1 Table 1: Parameter estimates (calibrating returns to scale, ) (1) (2) (3) (4) Method Unconstrained Unconstrained Unconstrained Unconstrained ( calibrated from Basu ( calibrated from ( calibrated at 0.5, and Fernald, 1997) Atkeson and Kehoe, Hsieh-Klenow, 2009) ( calibrated at 0.9) 2005), scale parameter , power law (0.059) (0.059) (0.059) (0.057) T = (0.018) (0.018) (0.018) (0.015) n u, upper employment threshold (0.024) (0.024) (0.024) (0.002) σ, variance of measurement error (0.025) (0.025) (0.025) (0.008), implicit tax, variable cost (0.005) (0.004) (0.013) (0.002) k/w, implicit tax, fixed cost (0.257) (0.192) (0.653) (0.099) Mean (Median) # of 55.7 (23) 55.7 (23) 55.7 (23) 55.7 (23) employees Observations 238, , , ,701 Firms 57,008 57,008 57,008 57,008 Ln Likelihood -1,065,936-1,065,936-1,065,936-1,066,165 Notes: Parameters estimated by ML with standard errors below in parentheses (clustered at the four digit level). Estimation is on unbalanced panel of population of French manufacturing firms with 10 to 1,000 employees. These estimates of the implicit tax are based on different estimates of ; the methods are indicated in the different columns.
2 Table 2: Parameter estimates (exploiting information from the Production Function to estimate returns to scale, ) (1) (2) (3) (4) (5) Using Production TFP/Size High-Tech Sectors Function estimates relationship (Using Production Method Baseline (column (1) of Table 1), scale parameter (0.013), power law (0.059) (0.052) T = (0.018) (0.020) n u, upper employment threshold (0.024) (1.133) σ, variance of measurement error (0.025) (0.020), implicit tax, variable cost (0.005) (0.004) k/w, implicit tax, fixed cost (0.257) (0.177) (0.025) (0.055) (0.023) (1.342) (0.024) (0.005) (0.219) Function estimates) (0.012) (0.063) (0.012) (2.474) (0.029) (0.003) (0.129) Low-Tech Sectors (Using Production Function estimates) (0.016) (0.063) (0.032) (1.603) (0.034) (0.007) (0.304) Mean (Median) # of 55.7 (23) 55.7 (23) 55.7 (23) 78.3 (29) 51.3 (23) employees Observations 238, , ,701 38, ,988 Firms 57,008 57,008 57,008 9,099 48,139 Ln Likelihood -1,065, Notes: Parameters estimated by ML with standard errors below in parentheses (clustered at the four digit level). Estimation is on unbalanced panel of population of French manufacturing firms with 10 to 1,000 employees. These estimates of the implicit tax are based on different estimates of ; the methods are indicated in the different columns. Standard errors are calculated using bootstrap in columns (2) to (5). Using TFP-Size relationship calculates 1 where is calculated from the coefficient of a regression of ln(tfp) on ln(employment) on firms with 10 to 45 workers. Using the production function calculates as the sum of the coefficients on the factor inputs obtained from TFP estimation (see Table A1). High tech sectors are based on R&D intensity as defined by the OECD.
3 Table 3: Variation in estimates across different sectors (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Method Industry Baseline Manufacturing industries Calibrated Transport Calibrated Construction Calibrated Trade Calibrated Business services , power law (0.059) (0.059) (0.098) (0.099) (0.147) (0.147) (0.100) (0.100) (0.087) (0.087) T = (0.018) (0.018) (0.029) (0.029) (0.016) (0.016) (0.022) (0.021) (0.020) (0.020) n u, upper employment (0.024) (0.024) (0.016) (0.016) (0.013) (0.013) (0.015) (0.015) (0.018) (0.018) threshold σ,measurement error, implicit variable tax k/w, implicit tax, fixed cost Mean (Median) # of employees (0.025) (0.005) (0.257) (0.025) (0.004) (0.192) (0.023) (0.010) (0.492) 55.7 (23) 55.7 (23) 48.2 (23) (0.023) (0.007) (0.367) 48.2 (23) (0.037) (0.004) (0.179) (0.037) (0.003) (0.133) 29.3 (17) 29.3 (17) (0.050) (0.006) (0.288) (0.050) (0.004) (0.215) (0.024) (0.004) (0.188) (0.024) (0.024) (0.150) 36.8 (19) 36.8 (19) 45.3 (20) 45.3 (20) Observations 238, ,701 70,479 70, , , , , , ,835 Firms 57,008 57,008 14,487 14,487 41,768 41,768 66,848 66,848 61,906 61,906 Notes: Parameters estimated by ML with standard errors below in parentheses (clustered at the four digit level). Estimation is on unbalanced panel of firms with 10 to 1,000 employees. These estimates of the implicit tax are based on different (calibrated) values of that are indicated in the different columns.
