Reallocation and the Role of Firm Composition Effects on Aggregate Wage Dynamics

Year	% of firms in industry	% of firms in manufacturing	wage Monthly per nominal employee		Firm size		N. of firms	N. of employees
			Mean	SD	Mean	SD
1990	0.49	0.32	1,102	457	7.96	182.28	1,116,988	8,886,276
1991	0.48	0.32	1,217	495	7.96	181.01	1,120,616	8,921,224
1992	0.48	0.31	1,288	539	7.86	188.06	1,122,465	8,823,486
1993	0.47	0.31	1,334	556	7.8	184.21	1,084,613	8,462,596
1994	0.47	0.31	1,382	579	7.83	180.24	1,059,330	8,297,098
1995	0.47	0.30	1,441	620	7.87	179.07	1,063,816	8,370,518
1996	0.47	0.30	1,492	646	7.94	172.87	1,069,946	8,494,919
1997	0.46	0.30	1,550	670	7.96	163.06	1,058,114	8,422,835
1998	0.46	0.29	1,580	697	7.97	156.18	1,082,870	8,627,422
1999	0.45	0.28	1,595	711	7.86	138.33	1,136,160	8,931,878
2000	0.44	0.27	1,637	766	7.97	139.11	1,181,331	9,411,951
2001	0.44	0.27	1,675	821	7.98	140.12	1,222,381	9,748,518
2002	0.44	0.26	1,693	788	7.73	133.23	1,293,289	9,993,794
2003	0.44	0.25	1,728	819	7.7	129.98	1,325,116	10,208,096
2004	0.43	0.24	1,765	837	7.59	127.86	1,369,570	10,388,312
2005	0.42	0.24	1,816	892	7.56	128.7	1,380,839	10,444,820
2006	0.42	0.23	1,872	938	7.55	131.95	1,403,808	10,592,187
2007	0.42	0.22	1,898	994	7.53	133.46	1,474,112	11,105,779
2008	0.41	0.22	1,973	1,030	7.57	128.97	1,496,808	11,335,465
2009	0.40	0.22	1,975	1,006	7.48	146.85	1,478,607	11,056,102
2010	0.39	0.21	2,031	1,061	7.43	169.79	1,471,727	10,941,586
2011	0.38	0.21	2,068	1,070	7.46	165.14	1,467,731	10,943,035
2012	0.37	0.21	2,073	1,086	7.35	167.58	1,468,616	10,790,006
2013	0.36	0.21	2,100	1,140	7.46	169.2	1,415,186	10,556,232
2014	0.36	0.21	2,128	1,149	7.61	174.12	1,371,093	10,440,510
2015	0.35	0.20	2,156	1,175	7.59	174.64	1,392,761	10,565,555

[i] Source: own calculations on INPS data for the universe of firms. Statistics of wages are weighted by the number of employees in the firm.

Table A2

Descriptive statistics on workers (at the contract level)

