Abstract
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.
6 Appendix
Turning points (T)/(P) | 1980 Aug (T) | 1981 Sep (P) | 1983 Feb (T) | 1984 Sep (P) | 1986 Dec (T) | 1990 May (P) | 1991 Oct (T) | 1994 Dec (P) | 1996 Mar (T) | 1998 Feb (P) | 1999 Feb (T) | 2000 Nov (P) | 2003 Sep (T) | 2008 Apr (P) | 2009 Jul (T) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Alabama | 0 | 2 | 2 | 5 | 1 | 1 | 1 | –2 | –11 | –4 | –1 | 4 | –2 | 0 | 0 |
Arizona | 1 | 2 | 2 | –12 | 0 | –1 | –16 | 0 | –11 | 0 | 1 | 0 | 1 | 2 | 0 |
Arkansas | 0 | 3 | 3 | 3 | –2 | 1 | 3 | –3 | –10 | 0 | 3 | 3 | 2 | –1 | –1 |
California | 1 | 2 | 1 | –2 | 2 | –2 | –23 | –10 | –8 | –4 | –5 | –1 | –1 | –1 | 0 |
Colorado | –2 | –3 | –2 | –1 | –2 | –1 | 0 | –1 | –7 | 1 | –4 | –1 | 1 | –1 | –1 |
Connecticut | 0 | 1 | 0 | 1 | 14 | 16 | –2 | 1 | –12 | –6 | –4 | 3 | 3 | 0 | 0 |
Delaware | 2 | 7 | –2 | –6 | 7 | 3 | 1 | –2 | –7 | 12 | 6 | 1 | 0 | –2 | |
Florida | 2 | 1 | 1 | 6 | 0 | –2 | 1 | –4 | –5 | –5 | 0 | 0 | 5 | 1 | |
Georgia | 2 | 4 | 1 | 1 | 1 | 0 | 0 | 1 | 7 | 0 | 1 | 1 | 0 | –1 | |
Idaho | 1 | 3 | 5 | –5 | 0 | 0 | 3 | 3 | –6 | 6 | 2 | 2 | –1 | 6 | 0 |
Illinois | –3 | –1 | 0 | –1 | 9 | –3 | –3 | –3 | –12 | –6 | –4 | 0 | 0 | –1 | –1 |
Indiana | 1 | 2 | 2 | 4 | 1 | 15 | 2 | –2 | 0 | 16 | 6 | 1 | 0 | 1 | –2 |
Iowa | 0 | 2 | 2 | 3 | 3 | 14 | –19 | –1 | –12 | 3 | 3 | –1 | –1 | 1 | |
Kansas | 0 | 1 | 2 | 2 | 14 | 2 | 5 | –1 | –6 | –4 | –5 | –2 | 1 | –1 | –1 |
Kentucky | –1 | 0 | –1 | –1 | 3 | 2 | 4 | 0 | –10 | –2 | 2 | 4 | 1 | 1 | 1 |
Louisiana | 2 | –2 | –3 | –1 | –1 | –2 | –19 | –4 | –9 | –3 | –4 | 8 | 9 | –4 | –3 |
Maine | 1 | 3 | 1 | 1 | 11 | 15 | 4 | –2 | –2 | 14 | 2 | 5 | 2 | 0 | –2 |
Maryland | 6 | 1 | –2 | 2 | 3 | –3 | 0 | –1 | 11 | 12 | 1 | 5 | 1 | –1 | |
Massachusetts | 5 | 6 | 2 | 0 | 5 | 15 | 3 | 15 | 6 | –3 | –3 | 0 | 2 | 1 | –1 |
Michigan | 0 | 2 | 3 | 6 | –12 | 1 | 1 | 0 | –9 | 2 | 1 | 6 | 2 | 0 | 1 |
Minnesota | 1 | 1 | 1 | –1 | 6 | 0 | 1 | –11 | –11 | 0 | 0 | 0 | –2 | –2 | –1 |
Mississippi | 0 | 1 | 2 | 6 | –1 | 0 | 3 | 4 | –12 | 10 | 3 | 0 | 0 | –4 | |
Missouri | 0 | 1 | –1 | 0 | –3 | 2 | 4 | 2 | –3 | 7 | 8 | 9 | –5 | –1 | 0 |
Montana | 1 | 3 | 6 | 3 | –7 | 18 | 7 | 2 | 5 | –5 | –2 | 5 | –1 | 5 | 1 |
Nebraska | 0 | 2 | 1 | –2 | –2 | –3 | –19 | 3 | –10 | –5 | –1 | –8 | 4 | 0 | –1 |
