The Microeconomic Implications of Labor Regulations: Cross-Country Evidence from Within the Firm

1. The Microeconomic Implications of Labor Regulations: Cross-Country Evidence from Within the Firm Francine LafontaineJagadeesh Sivadasan Ross School of Business, University of Michigan AEA Meetings, Jan 2007 (Updated Jan 2009)

2. Introduction • Goal: Assess the effects of regulations that create rigidities in the labor market • Important and controversial question: a number of papers have considered the effects of such regulations at the macro level (e.g. Botero et al (2004), Lazear (1990)) • We focus on the very micro level, using data from a single fast-food chain with operations in more than 40 countries around the world: • most importantly, we quantify the effect of the regulations on firm labor adjustment decisions • also examine effect on extent of operations

3. Introduction • Important advantages of our empirical setting: • Fundamentally same production technology • Same output (as close as can get) • Labor intensive - labor issues really matter • We have very detailed data: weekly revenues and costs information for each outlet outside the US (more than 2500 of them, over 4 years, in 43 countries)

4. Introduction • Important advantages of our empirical setting: • The high frequency of our data has two main advantages: • Gets around problems with annual data, which can hide a lot of within year turnover (Hamermesh and Pfann (1996)) • Allows us to adopt estimation strategies with lots of controls (i.e. fixed effects at the outlet, outlet-year, or outlet-year-season)

5. Introduction • We focus on questions that are particularly suited to our data • Companion paper – addresses the effect of regulation on productivity and labor demand • In some sense, more direct place to look for effects • But in reality, theory ambiguous on these effects, whereas clear predictions on hysteresis and effect on responsiveness to output changes • And our data – labor costs, not labor levels => potential biases due to “poor” measure of wages

6. Introduction – Preview of Results • We find strong evidence that in countries with more rigid labor laws: • Outlets’ choices of labor levels are less responsive, from period to period, to changes in revenues, and • there is more hysteresis in labor levels, that is labor in one period is more related to previous period labor • We also find some evidence that in such countries: • the Company enters later and operates fewer outlets -- and uses “local” franchising more as well

7. Organization of this Talk • Basic Model and Predictions • A Note on Dynamics • Data and Definition of Variables • Results for Labor Adjustment – Botero & GCS • Key Identification Issues • Contrasting with Materials Adjustment Results • IV Results • Results on Company’s Extent of Operations • Conclusions

8. Basic Model • Draws on Heckman and Pagés (2003), who drew on Holt, Modigliani, Muth and Simon (1960) • Given our weekly data, we take capital as given (or contributing to the Hicks neutral productivity term), and write a 2 input Cobb-Douglas production process with labor and materials where Ytis the quantity of output produced by the firm in period t, Ltis labor, and Mtrepresents materials used

9. Basic Model • Assume iso-elastic demand curve: where Ptis the price per unit output in period t,  represents demand shifters and  is the price elasticity of demand • The firm’s profit to be maximized is where Wtis the wage rate faced by the firm in period t, and Stis the per unit cost of materials

10. Basic Model • Each week, manager chooses labor and materials so FOCs for these are binding • Assume horizontal labor and materials supply in each local market (each outlet buys little, and even as a group they are a tiny part of the market) => obtain optimal labor and materials demand function in terms of the primitives (prices, demand & production function parameters) => can write total labor and material cost equations conditional on output

11. Basic Model • These input demand equations are where we use bt to denote log(WtLt) and ft to mean log(StMt), and • These equations then describe equilibrium input costs in the absence of adjustment costs

12. Basic Model • Now suppose that there are costs to adjusting labor. First, let the cost of being off the static optimum be quadratic in log labor • where o > 0. Second, suppose that the cost of changing labor levels from one period to the next are given by • where we expect ato be positive and increasing in the rigidity of labor regulations

13. Basic Model • Each outlet minimizes the sum of these costs. This yields optimal labor choice where outlet i is in country j, and • The optimal labor cost equation above can be rewritten as

14. Basic Model • Taking, as a first approximation, we get the following econometric specification for the labor costs of outlet i in country j at time t: where τj is the index of labor regulation, and is stands for store, store-year, or store-season-year fixed effects.

15. Basic Model • In this regression, we expect r to be negative and bto be positive. • In other words, our simple model yields two principal implications that we bring to data: • Labor costs should be less responsive to changes in revenues in countries where regulations are more stringent • Labor costs at time t should be more dependent on labor costs at time t-1 in countries with more stringent laws (hysteresis) • These predictions are intuitive, and the latter has been tested in a number of studies of the effect of regulation on labor demand (see survey in Heckman and Pagés, 2004).

