1. Estimating productivity when labor is a dynamic input Xulia González Daniel Miles Universidad de Vigo (Spain) xgzlez@uvigo.es dmiles@uvigo.es Very preliminar

2. Motivation Usually production function estimation assumes labor is a flexible input. This is a sensible assumption under low adjustment costs (for example, no dismissal costs). However, in the case of Spain this assumption doesn’t seem reasonable due to the dismissal cost for some workers with permanent contracts with high protection legislation.

3. Temporary workers and Employment Protection Legislation, 1999 (OECD indicator)

4. Why this high rate of temporary workers? There was an important labor reform in 1984 that liberalized the use of temporary contracts, with the objective of reducing the 21% rate of unemployment. –Temporary contracts could be used for non temporary activities As a result, temporary contracts were extensively used by firms as a way to avoid the severance payments in case to dismissal.

5. Research targets Estimate a production function in an Olley-Pakes framework assuming two types of labor input, depending on the dismissal costs. –Workers with fixed-term (or temporary) contracts are consider a flexible inpu. as they have not firing costs. –Workers with indefinide (permanent) contracts are considered as a dynamic input, costly to adjust. Measure the gap between the marginal revenue product of labor and its marginal costs (wage) as a measure of inefficiency. As Petrin and Sivadasan (2006) shows “firing costs drive a wedge between the marginal product of labor and the wage”. Study whether the gap was affected by the Spanish 1997 labor reform. –This reform diminished dismissal costs and introduced important fiscal incentives to promote the transformation of fixed-term to permanent labor contracts.

6. Labour reforms in the 90’s 1994 labor reform. Restricted the conditions for the use of fixed term contracts 1997 labor reform. Introduce a new type of permanent contract with lower firing costs: from 45 to 33 days per year of seniority with a maximum (with a maximum of 12 months instead of 42).

7. Outline Literature. Data. Methodological framework. Results.

8. Literature Productivity estimation, –Olley and Pakes, (Econometrica, 1996) –Fernandes and Pakes (mimeo, 2008) –Ackerberg, Caves and Frazer (mimeo, 2006) The economic effects of job security provisions: –International comparisons: Lazear (1990); Heckman and Pages (2000), Micco and Pagés (2006), Botero et al. (2005), Addison and Teixeira (2003). –Monograph about temporary work: The Economic Journal, 112, june 2002 –Productivity (India): Besley and Burgess, (QJE, 2004). Fernandes and Pakes (mimeo, 2008) –Factor utilization (Chile): Petrin and Sivadasan (NBER, 2006); –Employment, job turnover and productivity (Spain): Aguirregabiria and Alonso- Borrego, (1999)

9. Data Panel data set Spanish Manufacturing firms. Firms Strategies Survey (ESEE). Sample period 1991-2001 Total observations: 17,395 Number of firms: 2,175 Average number of years in the sample 8

10. Data Small and medium firms: less than 200 workers Big firms: more that 200 workers. Observations 11,7285,617 Percentage of firms with Temporary workers 77%90% Share: Temporary workers / total employment 31%17%

11. Data Information available. –Number of permanent workers= nº workers full time+ ½ workers part time. –Average of temporary workers. Firms report the number of temporary works in each quarter. We obtain the average. Capital stock, materials, production (sales+inventories). Also include information about: –Price changes of output and inputs. –Capacity utilization. –Labor cost, firing costs after 1993.

12. Share of fixed-term contract workers by cycle Fixed term contracts workers are more flexible to changes in demand.

13. Production function estimation Production function Classical problems in the estimation of this function : a. correlation between productivity and inputs. b. q is not observable (sales generation function). c. exit bias.

14. Production function estimation Olley-Pakes (1996) shows that the investment function can be written as a function of the productivity and other dynamic factors: If i>0 then this function is invertible and we can obtain the productivity as a function of observable variables.

15. Production function estimation Substituting into de production function and rearranging we have: And we estimate using the two-stage procedure as in O-P.

16. Marginal revenue product and wage gap Once we obtain the parameters of the production function we are able to calculate the Gap (if there is any) between the marginal revenue product and wage. This gap can be use as a measure of inefficiency of adjustment costs due to employment protection legislation (among other things).

17. Marginal revenue product and wage gap We know the proportion of temporary workers, then we define the labor firm’s gap as: A positive gap means that workers are receiving less than the value it contributes to the firms and firms would be able to hire more workers. Firms don’t hire more workers because it is costly to dismiss in case a negative shock.

