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Technology, routinization and wage inequality: gender differences in the case of Uruguay

Sandra Rodríguez López
Sandra Rodríguez López
 et 
Graciela Sanroman
Graciela Sanroman
   | 31 déc. 2023
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Publié en ligne: 31 déc. 2023
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Reçu: 05 mai 2022
DOI: https://doi.org/10.2478/izajodm-2023-0008
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© 2023 Sandra Rodríguez López et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1

Log hourly wage change between 2005 and 2015, by genderNote: i. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied. ii. Wages are expressed in real terms, 2010 prices.
Log hourly wage change between 2005 and 2015, by genderNote: i. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied. ii. Wages are expressed in real terms, 2010 prices.

Figure 2

Occupational category by genderNote: Percentage of private workers by occupational category and gender in 2005 and 2015.
Occupational category by genderNote: Percentage of private workers by occupational category and gender in 2005 and 2015.

Figure 3

Information Task Content measure by Occupational CategoryNote: Compiled by authors based on ECH-INE and National Center for O*NET Development. Blue bars correspond to men and violet ones to women.
Information Task Content measure by Occupational CategoryNote: Compiled by authors based on ECH-INE and National Center for O*NET Development. Blue bars correspond to men and violet ones to women.

Figure 4

Automation Task Content measure by Occupational CategoryNote: Compiled by authors based on ECH-INE and National Center for O*NET Development. Blue bars correspond to men and violet ones to women.
Automation Task Content measure by Occupational CategoryNote: Compiled by authors based on ECH-INE and National Center for O*NET Development. Blue bars correspond to men and violet ones to women.

Figure 5

Unconditional Quantile Partial Effects: Occupational Task. Forth vs First Quartile of Task Content. Dependent variable: log hourly wages. 2005 and 2015.Notes: i. Figures show the UQPE of the task indexes for the upper quartile when the bottom quartile is omitted. ii. Information/Automation covariates are defined as category variables that indicate the degree of information/automation task content of the job. Four quartiles are considered. iii. 2005 in red, 2015 in blue. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied.
Unconditional Quantile Partial Effects: Occupational Task. Forth vs First Quartile of Task Content. Dependent variable: log hourly wages. 2005 and 2015.Notes: i. Figures show the UQPE of the task indexes for the upper quartile when the bottom quartile is omitted. ii. Information/Automation covariates are defined as category variables that indicate the degree of information/automation task content of the job. Four quartiles are considered. iii. 2005 in red, 2015 in blue. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied.

Figure 6

Unconditional Quantile Partial Effects: Selected Education Covariates (Dummy 6 Years of Schooling omitted). Dependent variable: log hourly wages. 2005 and 2015.Notes: i. Figures show the UQPE of educational dummies, six years of education or less is omitted. ii. 2005 in red, 2015 in blue. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied.
Unconditional Quantile Partial Effects: Selected Education Covariates (Dummy 6 Years of Schooling omitted). Dependent variable: log hourly wages. 2005 and 2015.Notes: i. Figures show the UQPE of educational dummies, six years of education or less is omitted. ii. 2005 in red, 2015 in blue. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied.

Figure 7

Aggregated decomposition of log hourly wages changes, 2005 and 2015.Notes: i. Figures show the total change of wages by gender, as well as the aggregated decomposition into the structure and the composition effect. RIF-regression method is used to perform the decomposition. Covariates include: Information, Automation, Education, Experience, Informal worker, Region and Marital status. ii. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied.
Aggregated decomposition of log hourly wages changes, 2005 and 2015.Notes: i. Figures show the total change of wages by gender, as well as the aggregated decomposition into the structure and the composition effect. RIF-regression method is used to perform the decomposition. Covariates include: Information, Automation, Education, Experience, Informal worker, Region and Marital status. ii. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied.

Figure 8

Log-hourly wages gender gap, 2005 and 2015.Notes: i. The gender gap is calculated by subtracting men’s wages minus women’s wages. ii. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied. iii. Estimates of the gender gap without correction for self-selection are reported in the Supplementary Appendix.
Log-hourly wages gender gap, 2005 and 2015.Notes: i. The gender gap is calculated by subtracting men’s wages minus women’s wages. ii. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied. iii. Estimates of the gender gap without correction for self-selection are reported in the Supplementary Appendix.

Figure 9

Log-hourly wages gender gap variation between 2005 and 2015, unconditional and after controlling for observed characteristics. With correction for selection bias.Notes: i. Figures correspond to the estimation of the variation of the gender gap using an approach analogous to the diff in diff estimator. Unconditional stands for the estimation without any controls. Figure (a) compares the unconditional variation of the gap with respect to that which control for all selected characteristics (Information, Automation, Education, Experience, Informal worker, Region and Marital status). Figures (b) to (e) compares the model with all regressors with those excluding indicated variables. iii. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied.
Log-hourly wages gender gap variation between 2005 and 2015, unconditional and after controlling for observed characteristics. With correction for selection bias.Notes: i. Figures correspond to the estimation of the variation of the gender gap using an approach analogous to the diff in diff estimator. Unconditional stands for the estimation without any controls. Figure (a) compares the unconditional variation of the gap with respect to that which control for all selected characteristics (Information, Automation, Education, Experience, Informal worker, Region and Marital status). Figures (b) to (e) compares the model with all regressors with those excluding indicated variables. iii. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied.

Figure 10

Aggregated decomposition of the gender wage gap and the gender gap change.Notes: i. Figure (a) shows the aggregated composition and structure effects of gender wage gap in 2005 and 2015. Covariates include: Information, Automation, Education, Experience, Informal worker, Region and Marital status. ii. Figure (b) decompose gender wage gap change between 2005 and 2015 into the aggregated composition, structure and interaction effects, as defined in section 6) iii. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied.
Aggregated decomposition of the gender wage gap and the gender gap change.Notes: i. Figure (a) shows the aggregated composition and structure effects of gender wage gap in 2005 and 2015. Covariates include: Information, Automation, Education, Experience, Informal worker, Region and Marital status. ii. Figure (b) decompose gender wage gap change between 2005 and 2015 into the aggregated composition, structure and interaction effects, as defined in section 6) iii. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied.

Figure 11

Detailed decomposition of the gender gap changeNotes: Figures show the composition, structure and total effects of covariates Information, Automation and Education to the change of the gender wage gap between 2005 and 2015 as defined in section 6). ii. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied.
Detailed decomposition of the gender gap changeNotes: Figures show the composition, structure and total effects of covariates Information, Automation and Education to the change of the gender wage gap between 2005 and 2015 as defined in section 6). ii. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied.

