Figures and tables in Simulations of Policy Responses During the COVID-19 Crisis in Argentina: Effects on Socioeconomic Indicators

Abstract

This paper simulates the effects of policy responses to the COVID-19 pandemic on household income and employment in Argentina, using household survey data and administrative data on employment and wages by economic sectors. The paper also includes a gender and age group analysis. The results indicate that during the COVID-19 crisis, household income decreased. This welfare loss was nonlinear along the income distribution, with the lowest income earners suffering the most due to relatively higher informality at the bottom of the income distribution. The policy responses seem to ameliorate by around one-third what the average drop in household income, and prevented major increases in poverty and inequality.

This article contains 5 figures and 8 tables.

Figures

**Figure 1.** Pre-COVID-19 scenario. Distribution of workers by gender, age groups, and sectors.. Source: Authors’ own calculations based on Ministry of Labor, Employment and Social Security and INDEC. Notes: In percentage.

**Figure 2.** Pre-COVID-19 scenario. Share of informal workers by deciles, gender, and sectors.

**Figure 3.** Post-COVID-19 scenario. Simulation of employment loss and relative participation of woman. By sectors.. Source: Authors’ own calculations based on Ministry of Labor, Employment and Social Security and INDEC. Notes: In thousands of workers and percentage. Second quarter of 2020.

**Figure 4.** Post-COVID-19 scenario. Change on employment rate by age groups and gender.. Source: Authors’ own calculations based on Ministry of Labor, Employment and Social Security and INDEC. Notes: In percentage. Second quarter versus first quarter 2020.

**Figure 5.** Distribution of public assistance beneficiaries.. Source: Authors’ own calculations based on EPH-INDEC. Notes: In percentage. By deciles of per capita income. Second quarter of 2020.

Tables

Table 1. Average per capita income by deciles. Pre- and post-COVID-19 scenarios with and without policy responses.

	Pre-COVID-19	Post-COVID-19 scenario (without policy responses)						Post-COVID-19 scenario (with policy responses)
	1st quarter 2020	1 quarter ahead		2 quarters ahead		3 quarters ahead		1 quarter ahead		2 quarters ahead		3 quarters ahead
Decile	[1]	[2]	[3]	[4]	[5]	[6]	[7]	[8]	[9]	[10]	[11]	[12]	[13]
	Level	Level		Level	Change [4] - [1] (in %)	Level	Change [6] - [2] (in %)	Level	Change [8] - [2] (in %)	Level	Change [10] - [1] (in %)	Level	Change [12] - [6] (in %)
1	2841.8	1974.0		2396.1	-15.69	2387.2	20.9	2842.7	44.0	3205.8	12.8	2977.3	24.7
2	5822.4	4250.3		5004.4	-14.05	4813.7	13.3	4924.5	15.9	5623.7	-3.4	5272.9	9.5
3	8189.0	6176.9		7258.7	-11.36	6884.8	11.5	6762.2	9.5	7777.8	-5.0	7216.8	4.8
4	10367.5	7807.2		9200.6	-11.26	8641.3	10.7	8294.3	6.2	9628.1	-7.1	8882.4	2.8
5	12978.9	10347.1		11763.7	-9.36	11096.3	7.2	10769.7	4.1	12139.6	-6.5	11302.5	1.9
6	16019.6	12918.2		14818.0	-7.50	13599.7	5.3	13262.0	2.7	15125.3	-5.6	13778.4	1.3
7	19789.5	16368.6		18590.6	-6.06	17035.5	4.1	16628.7	1.6	18830.7	-4.8	17201.9	1.0
8	25037.3	21413.7		23702.7	-5.33	21507.7	0.4	21645.3	1.1	23917.8	-4.5	21648.2	0.7
9	33516.6	28733.5		32248.0	-3.78	28544.9	-0.7	28899.3	0.6	32393.6	-3.4	28632.5	0.3
10	64559.5	54499.1		62174.1	-3.69	53405.3	-2.0	54617.7	0.2	62283.2	-3.5	53458.7	0.1
All	19914.0	16450.4		18717.4	-6.01	16793.1	2.1	16866.1	2.5	19094.2	-4.1	17038.6	1.5

Source: Authors’ own calculations based on Ministry of Production and Labor and EPH-INDEC. Notes: In pesos and percentage change. Deciles of per capita income. Workers in Argentina benefit from a wage bonus, aguinaldos, that is paid twice a year in June and December. Therefore, they are registered and included in the first and third quarters of our simulations but not in the second and fourth ones. Thus, to provide an accurate comparison, the pre-COVID-19 scenario should be compared with the second-quarter-ahead scenario. This is valid for both the scenario with and without policy responses.