18. Production function estimates Note. L= hours of work OLSO-P Labor static inputO-P L P dynamic input Labor l T (std err.) Labor l P (std err.) Capital (std err.) Labor l T (std err.) Labor l P (std err.) Capital (std err.) Labor l T (std err.) Labor l P (std err.) Capital (std err.) (1)(2)(3)(4)(5)(6)(7)(8)(9) 1. Food, drink and tobacco 0.160 (0.017) 0.558 (0.032) 0.343 (0.020) 0.146 (0.034) 0.513 (0.061) 0.226 (0.068) 0.150 (0.032) 0.372 (0.032) 0.225 (0.096) 2. Textile, leather and shoes 0.185 (0.015) 0.523 (0.024) 0.232 (0.0) 0.176 (0.034) 0.512 (0.045) -0.167 (0.030) 0.502 (0.094) 0.282 (0.066) 3. Timber and furniture 0.221 (0.022) 0.538 (0.037) 0.253 (0.021) 0.194 (0.032) 0.456 (0.084) 0.236 (0.081) 0.191 (0.041) 0.454 (0.134) 0.348 (0.103) 4. Paper and printing products 0.160 (0.020) 0.564 (0.042) 0.341 (0.023) 0.152 (0.031) 0.556 (0.102) 0.270 (0.058) 0.148 (0.029) 0.722 (0.163) 0.213 (0.066) 5. Chemical products 0.082 (0.016) 0.558 (0.035) 0.409 (0.021) 0.077 (0.027) 0.56 (0.085) 0.415 (0.082) 0.079 (0.030) 0.515 (0.104) 0.446 (0.089) 6. Non-metalic minerals 0.162 (0.020) 0.590 (0.033) 0.270 (0.020) 0.172 (0.034) 0.584 (0.078) 0.236 (0.077) 0.148 (0.041) 0.290 (0.159) 0.250 (0.103) 7. Metals and metal products 0.205 (0.014) 0.557 (0.028) 0.293 (0.017) 0.197 (0.033) 0.560 (0.058) 0.227 (0.060) 0.181 (0.034) 0.656 (0.109) 0.165 (0.063) 8. Agric. and ind. machinery 0.173 (0.024) 0.690 (0.044) 0.139 (0.027) 0.170 (0.035) 0.666 (0.077) 0.069 (0.078) 0.165 (0.027) 0.386 (0.181) 0.129 (0.068) 9. Office mach., computers and electric material 0.194 (0.018) 0.704 (0.045) 0.180 (0.026) 0.165 (0.040) 0.660 (0.100) 0.206 (0.053) 0.163 (0.048) 0.512 (0.202) 0.199 (0.091) 10. Vehicles and accesories 0.215 (0.030) 0.512 (0.053) 0.308 (0.039) 0.200 (0.058) 0.505 (0.127) -0.190 (0.054) 0.581 (0.164) 0.280 (0.115)

19. Production function estimates Note. L= hours of work OLSO-P L P dynamic input Labor l T (std err.) Labor l P (std err.) Capital (std err.) Labor l T (std err.) Labor l P (std err.) Capital (std err.) (1)(2)(3)(7)(8)(9) 1. Food, drink and tobacco 0.160 (0.017) 0.558 (0.032) 0.343 (0.020) 0.150 (0.032) 0.372 (0.032) 0.225 (0.096) 2. Textile, leather and shoes 0.185 (0.015) 0.523 (0.024) 0.232 (0.0) 0.167 (0.030) 0.502 (0.094) 0.282 (0.066) 3. Timber and furniture 0.221 (0.022) 0.538 (0.037) 0.253 (0.021) 0.191 (0.041) 0.454 (0.134) 0.348 (0.103) 4. Paper and printing products 0.160 (0.020) 0.564 (0.042) 0.341 (0.023) 0.148 (0.029) 0.722 (0.163) 0.213 (0.066) 5. Chemical products 0.082 (0.016) 0.558 (0.035) 0.409 (0.021) 0.079 (0.030) 0.515 (0.104) 0.446 (0.089) 6. Non-metalic minerals 0.162 (0.020) 0.590 (0.033) 0.270 (0.020) 0.148 (0.041) 0.290 (0.159) 0.250 (0.103) 7. Metals and metal products 0.205 (0.014) 0.557 (0.028) 0.293 (0.017) 0.181 (0.034) 0.656 (0.109) 0.165 (0.063) 8. Agric. and ind. machinery 0.173 (0.024) 0.690 (0.044) 0.139 (0.027) 0.165 (0.027) 0.386 (0.181) 0.129 (0.068) 9. Office mach., computers and electric material 0.194 (0.018) 0.704 (0.045) 0.180 (0.026) 0.163 (0.048) 0.512 (0.202) 0.199 (0.091) 10. Vehicles and accesories 0.215 (0.030) 0.512 (0.053) 0.308 (0.039) 0.190 (0.054) 0.581 (0.164) 0.280 (0.115)

20. Marginal revenue product and wage gap First, we discuss whether the estimated gap is lower for those firms with a higher proportion of fixed-term contracts. Second, Have change the gap after the 1997 labor reform?

21. Gap and share of temporary workers

22. Effect of the MPR-Wage Gap on share temporary employment Independent variableDependent variable: gap mrp-wage (1)(2) Share Temporary Employment -488.6 (165.1) -361.3 (178.4) Share T Employment 1997 -278.6 (141.2) Step 1997 138.69 (69.3) Trend 53.7 (7.86) 42.6 (12.9) ______________________________________________________________________ All regressions include regional dummies, economic cycle variables, age, a third order polinomial in capital per worker the percentage growth of value added, the ratio of value added on capital.

23. Any changes after the 1997 reform? Small firms

24. Any changes after the 1997 reform? Big firms

25. Conclusions The 1997 reform was effective in reducing the share of temporary workers in the Spanish manufacturing industry. The gap did not diminish after the 1997 reform. –We do not find evidence that the distribution of the gap shrinks toward zero after the reform. –Due to the change of temporary workers into permanent. Future research: could we obtain the under/over utilization of labor due to adjustment costs?