Figure A1

Unconditional Quantile Partial Effects: Other Covariates. Dependent variable: log hourly wages. 2005 and 2015Notes: 2005 in red, 2015 in blue. Solid lines are point estimates, dashes lines report the lower and upper bound of the 95th confidence interval.
Unconditional Quantile Partial Effects: Other Covariates. Dependent variable: log hourly wages. 2005 and 2015Notes: 2005 in red, 2015 in blue. Solid lines are point estimates, dashes lines report the lower and upper bound of the 95th confidence interval.

Figure A2

Log-hourly wages gender gap variation between 2005 and 2015, unconditional and after controlling for observed characteristics. Without correction for selection bias.Notes: i. Figures correspond to the estimation (not corrected for self selection) of the variation of the gender gap using an approach analogous to the diff in diff estimator. Unconditional stands for the estimation without any controls. Figure (a) compares the unconditional variation of the gap with respect to that which control for all selected characteristics (Information, Automation, Education, Experience, Informal worker, Region and Marital status). Figures (b) to (e) compares the model with all regressors with those excluding indicated variables. iii. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied.
Log-hourly wages gender gap variation between 2005 and 2015, unconditional and after controlling for observed characteristics. Without correction for selection bias.Notes: i. Figures correspond to the estimation (not corrected for self selection) of the variation of the gender gap using an approach analogous to the diff in diff estimator. Unconditional stands for the estimation without any controls. Figure (a) compares the unconditional variation of the gap with respect to that which control for all selected characteristics (Information, Automation, Education, Experience, Informal worker, Region and Marital status). Figures (b) to (e) compares the model with all regressors with those excluding indicated variables. iii. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied.

Figure A3

Unconditional Quantile Partial Effects: Forth vs First Quartile of Information Task Content, two definition of the variable. Dependent variable: log hourly wages. 2005 and 2015.Notes: i. Figures show the UQPE of the information task using two alternative indexes, as well as using non imputed data for unemployed, for the upper quartile when the bottom quartile is omitted. ii. In red 2005 in blue 2015. iii. Definition 1 gives Cobb-Douglas weight of two-thirds to importance and one-third to level. In definition 2 we calculated this index by giving one-third to the former and two-thirds to the latter.iv. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied.
Unconditional Quantile Partial Effects: Forth vs First Quartile of Information Task Content, two definition of the variable. Dependent variable: log hourly wages. 2005 and 2015.Notes: i. Figures show the UQPE of the information task using two alternative indexes, as well as using non imputed data for unemployed, for the upper quartile when the bottom quartile is omitted. ii. In red 2005 in blue 2015. iii. Definition 1 gives Cobb-Douglas weight of two-thirds to importance and one-third to level. In definition 2 we calculated this index by giving one-third to the former and two-thirds to the latter.iv. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied.

Figure A4

Aggregated decomposition of log hourly wages changes, 2005 and 2015.Notes: i. Figures show the total change of wages by gender, as well as the aggregated decomposition into the structure and the composition effect. RIF-regression method is used to perform the decomposition. Covariates include: Information, Automation, Education, Experience, Informal worker, Region and Marital status. ii. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied.
Aggregated decomposition of log hourly wages changes, 2005 and 2015.Notes: i. Figures show the total change of wages by gender, as well as the aggregated decomposition into the structure and the composition effect. RIF-regression method is used to perform the decomposition. Covariates include: Information, Automation, Education, Experience, Informal worker, Region and Marital status. ii. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied.

Figure A5

Aggregated decomposition of the gender wage gap and the gender gap change.Notes: i. Figure (a) shows the aggregated composition and structure effects of gender wage gap in 2005 and 2015.Covariates include: Information, Automation, Education, Experience, Informal worker, Region and Marital status. ii. Figure (b) decompose gender wage gap change between 2005 and 2015 into the aggregated composition, structure and interaction effects, as defined in section 6) iii. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied.
Aggregated decomposition of the gender wage gap and the gender gap change.Notes: i. Figure (a) shows the aggregated composition and structure effects of gender wage gap in 2005 and 2015.Covariates include: Information, Automation, Education, Experience, Informal worker, Region and Marital status. ii. Figure (b) decompose gender wage gap change between 2005 and 2015 into the aggregated composition, structure and interaction effects, as defined in section 6) iii. Solid lines are point estimates, dashed lines indicate the lower and upper bound of the 95 confidence interval. Bootstrapped standard errors are calculated (200 replicates) within each 10 imputed data sets and then Rubin’s rules are applied.

Figure A6

Detailed decomposition of the gender gap change.
Detailed decomposition of the gender gap change.

Detailed Decomposition of the structure effect, wage variation between 2005 and 2015, based on Unconditional Quantile Partial Effects

Inequality measure All Males Females
90-10 90-50 50-10 90-10 90-50 50-10 90-10 90-50 50-10
Female 0.063*** (0.0043) 0.072*** (0.0032) −0.00909*** (0.0024)
Information −0.112*** (0.0086) −0.015** (0.0058) −0.09663*** (0.0071) −0.159*** (0.008) −0.08*** (0.006) −0.079*** (0.005) −0.062*** (0.018) 0.012 (0.01) −0.074*** (0.017)
Automation −0.021** (0.01) 0.012*** (0.0047) −0.03344*** (0.0078) −0.060*** (0.007) −0.009 (0.006) −0.051*** (0.005) −0.034** (0.016) −0.060*** (0.009) 0.026** (0.013)
Education −0.121*** (0.0082) −0.103*** (0.0057) −0.01804*** (0.0049) −0.136*** (0.013) −0.114*** (0.009) −0.021** (0.009) −0.052*** (0.014) −0.073*** (0.01) 0.021 (0.014)
Experience 0.013** (0.0053) 0.046*** (0.0057) −0.0334*** (0.0068) 0.006 (0.008) 0.037*** (0.008) −0.031*** (0.008) 0.021 (0.014) 0.052*** (0.009) −0.031*** (0.012)
Other 0.015*** (0.0044) −0.003 (0.0035) 0.01813*** (0.0044) 0.040*** (0.005) 0.005 (0.003) 0.035*** (0.004) −0.023** (0.011) −0.008 (0.006) −0.014 (0.01)
Constant −0.224*** (0.0176) −0.198*** (0.0075) −0.02593 (0.0177) −0.253*** (0.014) −0.165*** (0.014) −0.089*** (0.012) −0.216*** (0.043) −0.068*** (0.015) −0.148*** (0.037)
Total Structure Effect −0.387*** (0.0033) −0.188*** (0.0016) −0.1984*** (0.0026) −0.562*** (0.005) −0.326*** (0.002) −0.236*** (0.004) −0.365*** (0.01) −0.146*** (0.006) −0.219*** (0.008)