Table 2. Average per capita income by economic sector. Pre- and post-COVID-19 scenarios with and without policy responses.

	Pre-COVID-19	Post-COVID-19 scenario (without policy responses)						Post-COVID-19 scenario (with policy responses)
	1st quarter 2020	1 quarter ahead		2 quarters ahead		3 quarters ahead		1 quarter ahead		2 quarters ahead		3 quarters ahead
Sector	[1]	[2]	[3]	[4]	[5]	[6]	[7]	[8]	[9]	[10]	[11]	[12]	[13]
	Level	Level		Level	Change [4] - [1] (in %)	Level	Change [6] - [2] (in %)	Level	Change [8] - [2] (in %)	Level	Change [10] - [1] (in %)	Level	Change [12] - [6] (in %)
Primary activities	35381.3	21090.5		30087.9	-15.0	26230.8	24.4	21475.7	1.8	30381.8	-14.1	26371.9	0.5
Manufacturing	19020.6	15046.6		17550.6	-7.7	15612.8	3.8	15475.1	2.8	17934.3	-5.7	15805.1	1.2
Construction	14493.5	11537.6		13414.3	-7.4	12220.9	5.9	12143.8	5.3	13973.7	-3.6	12508.1	2.3
Commerce	18401.6	14723.3		17100.0	-7.1	15525.3	5.4	15222.0	3.4	17555.6	-4.6	15757.6	1.5
Hotels and rest.	17049.4	12384.3		15147.9	-11.2	13652.5	10.2	12929.2	4.4	15621.6	-8.4	13863.4	1.5
Transp. and com.	24533.3	19042.1		22514.4	-8.2	19857.1	4.3	19379.4	1.8	22809.8	-7.0	20001.7	0.7
Financial serv.	28750.7	23060.5		26614.6	-7.4	23550.0	2.1	23382.0	1.4	26895.2	-6.5	23663.0	0.5
Education	27983.4	23147.5		26406.0	-5.6	22707.2	-1.9	23274.8	0.5	26517.7	-5.2	22749.4	0.2
Social-health serv.	28959.7	24465.7		27630.8	-4.6	23864.5	-2.5	24641.0	0.7	27787.5	-4.0	23940.1	0.3
Domestic serv.	12080.5	9108.9		10891.3	-9.8	9969.3	9.4	9935.1	9.1	11662.2	-3.5	10285.2	3.2
Other serv.	21463.2	17921.9		20225.8	-5.8	18127.4	1.1	18367.1	2.5	20634.8	-3.9	18318.0	1.1
All	24460.2	19423.3		22737.1	-7.0	19843.0	2.2	19819.1	2.0	23093.7	-5.6	20004.7	0.8

Table 3. Poverty, inequality indicators, and number of poor by gender. Pre- and post-COVID-19 scenarios with and without policy responses.