	Daily nominal wage		Age		% female	% full time	% blue collars	% white collars	% middle managers	% industry	N. of employees	N. of firms
Year	Mean	SD	Mean	SD
1990	49.92	26.20	36.32	11.00	0.30	0.96	0.64	0.32		0.64	674,316	263,731
1991	51.53	25.64	36.38	10.97	0.30	0.95	0.64	0.33		0.63	683,562	267,286
1992	54.58	28.09	36.52	10.92	0.30	0.95	0.63	0.33		0.63	683,060	269,335
1993	56.64	28.77	36.70	10.79	0.31	0.94	0.63	0.34		0.61	656,778	261,026
1994	58.39	29.77	36.74	10.69	0.31	0.93	0.62	0.34		0.60	648,803	257,610
1995	60.17	30.64	36.60	10.57	0.32	0.92	0.63	0.34		0.60	654,221	259,404
1996	62.02	31.46	36.62	10.52	0.32	0.91	0.63	0.32	0.02	0.59	665,853	264,966
1997	64.28	32.91	36.64	10.42	0.32	0.91	0.63	0.32	0.02	0.58	665,207	262,301
1998	65.77	34.01	36.78	10.41	0.33	0.90	0.62	0.32	0.02	0.58	677,306	266,600
1999	66.64	34.31	36.75	10.37	0.33	0.89	0.62	0.31	0.02	0.56	702,670	277,117
2000	67.97	35.53	36.87	10.34	0.33	0.89	0.61	0.31	0.02	0.55	747,457	292,300
2001	69.39	36.51	37.04	10.32	0.34	0.88	0.61	0.31	0.03	0.54	774,424	303,645
2002	70.60	37.15	37.04	10.28	0.33	0.87	0.62	0.30	0.03	0.53	810,678	324,062
2003	72.30	37.94	37.30	10.26	0.34	0.86	0.62	0.30	0.03	0.52	818,378	329,247
2004	74.65	39.02	37.56	10.22	0.34	0.85	0.61	0.30	0.03	0.51	826,770	336,332
2005	76.51	39.87	37.94	10.24	0.34	0.84	0.60	0.31	0.03	0.50	821,421	336,031
2006	78.71	40.91	38.24	10.27	0.35	0.83	0.60	0.31	0.03	0.49	835,521	341,087
2007	80.38	41.51	38.34	10.35	0.35	0.82	0.60	0.30	0.03	0.49	879,014	362,206
2008	84.25	44.03	38.56	10.39	0.35	0.81	0.60	0.30	0.03	0.48	895,650	369,088
2009	85.83	44.42	39.11	10.43	0.36	0.80	0.59	0.31	0.03	0.46	882,614	365,012
2010	87.71	45.55	39.41	10.48	0.36	0.79	0.59	0.31	0.03	0.45	877,436	362,978
2011	89.07	46.39	39.69	10.52	0.36	0.79	0.60	0.31	0.03	0.44	880,748	363,405
2012	90.33	46.92	40.04	10.58	0.37	0.77	0.60	0.31	0.03	0.43	871,845	362,267
2013	92.29	47.79	40.47	10.59	0.37	0.75	0.59	0.32	0.03	0.42	844,600	346,920
2014	92.98	48.05	40.88	10.68	0.37	0.74	0.59	0.32	0.03	0.41	835,498	338,086
2015	93.94	48.03	41.12	10.80	0.37	0.73	0.59	0.32	0.03	0.40	856,844	345,811
2016	94.22	48.00	41.31	10.95	0.36	0.72	0.59	0.32	0.03	0.40	869,931	346,633

[i] Source: own calculations on INPS data; data are summarized at the contract level and refer to all employees born on the 1st and 9th day of each month. Note: Data on middle managers and white collars are reported together before 1997. Number of firms where at least one worker in the sample transited in the considered year.

Contribution of composition effects to the wage growth, distinguishing between employers’ and workers’ characteristics.
*Source:* own calculations on INPS data. *Note:* this figure plots the results on *composition effects* obtained from the BO decomposition (this is therefore the part of aggregate wage dynamics explained by changes in the average characteristics of employed individuals in the economy and of the firms where they are employed, keeping returns to these characteristics fixed over time). The results report the ratio between the 3-year moving average of the part of aggregate wage growth explained by changes in workers’ and employers’ composition and the 3-year moving average of aggregate wage growth. The blue line refers to the share of the yearly change in wage levels explained by changes in workers’ characteristics, and the red line refers to the share of the yearly change in wage levels explained by changes in employers’ characteristics.

The contribution of some employers’ and workers’ characteristics to the composition effect of aggregate nominal wages.
*Source:* own calculations on INPS data. *Note*: this figure plots the average contribution, for several subperiods, of the composition effects referred to changes in different workers’ and employers’ characteristics, as obtained from the BO decomposition. It therefore plots the average $({\bar{x}}_{i j t} - {\bar{x}}_{i j t - 1}) {\hat{β}}_{t}$ in each four- or five-year period for different x.

Contribution of composition effects to the wage growth, distinguishing between employers’ and workers’ characteristics, by sector.
*Source:* own calculations on INPS data. *Note:* this figure plots the results on composition effects obtained from the BO decomposition (this is therefore the part of aggregate wage dynamics explained by changes in the average characteristics of employed individuals in the economy and of the firms where they are employed, keeping returns to these characteristics fixed over time). The results report the ratio between the 3-year moving average of the part of aggregate wage growth explained by changes in workers’ and employers’ composition and the 3-year moving average of aggregate wage growth. The blue line refers to the share of the yearly change in wage levels explained by changes in workers’ characteristics, and the red line refers to the share of the yearly change in wage levels explained by changes in employers’ characteristics.

