Wage Incidence of Corporate Income Tax: Contrasting Individual and Firm-Level Estimates

Transcription

1 Wage Incidence of Corporate Income Tax: Contrasting Individual and Firm-Level Estimates C. Carbonnier 1,2, C. Malgouyres 3,4, L. Py 2,3, C. Urvoy 2,5 1 Université de Cergy-Pontoise, THEMA 2 Sciences Po, LIEPP 3 Banque de France 4 IPP 5 Sciences Po, Department of Economics May 18th, / 24

2 Introduction Motivation Decrease in corporate income tax rates in Western countries (US, France) Who bears the burden of this tax? How does a change in the tax rate affect the distribution of wages? A corporate income tax credit to gain insights on the wage incidence of the corporate income tax Policy A corporate tax credit that resembles a payroll tax cut The amount of tax credit is a proportion (4%, then 6%) of the wages of workers paid less than 2.5 minimum wages (MW) Schedule 2.5MW: trade off between labor economics and export competition Schedule allows disentangling individual- and firm-level incidence: Individual-level: employees paid more or less than 2.5 MW Firm-level: firms more or less intensive in workers paid below 2.5 MW 2 / 24

3 Introduction Data Exhaustive matched employer-employee data Data on wages, hours worked, position, tax credit, firms characteristics Methodology Individual-level: bunching in the distribution of wages Firm-level: DiD and event-studies leveraging the discontinuity in eligibility by comparing firms with similar wage structure, except around the notch 3 / 24

4 Introduction Individual-level findings No bunching in the wage distribution at the cutoff Implies discontinuity in labor costs that persists until 2015 Salience, uncertainty, tax-benefit linkage, equity? Firm-level findings Sizable incidence on wages, but only wages of white-collars An ex ante eligibility in tax credit amounting to 1% of the wagebill translates into a.5% increase of the wages of white-collars Treatment spills over onto workers less likely to be paid below 2.5MW No detectable employment effect: no scale and complementarity mechanism Key role of firm-level mechanisms, potentially large distributive impact 4 / 24

5 Literature Literature on incidence of payroll taxes Textbook view: born by workers, unchanged labor cost, due to market adjustments (labor demand more elastic than labor supply) Empirical evidence: labor income share (including payroll taxes) stable over time (OECD, 1990), SSC contributions fall on employees (Gruber, 1997) Recently challenged: Saez et al. (2012), Bozio et al. (2017), Saez et al. (2017) find limited incidence on employees and stress the importance of the tax-benefit linkage Firm-level mechanisms crucial to understand incidence (Saez et al., 2017) Literature on incidence of corporate taxation Seminal paper by Herberger (1962) predicts the tax is born by firms in a closed economy, by workers in an open economy Arulampalam et al. (2012), Suárez Serrato and Zidar (2016), Fuest et al. (2018), about half of corporate tax seems to passed on to workers Instead of local variaiton in tax rates, we use firm-level variation in treatment intensity, national policy 5 / 24

6 Data and estimation sample Data sources Data on the CETC, firm-level ( ) Amount and use of the CETC: tax deduction, cash flows (MVC, DGFiP) Balance sheet and income statement data, firm-level ( ) Data on turnover, employees, margins, etc. (FARE, INSEE) Job data, job level ( ) Wage, hours worked, SPC, type of contract, etc. (DADS, INSEE) Estimation sample Keep only firms present in the 3 datasets and eligible Drop outliers for eligible wagebill, wages, profits margins (P1 & P100) Balanced panel of 328,674 firms ( ) Very representative: 86% of jobs, 90% of eligible wage bill 6 / 24

7 Individual-level analysis The policy implies a decrease in the labor cost of workers paid less than 2.5 MW Expect labor market adjustment: increased demand in now cheaper labor and relatively less elastic supply translate in wage increases for workers below 2.5 MW Graphically, we should detect bunching in the wage distribution below the notch 7 / 24