4 Table 4: Welfare and Distributional Analysis Variable FLEXIBLE WAGES RIGID WAGES (World with regulation - World without regulation) 1. Unemployment rate 2. Percentage of firms avoiding the regulation 3. Percentage of firms paying tax (compliers) 0% 3.593% 9.036% 5.217% 3.438% 8.646% 4. Change in labor costs (wage reduction) for small firms (below 49) % 0 5. Change in labor costs (wage reduction but tax increase), 0.232% 1.306% Large firms (above 49) 6. Excess entry by small firms 4.419% 4.409% (percent increase in number of firms) 7. Increase in size of small firms 5.370% 0 8. Increase in size of large firms % % 9. Annual welfare loss (as a percentage of GDP): a. Tax b. Output loss 10. Winners and losers: a. Change in (expected) wages for workers who remain in labor force b. Average gain by entering entrepreneurs of small firms c. Average profit gain by small unconstrained firms d. Average profit gain by firms constrained at 49 e. Change in profit for large firms 0.804% 0.016% % 1.603% 4.296% 2.447% % 0.801% 4.302% % % % % Notes: This table is based on the baseline specification reported in tables 1 to 3, column 1, under the additional assumption that maximum firm size is 10,000. For the sake of simplicity, we neglect that the (insignificant) fixed cost component of the regulation. In the first column, the model is solved assuming that wages fully adjust (see section 2.4). In the second column, the model is solved assuming that wages do not adjust (see section 2.5). The "percentage of firms avoiding the regulation" corresponds to δ in the main part of the text. The "percentage of firms paying tax" corresponds to the mass of firms (agents) having productivity greater than in relative to firms having productivity greater than.the "change in labor costs for small firms" corresponds to the general equilibrium effect on wages. The "change in labor costs for large firms" corresponds to the previous quantity + the estimated tax (τ = 1.3%). "Excess entry" corresponds to the difference in the ln of the mass of firms (agents) having productivity greater than minus the ln of the mass of firms (agents) having productivity greater than. "Increase in size of small firms" corresponds to ln,, ln,, for firms having productivity smaller than ; increase in size of large firms corresponds to,, -,, for firms having productivity greater than. "Tax" corresponds to the total amount of taxes levied ( 1.,, as a share of total output,,. "Output loss" corresponds to ln,, ln,,. For "winners and losers", we compute the average (percentage point) changes in expected wages or profits for agent in each of the following bins: [α;α min [;[α min ;α fb min];[α fb min ; α c ]; [α c ; α u [;[α u ; + [. In the "rigid wages" case, expected wages are computed as 1.,, where is the unemployment rate.
5 Table 5: Comparison estimated results with of actual data (Actual data) Firms having 10 to 48 workers Firms having 49 to 57 workers Firms having 58 to 10,00 workers Distribution of firms (actual) Distribution of firms (predicted) (0.016) (0.007) (0.016) Distribution of employment (actual) Distribution of employment (predicted) n=n*[α].e ε ; σ ε = (0.024) (0.007) (0.027) Distribution of output (actual) Distribution of output (predicted) y = α.n θ =α. (n*[α]) θ.e θε, σ ε = (0.028) (0.007) (0.028) [0.219;0.331] [0.020;0.048] [0.635;0.747] Notes: The "actual" distribution is computed our estimation panel of population of French manufacturing firms with 10 to 1,000 employees. The "predicted" distribution is computed using our empirical model described in section 3 (incorporating a measurement error term ε) and our baseline estimate reported in tables 1 to 3, column1.
6 Figure 1: The Firm size distribution in the US and France (ln) Share of firms Firm size (employment) US firm size distribution FR firm size distribution Source: FICUS 2002 for France and Census, LBD 2003 for the US. Population databases of all firms. Notes: This is the distribution of firms (not plants). Authors' calculations
7 Figure 2: Number of Firms by employment size in France Source: FICUS, 2002 Notes: This is the population of manufacturing firms in France with between 31 and 69 employees. This plots the number of firms in each exact size category (i.e. raw data, no binning). There is a clear drop when regulations begin for firms with 50 or more employees.
