job security does affect economic efficiency: theory, …

NBER WORKING PAPER SERIES

JOB SECURITY DOES AFFECT ECONOMIC EFFICIENCY:THEORY, A NEW STATISTIC, AND EVIDENCE FROM CHILE

Amil PetrinJagadeesh Sivadasan

Working Paper 12757http://www.nber.org/papers/w12757

NATIONAL BUREAU OF ECONOMIC RESEARCH1050 Massachusetts Avenue

Cambridge, MA 02138December 2006

We have benefitted from discussions with Susanto Basu, Alejandra Cox-Edwards, James Heckman,Gunter Hitsch, Wendy Petropoulos, and seminar participants at the University of Chicago, MontanaState University, Yale University, and the University of Minnesota. The views expressed herein arethose of the author(s) and do not necessarily reflect the views of the National Bureau of EconomicResearch.

© 2006 by Amil Petrin and Jagadeesh Sivadasan. All rights reserved. Short sections of text, not toexceed two paragraphs, may be quoted without explicit permission provided that full credit, including© notice, is given to the source.

Job Security Does Affect Economic Efficiency: Theory, A New Statistic, and Evidence fromChileAmil Petrin and Jagadeesh SivadasanNBER Working Paper No. 12757December 2006JEL No. D21,J23,J63,J65

ABSTRACT

The extensive empirical macro- and micro-level evidence on the impact of job security provisionsis largely inconclusive. We argue that the weak evidence is a consequence of the weak power of statisticsused, which is suggested by a dynamic theory of plant-level labor demand that we develop. This modelspeaks clearly on one issue: firing costs drive a wedge between the marginal revenue product of laborand its marginal cost. We examine changes in this gap as our test statistic. It is easy to compute andhas a welfare interpretation. We use census data of Chilean manufacturing firms for the years 1979-1996to look for real effects induced by two significant increases in the costs of dismissing employees. Similar to previous findings in other data, the traditional labor demand statistics provide little evidenceof a negative impact from increases in firing costs. While we find no evidence that gaps increase forinputs that are not directly affected by firing costs, we find large and statistically significant increasesin the mean and variance of the within-firm gap between the marginal revenue product of labor andthe wage for both blue and white collar workers.

Amil PetrinGraduate School of BusinessUniversity of Chicago5807 Woodlawn AvenueChicago, IL 60637and [email protected]

Jagadeesh SivadasanUniversity of MichiganRoss School of Business701 Tappan, Room D4209AAnn Arbor, MI [email protected]

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Figure 1 Expected Discounted Cost of Firing a Worker

Multiples of Monthly Wages, Latin America and the OECD Countries, 1999

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Source: Heckman and Pages (2004). Firing costs are defined as the additional payment made to the worker at the time of dismissal. This definition does not include ``indirect'' payments, like those made by U.S. firms into an insurance fund based in part on the number of firings at the firm.

#�

Figure 2 The Change in Firing Costs in Chile

Expected discounted cost of dismissing a worker, in multiples of monthly wages

Source: Pages and Montenegro (1999).

0

0.5

1

1.5

2

2.5

3

3.5

1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996

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Quantity

Price

Wage

Demand

Marginal Revenue

QMONOPOLIST QSOCPLANNER

A B C D E

Figure 3 Relating Changes in Gaps to Changes in Welfare

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

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%.

Blue Collar White Collar Materials Electricity

(1) (2) (1) (2) (1) (2) (1) (2)

Base Period Gap (1979-1984) 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]**

Increase in Gap, 2nd Pd. (85-90) 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]**

Increase in Gap, 3rd Pd. (91-96) 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]**

Industry Output Growth Rate 2.01 5.16 -0.01 0.29

[1.35] [3.41] [0.01]+ [0.19]

Observations 54,230 54,226 54,269 54,265 55,329 55,325 55,329 55,325

R-squared 0.81 0.81 0.67 0.67 0.69 0.69 0.71 0.71

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Table 2 Average Dispersion of the Within-Firm Gap



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 1985-1990, 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%.


