchronization among regional or national business cycles has recently been attracting a growing interest within the economic literature. Far less attention has instead been devoted to a closely related issue: given a certain level of synchronization, some economies might be systematically ahead of others along the swings of the business cycle. We analyze this issue within a system of economies and show that leading (or lagging behind) is a feature that does not occur at random across the economies. In addition, we investigate the economic drivers that could explain this behavior. To do so, we employ data for 48 conterminous US states between 1990 and 2009.
|LL||Average (along national turning points) of the number of months by which a state’s business cycle anticipates or follows the national business cycle|
|ρ||Bilateral correlation among states’ cycles. Cycles have been identified using the Baxter-King band-pass filter|
|S||Time average of yearly pairwise differences across states in the industry mix:||US Bureau of Economic Analysis|
|where sn,i,t is the employment share of industry n in total employment at time t|
|HT||Time average of yearly pairwise differences across states in the share of high technology sector employment over total employment; high-tech sector is proxied by NAICS 340,000 “computer and electronic product manufacturing”||US Bureau of Economic Analysis|
|DL||Dummy variable which takes on a value of 1 if the first state of the pair is leading the second in terms of business cycle, 0 otherwise|
|T||Bilateral trade intensity||See text|
|F||Cross-state financial integration||See text|
|Amenity||Pairwise differences across states in the natural amenity index||Economic Research Service; US Dept. of Agriculture|
|Agriculture||Time average of yearly pairwise differences across states in the share of agriculture employment over total employment||US Bureau of Economic Analysis|
|Public||Time average of yearly pairwise differences across states in the share of public sector employment over total employment||US Bureau of Economic Analysis|
|Mining||Time average of yearly pairwise differences across states in the share of mining employment over total employment||US Bureau of Economic Analysis|
|Oil||Pairwise differences across states in 2010 oil production (in million barrels)||US Energy Information Administration|
|Distance||Logarithm of Euclidean distance across states’ capitals|
|Pop difference||Time average of yearly pairwise differences across states in population||US Bureau of Economic Analysis|
|ln GSPpc difference||Time average of yearly pairwise differences across states in log Gross State Product (GSP) per capita||US Bureau of Economic Analysis|
|ln GSP gap||Time average of yearly pairwise differences (in absolute terms) across states in log GSP||US Bureau of Economic Analysis|
|ln GSP product||Time average of yearly pairwise products across states in log GSP||US Bureau of Economic Analysis|
The coincident index is a macroeconomic indicator that summarizes in a single variable the current economic conditions of a state. It includes four main elements: non-farm payroll employment, average hours worked in manufacturing, unemployment rate, and wage and salary disbursements. Coincident index data are obtained from the website of the Federal Reserve Bank of Philadelphia.
As will be clarified in Section 3, the regression analysis covers a shorter period due to data availability problems for most variables introduced in the model.
Baxter and King (1999) propose a band-pass filter, based on Burns and Mitchell’s (1946) definition of a business cycle, designed to remove low and high frequencies from the data. As recommended, the applied filter passes through components of time series with fluctuations between 18 and 96 months while removing higher and lower frequencies.
Table A1 in the Appendix reports, for each state and for each turning point of the US business cycle, the number of months by which a state leads or lags behind due to differences in timing of cycle swings.
A detailed table with median lead/lag values for all the States and all sub-periods is provided in the Appendix (Table A2).
We do not impose any restriction on these coefficients in the estimation and subsequently check that the estimated values are compatible with the signs reported in Figure 5.
There is also a branch of the literature that studies directly the role of trade and financial integration on the degree of synchronization by estimating a single equation model and allowing for endogeneity via instrumental variables (among many other, Abbott et al. 2008; Baxter and Kouparitsas 2005; Inklaar et al. 2008; Kalemli-Ozcan et al. 2009; Kose et al. 2003; Otto et al. 2001).
See Imbs (2004) for details on the sign of these relationships.
Table A3 in the Appendix provides a detailed description of the variables and data sources.
The N industries that have been used are: agriculture, mining, utilities, construction, manufacturing, wholesale trade, retail trade, transportation, information, finance and insurance, real estate, rental and leasing, professional, scientific and technical services, management of companies and services, administrative services, educational services, health care and social assistance, arts, entertainment, recreation services, accommodation and food services, other services except government, and government sector.
Here we adopt the original coefficients estimated by Imbs (2003) so that inter-state trade between i and j is:
Both GSP and DY have been detrended using the Hodrick-Prescott (Hodrick and Prescott 1997) filter.
Estimates are obtained using the reg3 command in Stata 12.