		Pre-COVID-19	Post-COVID-19 scenario (without policy responses)						Post-COVID-19 scenario (with policy responses)
		1st quarter 2020	1 quarter ahead		2 quarters ahead		3 quarters ahead		1 quarter ahead		2 quarters ahead		3 quarters ahead
Sector	Group	[1]	[2]	[3]	[4]	[5]	[6]	[7]	[8]	[9]	[10]	[11]	[12]	[13]
		Level	Level		Level	Change [4] - [1] (in %)	Level	Change [6] - [2] (in %)	Level	Change [8] - [2] (in %)	Level	Change [10] - [1] (in %)	Level	Change [12] - [6] (in %)
Panel A. Indigence, poverty, and inequality indicators
	Extreme poverty
FGT (0)		8.6	18.0		12.5	45.3	13.3	-26.3	15.3	-14.8	10.0	16.6	11.2	-15.3
FGT (1)		3.2	10.1		6.1	90.9	6.1	-40.3	7.1	-30.4	4.1	28.9	4.8	-20.2
FGT (2)		1.9	7.7		4.3	123.7	4.2	-46.1	4.5	-42.4	2.5	31.0	3.3	-20.6
	Moderate poverty
FGT (0)		34.6	45.6		39.2	13.5	43.3	-5.2	43.8	-4.0	37.3	7.9	42.0	-3.0
FGT (1)		13.6	22.9		17.7	29.7	19.0	-16.9	20.2	-11.7	15.5	13.4	17.5	-7.9
FGT (2)		7.6	15.6		11.1	46.0	11.6	-25.5	12.6	-18.9	8.9	17.7	10.2	-12.0
	Inequality
Gini		0.441	0.465		0.455	3.1	0.444	-4.7	0.465	-0.1	0.450	1.9	0.435	-2.0
Theil		0.345	0.382		0.366	6.0	0.347	-9.1	0.381	-0.2	0.357	3.5	0.333	-4.0
ATK (0)		0.000	0.000		0.000		0.000		0.000		0.000		0.000
ATK (0.5)		0.160	0.181		0.171	6.9	0.162	-10.4	0.180	-0.4	0.167	4.2	0.154	-4.7
ATK (1)		0.300	0.347		0.323	7.9	0.306	-11.8	0.344	-0.8	0.315	5.1	0.289	-5.5
ATK (2)		0.546	0.645		0.596	9.0	0.562	-12.9	0.625	-3.1	0.582	6.4	0.525	-6.7
Panel B. Poverty and inequality indicators, by gender
	Moderate poverty
Poverty	Head male	31.8	43.3		36.4	14.3	41.3	-4.6	41.4	-4.5	34.3	8.0	40.2	-2.9
Incidence	Head female	38.6	49.0		43.4	12.5	46.1	-5.9	47.4	-3.4	41.6	7.9	44.6	-5.9
	Population	34.6	45.6		39.2	13.5	43.3	-5.2	43.8	-4.0	37.3	7.9	42.0	-4.2
	Inequality
Gini	Head male	0.429	0.444		0.439	2.4	0.428	-3.5	0.446	0.3	0.435	1.4	0.420	-5.6
	Head female	0.458	0.496		0.478	4.2	0.465	-6.2	0.492	-0.9	0.470	2.5	0.454	-7.6
	Population	0.441	0.465		0.455	3.1	0.444	-4.7	0.465	-0.1	0.450	1.9	0.435	-6.5
Panel C. Number of poor
	Head male	5,376,778	7,324,550		6,147,894	771,116	6,986,696	-337,854	6,997,154	-327,396	5,804,325	427,547	6,795,035	-191,661
	Head female	4,489,291	5,698,983		5,045,405	556,114	5,365,024	-333,959	5,505,334	-193,649	4,841,836	352,545	5,181,325	-183,699
	Population	9,866,069	13,023,533		11,193,299	1,327,230	12,351,720	-671,813	12,502,488	-521,045	10,646,161	780,092	11,976,360	-375,360

Source: Authors’ own calculations based on Ministry of Production and Labor and EPH-INDEC. Notes: Workers in Argentina benefit from a wage bonus, aguinaldos, that is paid twice a year in June and December. Therefore, they are registered and included in the first and third quarters of our simulations but not in the second and fourth ones. Thus, to provide an accurate comparison, the pre-COVID-19 scenario should be compared with the second-quarter-ahead scenario. This is valid for both the scenario with and without policy responses.

Table A1. Employment and wage variations.

Employment variations
Sector	1q ahead		2q ahead		3q ahead
	Formal	Informal	Formal	Informal	Formal	Informal
Primary	-0.63	-0.90	-0.36	-0.52	-0.30	-0.43
Manufacturing	-0.18	-0.33	-0.10	-0.19	-0.08	-0.16
Construction	-0.17	-0.27	-0.10	-0.15	-0.08	-0.13
Commerce	-0.12	-0.44	-0.07	-0.25	-0.06	-0.21
Hotels and rest.	-0.31	-0.45	-0.18	-0.26	-0.15	-0.21
Transp. and com.	-0.19	-0.44	-0.11	-0.25	-0.09	-0.21
Financial serv.	-0.25	-0.44	-0.14	-0.26	-0.12	-0.21
Education	-0.28	-0.72	-0.16	-0.42	-0.13	-0.34
Health	-0.17	-0.58	-0.10	-0.33	-0.08	-0.27
Domestic serv.	-0.24	-0.29	-0.14	-0.17	-0.11	-0.14
Other	-0.12	-0.33	-0.07	-0.19	-0.06	-0.15
Average	-0.24	-0.47	-0.14	-0.27	-0.11	-0.22
Wage variations
	Formal	Informal	Formal	Informal	Formal	Informal
	-0.0034	0.02	0.05	0.13	0.16	0.25