(1) (2) (1) (2) (1) (2) (1) (2)

Base Period Gap (1979-1984) 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]**


[3.48]** [3.49]** [3.68]** [3.704]** [0.01]** [0.01]** [0.18]** [0.19]**


[5.02]** [5.04]** [4.787]** [4.798]** [0.004]** [0.004]** [0.23]** [0.23]**


[1.09] [2.46] [0.01]+ [0.12]

Observations 54,230 54,226 54,269 54,265 55,329 55,325 55,329 55,325

R-squared 0.76 0.76 0.60 0.60 0.63 0.63 0.58 0.58

�^

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


All variables are in thousands of 1979 pesos. If markups exist, our focus should be on only those firms with positive gaps in juxtaposed time-periods. 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%.


(1) (2) (1) (2) (1) (2) (1) (2)

Base Period Gap (1979-1984) 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]**


[5.27]** [5.21]** [7.65]** [7.67]** [0.01]** [0.01]** [0.34]** [0.35]**


[9.78]** [9.76]** [10.55]** [10.59]** [0.01]** [0.01]** [0.41]** [0.42]**


[3.87]** [4.45] [0.01] [0.20]

Observations 37,923 37,921 38,167 38,165 28,647 28,644 50,519 50,515

R-squared 0.79 0.79 0.7 0.7 0.71 0.71 0.7 0.7

�#

Figure 4 Year-to-Year Differences in the Absolute Gap

Blue and White Collar Labor, Materials, and Electricity Levinsohn-Petrin Production Function Estimates

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.

40

60

80

100

120

140

160

180

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 BLUE WHITE MATERIALS ELECTRICITY

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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.)

95% Confidence Interval for Change in Gap

Average Absolute Value of Within−Firm GapBlue and White Collar Labor

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.

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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.)

95% Confidence Interval for Change in Dispersion

Average Dispersion of Within−Firm GapsBlue and White Collar Labor

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.

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Table 4 Labor Demand Regression

Blue Collar Employment, Instrumental Variables

Blue Collar Labor

Period 1 (1979-84)

Period 2 (1985-90)

Period 3 (1991-96)

All Periods

Constant -0.01 0.01 -0.04 -0.02

[0.01] [0.01]* [0.01]** [0.01]**

Blue wage -0.12 -0.23 -0.10 -0.19

[0.03]** [0.02]** [0.04]* [0.01]**

Blue wage_t-1 -0.04 0.08 0.23 0.06

[0.01]** [0.01]** [0.02]** [0.01]**

White wage 0.11 0.02 0.02 0.05

[0.02]** [0.01] [0.01] [0.01]**

White wage_t-1 -0.02 -0.03 -0.02 -0.04

[0.01]* [0.01]** [0.01]* [0.01]**

Value added 0.10 -0.01 0.07 0.08

[0.01]** [0.02] [0.01]** [0.01]**

Value added_t-1 0.04 -0.01 -0.01 0.01

[0.01]** [0.01] [0.01]+ [0.01]**

Blue Employment_t-1 0.12 0.43 0.66 0.45

[0.05]* [0.09]** [0.07]** [0.05]**

Period Dummy (1985-1990) 0.01

[0.01]

Period Dummy (1991-1996) 0.01

[0.01]

Period Dummy (1985-1990)*Blue Employment_t-1 0.35

[0.10]**

Period Dummy (1991-1996)*Blue Employment_t-1 -0.28

[0.09]**

Observations 8,129 15,272 18,499 41,900

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%.