Unconditional Quantile Partial Effects on Female Log Wages (2005 – 2015) - RIF Regression

Year 2005/06 2014/15
Covariates/Quantile 10 50 90 10 50 90
Task content indexes (1st. quartile omitted)
Information content Q2 0.132*** (0.019) 0.069*** (0.005) −0.018** (0.007) 0.065*** (0.009) 0.004 (0.006) 0.025*** (0.006)
Information content Q3 0.136*** (0.020) 0.384*** (0.011) 0.010 (0.010) 0.008 (0.008) 0.103*** (0.004) −0.107*** (0.006)
Information content Q4 0.602*** (0.016) 0.607*** (0.007) 0.886*** (0.008) 0.103*** (0.008) 0.374*** (0.005) 0.587*** (0.005)
Automation content Q2 0.162*** (0.013) 0.169*** (0.005) −0.068*** (0.006) 0.078*** (0.008) 0.098*** (0.005) −0.143*** (0.009)
Automation content Q3 0.171*** (0.024) 0.178*** (0.010) −0.186*** (0.014) 0.143*** (0.006) 0.186*** (0.005) −0.211*** (0.007)
Automation content Q4 0.096*** (0.018) 0.041*** (0.006) 0.012 (0.011) 0.006 (0.005) 0.106*** (0.006) −0.487*** (0.009)
Education (6 years or less omitted)
From 7 to 9 years 0.176*** (0.015) 0.115*** (0.011) 0.043*** (0.008) 0.133*** (0.007) 0.07*** (0.003) 0.019*** (0.005)
From 10 to 12 years 0.285*** (0.017) 0.248*** (0.011) 0.18*** (0.011) 0.31*** (0.008) 0.313*** (0.004) 0.138*** (0.004)
From 13 to 15 years 0.352*** (0.018) 0.492*** (0.012) 0.705*** (0.021) 0.39*** (0.010) 0.581*** (0.005) 0.599*** (0.009)
16 and more years 0.364*** (0.018) 0.648*** (0.011) 1.857*** (0.020) 0.424*** (0.010) 0.74*** (0.004) 1.712*** (0.013)
Experience (15<Experience<20 omitted)
Experience<5 0.138*** (0.027) −0.03* (0.016) −1.239*** (0.056) 0.053*** (0.004) −0.082*** (0.009) −0.957*** (0.040)
5<experience<10 0.054*** (0.021) −0.09*** (0.007) −0.499*** (0.018) 0.017** (0.007) −0.095*** (0.005) −0.372*** (0.010)
10<experience<15 −0.003 (0.021) −0.074*** (0.006) −0.159*** (0.009) −0.011 (0.009) −0.081*** (0.005) −0.112*** (0.006)
20<experience<25 0.048*** (0.017) 0.08*** (0.008) 0.102*** (0.007) 0.066*** (0.008) 0.012* (0.007) 0.044*** (0.009)
25<experience<30 0.105*** (0.020) 0.118*** (0.009) 0.117*** (0.006) 0.056*** (0.007) 0.056*** (0.008) 0.085*** (0.006)
30<experience<35 0.092*** (0.015) 0.121*** (0.009) 0.16*** (0.009) 0.079*** (0.008) 0.066*** (0.007) 0.183*** (0.009)
35<experience<40 0.134*** (0.018) 0.149*** (0.006) 0.154*** (0.005) 0.078*** (0.011) 0.06*** (0.007) 0.137*** (0.008)
Experience>40 0.08*** (0.018) 0.196*** (0.008) 0.163*** (0.009) 0.048*** (0.005) 0.048*** (0.006) 0.098*** (0.005)
Nonmarried −0.082*** (0.009) −0.078*** (0.004) −0.105*** (0.004) −0.04*** (0.003) −0.066*** (0.002) −0.076*** (0.003)
Region −0.266*** (0.008) −0.228*** (0.005) −0.164*** (0.005) −0.1*** (0.003) −0.109*** (0.002) −0.094*** (0.002)
Informal −0.451*** (0.022) −0.156*** (0.008) −0.085*** (0.007) −0.525*** (0.021) −0.131*** (0.006) −0.01** (0.005)
Constant 2.508*** (0.036) 3.107*** (0.008) 4.251*** (0.008) 3.179*** (0.013) 3.63*** (0.007) 4.706*** (0.011)

Descriptive statistics

Variable 2005/06 2014/15 Di · 2005 – 2015
With imputation Without imputation With imputation Without imputation With imputation Without imputation
A: Men
Age 40.388 40.510 40.281 40.418 −0.108 −0.093
Education
6 years or less 0.290 0.286 0.212 0.209 −0.079 −0.077
7 to 9 years 0.327 0.325 0.289 0.288 −0.038 −0.037
10 to 12 years 0.243 0.246 0.331 0.333 0.088 0.087
13 to 16 years 0.067 0.067 0.084 0.084 0.018 0.017
16 and more years 0.074 0.076 0.084 0.085 0.010 0.009
Non – married 0.276 0.261 0.304 0.291 0.028 0.030
Resto of the country 0.492 0.491 0.473 0.473 −0.019 −0.018
Not registered 0.231 0.208 0.096 0.088 −0.134 −0.121
B: Women
Age 40.902 41.244 40.887 41.149 −0.015 −0.095
Education
6 years or less 0.261 0.250 0.185 0.177 −0.076 −0.073
7 to 9 years 0.270 0.262 0.233 0.225 −0.037 −0.037
10 to 12 years 0.270 0.275 0.352 0.357 0.082 0.082
13 to 16 years 0.097 0.101 0.107 0.111 0.010 0.010
16 and more years 0.102 0.112 0.122 0.129 0.020 0.017
Non – married 0.430 0.437 0.391 0.391 −0.038 −0.045
Rest of the country 0.455 0.440 0.466 0.461 0.010 0.021
Not registered 0.327 0.282 0.136 0.115 −0.191 −0.167

Percentage of Workers in the Top Quartile of Task Content Indexes by Major Occupation Group in 2005/2006 – 2014/2015 (with self selection correction)

Task Content Indexes Percentage of workers Technology
Information Automation
2005 Men Women Men Women Men Women
Overall 100 100 20 25 26 23
Manager, Professionals, Technicians 13 12 87 84 0 0
Clerical support and sale workers 19 31 45 49 48 56
Plant and machines operators and assemblers 20 8 2 0 51 75
Agricultural, construction and transport workers 36 1 1 1 20 27
Service workers 13 48 0 0 2 0
2015
Overall 100 100 21 29 23 16
Manager, Professionals, Technicians 13 15 87 82 1 0
Clerical support and sale workers 20 36 44 48 23 30
Plant and machines operators and assemblers 20 6 2 0 49 69
Agricultural, construction and transport workers 33 2 1 0 24 38
Service workers 14 42 0 0 2 0