Source: Authors’ own calculations based on Ministry of Production and Labor and EPH-INDEC. Notes: We assume zero variations on public employment. For wage variations, we use the same criteria than for the rest of employees (INDEC). These are, for each quarter, 1,95 percent, 7.19 percent, and 15.22 percent, respectively.

Table A2. Logistic regression on the probability of being employed: Formal and informal workers.

	Pr (employed=1)
	Formal	Informal
Gender (male==1)	1.307***	1.275***
	(0.344)	(0.317)
Age	0.212***	0.0964***
	(0.0157)	(0.0135)
Age2	-0.00198***	-0.000818***
	(0.000184)	(0.000165)
Incomplete primary	-0.265	-0.891
	(0.976)	(0.804)
Complete primary	0.496	-0.561
	(0.925)	(0.782)
Incomplete secondary	0.334	-0.773
	(0.918)	(0.776)
Complete secondary	0.637	-0.86
	(0.915)	(0.774)
Incomplete tertiary	0.406	-0.897
	(0.917)	(0.776)
Complete tertiary	1.053	-1.14
	(0.904)	(0.763)
Head of HH	0.845***	0.484***
	(0.121)	(0.104)
# of children	0.237**	0.127
	(0.111)	(0.0968)
Marriage status	0.627***	0.368***
	(0.0796)	(0.0674)
# HH members	0.156***	0.0783***
	(0.0231)	(0.0177)
HH per capita income (log)	2.013***	0.552***
	(0.062)	(0.0439)
Observations	11,656	8,478
R2	0.3613	0.0645

Source: Authors’ own estimates based on the Permanent Household Survey (EPH). Notes: Robust standard errors are in parentheses. Statistical significance *** p < 0.01, ** p < 0.05, * p < 0.1. Interaction terms and intercept are included but not reported for briefness.

Table A3. Employment by gender. Pre-COVID-19 (1st quarter of 2020) versus Post-COVID-19 (2nd quarter of 2020). Number of employed people and percentage change.

	1st quarter 2020			2nd quarter 2020			Change
	Level			Level			%
	Male	Female	All	Male	Female	All	Male	Female	All
Primary	91,750	29,883	121,633	34,636	10,945	45,581	-62.2	-63.4	-62.5
Manufacturing	874,726	451,827	1,326,553	708,487	332,908	1,041,395	-19.0	-26.3	-21.5
Construction	1,044,273	22,001	1,066,274	810,563	19,909	830,472	-22.4	-9.5	-22.1
Commerce	1,311,071	883,783	2,194,854	1,012,360	608,865	1,621,225	-22.8	-31.1	-26.1
Hotels and rest.	278,457	183,943	462,400	191,980	104,309	296,289	-31.1	-43.3	-35.9
Transp. and com.	778,999	135,834	914,833	598,012	91,637	689,649	-23.2	-32.5	-24.6
Financial serv.	783,739	528,898	1,312,637	566,354	389,737	956,091	-27.7	-26.3	-27.2
Education	239,797	735,108	974,905	201,445	623,182	824,627	-16.0	-15.2	-15.4
Health	1,033,530	1,123,807	2,157,337	965,203	1,004,377	1,969,580	-6.6	-10.6	-8.7
Domestic serv.	47,380	942,717	990,097	24,948	697,099	722,047	-47.3	-26.1	-27.1
Other	246,806	276,809	523,615	214,327	211,890	426,217	-13.2	-23.5	-18.6
Total	6,730,528	5,314,610	12,045,138	5,328,315	4,094,858	9,423,173	-20.8	-23.0	-21.8

Source: Authors’ own estimates based on Permanent Household Survey (EPH). Notes: The table includes all types of employment (public, private, and self-employment).