8 Bunching in new hires wages distribution No discontinuity in the wage distribution of new hires at the cutoff Distribution of new hires' wages - estimating sample (as a fraction of the min. wage) Source: DADS. The y-axis refers to number of hires in thousands. Hires are defined as jobs starting in Feb. or later at year t that did not exist in year t-1 taken up by workers not employed in the same firm at t-1. Firms with no employment at year t-1 are excluded. 8 / 24

9 Bunching in new hires wages distribution Method from Chetty, Friedman, Olsen and Pistaferri (QJE 2011) Goal: estimate excess mass relative to local density Fit a counterfactual distirbution accounting for the fact that the excess mass locally must be compensated elsewhere No excess mass near the cutoff up to three year after the implementation of the policy New hires (2.3, 2.5) Year ˆb (.3401) (.411) (.3885) Additionally, we test for deformation in the wage growth distribution: no significant difference Wage Growth 9 / 24

10 Bunching in new hires wages distribution Rationalization Salience (Chetty et al., 2009): accounting vs. executive functions (Carbonnier et al., 2016), but persistent, and firm-level incidence in 2013 Uncertainty: policy not seen as persistent, downward wage rigidity Tax-benefit linkage (Bozio et al., 2017): tax credit not linked to employee benefits Wage equity constraint (Saez et al., 2018): relatively high wages 10 / 24

11 Firm-level analysis Persistent discontinuity in labor cost at the notch Does it means that the tax credit fully falls on the firm? We examine whether firm-level mechanisms play a role in the transmission of the tax credit 11 / 24

12 Measurement and empirical strategy Main idea: use variation in treatment intensity instead of treatment status as a vast majority of firms is eligible to the tax credit Threat to identification: treatment intensity is computed from the wage bill, whose dynamics can be influenced by the policy Use pre-reform (2012) wage bill No anticipation: new government elected in 2012, policy announced in November, voted in December Z i = j i w j,2012h j,2012 1(w j,2012 < 2.5 MW 2012 ) j i w j,2012h j,2012 where h jt and w jt denote respectively hourly wage and hours worked for employee j in firm i at time t. 5.3% is the average rate over the period studied ( ) Distribution of treatment 12 / 24

13 Measurement and empirical strategy Actual vs. predicted treatment intensity Actual Treatment Actual Treatment Intensity (2013) Intensity (2014) Predicted Treatment 0.776*** 0.754*** Intensity (2012) (0.002) (0.002) Observations 336, ,297 R / 24

14 Measurement and empirical strategy Reduced-form difference-in-difference ln(y it ) = β Z i 1(t 2013) + X itγ + α i + α cnst + ε it Reduced-form event study ln(y it ) = 2015 d=2009,d 2012 β t Z i 1(d = t) + X itγ + α i + α cnst + ε it where Y it stands for wages or employment of firm i at time t where Z i is the predicted treatment intensity of firm i where X it is a set of lagged controls (productivity, assets, % workers below 1.5 MW year dummies) α i are firm fixed-effects α cnst are cells industry size year fixed-effects 14 / 24

15 Measurement and empirical strategy Main idea: compare firms with similar wage distributions, except immediately around the cutoff Implications Comparability of firms: within-cell common trend assumption More balanced: variation in treatment intensity stemming from the repartition of wages around the notch, less likely correlated with counfouding factors Table: Ex ante correlation of treatment intensity with firm charasteristics Sector Sector size Statistic Uncondit. size FEs cells FEs ρ(z i, Assets i ) ρ(z i, (VA/L) i ) / 24

16 Measurement and empirical strategy We also restrict our analysis to firms whose employees paid between 2.2MW and 2.8MW account for at least 30% or 50% of the wage bill Table: Between/within cell variation in treatment Statistic Sample Std deviation Between Cells Within Cells ˆV(Z i ) all % 8.0% ˆV(Z i ) % WB > % 49.8% ˆV(Z i ) % WB > % 72.4% 16 / 24

