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

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

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

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)

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?

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

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.

Gap equations

• yt - yt* = ß0 - ß1(ut - ut*) + εt

• where the star denotes the long run equilibrium value of the variable.

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

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

Main problem with Okun’s coefficient papers today

• Okun’s coefficient has become the plaything of econometricians….

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?

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

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

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

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

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

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

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

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

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

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

Size of the state was the most important variable

• Small states rarely had good results, large states usually did

Put another way

• California does not care about what Nevada does, but Nevada cares very much about what California does.

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

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

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?