1. State Level Tests of Okun's Coefficient -- Implications for the current U.S. Recession

2. Okun’s Coefficient Key (along with Philip’s curve) macro variable Embedded into all sorts of practical models of the economy No theoretical basis – indeed anti theoretical It violates the principal of declining marginal utility A 3% decline in output should result in a 6% decline in employment, not the opposite

3. U.S. estimates Originally, estimated at 3:1 Current estimates put it closer to 2:1 Debate as to whether this reflects a change in the economy, or is just a better measurement

4. International Estimates Most European countries have a lower estimate Generally said to be due to labor rigidity and unionization Japan (had) a much higher estimate No good estimates for Thailand, due to a lack of reliable unemployment data (Bhanupong Nidhiprabha)

5. Theoretical Issues Asymmetries – is Okun’s coefficient different during upturns and downturns? Does Okun’s coefficient change over time? Can it be related to the Phillip’s curve What supply and demand (sectoral and labor) factors influence it?

6. Measuring Okun’s coefficient Yearly vs. Quarterly (with lags) data How to detrend How is the data gathered, comparisons across time/polities Co-integration, omitted variables, linearity

7. Differenced equation ∆yt = ß0 - ß1 ∆ut + εt Where ∆yt was the change in output, ß0 is the intercept, ß1 ∆ut estimates the change in unemployment, and εt is an error term.

8. Gap equations yt - yt* = ß0 - ß1(ut - ut*) + εt where the star denotes the long run equilibrium value of the variable.

9. Expanded out estimates (Prachowney’s formulation) yt - yt* = ά(c –c*) + βγ(l – l*) – βγ(u – u*) + βδ(h – h*) In the above, c is the utilization rate of capital, l is total employment, u is the rate of unemployment, and h is hours worked; in all cases a * indicates the trend variable

10. Measurement difficulties Gap equation estimates rely very much upon the construction of long run trend variables Data needs to be de-trended, both for seasonality, and for the long and short cycles Most papers now use a variety of de-trending methods HP, BK, Arima, BN, other types of Bandpass filters

11. Main problem with Okun’s coefficient papers today Okun’s coefficient has become the plaything of econometricians….

12. State Level Tests of Okun’s coefficient Will Okun’s coefficient vary between polities that share a common monetary policy? What factors within the states will cause the coefficient to vary? Can new insights be gained with a new, large and robust dataset?

13. Data Unemployment was U3 data from the BLE, 1950s for all states, monthly/quarterly/yearly Output data was much more difficult to find BEA maintains two data sets, the xxxx set, from 1977 (1970 for 26 states) to 1998 Approximates GNP, but in many ways is closer to an income measure The xxxx set, from 1998 to 2007 (updating) which is comparable to GDP measures

14. Data problems Unemployment data had no problems The output data from 1970 to 1998 had two major revisions in the method of data gathering (aside -- how does BEA gather data?) Data itself gave some strange results – it vastly overstated measured/taxable income

15. Results (I) 1977-1998 Differencing gave poor results, unless one added a dummy variable for 1987 Then good results, 31 states gave significant results, somewhat lower then national estimates This contradicted Blackley (1990), who got higher results Smaller states gave less significant results, with much more variance. 24 of 25 largest states had significant results, between.9 and 2.4

16. Results (II) 1977-1998 (BK method) Gap estimation gave betters results, (42 states), somewhat lower estimates Robust to the estimation method used. Estimates (generally) ranged between 1.4 and 2, again lower than national estimates

17. Results (III) 1998 – 2007 Differencing gave O.K. results (17 of 25 largest states) Gap estimates gave poor results (12 of 25 largest states) Primarily due to the short data-set, 2 more years of data should fix this

18. Implications State governments have less ability to use Okun’s coefficient to reduce unemployment This is especially true for small states The smaller the state, the greater the impact of the national business cycle There are still regional differences

19. Extensions – testing for asymmetries (1) Testing for asymmetries and lags (1977—1997) All tests for lags came out negative With 50 states, it was possible to test by year Okun’s coefficient was almost always significant during downturns Much less important during upturns Significant evidence that the coefficient is asymmetric Aside – risk aversion, threshold effects, or just clearer data

20. Extensions – testing for asymmetries (2) When the data was split into upturns and downturns….. Okun’s coefficient was consistently larger in upturns, and smaller in downturns Okun’s coefficient was always significant in downturns, not so in upturns Downturns did show lagged effects for one year

21. Extension – tests of labor mobility Moran I test – test of long range spatial relationships Ran for 8 regions, and for 48 continental states Regions showed some effect, state level tests did not Similar to results for Spain and Greece

22. Other Variables Used a host of demand and supply variables Taxes, female participation in the labor force, Age structure, manufacturing base, etc. Many things significant, but few important Noteworthy, unemployment insurance was not important

23. Size of the state was the most important variable Small states rarely had good results, large states usually did

24. Put another way California does not care about what Nevada does, but Nevada cares very much about what California does.

25. Extended form of Okun’s coefficient Used the Prachowney method, theoretically more rigorous But much harder to measure As a practical matter, used a reduced form of it. Did not get very good results

26. Implications for today I Okun’s coefficient has been decreasing coming out of recessions Labor markets are more sensitive to downturns then upturns Individual state economies do matter – some states much harder hit then others The ability of an individual state to “grow out” of a recession is limited Micro policies seem more effective

27. Implications for today II The crash in the housing market could be impacting labor mobility in a significant way Greater disparities between states then in past recessions Role of manufacturing and unions has declined, role of govt and unions has increased Can you achieve growth through investments in the least productive sectors of the economy?