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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 1.55 2.08 0.89

[0.09]** [0.18]** [0.23]** [0.04]**

Blue wage -0.15 -0.26 -0.28 -0.23

[0.02]** [0.02]** [0.02]** [0.01]**

Blue wage_t-1 -0.01 0.06 0.06 0.09

[0.01] [0.01]** [0.01]** [0.01]**

White wage 0.09 0.06 0.02 0.06

[0.01]** [0.01]** [0.01] [0.01]**

White wage_t-1 -0.02 -0.01 -0.01 -0.04

[0.01]** [0.01]* [0.01]* [0.01]**

Value added 0.11 0.10 0.11 0.11

[0.01]** [0.01]** [0.01]** [0.01]**

Value added_t-1 0.03 0.03 0.01 0.01

[0.01]** [0.01]** [0.01]* [0.01]**


[0.01]** [0.02]** [0.02]** [0.01]**

Period Dummy (1985-1990) -0.03

[0.01]*

Period Dummy (1991-1996) -0.02

[0.01]

Period Dummy (1985-1990)*Blue Employment_t-1

0.02

[0.01]**

Period Dummy (1991-1996)*Blue Employment_t-1

0.02

[0.01]**

Observations 17,650 20,416 23,155 61,221

R-squared 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%.

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Table 6 Probability of Not Adjusting Labor

(1) (2) (3) (4) (5) (6)

Constant 0.179 0.179 0.146 0.146 0.172 0.17

[0.013]** [0.013]** [0.006]** [0.007]** [0.023]** [0.032]**

Period dummy (1985-1990) -0.044 -0.044 -0.003 -0.003 0.001 0.012

[0.008]** [0.008]** [0.009] [0.009] [0.011] [0.011]

Period dummy (1991-1996) -0.044 -0.044 0.009 0.008 0.017 0.036

[0.006]** [0.006]** [0.010] [0.010] [0.015] [0.018]*

Industry output growth rate -0.002 0.001

[0.003] [0.003]

Lagged labor 0 0

[0.000]** [0.000]**

Firm wage rate 0 0

[0.000] [0.000]+

Observed productivity (LP estimate) -0.003

[0.005]

Observed productivity (Fixed Effects estimate) -0.008

[0.013]

Fixed effects None None Firm Firm Firm Firm

Observations 73,705 73,689 73,705 73,689 55,204 31,891

R-squared 0 0 0.25 0.25 0.24 0.25

Number of clusters 89 89 89 89 60 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%.

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Table 7 Firing Rates Conditional on Firing

Percentage change (1) (2) (3) (4) (5) (6)

Constant 0.172 0.17 0.174 0.169 0.232 0.208

[0.005]** [0.005]** [0.004]** [0.003]** [0.026]** [0.022]**

Period dummy (1985-1990) -0.019 -0.016 -0.025 -0.019 -0.034 -0.03

[0.005]** [0.005]** [0.008]** [0.007]** [0.010]** [0.013]*

Period dummy (1991-1996) -0.026 -0.023 -0.026 -0.02 -0.057 -0.06

[0.004]** [0.004]** [0.005]** [0.004]** [0.010]** [0.014]**

Industry output growth rate -0.011 -0.025

[0.005]* [0.006]**

Lagged labor 0.00 0.00

[0.000]** [0.000]**

Firm wage rate 0.00 0.00

[0.000]** [0.000]**

Observed productivity (LP estimate) -0.02

[0.006]**

Observed productivity (Fixed Effects estimate) -0.028

[0.007]**

Fixed effects None None Firm Firm Firm Firm

Observations 28,908 28,898 28,908 28,898 21,216 12,557

R-squared 0.01 0.01 0.37 0.37 0.39 0.410

Number of clusters 89 89 89 89 60 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%.

