INTERNATIONAL POLICY CENTER Gerald R. Ford School of Public Policy University of Michigan IPC Working Paper Series Number 21 Job Security Does Affect Economic Efficiency: Theory, A new Statistic, and Evidence from Chile Amil Petrin Jagadeesh Sivadasan December 2006 !"#%$ & (' )"*+ ,.- %/102 3465 798/&:;9&<0=8>"? 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Republic Uruguay Sweden Paraguay Austria Belgium Australia Finland Hungary France United Kingdom Switzerland Poland Germany Ireland Norway Netherlands Japan Canada New Zealand 0 Guatemala 2 United States # 10 Figure 2 The Change in Firing Costs in Chile Expected discounted cost of dismissing a worker, in multiples of monthly wages 3.5 3 2 # 2.5 1.5 1 0.5 0 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 Source: Pages and Montenegro (1999). Figure 3 Relating Changes in Gaps to Changes in Welfare Price Demand Marginal Revenue Wage E D A QMONOPOLIST B C QSOCPLANNER Quantity Qmonopolist sets marginal revenue equal to wage, while Qsocplanner sets demand equal to wage. In a competitive setting (no markups) we recover the difference between the demand curve and input marginal cost (wage). In this case, if the absolute value of the gap increases then economic efficiency decreases, as in a move from B to A or B to C. When there are markups we recover the gap between the marginal revenue curve and marginal cost (wage). In this case we can only sign the change in economic efficiency for firms that have positive gaps (i.e. to the left of Qmonopolist ). For example, welfare unambiguously decreases if we move from D to E. Table 1 The Absolute Value of the Gap Between the Marginal Revenue Product and the Input Price, 1979-1996 All Specifications Include Plant-level Fixed Effects Simultaneity-Corrected Production Function Estimates, Standard Errors in Brackets Blue Collar (1) Base Period Gap (1979-1984) (2) Materials (1) (2) (1) Electricity (2) (1) (2) 99.11 99.31 175.48 175.99 0.31 0.31 7.56 7.59 [4.53]** [4.53]** [4.48]** [4.50]** [0.01]** [0.01]** [0.28]** [0.28]** 20.99 20.68 9.51 8.69 -0.04 -0.04 -1.11 -1.15 [4.41]** [4.42]** [5.87] [5.89] [0.01]** [0.01]** [0.33]** [0.34]** 33.02 32.74 15.56 14.83 -0.02 -0.01 -1.57 -1.61 [7.95]** [7.97]** [6.86]* [6.89]* [0.01]* [0.01]* [0.40]** [0.41]** ñ Increase in Gap, 2nd Pd. (85-90) White Collar Increase in Gap, 3rd Pd. (91-96) Industry Output Growth Rate Observations R-squared 2.01 5.16 -0.01 0.29 [1.35] [3.41] [0.01]+ [0.19] 54,230 54,226 54,269 54,265 55,329 55,325 55,329 55,325 0.81 0.81 0.67 0.67 0.69 0.69 0.71 0.71 All variables are in thousands of 1979 pesos. We focus on the absolute value of the gap because increases in the gap are associated with decreases in economic efficiency (see Figure 3). Marginal product estimates are from a gross output Cobb-Douglas production function specification, which is estimated using Levinsohn-Petrin (2002) to address the simultaneous determination of inputs and productivity. The blue-collar input price is the total blue-collar wage bill divided by the number of blue-collar employees. We define the other input prices similarly. We estimate production functions separately for each 3-digit industry. We cluster standard errors at the 4-digit industry-period level. + significant at 10%; * significant at 5%; ** significant at 1%. Table 2 Average Dispersion of the Within-Firm Gap Between the