Unconditional Quantile Partial Effects on Male Log Wages (2005 – 2015) - RIF Regression

Year 2005/06 2014/15
Covariates/Quantile 10 50 90 10 50 90
Task content indexes (1st. quartile omitted)
Information content Q2 0.071*** (0.005) 0.087*** (0.005) −0.075*** (0.007) −0.022** (0.009) 0.010*** (0.003) 0.111*** (0.004)
Information content Q3 0.031*** (0.006) 0.081*** (0.004) 0.007 (0.007) 0.005 (0.005) −0.035*** (0.002) −0.133*** (0.003)
Information content Q4 0.14*** (0.006) 0.45*** (0.006) 1.112*** (0.011) 0.026*** (0.006) 0.163*** (0.002) 0.474*** (0.008)
Automation content Q2 −0.259*** (0.007) −0.147*** (0.004) −0.171*** (0.007) −0.050*** (0.003) −0.035*** (0.002) −0.096*** (0.004)
Automation content Q3 0.124*** (0.008) 0.227*** (0.010) −0.142*** (0.010) 0.057*** (0.003) 0.129*** (0.003) −0.014*** (0.004)
Automation content Q4 0.009* (0.005) −0.019*** (0.005) 0.008 (0.008) 0.003 (0.003) −0.02*** (0.003) −0.326*** (0.003)
Education (6 years or less omitted)
From 7 to 9 years 0.193*** (0.007) 0.164*** (0.004) 0.101*** (0.007) 0.173*** (0.005) 0.112*** (0.002) 0.096*** (0.003)
From 10 to 12 years 0.274*** (0.007) 0.331*** (0.005) 0.460*** (0.010) 0.269*** (0.004) 0.311*** (0.002) 0.345*** (0.004)
From 13 to 15 years 0.328*** (0.012) 0.535*** (0.009) 1.257*** (0.031) 0.353*** (0.006) 0.524*** (0.005) 0.899*** (0.008)
16 and more years 0.312*** (0.005) 0.649*** (0.007) 2.751*** (0.032) 0.403*** (0.006) 0.691*** (0.004) 1.953*** (0.012)
Experience (15<Experience<20 omitted)
Experience<5 −0.024** (0.011) −0.090*** (0.015) −2.119*** (0.044) 0.054*** (0.006) −0.077*** (0.015) −1.350*** (0.036)
5<experience<10 0.008 (0.012) −0.129*** (0.011) −0.916*** (0.017) −0.013*** (0.004) −0.128*** (0.005) −0.516*** (0.009)
10<experience<15 −0.112*** (0.013) −0.138*** (0.006) −0.156*** (0.010) −0.039*** (0.004) −0.072*** (0.003) −0.147*** (0.006)
20<experience<25 0.046*** (0.009) 0.108*** (0.007) 0.131*** (0.008) 0.053*** (0.004) 0.06*** (0.003) 0.096*** (0.005)
25<experience<30 0.075*** (0.007) 0.147*** (0.004) 0.26*** (0.008) 0.065*** (0.003) 0.094*** (0.004) 0.156*** (0.005)
30<experience<35 0.074*** (0.008) 0.159*** (0.006) 0.192*** (0.010) 0.073*** (0.003) 0.128*** (0.001) 0.204*** (0.005)
35<experience<40 0.096*** (0.010) 0.153*** (0.007) 0.202*** (0.008) 0.092*** (0.004) 0.129*** (0.004) 0.243*** (0.005)
Experience>40 0.052*** (0.012) 0.165*** (0.005) 0.235*** (0.011) 0.091*** (0.005) 0.091*** (0.003) 0.192*** (0.007)
Nonmarried −0.083*** (0.008) −0.159*** (0.003) −0.158*** (0.004) −0.071*** (0.002) −0.111*** (0.002) −0.134*** (0.003)
Region −0.108*** (0.004) −0.097*** (0.004) −0.147*** (0.007) −0.054*** (0.004) −0.016*** (0.002) −0.029*** (0.002)
Informal −0.484*** (0.011) −0.325*** (0.006) −0.017* (0.010) −0.515*** (0.008) −0.255*** (0.004) 0.008 (0.006)
Constant 2.853*** (0.008) 3.458*** (0.007) 4.327*** (0.011) 3.504*** (0.007) 4.021*** (0.004) 4.725*** (0.007)

Unconditional Quantile Partial Effects on Log Wages (2005 – 2015) - RIF Regression (Without correction for selection bias)