Nevada | 2 | 1 | 1 | 3 | 8 | 0 | –18 | 4 | 6 | 13 | 11 | –2 | 16 | 1 | 0 |
New Hampshire | 1 | 2 | 3 | 5 | 18 | 16 | 4 | –1 | –8 | –2 | –3 | –1 | 12 | 0 | –1 |
New Jersey | 0 | 1 | 1 | 2 | 10 | 4 | –1 | 0 | 0 | –3 | –2 | 3 | 7 | 0 | 0 |
New Mexico | 0 | 1 | 0 | –13 | –4 | –1 | 0 | –3 | –6 | 2 | –2 | –4 | 1 | –1 | –1 |
New York | 0 | 0 | 0 | 1 | 10 | 0 | –1 | –1 | –10 | 0 | 0 | –1 | –1 | –1 | |
North Carolina | 0 | 2 | 3 | 4 | 5 | 2 | 3 | –1 | –1 | 1 | 0 | 1 | 0 | –1 | |
North Dakota | –2 | –2 | 2 | 7 | 6 | 0 | –20 | 2 | 5 | 1 | 1 | –6 | 14 | 0 | 1 |
Ohio | 0 | 1 | 2 | 3 | –3 | 0 | 3 | –1 | –3 | –1 | 0 | 4 | 1 | 0 | 0 |
Oklahoma | –1 | –5 | –3 | –9 | –1 | 0 | 0 | 1 | 6 | –3 | –8 | –4 | 2 | –3 | –2 |
Oregon | 1 | 4 | 5 | 4 | 7 | 0 | –1 | –1 | 0 | 5 | 0 | 1 | 0 | 1 | 1 |
Pennsylvania | 0 | 1 | 1 | 3 | 8 | 2 | 0 | 0 | 2 | –3 | –1 | 2 | 2 | –1 | –1 |
Rhode Island | 2 | 4 | 2 | 0 | –2 | 15 | 1 | 14 | –9 | –5 | –3 | 2 | 15 | 10 | –1 |
South Carolina | 2 | 3 | 3 | 4 | 14 | –1 | 1 | –4 | –8 | –5 | –1 | 4 | 0 | 2 | 1 |
South Dakota | 0 | 2 | 3 | 3 | –9 | –1 | 5 | –4 | –7 | 5 | 4 | 9 | 1 | 0 | 0 |
Tennessee | 0 | 1 | 1 | 3 | 3 | 4 | 3 | –3 | –8 | –3 | –1 | 3 | 1 | 0 | 0 |
Texas | –1 | –3 | –3 | –10 | –2 | –3 | –8 | –2 | –4 | –3 | –6 | –2 | 1 | –3 | –2 |
Utah | –2 | –2 | –1 | 1 | –8 | –4 | –11 | 8 | –1 | –1 | 2 | 2 | 0 | 0 | |
Vermont | 0 | 2 | 3 | 6 | 2 | 13 | 4 | –4 | –11 | –1 | 0 | 1 | 6 | 0 | 0 |
Virginia | 7 | 3 | –4 | 7 | 2 | 0 | 1 | 1 | –9 | –6 | 1 | 3 | 0 | –1 | |
Washington | 2 | 4 | 4 | 4 | 2 | 0 | 2 | 2 | 2 | 0 | 1 | 3 | 1 | 0 | 0 |
West Virginia | –1 | 0 | 1 | 2 | 10 | –1 | 2 | –2 | –7 | 1 | 4 | 3 | 1 | –2 | –1 |
Wisconsin | –2 | –2 | –1 | 3 | –3 | 0 | 1 | 0 | 1 | –2 | –2 | 5 | 4 | –4 | –2 |
Wyoming | 1 | –3 | –3 | –15 | –3 | –3 | –14 | 2 | –2 | 3 | 2 | –12 | 7 | –4 | –3 |
Lead/Lag | 1980:7–1982:11 | 1982:11–1991:3 | 1991:3–2001–11 | 2001:11–2009:7 |
---|---|---|---|---|
Alabama | 1 | 1.5 | –1.5 | 0 |
Arizona | 1.5 | –0.5 | 0 | 1 |
Arkansas | 1.5 | 2 | 1.5 | –1 |
California | 1.5 | –0.5 | –6.5 | –1 |
Colorado | –2.5 | –1.5 | –1 | –1 |
Connecticut | 0.5 | 7.5 | –3 | 0 |
Delaware | 4.5 | 0.5 | 1 | –1 |
Florida | 2 | 1 | –3 | 1 |
Georgia | 3 | 1 | 1 | –0.5 |
Idaho | 2 | 0 | 2.5 | 0 |
Illinois | –2 | –0.5 | –3.5 | –1 |
Indiana | 1.5 | 3 | 1.5 | 0 |
Iowa | 1 | 3 | –1 | 0 |
Kansas | 0.5 | 2 | –3 | –1 |