Marginal Revenue Product and the Input Price, 1979-1996 All Specifications Include Plant-level Fixed Effects Simultaneity-Corrected Production Function Estimates, Standard Errors in Brackets Blue Collar Increase in Gap, 2nd Pd. (85-90) Materials (1) Electricity (1) (2) (1) (2) (2) (1) (2) 102.71 102.87 144.67 145.07 0.26 0.26 7.13 7.15 [2.97]** [2.98]** [3.05]** [3.06]** [0.01]** [0.01]** [0.16]** [0.16]** 20.91 20.67 13.79 13.16 -0.04 -0.04 -0.97 -1.00 [3.48]** [3.49]** [3.68]** [3.704]** [0.01]** [0.01]** [0.18]** [0.19]** 43.29 43.08 25.7 25.14 -0.02 -0.02 -1.19 -1.21 [5.02]** [5.04]** [4.787]** [4.798]** [0.004]** [0.004]** [0.23]** [0.23]** ^ Base Period Gap (1979-1984) White Collar Increase in Gap, 3rd Pd. (91-96) Industry Output Growth Rate Observations R-squared 1.52 3.99 -0.01 0.17 [1.09] [2.46] [0.01]+ [0.12] 54,230 54,226 54,269 54,265 55,329 55,325 55,329 55,325 0.76 0.76 0.60 0.60 0.63 0.63 0.58 0.58 All variables are in thousands of 1979 pesos. We measure dispersion by predicting expected plant-year gaps by regressing observed plant-level gaps used in Table 1 on plant fixed effects and two indicator variables that allow changes in firing cost to affect the prediction, one for 19851990, and one for 1991-1996. We then deduct the prediction from the observed plant-level gap, and take the absolute value. We cluster standard errors at the 4-digit industry-period level. + significant at 10%; * significant at 5%; ** significant at 1%. Table 3 The Gap between the Marginal Revenue Product and the Input Price, 1979-1996 All Specifications Only Use Plants with Positive Gaps in Juxtaposed Time-Periods to Allow for Markups All Specifications Include Plant-level Fixed Effects Simultaneity-Corrected Production Function Estimates, Standard Errors in Brackets Blue Collar Base Period Gap (1979-1984) Materials (1) Electricity (1) (2) (1) (2) (2) (1) (2) 113.66 114.72 197.63 198.21 0.44 0.44 8.24 8.27 [5.62]** [5.59]** [6.77]** [6.79]** [0.01]** [0.01]** [0.29]** [0.29]** 30.47 28.47 28.34 27.33 -0.09 -0.08 -1.21 -1.26 [5.27]** [5.21]** [7.65]** [7.67]** [0.01]** [0.01]** [0.34]** [0.35]** 46.92 45.23 33.15 32.26 -0.03 -0.03 -1.70 -1.74 [9.78]** [9.76]** [10.55]** [10.59]** [0.01]** [0.01]** [0.41]** [0.42]** # Increase in Gap, 2nd Pd. (85-90) White Collar Increase in Gap, 3rd Pd. (91-96) Industry Output Growth Rate Observations R-squared 14.32 7.10 -0.01 0.30 [3.87]** [4.45] [0.01] [0.20] 37,923 37,921 38,167 38,165 28,647 28,644 50,519 50,515 0.79 0.79 0.7 0.7 0.71 0.71 0.7 0.7 All variables are in thousands of 1979 pesos. If markups exist, our focus should be on only those firms with positive gaps in juxtaposed timeperiods. Increases in the value of the gap for these firms are associated with decreases in economic efficiency (see Figure 3). Marginal product estimates are from a gross output Cobb-Douglas production function specification, which is estimated using Levinsohn-Petrin (2002) to address the simultaneous determination of inputs and productivity. The blue-collar input price is the total blue-collar wage bill divided by the number of blue-collar employees. We define the other input prices similarly. We estimate production functions separately for each 3-digit industry. We cluster standard errors at the 4-digit industry-period level. + significant at 10%; * significant at 5%; ** significant at 1%. Figure 4 Year-to-Year