Year 2005/06 2014/15
Covariates/Quantile 10 50 90 10 50 90
Female −0.141*** (0.001) −0.166*** (0.001) −0.337*** (0.002) −0.210*** (0.001) −0.234*** (0.0005) −0.284*** (0.001)
Task content indexes (1st. quartile omitted)
Information content Q2 0.143*** (0.001) 0.097*** (0.001) 0.0190*** (0.001) 0.009*** (0.001) −0.089*** (0.001) −0.033*** (0.001)
Information content Q3 0.108*** (0.001) 0.2190*** (0.001) −0.026*** (0.001) −0.009*** (0.001) 0.024*** (0.001) −0.044*** (0.001)
Information content Q4 0.166*** (0.001) 0.512*** (0.001) 0.988*** (0.003) 0.109*** (0.001) 0.336*** (0.001) 0.578*** (0.001)
Automation content Q2 −0.017*** (0.001) −0.000 (0.001) −0.147*** (0.001) 0.042*** (0.001) −0.030*** (0.001) −0.135*** (0.001)
Automation content Q3 0.192*** (0.001) 0.217*** (0.001) −0.390*** (0.002) 0.089*** (0.001) 0.100*** (0.001) −0.014*** (0.001)
Automation content Q4 0.100*** (0.001) 0.044*** (0.001) −0.192*** (0.002) 0.098*** (0.001) 0.019*** (0.001) −0.324*** (0.001)
Education (6 years or less omitted)
From 7 to 9 years 0.166*** (0.001) 0.145*** (0.001) 0.097*** (0.001) 0.150*** (0.001) 0.103*** (0.001) 0.055*** (0.001)
From 10 to 12 years 0.235*** (0.001) 0.280*** (0.001) 0.363*** (0.001) 0.268*** (0.001) 0.259*** (0.001) 0.215*** (0.001)
From 13 to 15 years 0.235*** (0.001) 0.280*** (0.001) 0.363*** (0.001) 0.268*** (0.001) 0.259*** (0.001) 0.215*** (0.001)
16 and more years 0.280*** (0.001) 0.541*** (0.001) 0.954*** (0.004) 0.331*** (0.001) 0.49*** (0.001) 0.659*** (0.002)
Experience (15<Experience<20 omitted)
Experience<5 0.027*** (0.002) −0.090*** (0.002) −1.658*** (0.009) 0.015*** (0.001) −0.112*** (0.001) −1.096*** (0.005)
5<experience<10 0.022*** (0.001) −0.134*** (0.001) −0.662*** (0.003) −0.015*** (0.001) −0.124*** (0.001) −0.435*** (0.002)
10<experience<15 −0.057*** (0.001) −0.123*** (0.001) −0.154*** (0.001) −0.046*** (0.001) −0.089*** (0.001) −0.13*** (0.001)
20<experience<25 0.019*** (0.001) 0.099*** (0.001) 0.123*** (0.002) 0.022*** (0.001) 0.026*** (0.001) 0.077*** (0.001)
25<experience<30 0.058*** (0.001) 0.142*** (0.001) 0.223*** (0.002) 0.023*** (0.001) 0.057*** (0.001) 0.134*** (0.001)
30<experience<35 0.050*** (0.002) 0.160*** (0.001) 0.234*** (0.002) 0.034*** (0.001) 0.077*** (0.001) 0.198*** (0.001)
35<experience<40 0.100*** (0.001) 0.178*** (0.001) 0.220*** (0.002) 0.041*** (0.001) 0.077*** (0.001) 0.197*** (0.001)
Experience>40 0.032*** (0.001) 0.202*** (0.001) 0.234*** (0.002) 0.027*** (0.001) 0.076*** (0.001) 0.155*** (0.001)
Nonmarried −0.082*** (0.001) −0.146*** (0.001) −0.157*** (0.001) −0.0550*** (0.001) −0.099*** (0.0004) −0.095*** (0.001)
Region −0.198*** (0.001) −0.159*** (0.001) −0.150*** (0.001) −0.088*** (0.001) −0.058*** (0.0004) −0.065*** (0.001)
Informal −0.480*** (0.001) −0.239*** (0.001) 0.004*** (0.001) −0.550*** (0.001) −0.185*** (0.001) 0.038*** (0.001)
Constant 2.775*** (0.002) 3.351*** (0.001) 4.425*** (0.002) 3.476*** (0.001) 4.041*** (0.001) 4.819*** (0.001)

Aggregate Decomposition of wage change between 2005 and 2015 (without selection correction)

90-10 90-50 50-10
A. All
Total Change −0.457*** (0.001) −0.284*** (0.001) −0.173*** (0.001)
Composition −0.118*** (0.001) −0.119*** (0.001) 0.001 (0.001)
Structure −0.339*** (0.001) −0.165*** (0.001) −0.174*** (0.001)
B. Males
Total Change −0.555*** (0.002) −0.346*** (0.001) −0.208*** (0.001)
Composition −0.001 (0.001) −0.001 (0.001) 0 (0.001)
Structure −0.553*** (0.002) −0.345*** (0.001) −0.208*** (0.001)
C. Females
Total Change −0.415*** (0.002) −0.243*** (0.001) −0.172*** (0.001)
Composition −0.147*** (0.001) −0.150*** (0.001) 0.003*** (0.001)
Structure −0.268*** (0.002) −0.093*** (0.001) −0.176*** (0.001)

Detailed Decomposition of the composition effect, based on Unconditional Quantile Partial Effects (without selection correction)

Inequality measure All Males Females
90-10 90-50 50-10 90-10 90-50 50-10 90-10 90-50 50-10
Female −0.002*** (0.00003) −0.002*** (0.00003) −0.00024*** (0.00001)
Information 0.008*** (0.0004) −0.022*** (0.0003) 0.03*** (0.0003) 0.018*** (0.0005) 0.01*** (0.0004) 0.008*** (0.0003) 0.039*** (0.0008) −0.039*** (0.0007) 0.077*** (0.0006)
Automation −0.072*** (0.00033) −0.073*** (0.00032) 0.001*** (0.00023) 0.018*** (0.0003) 0.00853*** (0.0003) 0.009*** (0.0002) −0.117*** (0.0013) −0.09796*** (0.0011) −0.019*** (0.0008)
Education 0.043*** (0.0004) 0.03*** (0.0003) 0.012*** (0.0002) 0.057*** (0.0006) 0.044*** (0.0005) 0.013*** (0.0002) 0.018*** (0.0005) 0.013*** (0.0003) 0.005*** (0.0003)
Experience −0.02*** (0.0002) −0.015*** (0.00013) −0.005*** (0.0001) −0.025*** (0.00027) −0.02*** (0.00024) −0.005*** (0.0001) −0.014*** (0.00021) −0.01*** (0.00018) −0.003*** (0.0001)
Other −0.075*** (0.00034) −0.038*** (0.00021) −0.037*** (0.00029) −0.07*** (0.0004) −0.043*** (0.0003) −0.026*** (0.0003) −0.073*** (0.0006) −0.016*** (0.0003) −0.057*** (0.0005)
Total Composition Effect −0.118*** (0.001) −0.119*** (0.001) 0.001 (0.001) −0.001 (0.001) −0.001 (0.0008) −0.000 (0.0006) −0.147*** (0.0015) −0.150*** (0.0012) 0.003*** (0.0009)

Average O*NET Indexes by Major Occupation Group 2005

A: WITH IMPUTATION 2005
MEN WOMEN
O*Net Indexes Information Automation Information Automation
Overall Mean 0.596 0.739 0.602 0.732
Standard Deviation 0.118 0.062 0.112 0.061
Manager, Professionals, Technicians 0.788 0.702 0.771 0.676
Clerical support and sale workers 0.666 0.738 0.672 0.744
Plant and machines operators and assemblers 0.546 0.772 0.518 0.803
Agricultural, construction and transport workers 0.543 0.744 0.522 0.747
Service workers 0.530 0.713 0.531 0.726
B: WITHOUT IMPUTATION
Overall Mean 0.598 0.739 0.607 0.732
Standard Deviation 0.119 0.062 0.114 0.061
Manager, Professionals, Technicians 0.789 0.701 0.771 0.676
Clerical support and sale workers 0.667 0.738 0.675 0.747
Plant and machines operators and assemblers 0.547 0.772 0.518 0.804
Agricultural, construction and transport workers 0.544 0.744 0.531 0.750
Service workers 0.530 0.712 0.531 0.726