Representativeness of INPS and ESBS databases, class size.
*Source:* our calculation based on INPS and Eurostat, *Structural Business Statistics* data.

Contribution of the OP term to aggregate wage changes $({Δ {OP}_{t}^{m} / Δ \bar{W}}_{t}^{m}),$ by sector.
*Source*: our calculations based on INPS data on the universe of firms. Data on 2016 are not yet available for all firms and are thus discarded. *Note*: $Δ x_{t}^{m} = \sum_{h = - 1}^{1} Δ x_{t + h}$ with Δ denoting first differences. TOT = private nonagricultural sector (blue line), MAN = manufacturing sector (red line), and SER = private services (green line; right axis)

Table 1

Percentage contribution of the OP term to aggregate wage growth in different periods

	Private Sector			Manufacturing			Private Services
Years	Wage growth	Counterfactual wage growth	Fraction due to OP term	Wage growth	Counterfactual wage growth	Fraction due to OP term	Wage growth	Counterfactual wage growth	Fraction due to OP term
Wages (%)

2002–2015	27.3	19.2	29.7	41.6	28.8	30.8	17.6	12.7	27.7
2004–2015	22.2	15.1	31.8	33.3	22.9	31.1	14.5	9.6	33.9
2004–2008	11.8	8.9	24.7	15.1	10.8	28.3	9.9	7.2	26.6
2008–2015	9.3	5.7	38.4	15.8	10.9	31.0	4.2	2.2	48.7

Wages net of differences in firm occupation structure across firms (%)

2002–2015	68.0	49.2	27.6	89.1	64.1	28.1	58.8	41.5	29.4
2004–2015	70.5	53.4	24.3	88.8	67.2	24.3	62.8	46.5	26.0
2004–2008	28.5	22.7	20.3	34.3	26.7	22.2	25.5	19.5	23.7
2008–2015	32.7	25.0	23.6	40.6	32.0	21.1	29.7	22.6	23.9

[i] Notes: The table displays, for different time intervals, actual wage growth and the counterfactual wage growth (obtained keeping the contribution of the OP term to the aggregate wage constant, i.e. keeping the distribution of workers between low- and high-paying firms constant). Results in the bottom half of the table are obtained by applying the OP decomposition to log-firm wages after controlling for the share of middle managers, white collars, and blue collars.

Contribution of the OP term to aggregate wage changes $(Δ {OP}_{t}^{m} / Δ {\bar{W}}_{t}^{m}),$ by sector and net of changes in workers’ composition.
*Note*: our calculations based on INPS data on the universe of firms. Data on 2016 are not yet available for all firms and are thus discarded. *Note*: $Δ x_{t}^{m} = \sum_{h = - 1}^{1} Δ x_{t + h}$ with Δ denoting first differences. TOT= private nonagricultural sector (blue line), MAN = manufacturing sector (red line), and SER = private services (green line; right axis). We correct for workers’ composition by using the residual of a regression of wages at the firm level on the occupational composition of workers in each firm, as a measure of net wages of workers’ composition.

Average labor productivity and average wage by (log) class size, and fractions of incorporated businesses and of firms with balance sheet data within the universe of employer businesses.
*Note*: our calculations based on INPS and Cerved. The figure displays the average labor productivity for the sample of limited companies in Cerved that can be merged to firms in INPS and the average wage for the firms in INPS, i.e. for the entire population of employer businesses, conditional on (the natural logarithm of) class size (left scale). It also reports the fraction of firms in INPS that are incorporated businesses and the fraction of firms in INPS that can be merged with Cerved and, therefore, for which we have labour productivity data (right scale).