Differences in the Absolute Gap Blue and White Collar Labor, Materials, and Electricity Levinsohn-Petrin Production Function Estimates 180 160 140 120 100 80 60 40 1980 1981 1982 1983 BLUE 1984 1985 1986 WHITE 1987 1988 1989 1990 MATERIALS 1991 1992 1993 1994 1995 1996 ELECTRICITY The graph plots the coefficient on year dummies in regression of absolute gap between marginal product of an input and its normalized price. The regression includes firm fixed effects and industry output growth rate. Year 1980 is normalized to 100. Figure 5 Average Absolute Value of Within−Firm Gap Blue and White Collar Labor 1000’s of 1979 Chilean Pesos 80 100 120 140 160 180 200 220 Z 95% Confidence Interval for Change in Gap 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 Year Blue Collar Gap (Mean) White Collar Gap (Mean) Blue Collar Gap (95% C.I., l.b.) White Collar Gap (95% C.I., l.b.) Blue Collar Gap (95% C.I., u.b.) White Collar Gap (95% C.I., u.b.) Gaps are those implied by the Levinsohn-Petrin Estimator. The figure plots the coefficients from the regression of the absolute value of the gaps on yearly indicator variables, plant-level fixed effects, and the percentage change in the industry growth rate. The two lines demark the level of the average gap for blue and white collar labor in 1984, so the years for which the line is not within the confidence interval are the years for which the change in the gap is significantly different from the 1984 gap. Figure 6 Average Dispersion of Within−Firm Gaps Blue and White Collar Labor 1000’s of 1979 Chilean Pesos 80 100 120 140 160 180 200 N 95% Confidence Interval for Change in Dispersion 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 Year Blue Collar (Avg. Dispersion) White Collar (Avg. Dispersion) Blue Collar (95% C.I., l.b.) White Collar (95% C.I., l.b.) Blue Collar (95% C.I., u.b.) White Collar (95% C.I., u.b.) We measure dispersion by predicting expected plant-year gaps by regressing observed plant-level gaps used in Table 1 on plant fixed effects and two indicator variables that allow changes in firing cost to affect the prediction, one for 1985-1990, and one for 1991-1996. We then deduct the prediction from the observed plant-level gap, and take the absolute value. The figure plots the coefficients from the regression of this dispersion on yearly indicator variables, plant-level fixed effects, and the percentage change in the industry growth rate. The two lines demark the level of the average dispersion for blue- and white- collar labor in 1984, so the years for which the line is not within the confidence interval are the years for which the change in the gap is significantly different from the 1984 gap. Table 4 Labor Demand Regression Blue Collar Employment, Instrumental Variables Period 1 (1979-84) Constant -0.01 [0.01] -0.12 [0.03]** -0.04 [0.01]** 0.11 [0.02]** -0.02 [0.01]* 0.10 [0.01]** 0.04 [0.01]** 0.12 [0.05]* Blue wage Blue wage_t-1 White wage White wage_t-1 Value added Value added_t-1 Blue Employment_t-1 Blue Collar Labor Period 2 Period 3 (1985-90) (1991-96) All Periods 0.01 [0.01]* -0.04 [0.01]** -0.02 [0.01]** -0.23 [0.02]** -0.10 [0.04]* -0.19 [0.01]** 0.08 [0.01]** 0.02 [0.01] -0.03 [0.01]** -0.01 [0.02] -0.01 [0.01] 0.43 [0.09]** 0.23 [0.02]** 0.02 [0.01] -0.02 [0.01]* 0.07 [0.01]** -0.01 [0.01]+ 0.66 [0.07]** 15,272 18,499 0.06 [0.01]** 0.05 [0.01]** -0.04 [0.01]** 