8 Figure 3: Theoretical Firm size distribution with regulatory constraint Share of firms Notes: This figure shows the theoretical firm size distribution with exponentially increasing bins. The tallest bar represents the point at which the size constraint bins. Parameters: β α = 1.6, τ = 1.01, n u = 60, θ = 0.9, β =1.06.
9 Figure 4: Theoretical Relationship between TFP (managerial talent) and firm size Productivity index Notes: This figure shows the theoretical relationship between TFP and firm size. There is a mass of firms at employment size=50 where the regulatory constraint binds. Parameters: β α = 1.6, τ = 1.01, n u = 60, θ = 0.9, β =1.06.
10 Figure 5: The Theoretical Firm Size Distribution when employment is measured with error (ln) Share of firms n* (sigma = 0) n, sigma = 0.15 n, sigma = 0.5 Note: The solid (blue) line shows the theoretical firm size distribution (broken power law), n*. The dashed line shows the new firm size distribution when we extend the model, to allow employment size to be measured with error with σ = The solid dark line increases the measurement error to σ = 0.5
11 Figure 6: The effect on the measured firm size distribution using Alternative Datasets and definitions of employment Bar plot Log-log plot FICUS 2002: Fiscal source (corporate tax collection to fiscal administration) Arithmetic average of quarterly head counts (ln) Share of firms DADS 2002: Payroll tax reporting to social administration "Declared" workers on Dec. 31st: cross-sectional count, taking part of part-timers (ln) Share of firms DADS 2002: Payroll tax reporting to social administration "Full-time equivalent", computed by the French statistical institute as min,1, (ln) Share of firms Note: Data sources are indicated in the headers of the table. All datasets relate to the year 2002.
12 Figure 7: Share of Firms by employment size, 2002 (ln) Number of firms Source: FICUS, 2002 Notes: This is the population of manufacturing firms in France with between 1 and 1000 employees. This plots the number of firms in each exact size category (i.e. raw data, no binning). As in figure 2, there is a clear drop at 50, but also at 10 employees. These thresholds correspond to various size based regulations.
13 Figure 8: TFP Distribution around the regulatory threshold of 50 employees Panel A: Short span Av. TFP, Lev. Pet. estimates Size Panel B: Longer span Av. TFP, Lev. Pet. estimates Size Notes: This figures plots the mean level of TFP by firm employment size using an upper support of 100 (Panel A) or 500 (Panel B). A fourth order polynomial is displayed in both panels using only data from the "undistorted" points (potentially "distorted" points are shown in red).
14 Figure 9: Firm Size Distribution and Broken Power Law: Data and Fit of Model (ln) Share of firms n* n n, actual Notes: This shows the difference between the fit of the model (dashed red line) which allows for measurement error with the actual data. Estimates correspond to the baseline specification reported in tables 1 to 3, column 1. We also include the pure theoretical predictions (in blue).
15 Figure 10: Firm Size Distribution with and without regulation (ln) Number of firms with regulation without regulation Notes: This graph compares the firm size distribution in a world with regulation (blue line) from a world without regulation (red line) based on the estimated parameters from our model (baseline specification reported in tables 1 to 3, column 1).
16 Figure 11: Output Across Entrepreneurs (ln) Output (ln) Ability with regulation without regulation Notes: This graph compares the relation between ability (productivity) and output for individuals of different managerial ability in a world with regulation (blue line) from a world without regulation (red line) based on the estimated parameters from our model. Maximum ability has been normalized to 1; an ability level of corresponds to a firm size of 49 and an ability level of corresponds to a firm size of 58. Underlying estimates correspond to the baseline specification reported in tables 1 to 3, column 1.
17 Figure 12: Distributional Effects of the Regulation Diff. in ln(income) (with tax - w/o tax) (ln) Ability Notes: This graph compares the change in ln(income) for individuals of different managerial ability in a world with regulation reklative to one without regulation based on the estimated parameters from our model. The blue line is our baseline base. Maximum ability has been normalized to 1. Individuals with an ability level below 0.21 are workers in the world with regulation. An ability level of 0.34 corresponds to a firm size of 49 and an ability level of 0.37 corresponds to a firm size of 58. Underlying estimates correspond to the baseline specification reported in tables 1 to 3, column 1.