Unconditional Quantile Partial Effects on Male Log Wages (2005 – 2015) - RIF Regression (Without correction for selection bias)

Year 2005/06 2014/15
Covariates/Quantile 10 50 90 10 50 90
Task content indexes (1st. quartile omitted)
Information content Q2 0.021*** (0.001) 0.064*** (0.001) 0.067*** (0.001) −0.072*** (0.001) −0.042*** (0.001) −0.070*** (0.001)
Information content Q3 0.033*** (0.001) 0.030*** (0.002) 0.001 (0.001) 0.001 (0.001) −0.002* (0.001) −0.023*** (0.001)
Information content Q4 0.479*** (0.001) 0.124*** (0.001) 0.390*** (0.001) 0.322*** (0.001) 0.106*** (0.001) 0.296*** (0.001)
Automation content Q2 −0.199*** (0.001) −0.281*** (0.002) −0.160*** (0.001) −0.066*** (0.001) −0.072*** (0.001) −0.049*** (0.001)
Automation content Q3 0.017*** (0.001) 0.104*** (0.001) 0.179*** (0.001) 0.062*** (0.001) 0.032*** (0.001) 0.122*** (0.001)
Automation content Q4 −0.044*** (0.002) 0.017*** (0.001) 0.001 (0.001) 0.001 (0.001) 0.012*** (0.001) −0.007*** (0.001)
Education (6 years or less omitted)
From 7 to 9 years 0.162*** (0.001) 0.188*** (0.001) 0.167*** (0.001) 0.118*** (0.001) 0.175*** (0.001) 0.106*** (0.001)
From 10 to 12 years 0.336*** (0.001) 0.272*** (0.002) 0.332*** (0.001) 0.27*** (0.001) 0.258*** (0.001) 0.272*** (0.001)
From 13 to 15 years 0.619*** (0.002) 0.317*** (0.002) 0.543*** (0.002) 0.487*** (0.001) 0.307*** (0.001) 0.435*** (0.001)
16 and more years 1.070*** (0.002) 0.321*** (0.002) 0.678*** (0.002) 0.88*** (0.001) 0.334*** (0.001) 0.573*** (0.001)
Experience (15<Experience<20 omitted)
Experience<5 −0.578*** (0.003) −0.029*** (0.004) −0.086*** (0.004) −0.44*** (0.002) 0.046*** (0.001) −0.0850*** (0.002)
5<experience<10 −0.303*** (0.001) −0.000 (0.002) −0.135*** (0.002) −0.218*** (0.001) −0.013*** (0.001) −0.131*** (0.001)
10<experience<15 −0.133*** (0.001) −0.100*** (0.002) −0.146*** (0.001) −0.087*** (0.001) −0.039*** (0.001) −0.079*** (0.001)
20<experience<25 0.089*** (0.001) 0.037*** (0.002) 0.107*** (0.001) 0.056*** (0.001) 0.052*** (0.001) 0.054*** (0.001)
25<experience<30 0.152*** (0.001) 0.075*** (0.002) 0.136*** (0.001) 0.099*** (0.001) 0.051*** (0.001) 0.085*** (0.001)
30<experience<35 0.156*** (0.001) 0.075*** (0.002) 0.155*** (0.001) 0.133*** (0.001) 0.064*** (0.001) 0.116*** (0.001)
35<experience<40 0.157*** (0.001) 0.091*** (0.002) 0.147*** (0.001) 0.128*** (0.001) 0.083*** (0.001) 0.113*** (0.001)
Experience>40 0.154*** (0.001) 0.043*** (0.002) 0.156*** (0.001) 0.11*** (0.001) 0.11*** (0.001) 0.089*** (0.001)
Nonmarried −0.139*** (0.001) −0.087*** (0.001) −0.169*** (0.001) −0.100*** (0.001) −0.072*** (0.001) −0.114*** (0.001)
Region −0.119*** (0.001) −0.104*** (0.001) −0.102*** (0.001) −0.026*** (0.0005) −0.054*** (0.001) −0.016*** (0.001)
Informal −0.302*** (0.001) −0.506*** (0.002) −0.331*** (0.001) −0.276*** (0.001) −0.555*** (0.002) −0.243*** (0.001)
Constant 3.566*** (0.001) 2.866*** (0.002) 3.483*** (0.002) 4.092*** (0.001) 3.528*** (0.001) 4.045*** (0.001)

Unconditional Quantile Partial Effects on Log Wages (2005 – 2015) - RIF Regression