0.08 [0.01]** 0.01 [0.01]** 0.45 [0.05]** 0.01 [0.01] 0.01 [0.01] 0.35 [0.10]** -0.28 [0.09]** 41,900 Period Dummy (1985-1990) Period Dummy (1991-1996) Period Dummy (1985-1990)*Blue Employment_t-1 Period Dummy (1991-1996)*Blue Employment_t-1 Observations 8,129 Dependent variable is the log (Blue Collar Employment). All other variables are also in logs. The regressions are estimated in first differences. The endogenous variables are Blue Wage, White Wage, Value Added and Blue Employment_t-1. Instruments are lagged industry output, lagged industry average Blue Wage, Lagged industry average White Wage, two-period lagged industry output, double lagged industry average blue wage, double lagged industry average white wage, two-period lagged Blue Wage, two-period lagged White Wage, two-period lagged Materials, two-period lagged Capital, two-period lagged Value Added and three-period lagged Blue Collar employment. Standard errors are adjusted for clustering at the 4-digit industry level. + significant at 10%; * significant at 5%; ** significant at 1%. K Table 5 Labor Demand Regression Blue Collar Employment, Fixed Effects Blue Collar Labor Period 1 (1979-84) Period 2 (1985-90) Period 3 (1991-96) All Periods Constant 1.59 [0.09]** 1.55 [0.18]** 2.08 [0.23]** 0.89 [0.04]** Blue wage -0.15 [0.02]** Blue wage_t-1 -0.01 [0.01] 0.09 [0.01]** -0.02 [0.01]** 0.11 [0.01]** 0.03 [0.01]** 0.22 [0.01]** -0.26 [0.02]** 0.06 [0.01]** 0.06 [0.01]** -0.28 [0.02]** 0.06 [0.01]** 0.02 [0.01] -0.23 [0.01]** 0.09 [0.01]** 0.06 [0.01]** -0.01 [0.01]* 0.10 [0.01]** 0.03 [0.01]** 0.36 [0.02]** -0.01 [0.01]* 0.11 [0.01]** 0.01 [0.01]* 0.32 [0.02]** -0.04 [0.01]** 0.11 [0.01]** 0.01 [0.01]** 0.55 [0.01]** White wage White wage_t-1 Value added Value added_t-1 Blue Employment_t-1 Period Dummy (1985-1990) -0.03 [0.01]* -0.02 [0.01] 0.02 Period Dummy (1991-1996) Period Dummy (1985-1990)*Blue Employment_t-1 [0.01]** 0.02 Period Dummy (1991-1996)*Blue Employment_t-1 Observations R-squared 17,650 20,416 23,155 [0.01]** 61,221 0.95 0.95 0.94 0.93 Dependent variable is the log (Blue Collar Employment). All other variables are also in logs. Standard errors are adjusted for clustering at the 4-digit industry level. + significant at 10%; * significant at 5%; ** significant at 1%. Table 6 Probability of Not Adjusting Labor Constant Period dummy (1985-1990) Period dummy (1991-1996) (1) (2) (3) (4) (5) (6) 0.179 [0.013]** -0.044 [0.008]** -0.044 [0.006]** 0.179 [0.013]** -0.044 [0.008]** -0.044 [0.006]** -0.002 [0.003] 0.146 [0.006]** -0.003 [0.009] 0.009 [0.010] 0.146 [0.007]** -0.003 [0.009] 0.008 [0.010] 0.001 [0.003] 0.172 [0.023]** 0.001 [0.011] 0.017 [0.015] 0.17 [0.032]** 0.012 [0.011] 0.036 [0.018]* 0 [0.000]** 0 [0.000] -0.003 [0.005] 0 [0.000]** 0 [0.000]+ Industry output growth rate Lagged labor Firm wage rate Observed productivity (LP estimate) Observed productivity (Fixed Effects estimate) Fixed effects Observations R-squared Number of clusters None 73,705 0 89 None 73,689 0 89 Firm 73,705 0.25 89 Firm 73,689 0.25 89 Firm 55,204 0.24 60 -0.008 [0.013] Firm 31,891 0.25 84 Dependent variable is one if the firm does not adjust its total employment level from the previous year. All other variables are in logs. Standard errors are adjusted for clustering at the 4-digit industry level. + significant at 10%; * significant at 5%; ** significant at 1%. Table 7 