18 Figure 13: Corporate Restructuring in Response to the Regulation? Independent Firms vs. Corporate groups (ln) Share of firms Standalone Under control, aggregated at the group level Notes: "Standalone" are independent firms (blue crosses). Under Control are firms who are subsidiaries of larger business groups; and employment has been aggregated at the group level in this case (black crosses). This shows that the discontinuity at 50 is observable in both populations of firms. The discontinuity at 50 is therefore not simply due to subsidiaries of larger business groups as we can see the relationship even at the group level..
19 Figure 14: Adjustment in the hours margin around the threshold (annual hours per worker) Hours per employee Notes: Annual average hours per worker - combined FICUS and DADs data for % confidence intervals shown.
20 Figure 15: Adjustment in Types of Labor A. Share of Managers Share of mangers (CS3) B. Share of manual workers Share of manual workers (CS6) C. Share of Clerical Workers Share of clerical workers (CS6) Notes: This looks at the share of the main three occupational groups using combined FICUS and DADs data for Managers and professionals are the most skilled group and manual workers are the least skilled group (clerical workers are in between). 95% confidence intervals shown.
21 Figure 16: Workers do not appear to be accepting significantly lower wages in return for insurance of employment protection Wages (net of employer payroll taxes) per worker (ke) Source: FICUS, Notes: Wages is the nominal wage (net of payroll tax) by employer size. 95% confidence intervals shown.
22 Appendices Table A1: Production Function Estimation (1) (2) (3) Baseline High Tech Sectors Low Tech Sectors Labor (0.004) (0.011) (0.004) Capital (0.004) (0.012) (0.003) Observations 219,938 35, ,705 Firms 53,127 8,410 44,931 Source: FICUS, 2001 to Notes: Parameters estimated by Levinsohn-Petrin (2003) method. Estimation on unbalanced panel of population of French manufacturing firms with 10 to 1,000 employees (with retrospective information for 2001). High tech sectors are based on R&D intensity as defined by the OECD.
23 Table A2: Welfare Analysis under alternative assumptions for the upper bound Variable (World with tax -World without) Upper bound = 500 Upper bound = 1,000 Upper bound = 5,000 Unemployment rate Percentage of firms avoiding the regulation Percentage of firms paying tax (compliers) Change in labor costs (wage reduction), Small firms (below 49) Change in labor costs (wage reduction but tax increase), Large firms (below 49) Excess entry by small firms Rigid Wages Flexible Wages Rigid Wages Flexible Wages Rigid Wages Flexible Wages 4.066% 0% 4.433% 0% 5.032% 0% 3.528% 3.652% 3.490% 3.624% 3.447% 3.597% 7.259% 7.516% 7.939% 8.244% 8.557% 8.928% % % % 1.306% 0.473% 1.306% 0.396% 1.306% 0.271% 3.472% 3.487% 3.763% 3.778% 4.254% 4.265% (percent increase in number of firms) Increase in size of small firms % % % Increase in size of large firms % % % % % % Annual welfare loss: Tax (as a share of GDP) Output loss Winners and losers: Change in wages for workers who remain workers Average gain by entering entrepreneurs Average profit gain by small unconstrained firms Average profit gain by firms constrained at 49 Change in profit for large firms 0.522% 3.353% % % % % 0.525% 0.033% % 1.245% 3.333% 1.484% % 0.615% 3.655% % % % % 0.618% 0.028% % 1.359% 3.639% 1.790% % 0.759% 4.149% % % % % 0.762% 0.019% % 1.546% 4.141% 2.292% % Notes: This table is based on the baseline specification reported in tables 1 to 3, column 1, under the additional assumption that maximum firm size is 10,000. For the sake of simplicity, we neglect that the (insignificant) fixed cost component of the regulation. In the first column, the model is solved assuming that wages fully adjust (see section 2.4). In the second column, the model is solved assuming that wages do not adjust (see section 2.5). The "percentage of firms avoiding the regulation" corresponds to δ in the main part of the text. The "percentage of firms paying tax" corresponds to the mass of firms (agents) having productivity greater than in relative to firms having productivity greater than.the "change in labor costs for small firms" corresponds to the general equilibrium effect on wages. The "change in labor costs for large firms" corresponds to the previous quantity + the estimated tax (τ = 1.3%). "Excess entry" corresponds to the difference in the ln of the mass of firms (agents) having productivity greater than minus the ln of the mass of firms (agents) having productivity greater than. "Increase in size of small firms" corresponds to,, -,, for firms having productivity smaller than ; increase in size of large firms corresponds to ln,, ln,, for firms having productivity greater than. "Tax" corresponds to the total amount of taxes levied ( 1.,, as a share of total output,,. "Output loss" corresponds to ln,, ln,,. For "winners and losers", we compute the average (percentage point) changes in expected wages or profits for agent in each of the following bins: [α;α min [;[α min ;α fb min];[α fb min ; α c ]; [α c ; α u [;[α u ; + [. In the "rigid wages" case, expected wages are computed as 1.,, where is the unemployment rate.