By “representative leading” state we mean the hypothetical state for which all independent variables take on their sample mean value conditional on the dummy DL being equal to 1. A similar concept applies for the “representative lagging behind” state with the only difference that the dummy DL is equal to 0.
Abbott, A., J. Easaw, and T. Xing. (2008). Trade Integration and Business Cycle Convergence: Is the Relation Robust across Time and Space? Scandinavian Journal of Economics, 110:403–17.10.1111/j.1467-9442.2008.00539.xSearch in Google Scholar
Artis, M. and T. Okubo. (2011). The Intranational Business Cycle in Japan. Oxford Economic Papers, 63:111–33.Search in Google Scholar
Baxter, M. and R.G. King. (1999). Measuring Business Cycles: Approximate Bandpass Filters. Review of Economics and Statistics, 81:575–93.Search in Google Scholar
Baxter, M. and M.A. Kouparitsas. (2005). Determinants of Business Cycle Comovements: A Robust Analysis. Journal of Monetary Economics, 52:113–57.Search in Google Scholar
Bry, G. and C. Boschan. (1971). Cyclical Analysis of Time Series: Selected Procedures and Computer Programs”. NBER Technical Paper No. 20.Search in Google Scholar
Beckworth, D. (2010). One Nation Under the Fed? The Asymmetric Effect of US Monetary Policy and Its Implications for the United States as an Optimal Currency Area. Journal of Macroeconomics, 32:732–46.Search in Google Scholar
Burns, A.F. and W.C. Mitchell. (1946). Measuring business cycles. New York: NBER.Search in Google Scholar
Carlino, G. and R. DeFina. (1998). The Differential Regional Effects of Monetary Policy. Review of Economics and Statistics, 80:572–87.Search in Google Scholar
Carlino, G. and R. DeFina. (1999a). The Differential Regional Effects of Monetary Policy: Evidence from the US States. Journal of Regional Science, 39:339–87.10.1111/1467-9787.00137Search in Google Scholar
Carlino, G. and R. DeFina. (1999b). Do States Respond Differently to Changes in Monetary Policy? Federal Reserve Bank of Philadelphia Business Review, 17–27.Search in Google Scholar
Carlino, G. and K. Sill. (2001). Regional Income Fluctuations: Common Trends and Common Cycles. Review of Economics and Statistics, 83:446–56.Search in Google Scholar
Crone, T.M. (2005). An alternative Definition of Economic Regions in the United States Based on Similarities in State Business Cycles. Review of Economics and Statistics, 87:617–26.Search in Google Scholar
Crone, T.M. (2006). What a New Set of Indexes Tells Us About State and National Business Cycles. Federal Reserve Bank of Philadelphia Business Review, Q1:11–24.Search in Google Scholar
Crone, T.M. (2007). Pattern of Regional Differences in the Effects of Monetary Policy. Federal Reserve Bank of Philadelphia Business Review, Q3:9–19.Search in Google Scholar
Dées, S. and N. Zorell. (2011). Business Cycle Synchronization – Disentangling Trade and Financial Linkages. European Central Bank Working Paper No. 1322.Search in Google Scholar
DeVol, R.C, P. Wong, J. Catapano, and G. Robitshek. (1999). America’s High-Tech Economy. Growth, Development, and Risks for Metropolitan Areas. Santa Monica, CA: Milken Institute.Search in Google Scholar
Dorfman, J.H., M.D. Partridge, and H. Galloway. (2008) Are High-tech Employment and Natural Amenities Linked? Answers from a Smoothed Bayesian Spatial Model. Paper presented at the American Agricultural Economics Association Meeting, 27–29 July 2008, Orlando.Search in Google Scholar
Fidrmuc, J. (2004). The Endogeneity of the Optimum Currency Area Criteria, Intra-Industry Trade, and EMU Enlargement. Contemporary Economic Policy, 22:1–12.Search in Google Scholar
Fidrmuc, J., K. Iwatsubo, and T. Ikeda. (2010). Financial Integration and International Transmission of Business Cycles: Evidence from Dynamic Correlations. Graduate School of Economics, Kobe University Discussion Paper No. 1007.Search in Google Scholar
Frankel, J.A. and A.K. Rose. (1998). The Endogeneity of the Optimum Currency Area Criteria. Economic Journal, 108:1009–25.Search in Google Scholar
Garcia-Herrero, A. and J.M. Ruiz. (2008). Do Trade and Financial Links Foster Business Cycle Synchronization in a Small Open Economy? Moneda y Credito, 226:187–226.10.2139/ssrn.886287Search in Google Scholar