Table A4. Average per capita income by deciles and by gender. Pre- and post-COVID-19 scenarios with and without policy responses. In pesos and percentage change.

		Pre-COVID-19	Post-COVID-19 scenario (without policy responses)						Post-COVID-19 scenario (with policy responses)
		1st quarter 2020	1 quarter ahead		2 quarters ahead		3 quarters ahead		1 quarter ahead		2 quarters ahead		3 quarters ahead
Decil	Group	[1]	[2]	[3]	[4]	[5]	[6]	[7]	[8]	[9]	[10]	[11]	[12]	[13]
		Level	Level		Level	Change[4] - [1](in %)	Level	Change[6] - [2](in %)	Level	Change [8] - [2](in %)	Level	Change [10] - [1](in %)	Level	Change [12] - [6] (in %)
1	Head male	2876.8	2019.1		2386.4	-17.05	2382.7	18.0	2812.6	39.3	3114.0	8.2	2856.4	19.9
	Head female	2810.4	1933.5		2404.7	-14.43	2391.3	23.7	2869.8	48.4	3288.3	17.0	3085.9	29.1
	Decile	2841.8	1974.0		2396.1	-15.69	2387.2	20.9	2842.7	44.0	3205.8	12.8	2977.3	24.7
2	Head male	5881.1	4534.6		5227.2	-11.12	5008.1	10.4	5173.8	14.1	5795.6	-1.5	5413.6	8.1
	Head female	5754.5	3920.8		4746.2	-17.52	4588.4	17.0	4635.6	18.2	5424.5	-5.7	5109.7	11.4
	Decile	5822.4	4250.3		5004.4	-14.05	4813.7	13.3	4924.5	15.9	5623.7	-3.4	5272.9	9.5
3	Head male	8223.8	6429.1		7382.5	-10.23	6966.7	8.4	7001.6	8.9	7889.2	-4.1	7301.9	4.8
	Head female	8138.5	5810.2		7078.7	-13.02	6765.8	16.4	6414.1	10.4	7615.7	-6.4	7093.1	4.8
	Decile	8189.0	6176.9		7258.7	-11.36	6884.8	11.5	6762.2	9.5	7777.8	-5.0	7216.8	4.8
4	Head male	10352.4	7943.7		9356.1	-9.62	8676.8	9.2	8400.7	5.8	9757.7	-5.7	8876.9	2.3
	Head female	10389.5	7609.1		8974.6	-13.62	8589.7	12.9	8139.7	7.0	9439.9	-9.1	8890.4	3.5
	Decile	10367.5	7807.2		9200.6	-11.26	8641.3	10.7	8294.3	6.2	9628.1	-7.1	8882.4	2.8
5	Head male	12904.4	10491.4		11875.5	-7.97	11122.0	6.0	10852.8	3.4	12204.3	-5.4	11323.5	1.8
	Head female	13082.1	10147.3		11608.8	-11.26	11060.8	9.0	10654.5	5.0	12050.0	-7.9	11273.4	1.9
	Decile	12978.9	10347.1		11763.7	-9.36	11096.3	7.2	10769.7	4.1	12139.6	-6.5	11302.5	1.9
6	Head male	16017.4	13231.7		14856.3	-7.25	13547.2	2.4	13519.3	2.2	15123.6	-5.6	13710.4	1.2
	Head female	16023.9	12326.6		14745.6	-7.98	13698.8	11.1	12776.4	3.6	15128.6	-5.6	13906.7	1.5
	Decile	16019.6	12918.2		14818.0	-7.50	13599.7	5.3	13262.0	2.7	15125.3	-5.6	13778.4	1.3
7	Head male	19768.0	16373.9		18662.9	-5.59	17113.0	4.5	16612.0	1.5	18883.1	-4.5	17261.5	0.9
	Head female	19819.5	16361.2		18490.0	-6.71	16927.5	3.5	16651.9	1.8	18757.7	-5.4	17118.9	1.1
	Decile	19789.5	16368.6		18590.6	-6.06	17035.5	4.1	16628.7	1.6	18830.7	-4.8	17201.9	1.0
8	Head male	24920.4	21226.2		23533.0	-5.57	21187.1	-0.2	21430.0	1.0	23721.8	-4.8	21320.8	0.6
	Head female	25243.5	21744.7		24002.2	-4.92	22073.2	1.5	22025.2	1.3	24263.6	-3.9	22225.9	0.7
	Decile	25037.3	21413.7		23702.7	-5.33	21507.7	0.4	21645.3	1.1	23917.8	-4.5	21648.2	0.7
9	Head male	33505.8	28632.5		32187.2	-3.94	28267.5	-1.3	28791.9	0.6	32330.1	-3.5	28354.1	0.3
	Head female	33536.6	28922.2		32361.5	-3.50	29063.2	0.5	29100.1	0.6	32512.2	-3.1	29152.7	0.3
	Decile	33516.6	28733.5		32248.0	-3.78	28544.9	-0.7	28899.3	0.6	32393.6	-3.4	28632.5	0.3
10	Head male	64176.4	53944.6		61847.3	-3.63	52654.1	-2.4	54050.2	0.2	61947.0	-3.5	52708.0	0.1
	Head female	65193.7	55416.9		62714.9	-3.80	54648.5	-1.4	55557.0	0.3	62839.6	-3.6	54701.1	0.1
	Decile	64559.5	54499.1		62174.1	-3.69	53405.3	-2.0	54617.7	0.2	62283.2	-3.5	53458.7	0.1
All	Head male	20717.0	17218.3		19557.8	-5.60	17406.4	1.1	17583.7	2.1	19888.1	-4.0	17616.4	1.2
	Head female	18746.6	15334.1		17495.6	-6.67	15901.6	3.7	15823.0	3.2	17940.1	-4.3	16198.6	1.9
	Population	19914.0	16450.4		18717.4	-6.01	16793.1	2.1	16866.1	2.5	19094.2	-4.1	17038.6	1.5

