What Lies beneath? A Sub-National Look at Okun’s Law in the United States

Abstract

We find that Okun’s Law holds quite well for most U.S. states but the Okun coefficient—the responsiveness of unemployment to output—varies substantially across states. We are able to explain a significant part of this cross-state heterogeneity on the basis of the state’s industrial structure. Our results have implications for the design of state and federal policies and may also be able to explain why Okun’s Law at the national level has remained quite stable over time despite an enormous shift in the structure of the U.S. economy from manufacturing to services.

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Notes

  1. 1.

    As a robustness check we also use Hamilton’s filtering technique. In table 1, we include the correlation between the coefficients calculated using Hodrick Prescott and using Hamilton. The results are robust to this check.

  2. 2.

    We test the stability of the coefficients by splitting the sample in two and testing whether the coefficients for both periods were the same. Results of this test indicate that for 41 of the states the Okun’s coefficient is stable. However – Colorado, Georgia, Idaho, Missouri, Oklahoma, South Dakota, Texas and West Virginia have a significant difference in their coefficients.

  3. 3.

    In a previous version we used deflator with different aggregation levels (metropolitan area) but the results don’t change significantly.

  4. 4.

    This variables are easily downloadable using the multiscreen function in the BLS website and using the codes in http://www.bls.gov/help/hlpforma.htm#OE

References

  1. Ball L, Leigh D, Loungani P (2017). Okun’s Law: Fit at 50? Journal of Money Credit and Banking

  2. Binet M, Facchini F (2013) Okun’s law in the French regions: a cross-regional comparison. Economics Bulletin, Economics

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  3. Blanchard O, Katz L (1992) Regional Evolutions. Brook Pap Econ Act 1992:1

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  4. Crivelli E, Furceri D, Toujas-Bernate J (2012) Can policies affect employment intensity of growth? A cross Country Analysis, IMF Working Paper

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  5. Estevao M, Tsounta E (2011) Has the Great Recession Raised U.S. Structural Unemployment? IMF working paper

  6. Okun A (1962) Potential GNP, its measurement and significance. Yale University, Cowles Foundation

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Correspondence to Prakash Loungani.

Additional information

The views expressed in this paper are those of the authors and do not necessarily represent those of the IMF or IMF policy.

Data Appendix

Data Appendix

The goal of this appendix is to clarify the exact variables and modifications that we used in the paper. It is going to be organized by type of variable: output, labor market, and determinants. It will also include the source, period of availability, a link the raw data, and finally descriptions of any modification performed to produce the variables used to generate the results.

Output measures

Bureau of Economic Analysis

  • Personal income at the state level (1929–2015): http://www.bea.gov/newsreleases/regional/spi/sqpi_newsrelease.htm This variable is in nominal terms so as a first step we used CPI at the national level from the IFSFootnote 3 to deflate it. After that, for the gaps specification we estimated the potential output using the Hodrick Prescott filter on the logarithm of the real series with a smoothing parameter of 100 and use that as \( {y}_t^{\ast } \). For the changes specification Δyt is equal to the growth rate of the real variable.

  • Value added at the industry level (1947–2015): http://www.bea.gov/industry/gdpbyind_data.htm The ωi where calculated using the changes specification. So in that case, after deflating the variable using the national CPI we simply calculated the growth rate ΔVAI, t

Labor market variables

Bureau of Labor StatisticsFootnote 4

  • Labor market variables-State level (1976–2015): http://www.bls.gov/lau/ this is the source for all the labor market related data used in the estimation of the Okun coefficients as the state level. For the gaps specification we used the HP filter with a smoothing parameter of 100 to obtain the potential value for all variables: \( {u}_t^{\ast },{l}_t^{\ast },{e}_t^{\ast } \). In the case of employment and labor force we filtered the logarithm of the series and for unemployment rate we filtered the series directly. For the changes specification, in the case of labor force and employment we calculated the growth rate to obtain Δet, Δlt and for the unemployment rate we used the difference between the current and the previous value to get Δut.

  • Employment by industry (1939–2015): http://www.bls.gov/ces/ this variable is used in the estimation of the employment elasticity at the industry level before including it in the regression we calculate it’s growth rate to get ΔEmplI, t

  • Employment by industry at the State level (1990–2015): http://www.bls.gov/sae/ In this case we used the value for 1990 to define the weights of each industry in the construction of the industrial structure variable.\( \frac{Emp{l}_{S,I}}{Total\ Emp{l}_S} \)

Determinants

  • Average unemployment rate: Average over 1976–2015

  • Lag Labor Force: logarithm of the average labor force for the period 1976–2015.

  • Entrepreneurial Index: The entrepreneurship index is the percent of individuals (ages 20–64) who do not own a business in the first survey month that start a business in the following month with 15 or more hours worked. Kauffman foundation. The data corresponds to 1996, the first year with available data.

  • Skill Mismatch Index: comes from appendix B in Estevao and Tsounta (2011). A Higher number indicates a higher mismatch.

  • Oil: Following the classification made by Blanchard and Katz (1992), we define oil states as those states in which earnings from oil, gas, and other minerals accounted for more than 2% of earnings in 1980.

  • Oil 2: equal to one for the five states with more average barrel production between 1981 and 2015. Source: Energy information administration. Louisiana, Oklahoma Texas, Alaska and California.

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Gonzalez Prieto, N., Loungani, P. & Mishra, S. What Lies beneath? A Sub-National Look at Okun’s Law in the United States. Open Econ Rev 29, 835–852 (2018). https://doi.org/10.1007/s11079-018-9491-2

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Keywords

  • Okun’s Law
  • Unemployment
  • US states
  • Cyclicality