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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?