Table A5. Average per capita income by economic sector and by gender. Pre- and post-COVID-19 scenarios with and without policy responses. In pesos and percentage change.

		Pre-COVID-19	Post-COVID-19 scenario (without policy responses)						Post-COVID-19 scenario (with policy responses)
		1st quarter 2020	1 quarter ahead		2 quarters ahead		3 quarters ahead		1 quarter ahead		2 quarters ahead		3 quarters ahead
Sector	Group	[1]	[2]	[3]	[4]	[5]	[6]	[7]	[8]	[9]	[10]	[11]	[12]	[13]
		Level	Level		Level	Change [4] - [1] (in %)	Level	Change [6] - [2] (in %)	Level	Change [8] - [2] (in %)	Level	Change [10] - [1] (in %)	Level	Change [12] - [6] (in %)
Primary act.	Head male	36813.8	22837.4		32161.1	-12.6	27830.7	21.9	23202.8	1.6	32424.3	-11.9	27945.6	0.4
	Head female	30860.1	15576.9		23544.3	-23.7	21181.2	36.0	16024.4	2.9	23934.9	-22.4	21405.0	1.1
	Sector	35381.3	21090.5		30087.9	-15.0	26230.8	24.4	21475.7	1.8	30381.8	-14.1	26371.9	0.5
Manufac- turing	Head male	19989.8	16026.3		18621.2	-6.8	16331.7	1.9	16396.8	2.3	18954.0	-5.2	16485.8	0.9
	Head female	17260.7	13267.5		15606.6	-9.6	14307.4	7.8	13801.5	4.0	16082.7	-6.8	14568.9	1.8
	Sector	19020.6	15046.6		17550.6	-7.7	15612.8	3.8	15475.1	2.8	17934.3	-5.7	15805.1	1.2
Constru- ction	Head male	14908.3	12140.8		13967.8	-6.3	12659.0	4.3	12779.0	5.3	14558.4	-2.3	12928.4	2.1
	Head female	13641.8	10299.1		12277.8	-10.0	11321.5	9.9	10839.4	5.2	12773.2	-6.4	11645.0	2.9
	Sector	14493.5	11537.6		13414.3	-7.4	12220.9	5.9	12143.8	5.3	13973.7	-3.6	12508.1	2.3
Commerce	Head male	18750.6	15335.0		17579.7	-6.2	15853.3	3.4	15799.7	3.0	18006.8	-4.0	16066.3	1.3
	Head female	17827.3	13716.8		16310.7	-8.5	14985.7	9.3	14271.4	4.0	16813.1	-5.7	15249.8	1.8
	Sector	18401.6	14723.3		17100.0	-7.1	15525.3	5.4	15222.0	3.4	17555.6	-4.6	15757.6	1.5
Hotels and rest.	Head male	17423.5	13116.4		15739.9	-9.7	14196.0	8.2	13640.7	4.0	16204.1	-7.0	14368.0	1.2
	Head female	16560.2	11427.0		14373.7	-13.2	12941.8	13.3	11998.8	5.0	14859.8	-10.3	13203.5	2.0