Firing Rates Conditional on Firing Percentage change Constant Period dummy (1985-1990) Period dummy (1991-1996) (1) (2) (3) (4) (5) (6) 0.172 [0.005]** -0.019 [0.005]** -0.026 [0.004]** 0.17 [0.005]** -0.016 [0.005]** -0.023 [0.004]** -0.011 [0.005]* 0.174 [0.004]** -0.025 [0.008]** -0.026 [0.005]** 0.169 [0.003]** -0.019 [0.007]** -0.02 [0.004]** -0.025 [0.006]** 0.232 [0.026]** -0.034 [0.010]** -0.057 [0.010]** 0.208 [0.022]** -0.03 [0.013]* -0.06 [0.014]** 0.00 [0.000]** 0.00 [0.000]** -0.02 [0.006]** 0.00 [0.000]** 0.00 [0.000]** Z Industry output growth rate Lagged labor Firm wage rate Observed productivity (LP estimate) Observed productivity (Fixed Effects estimate) Fixed effects Observations R-squared Number of clusters None 28,908 0.01 89 None 28,898 0.01 89 Firm 28,908 0.37 89 Firm 28,898 0.37 89 Firm 21,216 0.39 60 -0.028 [0.007]** Firm 12,557 0.410 81 Firing rate is the absolute percentage decrease in employment over the previous year, defined for firms that decrease employment. Standard errors are adjusted for clustering at the 4-digit industry level. + significant at 10%; * significant at 5%; ** significant at 1%. Table 8 Hiring Rates Conditional on Hiring Percentage Changes Constant Period dummy (1985-1990) Period dummy (1991-1996) (1) (2) (3) (4) (5) (6) 0.244 [0.011]** -0.012 [0.006]+ 0.244 [0.011]** -0.013 [0.006]* 0.258 [0.007]** -0.023 [0.007]** 0.258 [0.007]** -0.023 [0.007]** 0.297 [0.024]** 0.014 [0.006]* 0.269 [0.033]** 0.023 [0.007]** -0.045 [0.010]** -0.046 [0.010]** 0.01 [0.007] -0.074 [0.013]** -0.074 [0.013]** 0.004 [0.005] 0.004 [0.013] 0.026 [0.009]** 0.00 0.00 [0.000]** 0.00 [0.000]** 0.007 [0.005] [0.000]** 0.00 [0.000]** ¥ñZ Industry output growth rate Lagged labor Firm wage rate Observed productivity (LP estimate) Observed productivity (Fixed Effects estimate) Fixed effects Observations R-squared Number of clusters None 33,903 0.01 88 None 33,899 0.01 88 Firm 33,903 0.34 88 Firm 33,899 0.34 88 Firm 25,706 0.39 59 0.02 [0.014] Firm 14,585 0.410 82 Hiring rate is the percentage change in total employment over previous year, defined for firms that increase employment. Standard errors are adjusted for clustering at the 4-digit industry level. + significant at 10%; * significant at 5%; ** significant at 1%. Table 9 Explaining Movements in Real Wages (1) (2) (3) (4) (5) -0.37 -0.37 -0.40 -0.36 -0.53 [0.02]** [0.02]** [0.02]** [0.02]** [0.02]** 0.08 0.07 -0.007 0.09 -0.11 [0.02]** [0.02]** [0.02] [0.02]** [0.02]** Period Dummy (1985-1990) Period Dummy (1991-1996) Firm Output Growth Rate 0.008 [0.002]** Log(Industry Output) 0.12 [0.02]** Industry Output Growth Rate 0.001 [0.006] Unemployment Rate -2.13 [0.19]** Constant 4.49 4.52 2.52 4.48 4.81 [0.01]** [0.01]** [0.31]** [0.01]** [0.02]** 86,176 73,701 86,160 80,346 86,176 R-squared 0.8 0.82 0.81 0.81 0.81 Number of clusters 89 89 89 89 89 Observations Dependent variable is Log (real wage rate). Real wage rate is the nominal wage rate deflated by the producer price index. Nominal wage rate is defined as the total wage bill/ number of employees. For each independent variable the first row gives the coefficient values and the second row gives the related t-values. All regressions include firm fixed effects. Standard errors are adjusted for clustering at the 4-digit industry level. + significant at 10%; * significant at 5%; ** significant at 1%. Z^ Table