Table 2

Correlations between log size, log firm wage and log labor productivity

Year 2007
All firms				E ≥ 20
	ln(E)	ln(W)	ln(VA/E)	ln(E)	ln(W)	ln(VA/E)
ln(W)	29.8%			13.7%
ln(VA/E)	-4.5%	51.2%		11.8%	79.6%
ln(LC)	19.4%	77.4%	61.8%	12.9%	90.6%	80.8%

[i] Source: own calculations on INPS–Cerved data. The first panel shows correlations for the entire sample of firms for which these data are available (entire population of employers with at least one employee in the nonfarm business sector for employment, E, and wages, W, and limited companies for value added per capita, VA/E). The second panel computes these same correlations only for the firms with more than 20 employees. The table shows that data on value added are more reliable, on average, for large enough firms.

Table 3

Regressions at the sectoral level

Dep var:	Delta OP share
	(1)	(2)	(3)	(4)
%Δ (productivity)	0.047** (0.021)			0.040* (0.021)
%Δ (productivity)	–0.017 (0.030)			–0.008 (0.029)
*post 2009
Herfindahl index		–0.194* (0.112)		–0.346* (0.224)
Herfindahl index		–0.061 (0.153)		–0.204* (0.125)
*post 2009
%Δ (employment)			–0.005*** (0.000)	–0.038 (0.043)
%Δ (employment)			0.068 (0.052)	0.113* (0.070)
*post 2009
No observations	812	1,392	1,392	812
Sector FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes

[i] Notes: “Delta OP share” is the difference between years t and t-1 of the share of the average wage explained by the OP term (from INPS data) and captures the change in the allocation of workers across firms over time, “%Δ (productivity)” is the percentage variation in the sectoral average value added per worker between years t and t-1 (from Cerved data), “Herfindahl index” is the Herfindahl index computed using firm employment data in each sector (from INPS data), and “%Δ (employment)” is the percentage variation in the sectoral employment between years t and t-1 (from INPS data). Robust standard errors are given in parenthesis. Columns 1 and 4 include only years from 2000 onward, when the value-added data are reliable from Cerved. Columns 2 and 3 include years from 1990 onward. Sectors: NACE Rev. 2, two digits, private sector excluding agriculture and mining.

Representativeness of INPS and ESBS entry and exit rates.
*Source:* our calculation based on INPS and Eurostat, *Structural Business Statistics* data. *Note:* the blue line displays statistics from INPS and the red line from ESBS data.

Firm-level evolution of employment, average wages, and value added per employee over time.
*Source*: our calculation based on INPS and Istat, ENA data.

Percentage of firms in INPS with balance sheet information (from CERVED), by employment size class.
*Source*: our calculation based on INPS and Cerved data.

Contribution of composition effects to the wage growth, distinguishing between employers’ and workers’ characteristics and different types of fixed effects.
*Source:* own calculation based on INPS data. *Note:* this figure plots the results on composition effects obtained from the BO decomposition (this is therefore the part of aggregate wage dynamics explained by changes in the average characteristics of employed individuals in the economy and of the firms where they are employed, keeping returns to these characteristics fixed over time). The results report the ratio between the 3-year moving average of the part of aggregate wage growth explained by changes in workers’ and employers’ composition and the 3-year moving average of aggregate wage growth. The blue line refers to the share of the yearly change in wage levels explained by changes in workers’ characteristics, and the red line refers to the share of the yearly change in wage levels explained by changes in employers’ characteristics.

Dynamic OP decomposition and contribution of OP and net entry to aggregate wage growth.
*Source*: our calculation based on INPS data.

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DOI: https://doi.org/10.2478/izajole-2019-0003 | Journal eISSN: 2193-8997

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Language: English

Published on: Aug 1, 2019

Published by: Sciendo

In partnership with: Paradigm Publishing Services

Keywords:

aggregate wage dynamics,

reallocation,

firm composition effects

JEL:

D61,

E24

Related subjects:

Business and economics,

Mathematics and statistics for economists,

Econometrics

© 2019 Effrosyni Adamopoulou, Emmanuele Bobbio, Marta De Philippis, Federico Giorgi, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 8 (2019): Issue 1 (June 2019)

Reallocation and the Role of Firm Composition Effects on Aggregate Wage Dynamics

Figures & Tables

Table A1

Table A2

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Figure 2

Figure 3

Figure A1

Figure 4

Table 1

Figure 5

Figure 6

Table 2

Table 3

Figure A2

Figure A3

Figure A4

Figure A5

Figure A6

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