17 Measurement and empirical strategy Table: Comparison of subsamples (2012) All > 30% > 50% Employment and wage # of employees # of white-collars # of blue-collars Mean wage Mean wage white-collars Mean wage blue-collars Wage stucture Wabe bill Share of <1.6MW in WB Share of <2.5MW in WB Firm performance Sales EBIT VA per worker Assets Industries Manufacturing Construction Retail Services Other Observations / 24

18 Firm-level results: hourly wages Effect on hourly wages: Difference in difference, all employees Table: Impact on mean hourly wage of stayers per firm Z i 1{t 2013} 0.443*** 0.438*** 0.409*** 0.517*** 0.493*** 0.453*** 0.620*** 0.601*** 0.568*** (0.0757) (0.0734) (0.0687) (0.0936) (0.0909) (0.0850) (0.134) (0.131) (0.122) Observations R Window defining cells (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) % WB in window Width Cells Lagged Controls # firms * p < 0.05, ** p < 0.01, *** p < Sources: DADS, FARE, MVC cells. Significant positive impact on the wages of permanent employees An ex ante eligibility in tax credit amounting to 1% of the wagebill translates into a.5% increase of wages 18 / 24

19 Firm-level results: hourly wages Effect on mean hourly wage of stayers: Event study, all employees 2015 ln(y it ) = β t PredictedTreatment i 1(d = t) + X itγ + α i + α cnst + ε it d=2009,d cells, at least 30% of the wage bill is between 2.2 and 2.8 MW, with controls All 50% Finer grid Thicker grid 19 / 24

20 Firm-level results: hourly wages Effect on mean hourly wage of stayers: Event study Figure: Blue-collars (left) and white-collars (right) event studies Significant positive impact only on the wages of white-collar permanent employees About 30% of them earn less than 2.5 MW, treatment spills over DiD blue-collars DiD white-collars Robustness 20 / 24

21 Firm-level results: employment Table: Impact on hours worked per firm Blue-collars Z i 1{t 2013} ** * * * (0.239) (0.233) (0.229) (0.290) (0.284) (0.282) (0.416) (0.410) (0.407) Observations R White-collars Z i 1{t 2013} (0.260) (0.253) (0.252) (0.304) (0.296) (0.296) (0.409) (0.403) (0.407) Observations R Window defining cells (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) % WB in window Width Cells Lagged Controls # firms * p < 0.05, ** p < 0.01, *** p < Sources: DADS, FARE, MVC Scale effect and skill complementarity? No impact on blue-collars employment Local estimation 21 / 24

22 Firm-level results: heterogeneity in wage incidence Heterogeneity Gender: larger incidence on male workers, but can be because of of composition effects Industries: larger incidence in the services sector compared to manufacturing Profits: firms that have paid CIT at least once in increase wages by more Gender Industries Profits By herfindahl 22 / 24

23 Conclusion No detactable incidence at the individual level But substantial incidence on workers at the firm-level White-collars wages increase, but not blue-collars Treatment actually impacts less targeted categories Possible rationalization: rent-sharing at firm-level 23 / 24

24 Thank you 24 / 24

25 Introduction Comparison with pre-existing cuts in payroll taxes Back 1 / 15

26 Wage growth distribution No discontinuity in the wage growth at the cutoff bin_ceil bin_ceil bin_ceil bin_ceil bin_ceil bin_ceil 2 / 15

27 Wage growth distribution Detection of wage growth distribution deformation at the threshold OLS polynomial interpolation on [1.5MW ; 2.3MW ] [2.7MW ; 3.5MW ] Aggregate distance between actual and interpolated on [2.3MW ; 2.7MW ] Standard error computation from variance-covariance matrix Table: Estimation of wage growth distribution deformation Year ĝ ḡ Diff. z-stat Back 3 / 15

