Rethinking Unemployment: The European Challenge

Rethinking Unemployment: The European Challenge

The Problem of Unemployment in Europe and America A Lecture at the American University of Paris September 23, 2004 by James K. Galbraith The University of Texas Inequality Project http://utip.gov.utexas.edu Based on an article co-authored with Enrique Garcilazo published in Banca Nazionale del Lavoro Quarterly Review No. 228, March 2004 The Standard View Employment is determined in a labor market. Labor markets are national. Flexibility reduces unemployment. The United States has more jobs than Europe, but only at the expense of more inequality. Is this good or bad? A political question Data! Data! Data! I cant make bricks without clay. Sherlock Holmes The Adventure of the Copper Beeches

The U.T. Inequality Project Measures global pay inequality. Estimates global income inequalities. Shows now inequality has risen under globalization. Is changing our understanding of the relationship of inequality to unemployment. General Technique We use Theils T statistic, measured across sectors within each country, region or province, to show the evolution of economic inequality. The components of the statistic provide a measure of the contribution of each province-sector cell to inequality. This measure takes account both of the relative income of the cell and its size in relation to the whole employed population. The method permits us to map changes in the flow of incomes across regions and across sectors very accurately through time, using national data sources & without relying on sample surveys. A brief review of the Theil Statistic: The Theil Element m m

T p j R j log R j p j R j T j j 1 j 1 1 T j ri log ri n j i g j pj nj n Rj j Y n ~ employment; mu ~ average income; j ~ subscript denoting group Advantages Our method permits us to assess the value of inequality at each geographic level:

Within provinces Within countries Across large regions such as Europe. The U.S. Case In the American case, we have measured inequalities of pay (weekly earnings) in the manufacturing sector on a monthly basis going back to January, 1947, for sectors that are continuously measured since that time. The result gives us a time series of pay inequalities in a key part of the American industrial economy. Wage Inequality and Some Historical Events Recession Vietnam War Recession Recession Korean War TRUMAN Recession Recession EISENHOWER

JFK LBJ NIXON FORD CARTER REAGAN BUSH CLINTON Wage Inequality and Unemployment Open Unemployment Rate A strong positive correlation between the unemployment rate and wage inequality in the US is exhibited here. The U.S and Europe First, lets compare U.S. inequality to that in each European country. Then, lets compare U.S. inequality to that in Europe-as-a-whole Finally, we ask, what is the relationship between unemployment and inequality in Europe? EHII -- Estimated Household Income Inequality for OECD Countries GBR

LUX DEU NLD FRA AUT BEL ITA USA ESP GRC 1999 45 1963 1999 1998 1996

1963 1999 Gini coefficient 40 1999 1999 1997 1999 1999 35 30 1998 1963 1963 1998 1977 1999

1998 1963 1968 1994 1999 1998 1999 1963 1989 1967 1996 1992 1963 1963 1963 1963 1963 1963 1963 1994

1963 1963 1963 1963 1963 1963 25 SWE Low DNK FIN NOR AUS ISL NZL CAN

JPN IRL PRT High Now, is pay inequality in Europe really lower than in the U.S.? It depends on how you count Inequality in Europe Manufacturing Earnings T h e il V a lu e 0.05 0.04 0.03 0.02 0.01 0 1978 1980 1982 1984 1986 1988 1990 1992 1979 1981 1983 1985 1987 1989 1991 Within Country Between Countries The value for the U.S. on this scale is about 0.29, or roughly the height of the blue bar. Overall European manufacturing

pay inequality including differences between countries is higher than in the US. European Regional Panel Data Set Pay across Sectors by European Region From Eurostats REGIO Annual 1984-2000, up to 159 Regions Enables us to compute measures of inequality within and between regions. Permits construction of a panel with which we can isolate regional, national and continental effects Table 1. Population differentials for nations and regions in Europe. Variable Obs Mean Std. Dev. Min Max -------------------------------------------------------------------------Nations: Population 169 28128 25164 355.9 80759.6