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Table 8 Hiring Rates Conditional on Hiring

Percentage Changes (1) (2) (3) (4) (5) (6) Constant 0.244 0.244 0.258 0.258 0.297 0.269 [0.011]** [0.011]** [0.007]** [0.007]** [0.024]** [0.033]** Period dummy (1985-1990) -0.012 -0.013 -0.023 -0.023 0.014 0.023 [0.006]+ [0.006]* [0.007]** [0.007]** [0.006]* [0.007]** Period dummy (1991-1996) -0.045 -0.046 -0.074 -0.074 0.004 0.026 [0.010]** [0.010]** [0.013]** [0.013]** [0.013] [0.009]** Industry output growth rate 0.01 0.004 [0.007] [0.005] Lagged labor 0.00 0.00 [0.000]** [0.000]** Firm wage rate 0.00 0.00 [0.000]** [0.000]** Observed productivity (LP estimate) 0.007 [0.005] Observed productivity (Fixed Effects estimate) 0.02 [0.014] Fixed effects None None Firm Firm Firm Firm Observations 33,903 33,899 33,903 33,899 25,706 14,585 R-squared 0.01 0.01 0.34 0.34 0.39 0.410 Number of clusters 88 88 88 88 59 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%.

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Table 9 Explaining Movements in Real Wages

(1) (2) (3) (4) (5)

Period Dummy (1985-1990) -0.37 -0.37 -0.40 -0.36 -0.53

[0.02]** [0.02]** [0.02]** [0.02]** [0.02]**

Period Dummy (1991-1996) 0.08 0.07 -0.007 0.09 -0.11

[0.02]** [0.02]** [0.02] [0.02]** [0.02]**

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]**

Observations 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

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%.

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Table A1 The Absolute Value of the Gap


Fixed Effects Production Function Estimates Standard Errors in Brackets

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%.


(1) (2) (1) (2) (1) (2) (1) (2)

Base Period Gap (1979-1984) 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]**


[6.11]** [6.10]** [4.87]+ [4.93]+ [0.01]** [0.01]** [0.14]** [0.14]**


[7.92]** [7.93]** [5.83]* [5.89]* [0.01]** [0.01]** [0.19]** [0.19]**


[1.83] [2.45]+ [0.01]+ [0.13]

Observations 60,796 60,781 60,824 60,809 62,287 62,272 62,287 62,272

R-squared 0.81 0.81 0.68 0.68 0.67 0.67 0.70 0.70

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Table A2 Labor Demand Regression

Blue Collar Employment, OLS

OLS

Period 1 (1979-84)

Period 2 (1985-90)

Period 3 (1991-96)

All Periods

Constant 0.17 0.08 0.09 0.14

[0.03]** [0.02]** [0.03]** [0.01]**

Blue wage -0.19 -0.27 -0.29 -0.25

[0.01]** [0.01]** [0.02]** [0.01]**

Blue wage_t-1 0.09 0.17 0.20 0.15

[0.01]** [0.01]** [0.01]** [0.01]**

White wage 0.08 0.05 0.04 0.06

[0.01]** [0.01]** [0.01]** [0.01]**

White wage_t-1 -0.08 -0.04 -0.03 -0.05

[0.01]** [0.01]** [0.01]** [0.01]**

Value added 0.10 0.10 0.12 0.11

[0.01]** [0.01]** [0.01]** [0.01]**

Value added_t-1 -0.03 -0.02 -0.04 -0.03

[0.01]** [0.01]** [0.01]** [0.01]**


[0.01]** [0.01]** [0.01]** [0.01]**

Period Dummy (1985-1990) -0.04

[0.01]**

Period Dummy (1991-1996) -0.05

[0.01]**


[0.003]**


[0.004]**

Observations 17,650 20,416 23,155 61,221

R-squared 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%.

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Figure A1 Trends in Real Wages

Source: Authors’ calculations

Figure A2 Inflation and Unemployment Rate

Source: Edwards & Edwards (2000), ILO statistics, authors’ calculations.

0

20

40

60

80

100

120

140

16019

79

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

Median_Real_White_Wage Median_Real_Blue_Wage

0%

5%

10%

15%

20%

25%

30%

35%

40%

1979

1981

1983

1985

1987

1989

1991

1993

1995

Inflation_rate (cpi) Unemployment_rate

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job security does affect economic efficiency: theory, …

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