24 Figure A1: Heterogeneity of Results by three digit sector in manufacturing tau (and implied tax) theta (returns to scale) Manufacturing Transport Construction Trade Business Services Notes: These are the results from industry-specific estimation on the same lines as column (2) of Table 2
25 Figure A2: Who are the firms having 50 to 58 employees? (a) Outsourced workers Expenditure as share of total wage bill (b) Probability of large annual increase (by more than 10 %) (average probability ) Probability of increase in (c) For Comparison: Probability of "large" decrease (by more than 10 %) (average probability ) Probability of decrease in Notes: Annual averages, combined FICUS and EAE (French Annual Survey of Manufacturing) data for in panel (a), and FICUS data in panels (b) and (c).
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INDUSTRY GROWTH, FIRM SIZE AND THE BUSINESS ENVIRONMENT SEPTEMBER 2008 THIS MICROREPORT WAS PRODUCED FOR REVIEW BY THE UNITED STATES AGENCY FOR INTERNATIONAL DEVELOPMENT. IT WAS PREPARED BY THE IRIS CENTER,
Online Appendix: Structural Transformation and the Oil Price 1 Radoslaw (Radek) Stefanski Laval University and OxCarre December 1, 2013 1 All errors are my own. Contact: email@example.com 1
Data exploration with Microsoft Excel: univariate analysis Contents 1 Introduction... 1 2 Exploring a variable s frequency distribution... 2 3 Calculating measures of central tendency... 16 4 Calculating
SUMAN DUVVURU STAT 567 PROJECT REPORT SURVIVAL ANALYSIS OF HEROIN ADDICTS Background and introduction: Current illicit drug use among teens is continuing to increase in many countries around the world.
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Volatility, Productivity Correlations and Measures of International Consumption Risk Sharing. Ergys Islamaj June 2014 Abstract This paper investigates how output volatility and productivity correlations
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Principles of Macroeconomics Prof. Yamin Ahmad ECON 202 Fall 2004 Sample Final Exam Name Id # Part B Instructions: Please answer in the space provided and circle your answer on the question paper as well.
INVESTMENT PLANNING COSTS AND THE EFFECTS OF FISCAL AND MONETARY POLICY Susanto Basu and Miles S. Kimball University of Michigan and NBER MAIN RESULTS. Show that a model with capital accumulation and sticky
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Multiple Optimization Using the JMP Statistical Software Kodak Research Conference May 9, 2005 Philip J. Ramsey, Ph.D., Mia L. Stephens, MS, Marie Gaudard, Ph.D. North Haven Group, http://www.northhavengroup.com/
Compensating Wage Differentials and Seasonal Employment in Austria: Evidence from Administrative Data Emilia Del Bono, Andrea Weber First draft February 8, 2005 Abstract In this paper we investigate the
On the Dual Effect of Bankruptcy Daiki Asanuma Abstract This paper examines whether the survival of low-productivity firms in Japan has prevented economic recovery since the bursting of the financial bubble
My presentation is about data visualization. How to use visual graphs and charts in order to explore data, discover meaning and report findings. The goal is to show that visual displays can be very effective
Biostatistics: DESCRIPTIVE STATISTICS: 2, VARIABILITY 1. Introduction Besides arriving at an appropriate expression of an average or consensus value for observations of a population, it is important to
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FDI as a source of finance in imperfect capital markets Firm-Level Evidence from Argentina Paula Bustos CREI and Universitat Pompeu Fabra September 2007 Abstract In this paper I analyze the financing and