Year 2005/06 2014/15
Covariates/Quantile 10 50 90 10 50 90
Female −0.141*** (0.005) −0.158*** (0.003) −0.327*** (0.004) −0.203*** (0.002) −0.237*** (0.002) −0.272*** (0.002)
Task content indexes (1st. quartile omitted)
Information content Q2 0.136*** (0.009) 0.105*** (0.009) 0.080*** (0.006) −0.001 (0.005) −0.059*** (0.003) 0.047*** (0.003)
Information content Q3 0.105*** (0.008) 0.220*** (0.008) 0.061*** (0.006) 0.005 (0.004) 0.004*** (0.001) −0.118*** (0.002)
Information content Q4 0.185*** (0.008) 0.543*** (0.008) 1.013*** (0.011) 0.071*** (0.004) 0.230*** (0.002) 0.514*** (0.005)
Automation content Q2 −0.010 (0.009) 0.011 (0.009) −0.112*** (0.005) 0.045*** (0.001) 0.002 (0.002) −0.151*** (0.005)
Automation content Q3 0.205*** (0.009) 0.281*** (0.009) −0.125*** (0.008) 0.101*** (0.002) 0.11*** (0.002) −0.093*** (0.003)
Automation content Q4 0.088*** (0.008) 0.037*** (0.008) −0.304*** (0.007) 0.1*** (0.003) 0.045*** (0.003) −0.412*** (0.004)
Education (6 years or less omitted)
From 7 to 9 years 0.167*** (0.009) 0.141*** (0.004) 0.093*** (0.004) 0.148*** (0.004) 0.105*** (0.002) 0.064*** (0.002)
From 10 to 12 years 0.243*** (0.008) 0.278*** (0.005) 0.356*** (0.008) 0.281*** (0.004) 0.294*** (0.002) 0.267*** (0.003)
From 13 to 15 years 0.243*** (0.008) 0.278*** (0.005) 0.356*** (0.008) 0.281*** (0.004) 0.294*** (0.002) 0.267*** (0.003)
16 and more years 0.296*** (0.009) 0.525*** (0.008) 0.978*** (0.015) 0.359*** (0.005) 0.566*** (0.004) 0.765*** (0.006)
Experience (15<Experience<20 omitted)
Experience<5 0.042*** (0.012) −0.079*** (0.014) −1.581*** (0.034) 0.020*** (0.004) −0.099*** (0.009) −1.067*** (0.017)
5<experience<10 0.023** (0.010) −0.130*** (0.005) −0.670*** (0.009) −0.017*** (0.004) −0.119*** (0.003) −0.436*** (0.007)
10<experience<15 −0.051*** (0.009) −0.116*** (0.004) −0.153*** (0.007) −0.046*** (0.004) −0.088*** (0.003) −0.128*** (0.003)
20<experience<25 0.035*** (0.009) 0.102*** (0.004) 0.118*** (0.006) 0.031*** (0.003) 0.031*** (0.004) 0.074*** (0.005)
25<experience<30 0.075*** (0.008) 0.144*** (0.003) 0.213*** (0.007) 0.034*** (0.004) 0.067*** (0.003) 0.132*** (0.003)
30<experience<35 0.056*** (0.008) 0.16*** (0.005) 0.232*** (0.007) 0.049*** (0.004) 0.089*** (0.003) 0.202*** (0.004)
35<experience<40 0.113*** (0.010) 0.176*** (0.006) 0.222*** (0.005) 0.055*** (0.005) 0.086*** (0.003) 0.195*** (0.003)
Experience>40 0.046*** (0.008) 0.208*** (0.004) 0.225*** (0.006) 0.048*** (0.004) 0.094*** (0.003) 0.167*** (0.004
Nonmarried −0.073*** (0.005) −0.131*** (0.004) −0.149*** (0.003) −0.050*** (0.002) −0.101*** (0.002) −0.101*** (0.001)
Region −0.190*** (0.003) −0.161*** (0.003) −0.150*** (0.004) −0.086*** (0.002) −0.060*** (0.001) −0.059*** (0.001)
Informal −0.438*** (0.007) −0.244*** (0.005) −0.0220*** (0.006) −0.506*** (0.008) −0.196*** (0.003) 0.001 (0.003)
Constant 2.750*** (0.013) 3.334*** (0.005) 4.358*** (0.007) 3.450*** (0.007) 4.008*** (0.004) 4.834*** (0.003)

Avarage O*NET Indexes by Major Occupation Group 2015

A: WITH IMPUTATION 2015
MEN WOMEN
O*Net Indexes Information Automation Information Automation
Overall Mean 0.608 0.738 0.621 0.732
Standard Deviation 0.117 0.063 0.114 0.061
Manager, Professionals, Technicians 0.799 0.706 0.776 0.685
Clerical support and sale workers 0.670 0.744 0.668 0.746
Plant and machines operators and assemblers 0.552 0.772 0.518 0.802
Agricultural, construction and transport workers 0.559 0.738 0.561 0.757
Service workers 0.536 0.713 0.545 0.725
B: WITHOUT IMPUTATION
Overall Mean 0.610 0.738 0.625 0.732
Standard Deviation 0.117 0.063 0.115 0.062
Manager, Professionals, Technicians 0.799 0.706 0.777 0.685
Clerical support and sale workers 0.671 0.744 0.671 0.748
Plant and machines operators and assemblers 0.552 0.772 0.519 0.804
Agricultural, construction and transport workers 0.560 0.737 0.576 0.761
Service workers 0.536 0.712 0.544 0.725

Detailed Decomposition of the structure effect, wage variation between 2005 and 2015, based on UQPE (without selection correction)

Inequality measure All Males Females
90-10 90-50 50-10 90-10 90-50 50-10 90-10 90-50 50-10
Female 0.066*** (0.0012) 0.066*** (0.0012) 0.00048 (0.0007)
Information −0.048*** (0.0016) 0.018*** (0.0013) −0.06594*** (0.0013) −0.108*** (0.002) −0.065*** (0.002) −0.043*** (0.001) 0.051*** (0.003) 0.082*** (0.003) −0.030*** (0.002)
Automation 0.078*** (0.002) 0.110*** (0.0016) −0.03173*** (0.0012) 0.017*** (0.003) 0.047*** (0.002) −0.030*** (0.002) 0.082*** (0.003) 0.077*** (0.002) 0.005*** (0.002)
Education −0.151*** (0.0021) −0.103*** (0.0018) −0.04793*** (0.0016) −0.166*** (0.003) −0.119*** (0.002) −0.047*** (0.002) −0.076*** (0.003) −0.087*** (0.002) 0.011*** (0.002)
Experience 0.007*** (0.002) 0.047*** (0.0018) −0.03913*** (0.0014) −0.003 (0.003) 0.028*** (0.002) −0.031*** (0.002) 0.009*** (0.003) 0.059*** (0.003) −0.051*** (0.002)
Other 0.015*** (0.0012) −0.006*** (0.0009) 0.02106*** (0.001) 0.052*** (0.002) 0.008*** (0.001) 0.043*** (0.001) −0.038*** (0.002) −0.02*** (0.001) −0.018*** (0.001)
Constant −0.307*** (0.0042) −0.297*** (0.0036) −0.01043*** (0.003) −0.345*** (0.006) −0.244*** (0.005) −0.1*** (0.004) −0.296*** (0.006) −0.204*** (0.005) −0.092*** (0.005)
Total Structure Efect −0.339*** (0.001) −0.165*** (0.0009) −0.17362*** (0.0006) −0.553*** (0.002) −0.345*** (0.001) −0.208*** (0.001) −0.268*** (0.002) −0.093*** (0.001) −0.176*** (0.001)

Aggregate Decomposition of wage change between 2005 and 2015

90–10 90–50 50–10
A. All
Total Change −0.453*** (0.004) −0.255*** (0.002) −0.197*** (0.002)
Composition −0.066*** (0.003) −0.067*** (0.002) 0.001 (0.002)
Structure −0.387*** (0.003) −0.188*** (0.002) −0.198*** (0.003)
B. Males
Total Change −0.582*** (0.005) −0.34*** (0.002) −0.242*** (0.004)
Composition −0.02*** (0.002) −0.015*** (0.002) −0.005*** (0.002)
Structure −0.562*** (0.005) −0.326*** (0.002) −0.236*** (0.004)
C. Females
Total Change −0.418*** (0.012) −0.184*** (0.004) −0.233*** (0.009)
Composition −0.052*** (0.01) −0.038*** (0.006) −0.014** (0.006)
Structure −0.365*** (0.01) −0.146*** (0.006) −0.219*** (0.008)

Detailed Decomposition of the composition effect, based on Unconditional Quantile Partial Effects

