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Business cycle dynamics across the US states

Stefano Magrini EMAIL logo , Margherita Gerolimetto and Hasan Engin Duran

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.


Corresponding author: Stefano Magrini, Department of Economics, Università Ca’ Foscari Venezia, Cannaregio 873 Venezia 30121, Italy, e-mail:

6 Appendix

Table A1

Leads and lags with respect to US turning points.

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)
Alabama0225111–2–11–4–14–200
Arizona122–120–1–160–11010120
Arkansas0333–213–3–100332–1–1
California121–22–2–23–10–8–4–5–1–1–10
Colorado–2–3–2–1–2–10–1–71–4–11–1–1
Connecticut01011416–21–12–6–43300
Delaware27–2–6731–2–712610–2
Florida21160–21–4–5–50051
Georgia2411100170110–1
Idaho135–50033–6622–160
Illinois–3–10–19–3–3–3–12–6–400–1–1
Indiana12241152–20166101–2
Iowa0223314–19–1–1233–1–11
Kansas01221425–1–6–4–5–21–1–1
Kentucky–10–1–13240–10–224111
Louisiana2–2–3–1–1–2–19–4–9–3–489–4–3
Maine131111154–2–2142520–2
Maryland61–223–30–11112151–1
Massachusetts56205153156–3–3021–1
Michigan0236–12110–9216201
Minnesota111–1601–11–11000–2–2–1
Mississippi0126–1034–1210300–4
Missouri01–10–3242–3789–5–10
Montana1363–718725–5–25–151
Nebraska021–2–2–3–193–10–5–1–840–1
Nevada211380–18461311–21610
New Hampshire123518164–1–8–2–3–1120–1
New Jersey0112104–100–3–23700
New Mexico010–13–4–10–3–62–2–41–1–1
New York0001100–1–1–1000–1–1–1
North Carolina0234523–1–11010–1
North Dakota–2–22760–202511–61401
Ohio0123–303–1–3–104100
Oklahoma–1–5–3–9–10016–3–8–42–3–2
Oregon145470–1–10501011
Pennsylvania011382002–3–122–1–1
Rhode Island2420–215114–9–5–321510–1
South Carolina233414–11–4–8–5–14021
South Dakota0233–9–15–4–7549100
Tennessee0113343–3–8–3–13100
Texas–1–3–3–10–2–3–8–2–4–3–6–21–3–2
Utah–2–2–11–8–4–118–1–12200
Vermont02362134–4–11–101600
Virginia73–472011–9–6130–1
Washington244420222013100
West Virginia–101210–12–2–71431–2–1
Wisconsin–2–2–13–30101–2–254–4–2
Wyoming1–3–3–15–3–3–142–232–127–4–3
Table A2

Median leads and lags with respect to US cycle in sub-periods.

Lead/Lag1980:7–1982:111982:11–1991:31991:3–2001–112001:11–2009:7
Alabama11.5–1.50
Arizona1.5–0.501
Arkansas1.521.5–1
California1.5–0.5–6.5–1
Colorado–2.5–1.5–1–1
Connecticut0.57.5–30
Delaware4.50.51–1
Florida21–31
Georgia311–0.5
Idaho202.50
Illinois–2–0.5–3.5–1
Indiana1.531.50
Iowa13–10
Kansas0.52–3–1
Kentucky–0.50.511
Louisiana0–1.5–4–3
Maine2630
Maryland61.50.51
Massachusetts5.53.51.51
Michigan1211
Minnesota10.50–2
Mississippi0.513–2
Missouri0.5–0.55.5–1
Montana24.53.51
Nebraska1–2–6.50
Nevada1.5251
New Hampshire1.510.5–1.50
New Jersey0.53–0.50
New Mexico0.5–2.5–2.5–1
New York00.5–1–1
North Carolina13.50.5–0.5
North Dakota–2411
Ohio0.51–0.50
Oklahoma–3–2–1.5–2
Oregon2.54.501
Pennsylvania0.52.50–1
Rhode Island31–110
South Carolina2.53.5–2.51
South Dakota114.50
Tennessee0.53–20
Texas–2–3–3.5–2
Utah–2–2.50.50
Vermont14.5–0.50
Virginia72.50.50
Washington3320
West Virginia–0.51.51.5–1
Wisconsin–2–0.50.5–2
Wyoming–1–30–3
Table A3

Variables and data sources.

VariableDefinitionData source
LLAverage (along national turning points) of the number of months by which a state’s business cycle anticipates or follows the national business cycle
ρBilateral correlation among states’ cycles. Cycles have been identified using the Baxter-King band-pass filter
STime 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
HTTime 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
DLDummy 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
TBilateral trade intensitySee text
FCross-state financial integrationSee text
AmenityPairwise differences across states in the natural amenity indexEconomic Research Service; US Dept. of Agriculture
AgricultureTime average of yearly pairwise differences across states in the share of agriculture employment over total employmentUS Bureau of Economic Analysis
PublicTime average of yearly pairwise differences across states in the share of public sector employment over total employmentUS Bureau of Economic Analysis
MiningTime average of yearly pairwise differences across states in the share of mining employment over total employmentUS Bureau of Economic Analysis
OilPairwise differences across states in 2010 oil production (in million barrels)US Energy Information Administration
DistanceLogarithm of Euclidean distance across states’ capitals
Pop differenceTime average of yearly pairwise differences across states in populationUS Bureau of Economic Analysis
ln GSPpc differenceTime average of yearly pairwise differences across states in log Gross State Product (GSP) per capitaUS Bureau of Economic Analysis
ln GSP gapTime average of yearly pairwise differences (in absolute terms) across states in log GSPUS Bureau of Economic Analysis
ln GSP productTime average of yearly pairwise products across states in log GSPUS Bureau of Economic Analysis
  1. 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. 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. 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. 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. 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. 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. 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. 8

    See Imbs (2004) for details on the sign of these relationships.

  9. 9

    Table A3 in the Appendix provides a detailed description of the variables and data sources.

  10. 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. 11

    Here we adopt the original coefficients estimated by Imbs (2003) so that inter-state trade between i and j is:

  12. 12

    Both GSP and DY have been detrended using the Hodrick-Prescott (Hodrick and Prescott 1997) filter.

  13. 13

    Estimates are obtained using the reg3 command in Stata 12.

  14. 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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Published Online: 2013-04-09
Published in Print: 2013-01-01

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