Kentucky | –0.5 | 0.5 | 1 | 1 |
Louisiana | 0 | –1.5 | –4 | –3 |
Maine | 2 | 6 | 3 | 0 |
Maryland | 6 | 1.5 | 0.5 | 1 |
Massachusetts | 5.5 | 3.5 | 1.5 | 1 |
Michigan | 1 | 2 | 1 | 1 |
Minnesota | 1 | 0.5 | 0 | –2 |
Mississippi | 0.5 | 1 | 3 | –2 |
Missouri | 0.5 | –0.5 | 5.5 | –1 |
Montana | 2 | 4.5 | 3.5 | 1 |
Nebraska | 1 | –2 | –6.5 | 0 |
Nevada | 1.5 | 2 | 5 | 1 |
New Hampshire | 1.5 | 10.5 | –1.5 | 0 |
New Jersey | 0.5 | 3 | –0.5 | 0 |
New Mexico | 0.5 | –2.5 | –2.5 | –1 |
New York | 0 | 0.5 | –1 | –1 |
North Carolina | 1 | 3.5 | 0.5 | –0.5 |
North Dakota | –2 | 4 | 1 | 1 |
Ohio | 0.5 | 1 | –0.5 | 0 |
Oklahoma | –3 | –2 | –1.5 | –2 |
Oregon | 2.5 | 4.5 | 0 | 1 |
Pennsylvania | 0.5 | 2.5 | 0 | –1 |
Rhode Island | 3 | 1 | –1 | 10 |
South Carolina | 2.5 | 3.5 | –2.5 | 1 |
South Dakota | 1 | 1 | 4.5 | 0 |
Tennessee | 0.5 | 3 | –2 | 0 |
Texas | –2 | –3 | –3.5 | –2 |
Utah | –2 | –2.5 | 0.5 | 0 |
Vermont | 1 | 4.5 | –0.5 | 0 |
Virginia | 7 | 2.5 | 0.5 | 0 |
Washington | 3 | 3 | 2 | 0 |
West Virginia | –0.5 | 1.5 | 1.5 | –1 |
Wisconsin | –2 | –0.5 | 0.5 | –2 |
Wyoming | –1 | –3 | 0 | –3 |
Variable | Definition | Data source |
---|---|---|
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 |
- 1
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.
- 2
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.
- 3
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.
- 4
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.
- 5
A detailed table with median lead/lag values for all the States and all sub-periods is provided in the Appendix (Table A2).
- 6
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.
- 7
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).
- 8
See Imbs (2004) for details on the sign of these relationships.
- 9
Table A3 in the Appendix provides a detailed description of the variables and data sources.
- 10
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.
- 11
Here we adopt the original coefficients estimated by Imbs (2003) so that inter-state trade between i and j is:
- 12
Both GSP and DY have been detrended using the Hodrick-Prescott (Hodrick and Prescott 1997) filter.
- 13
Estimates are obtained using the reg3 command in Stata 12.
- 14
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.
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