	Sector	17049.4	12384.3		15147.9	-11.2	13652.5	10.2	12929.2	4.4	15621.6	-8.4	13863.4	1.5
Tran. and com.	Head male	24247.9	19141.4		22432.7	-7.5	19678.4	2.8	19477.1	1.8	22731.1	-6.3	19824.2	0.7
	Head female	25173.4	18819.4		22697.5	-9.8	20257.9	7.6	19160.1	1.8	22986.2	-8.7	20399.6	0.7
	Sector	24533.3	19042.1		22514.4	-8.2	19857.1	4.3	19379.4	1.8	22809.8	-7.0	20001.7	0.7
Financial serv.	Head male	29727.0	24441.8		27713.6	-6.8	24574.5	0.5	24728.0	1.2	27967.8	-5.9	24683.9	0.4
	Head female	26969.8	20540.5		24609.8	-8.8	21681.1	5.6	20926.5	1.9	24938.6	-7.5	21800.6	0.6
	Sector	28750.7	23060.5		26614.6	-7.4	23550.0	2.1	23382.0	1.4	26895.2	-6.5	23663.0	0.5
Education	Head male	28563.8	23565.4		26985.3	-5.5	22982.5	-2.5	23658.5	0.4	27069.8	-5.2	23015.4	0.1
	Head female	27261.2	22627.4		25685.2	-5.8	22364.7	-1.2	22797.2	0.8	25830.7	-5.2	22418.4	0.2
	Sector	27983.4	23147.5		26406.0	-5.6	22707.2	-1.9	23274.8	0.5	26517.7	-5.2	22749.4	0.2
Soc. and health	Head male	30351.3	25821.1		29034.2	-4.3	25062.0	-2.9	25963.8	0.6	29162.2	-3.9	25120.4	0.2
	Head female	26952.0	22510.3		25606.0	-5.0	22136.8	-1.7	22732.7	1.0	25804.1	-4.3	22237.4	0.5
	Sector	28959.7	24465.7		27630.8	-4.6	23864.5	-2.5	24641.0	0.7	27787.5	-4.0	23940.1	0.3
Domestic serv.	Head male	12683.2	9882.2		11586.0	-8.7	10526.5	6.5	10427.7	5.5	12094.6	-4.6	10771.9	2.3
	Head female	11652.9	8560.4		10398.5	-10.8	9574.0	11.8	9585.7	12.0	11355.6	-2.6	9939.9	3.8
	Sector	12080.5	9108.9		10891.3	-9.8	9969.3	9.4	9935.1	9.1	11662.2	-3.5	10285.2	3.2
Other serv.	Head male	21020.8	17336.8		19808.2	-5.8	17438.2	0.6	17710.4	2.2	20149.1	-4.1	17577.5	0.8
	Head female	22221.4	18924.4		20941.3	-5.8	19308.4	2.0	19492.4	3.0	21467.2	-3.4	19586.9	1.4
	Sector	21463.2	17921.9		20225.8	-5.8	18127.4	1.1	18367.1	2.5	20634.8	-3.9	18318.0	1.1
All	Head male	24977.5	20058.8		23392.5	-6.3	20294.4	1.2	20405.7	1.7	23705.5	-5.1	20434.2	0.7
	Head female	23659.2	18439.3		21722.3	-8.2	19144.0	3.8	18910.9	2.6	22146.5	-6.4	19339.6	1.0
	All Sectors	24460.2	19423.3		22737.1	-7.0	19843.0	2.2	19819.1	2.0	23093.7	-5.6	20004.7	0.8