Graves, P.E. (1979). A Life-Cycle Empirical Analysis of Migration and Climate by Race. Journal of Urban Economics, 6:135–47.Search in Google Scholar
Graves, P.E. (1980). Migration and Climate. Journal of Regional Science, 20:227–37.Search in Google Scholar
Hausman, J.A. (1978). Specification Tests in Econometrics. Econometrica, 46:1251–71.Search in Google Scholar
Heathcote, J. and F. Perri. (2004). Financial Globalization and Real Regionalization. Journal of Economic Theory, 119:207–43.Search in Google Scholar
Hodrick, R. and E.C. Prescott. (1997). Postwar US Business Cycles: An Empirical Investigation. Journal of Money, Credit and Banking, 29:1–16.Search in Google Scholar
Imbs, J. (2004). Trade, Specialization and Synchronization. Review of Economics and Statistics, 86:723–734.Search in Google Scholar
Inklaar, R., R. Jong-A-Pin, and J. de Haan. (2008). Trade and Business Cycle Synchronization in OECD Countries – A Re-examination. European Economic Review, 52:646–66.Search in Google Scholar
Irvine, F.O. and S. Shuh. (2005). Interest Sensitivity and Volatility Reductions: Cross-Section Evidence. Federal Reserve Bank of Boston Working Paper No. 4.Search in Google Scholar
Kalemli-Ozcan, S., B. Sørensen, and O. Yosha. (2003). Risk Sharing and Industrial Specialization: Regional and International Evidence. American Economic Review, 93:903–918.Search in Google Scholar
Kalemli-Ozcan, S., E. Papaioannou, and J.L. Peydró, (2009). Financial Regulation, Financial Globalization and the Synchronization of Economic Activity. NBER Working Paper No. 14887.10.3386/w14887Search in Google Scholar
Kouparitsas, M. (2001). Is the United States an Optimal Currency Area? An Empirical Analysis of Regional Business Cycles. Federal Reserve Bank of Chicago Working Paper No. 22.Search in Google Scholar
Kose, M.A., E.S. Prasad, and M.E. Terrones. (2003). How Does Globalization Affect the Synchronization of Business Cycles? American Economic Review, 93:57–62.10.1257/000282803321946804Search in Google Scholar
Moretti, E. (2010). Local Multipliers. American Economic Review, Papers and Proceedings, 100:1–7.Search in Google Scholar
Obstfeld, M. (1994). Risk-Taking, Global Diversification, and Growth. American Economic Review, 84:1310–29.Search in Google Scholar
Otto, G., G. Voss, and L. Willard. (2001). Understanding OECD Output Correlations. Reserve Bank of Australia Research Discussion Paper, No. 2001–5.Search in Google Scholar
Owyang, M.T. and H.J. Wall. (2004). Structural Breaks and Regional Disparities in the Transmission of Monetary Policy. Federal Reserve Bank of St. Louis Working Paper No. 2003–008B.Search in Google Scholar
Owyang, M.T. and H.J. Wall. (2009). Regional VARs and the Channels of Monetary Policy. Applied Economic Letters, 16:1191–94.Search in Google Scholar
Owyang, M.T., J.M. Piger, and H.J. Wall. (2005). Business Cycle Phases in U.S. States. Review of Economics and Statistics, 87:604–16.Search in Google Scholar
Park, Y. and G.J.D. Hewings. (2003). Does Industry Mix Matter in Regional Business Cycles? Regional Economics Applications Laboratory Discussion Paper No. 29.Search in Google Scholar
Partridge, M. and D. Rickman. (2005). Changes in Asymmetric Shocks in an Optimal Currency Area: An Analysis Using US State Data. Oxford Economic Papers, 5:373–97.Search in Google Scholar
Partridge, M.D., D.S. Rickman, K. Ali, and M.R. Olfert. (2008). The Geographic Diversity of US Nonmetropolitan Growth Dynamics: A Geographically Weighted Regression Approach. Land Economics, 84:241–66.Search in Google Scholar
Schiavo, S. (2008). Financial Integration, GDP Correlation and the Endogeneity of Optimum Currency Areas. Economica, 75:168–89.Search in Google Scholar
Van Biesebroeck, J. (2010). Dissecting Intra-Industry Trade. Economic Letters, 110:71–75.Search in Google Scholar
Xing, T. and A. Abbott. (2007). The Effects of Trade, Specialisation and Financial Integration for Business Cycle Synchronisation. Paper presented at the 9th European Trade Study Group Conference. 13–15 September 2007, Athens.Search in Google Scholar
©2013 by Walter de Gruyter Berlin Boston