(000s) Regions: Population 1853 2306 2556 22.5 17663.2 Contribution of European Provinces in Inequality Across the European continent, late 1990s. Impoverished Far Below Average Below Average Low low Neutral Low Neutral Neutral High Neutral Above Average Prosperous Wealthy European Inequality Across Sectors, Within Provinces, 1996

Within Region 1996 0.003 - 0.015 0.015 - 0.026 0.026 - 0.027 0.027 - 0.036 0.036 - 0.045 0.045 - 0.066 0.066 - 0.081 0.081 - 0.105 0.105 - 0.147 0.147 - 0.222 N W 1000 0 1000 2000 Miles E S A Simple Theory of European Unemployment Demand Factors: GDP Growth and Investment Wealth and Demand for Services

Supply Factors: Inequalities of Pay Transition to Work for Youth Hypotheses Growth reduces unemployment. (-) Higher incomes mean fewer unemployed. (-) Inequality increases unemployment (+) More younger workers means more unemployed. (+) Regression analysis of European unemployment Table 2. Coefficient Estimates: Linear Model - (1984-2000). Theil PopUn24 RelWage G-GDP R^2 N Total Beta Pvalue 4.969 0.039 57.019 0.000

-7.085 0.000 -4.485 0.025 0.6140 1465 Male Beta Pvalue 3.221 0.126 50.581 0.000 -4.951 0.000 -5.670 0.001 0.5869 1465 Female Beta Pvalue 6.805 0.039 76.462 0.000 -9.907 0.000

-2.347 0.393 0.6535 1465 < 25 Yrs Beta Pvalue 11.967 0.032 112.319 0.000 -6.371 0.004 -6.299 0.175 0.6172 1465 Table 3. Analysis of Variance Explained Under Different Specifications. Theil PopUn24 RelWage G-GDP Regional Country Time R^2

Beta Pvalue Beta Pvalue Beta Pvalue Beta Pvalue 4.027 50.205 -2.816 -11.830 0.180 0.000 0.000 0.000 4.808 48.640 -6.809 -8.561

0.039 0.000 0.000 0.000 5.393 54.227 -2.210 -9.494 0.087 0.000 0.002 0.001 4.969 57.019 -7.085 -4.485 0.039 0.000 0.000 0.025 X X X

0.1644 0.5702 X X 0.2057 X X X 0.6140 Beta 4.081 38.037 -7.434 -4.687 > 25 Yrs Pvalue 0.042 0.000 0.000 0.005 0.5831 1465 Country Fixed Effects Show the Differences Between

Countries Not Explained by the Explanatory Variables. Centralized wage bargains? All Workers -11 - -5 -4 -3 - 3 4-5 Emigration? Table A7. Ratio of Austrian to German Average Wages, by Major Sectors Mining and quarrying Manufacturing Electricity, gas and water supply Construction Transport, storage and communication Financial intermediation Real estate, renting and business activities Public administration and defence; compulsory social security 1995 1996 1997 1998

1999 2000 1.04 0.88 1.22 1.04 1.03 1.06 0.99 1.16 1.01 0.88 1.19 1.03 1.00 1.07 0.96 1.15 1.01 0.88 1.21 1.06 1.03 1.08 0.94

1.13 1.06 0.89 1.26 1.11 1.07 1.09 0.90 1.10 1.09 0.92 1.22 1.27 1.18 1.23 1.09 1.12 0.98 0.86 1.14 1.20 1.14 1.18 0.95 1.12 Time Fixed Effects Show the Movements of Unemployment

Across All Regions, After Taking Account of the Regressors Unemployment European Effects 8 Growth and Stability pact 6 4 Maastricht Treaty 2 19 92 19 93 19 94 19 95 19 96 19 97 19 98

19 99 20 00 91 19 90 19 89 19 88 19 87 19 86 85 19

19 -2 19 84 0 -4 Single European Act Birth of the Euro -6 -8 Years Total Male Female <25 Yrs >25 Yrs Conclusions Labor markets are not national. Macroeconomic conditions matter. Youth is a problem. Equality of pay helps. Flexibility does not. Small countries have an advantage. EU policies started off very poorly. But there is hope for the future. For more information: The University of Texas Inequality Project http://utip.gov.utexas.edu Type Inequality into Google to find us on the Web

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