A1 The Absolute Value of the Gap Between the Marginal Revenue Product and the Input Price, 1979-1996 All Specifications Include Plant-level Fixed Effects Fixed Effects Production Function Estimates Standard Errors in Brackets Blue Collar Materials (1) Electricity (1) (2) (1) (2) (2) (1) (2) 112.39 112.73 167.71 168.11 0.30 0.30 6.72 6.75 [5.02]** [5.03]** [3.63]** [3.68]** [0.01]** [0.01]** [0.13]** [0.13]** 27.78 27.19 9.62 8.89 -0.03 -0.03 -0.89 -0.92 [6.11]** [6.10]** [4.87]+ [4.93]+ [0.01]** [0.01]** [0.14]** [0.14]** 37.18 36.67 14.13 13.48 -0.02 -0.02 -1.4 -1.44 [7.92]** [7.93]** [5.83]* [5.89]* [0.01]** [0.01]** [0.19]** [0.19]** Z !# Base Period Gap (1979-1984) White Collar Increase in Gap, 2 nd Pd. (85-90) Increase in Gap, 3rd Pd. (91-96) Industry Output Growth Rate Observations R-squared 3.04 4.30 -0.01 0.22 [1.83] [2.45]+ [0.01]+ [0.13] 60,796 60,781 60,824 60,809 62,287 62,272 62,287 62,272 0.81 0.81 0.68 0.68 0.67 0.67 0.70 0.70 All variables are in thousands of 1979 pesos. We focus on the absolute value of the gap because increases in the gap are associated with decreases in economic efficiency (see Figure 3). Marginal product estimates are from a gross output Cobb-Douglas production function specification, which is estimated using a fixed effects estimator to address the simultaneous determination of inputs and productivity. The blue-collar input price is the total blue-collar wage bill divided by the number of blue-collar employees. We define the other input prices similarly. We estimate production functions separately for each 3-digit industry. We cluster standard errors at the 4-digit industry-period level. + significant at 10%; * significant at 5%; ** significant at 1%. Table A2 Labor Demand Regression Blue Collar Employment, OLS OLS Period 1 (1979-84) Constant Blue wage Blue wage_t-1 White wage White wage_t-1 Value added Value added_t-1 Blue Employment_t-1 Period 2 (1985-90) Period 3 (1991-96) 0.17 0.08 0.09 0.14 [0.03]** [0.02]** [0.03]** [0.01]** -0.19 -0.27 -0.29 -0.25 [0.01]** [0.01]** [0.02]** [0.01]** 0.09 0.17 0.20 0.15 [0.01]** [0.01]** [0.01]** [0.01]** 0.08 0.05 0.04 0.06 [0.01]** [0.01]** [0.01]** [0.01]** -0.08 -0.04 -0.03 -0.05 [0.01]** [0.01]** [0.01]** [0.01]** 0.10 0.10 0.12 0.11 [0.01]** [0.01]** [0.01]** [0.01]** -0.03 -0.02 -0.04 -0.03 [0.01]** [0.01]** [0.01]** [0.01]** 0.85 0.87 0.88 0.85 [0.01]** [0.01]** [0.01]** [0.01]** Period Dummy (1985-1990) All Periods -0.04 [0.01]** Period Dummy (1991-1996) -0.05 [0.01]** Period Dummy (1985-1990)*Blue Employment_t-1 0.02 [0.003]** Period Dummy (1991-1996)*Blue Employment_t-1 0.01 [0.004]** Observations R-squared 17,650 20,416 23,155 61,221 0.91 0.92 0.9 0.91 Dependent variable is the log (Blue Collar Employment). All other variables are also in logs. Standard errors are adjusted for clustering at the 4-digit industry level. + significant at 10%; * significant at 5%; ** significant at 1%. Z Figure A1 Trends in Real Wages 160 140 120 100 80 60 40 Median_Real_White_Wage Median_Real_Blue_Wage Source: Authors’ calculations Figure A2 Inflation and Unemployment Rate 40% 35% 30% 25% 20% 15% 10% 5% Inflation_rate (cpi) Unemployment_rate Source: Edwards & Edwards (2000), ILO statistics, authors’ calculations. ZZ 1995 1993 1991 1989 1987 1985 1983 1981 1979 0% 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 1985 1984 1983 1982 1981 1980 0 1979 20