16. Note on Dynamics • Our two testable implications are derived from a very simple model • Heckman and Pagés (2004) express concern that the labor hysteresis prediction may not arise in a more general dynamic model • We solved a more general dynamic stochastic programming model: • Two state variables are current productivity and last period labor. We solve numerically for four scenarios: • with both symmetric (quadratic) and asymmetric (i.e. severance pay only) adjustment costs. and • for iid as well as persistent demand /productivity shocks processes

17. Note on Dynamics • To approximate our actual data, using optimal policy functions, we ran regressions on simulated behavior of 75 outlets for 104 periods across 45 regimes

18. The Data • Mostly from internal firm records • Cover over 2500 outlets in more than 40 countries worldwide, weekly from 2000 to 2003 • Data on • Revenues per week • Total labor costs per week • Total materials costs per week • Number of items (standardized notion of output)

19. The Data • We measure the rigidity of the labor regulations in each country using the Botero et al (2004) index (see appendix in paper for details) • Main advantage – computed similarly across countries • Main disadvantage – laws may not be enforced as strongly everywhere • We verify our results using an index of hiring and firing flexibility from the Global Competitiveness Survey (2002) of business executives

22. Panel Characteristics (baseline sample)

23. Descriptive Statistics

24. Correlation between changes in input costs and revenue

25. Table 4: Baseline Results

26. Table 4: Baseline Results

27. Table 5: GCS Index Results

28. Table 5: GCS Index Results

29. Key Identification Issues • As per our model above, the error term is: • Thus the error term includes omitted supply-side parameter it (output elasticity with respect to labor) and demand side parameter it (elasticity of demand) • Our store or store-year-season fixed effects control implicitly for the differences in τj across countries, and for these supply and demand parameters insofar as they are fixed within a store or store-year or store-year-season

30. Key Identification Issues • Unanticipated demand and productivity shocks can also add to the error term • if labor is set early, then a high (low) demand shock shows up as too little (much) labor conditional on output • Since we are interested in the coefficient on lagged labor or revenue interacted with regulation, a bias arises only when: • the omitted demand and supply parameters vary within store-year-seasons, and are correlated with lagged labor/revenue in a different way across different regulation regimes • prediction errors (with regard to demand and productivity shocks) are correlated with levels of regulation

31. Key Identification Issues • We address these potential identification issues in two different ways: • using the material demand equation as a control • with instrumental variables • The material demand equation is a good control because: • omitted demand and supply parameters are the same for the material as for the labor demand equation • prediction error also would bias the material demand specification in a similar way • IV approach • draws from traditional approaches in the literature (Blundell and Bond 1998) • uses lagged endogenous variables that are uncorrelated with unpredicted component of current demand and productivity shocks • we have more instruments than endogenous variables, so we can perform an overidentification test – good results!

32. Table 6: Material Costs Results (Botero et al. Index)

33. Table 6: Material Costs Results (Botero et al. Index)

34. Table 7: Robustness check:OECD Sample and Interaction Terms

35. Table 8: Robustness check:DID comparison of top and bottom decile of change in Index of Inflexibility between 2002 and 2004

36. Table 8: Robustness check:Case study of labor reform in South Korea (1996-98)

37. Table 8: Robustness check:Case study of labor reform in South Korea (1996-98)

38. Impulse response functions based on VAR analysis

39. Implied Rigidity Estimates • The underlying structural parameters and are not simultaneously identified. • However is identified • Model implies following relationship between optimal adjustment and actual adjustment of labor • More regulation => higher => greater dampening of adjustment

40. Table 11: Estimates of Dampening Factor(change in labor costs in the absence of adjustment costs / actual change in labor costs)

41. A Look at the Firm’s Expansion • If more rigid labor regulations imply that individual outlets cannot adjust labor as much as they otherwise would, then all else the same, outlets in these markets will be less profitable. • This suggests the firm should • Enter later • Expand less rapidly • Franchise more ? in highly regulated markets

42. Labor Regulation and International Expansion: Time to Entry

43. Labor Regulation and International Expansion: Number of Outlets

44. Labor Regulation and International Expansion: Use of Franchising

45. Conclusion • Using weekly data from outlets of a multinational fast-food chain, we have shown evidence of a statistically and economically important effect of labor regulations on labor decisions at the micro level • To our knowledge this is the first time that effects of such policies are documented in a cross-country context at such a micro level

46. Conclusion • Specifically, using our most conservative estimates, we find that an increase of one standard deviation in the labor regulation rigidity index • reduces the response of labor cost to a one standard deviation increase in output (revenue) by about 4.4 percentage points (from 26.4 per cent to 22.0 percent) • increases the response of labor cost to a one standard deviation increase in lagged labor cost by about 9.6 percentage points (from 17.0 per cent to 26.6 per cent)

47. Conclusion • We have also shown that • results are similar whether we use the Botero et al. index, or an alternative measure of labor regulation from the Global Competitiveness Survey • The effects do not hold for material costs, confirming that they are not spurious – the increased rigidity in labor is not driven by omitted variables that are also likely to affect other variable costs • The effects are even stronger when we estimate using an IV approach

48. Conclusion • Consistent with the impact on adjustment behavior, we also find that the Company • delayed entry, and • operates fewer outlets • and its partners rely on franchising more in countries with more rigid labor laws

49. Conclusion • So we have shown that labor levels are more persistent in countries that enact more rigid labor laws, an effect these policies are meant to achieve • However, increases (responses to positive shocks) as well as decreases in labor levels are affected • Consistent with our earlier findings that outlet level labor demand was lower in more heavily regulated markets, our results on timing of entry and level of operations of the Company across markets suggest that easing these laws would increase employment and output in this industry

50. Other paper (Lafontaine and Sivadasan, “Within-firm Labor Productivity across Countries: A case study): Quantifying the Labor Demand effect • We find coefficients for regulation of about -0.4 • => an increase in labor regulation (Botero Index) from its 25th percentile to its 75th percentile value reduces labor per outlet by 0. 4 * 0.31 = 12% • Alternatively, a one standard deviation increase in the labor regulation rigidity index leads to a reduction in conditional labor demand of 6.4 per cent