28 Measurement and empirical strategy Distribution of actual treatment intensity, by firm size Frequency (thousands) Frequency (thousands) CICE / wagebill (in %) CICE / wagebill (in %) All < 50 Frequency Frequency CICE / wagebill (in %) CICE / wagebill (in %) Back 4 / 15

29 Firm-level results: hourly wages cells, all firms, with controls Back 5 / 15

30 Firm-level results: hourly wages cells, at least 50% of WB in the window, with controls Back 6 / 15

31 Firm-level results: hourly wages cells, at least 30% of WB in the window, with controls Back 7 / 15

32 Firm-level results: hourly wages cells, at least 30% of WB in the window, with controls Back 8 / 15

33 Firm-level results: hourly wages Effect on hourly wages: Difference in difference, blue-collars Table: Impact on mean hourly wage of blue-collar stayers per firm Z i 1{t 2013} (0.0821) (0.0802) (0.0776) (0.108) (0.106) (0.103) (0.174) (0.168) (0.163) Observations R Window defining cells (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) % WB in window Width Cells Lagged Controls # firms * p < 0.05, ** p < 0.01, *** p < Sources: DADS, FARE, MVC No impact of the tax credit on wages of blue-collar permanent employees Back 9 / 15

34 Firm-level results: hourly wages Effect on hourly wages: Difference in difference, white-collars Table: Impact on mean hourly wage of white-collar stayers per firm Z i 1{t 2013} 0.348*** 0.437*** 0.437*** 0.393*** 0.438*** 0.420*** 0.584*** 0.593*** 0.605*** (0.108) (0.103) (0.102) (0.125) (0.120) (0.118) (0.168) (0.159) (0.157) Observations R Window defining cells (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) (2.2,2.8) % WB in window Width Cells Lagged Controls # firms * p < 0.05, ** p < 0.01, *** p < Sources: DADS, FARE, MVC Significant positive impact on the wages of white-collar permanent employees About 30% of them earn less than 2.5 MW Treatment spills over onto less targeted workers Back 10 / 15

35 Firm-level results: hourly wages Effect on mean hourly wage of stayers: Event study Figure: Blue-collars (left) and white-collars (right) event studies No ctrls; 31-pt grid No ctrls, 51-pt grid Ctrls, 31-pt grid Ctrls, 51-pt grid No ctrls; 31-pt grid No ctrls, 51-pt grid Ctrls, 31-pt grid Ctrls, 51-pt grid At least 50% of the wage bill is between 2.2 and 2.8 MW Back 11 / 15

36 Heterogeneity, by profits Table: Incidence on wages, by profitability (paid CIT at least once over ) All workers Z i 1{t 2013} 1{profitable} 0.143*** (0.0158) Z i 1{t 2013} 0.191** (0.0896) R Observations 140,930 Back 12 / 15

37 Heterogeneity, by gender Table: Incidence on wages, by gender Gender Females Males Z i 1{t 2013} 0.299*** 0.457*** (0.097) (0.087) R Observations 572, ,855 # firms 107, ,066 Back 13 / 15

38 Heterogeneity, by industry Table: Incidence on wages, by industry Industry Manufacturing Services All High skills Low skills All High skills Low skills Z i 1{t 2013} * *** 0.427*** (0.207) (0.339) (0.215) (0.082) (0.112) (0.096) R Observations 103,201 85,998 98, , , ,096 # firms 17,988 16,024 17,594 78,052 68,401 70,370 Back 14 / 15

39 Heterogeneity, by employment herfindahl Table: Incidence on wages, by monopsony power Monopsony power Below median Above median All workers High skills Low skills All workers High skills Low skills Z i 1{t 2013} 0.413*** 0.411*** *** 0.676*** (0.106) (0.137) (0.118) (0.108) (0.198) (0.121) R Observations 278, , , , , ,554 # firms 50,165 50,881 42,674 44,375 25,370 43,333 Back 15 / 15