Inequality measure All Males Females
90-10 90-50 50-10 90-10 90-50 50-10 90-10 90-50 50-10
Female −0.002*** (0.00007) −0.002*** (0.00005) −0.00016*** (0.00004)
Information 0.018*** (0.0009) 0.000 (0.0007) 0.018*** (0.0005) 0.001 (0.0008) −0.005*** (0.0008) 0.006*** (0.0006) 0.040*** (0.002) 0.003*** (0.001) 0.037*** (0.001)
Automation −0.041*** (0.00146) −0.048*** (0.00087) 0.007*** (0.0015) 0.012*** (0.0008) 0.00667*** (0.001) 0.006*** (0.0009) −0.036*** (0.006) −0.03539*** (0.005) −0.00058 (0.005)
Education 0.042*** (0.001) 0.032*** (0.0007) 0.011*** (0.0005) 0.057*** (0.0018) 0.045*** (0.0014) 0.013*** (0.0012) 0.019*** (0.001) 0.015*** (0.001) 0.004*** (0.001)
Experience −0.020*** (0.0003) −0.015*** (0.0003) −0.005*** (0.0002) −0.025*** (0.00054) −0.020*** (0.00051) −0.005*** (0.0003) −0.013*** (0.001) −0.010*** (0.001) −0.003*** (0.001)
Other −0.064*** (0.002) −0.035*** (0.001) −0.030*** (0.001) −0.065*** (0.0018) −0.041*** (0.0015) −0.024*** (0.0013) −0.062*** (0.004) −0.010*** (0.002) −0.051*** (0.003)
Total Composition Effect −0.066*** (0.003) −0.067*** (0.002) 0.001 (0.002) −0.020*** (0.0024) −0.015*** (0.0022) −0.005*** (0.0015) −0.052*** (0.01) −0.038*** (0.006) −0.014** (0.006)

Unconditional Quantile Partial Effects on Female Log Wages (2005 – 2015) - RIF Regression (Without correction for selection bias)

Year 2005/06 2014/15
Covariates/Quantile 10 50 90 10 50 90
Task content indexes (1st. quartile omitted)
Information content Q2 0.169*** (0.002) 0.078*** (0.001) −0.078*** (0.001) 0.076*** (0.001) 0.004*** (0.001) −0.076*** (0.001)
Information content Q3 0.156*** (0.003) 0.438*** (0.002) 0.0030 (0.003) 0.002 (0.001) 0.140*** (0.001) 0.009*** (0.002)
Information content Q4 0.601*** (0.002) 0.644*** (0.002) 0.851*** (0.003) 0.082*** (0.001) 0.433*** (0.001) 0.620*** (0.002)
Automation content Q2 0.172*** (0.002) 0.190*** (0.001) −0.078*** (0.001) 0.092*** (0.001) 0.065*** (0.001) −0.121*** (0.002)
Automation content Q3 0.175*** (0.002) 0.130*** (0.001) −0.481*** (0.003) 0.141*** (0.001) 0.162*** (0.001) −0.105*** (0.001)
Automation content Q4 0.080*** (0.002) 0.056*** (0.001) 0.003 (0.003) 0.001 (0.001) 0.066*** (0.001) −0.446*** (0.002)
Education (6 years or less omitted)
From 7 to 9 years 0.193*** 3241(0.002) 0.108*** (0.001) 0.051*** (0.001) 0.134*** (0.001) 0.075*** (0.001) 0.021*** (0.001)
From 10 to 12 years 0.290*** (0.002) 0.257*** (0.001) 0.192*** (0.001) 0.308*** (0.001) 0.301*** (0.001) 0.095*** (0.001)
From 13 to 15 years 0.346*** (0.002) 0.508*** (0.002) 0.664*** (0.004) 0.392*** (0.001) 0.563*** (0.001) 0.524*** (0.002)
16 and more years 0.366*** (0.002) 0.687*** (0.002) 1.8*** (0.007) 0.437*** (0.001) 0.705*** (0.001) 1.608*** (0.004)
Experience (15<Experience<20 omitted)
Experience<5 0.112*** (0.003) −0.045*** (0.003) −1.325*** (0.011) 0.042*** (0.001) −0.093*** (0.001) −0.980*** (0.007)
5<experience<10 0.056*** (0.002) −0.103*** (0.002) −0.467*** (0.004) 0.027*** (0.001) −0.087*** (0.001) −0.374*** (0.002)
10<experience<15 −0.004** (0.002) −0.075*** (0.001) −0.15*** (0.002) −0.004*** (0.001) −0.081*** (0.001) −0.120*** (0.002)
20<experience<25 0.020*** (0.002) 0.093*** (0.001) 0.097*** (0.003) 0.065*** (0.001) 0.010*** (0.001) 0.044*** (0.002)
25<experience<30 0.087*** (0.002) 0.13*** (0.001) 0.126*** (0.003) 0.050*** (0.001) 0.054*** (0.001) 0.088*** (0.001)
30<experience<35 0.078*** (0.002) 0.126*** (0.001) 0.182*** (0.003) 0.059*** (0.001) 0.056*** (0.001) 0.188*** (0.002)
35<experience<40 0.132*** (0.002) 0.156*** (0.001) 0.144*** (0.003) 0.068*** (0.001) 0.051*** (0.001) 0.153*** (0.002)
Experience>40 0.051*** (0.003) 0.215*** (0.002) 0.171*** (0.002) 0.042*** (0.001) 0.042*** (0.001) 0.108*** (0.001)
Nonmarried −0.093*** (0.001) −0.088*** (0.001) −0.11*** (0.001) −0.045*** (0.001) −0.076*** (0.001) −0.086*** (0.001)
Region −0.291*** (0.001) −0.242*** (0.001) −0.167*** (0.001) −0.110*** (0.001) −0.113*** (0.001) −0.093*** (0.001)
Informal −0.487*** (0.002) −0.158*** (0.001) −0.056*** (0.001) −0.541*** (0.002) −0.114*** (0.001) 0.006*** (0.001)
Constant 2.515*** (0.003) 3.072*** (0.002) 4.307*** (0.003) 3.193*** (0.002) 3.659*** (0.001) 4.689*** (0.002)

Log hourly wages 2005/2006 and 2014/2015, by gender.

Variable 2005/06 2014/15
With imputation Without imputation With imputation Without imputation
Mean s.e Mean s.e Mean s.e. Mean s.e.
All 3.632 (0.004) 3.658 (0.004) 4.213 (0.003) 4.228 (0.003)
Male 3.724 (0.005) 3.736 (0.005) 4.314 (0.004) 4.320 (0.004)
Female 3.537 (0.005) 3.572 (0.006) 4.114 (0.004) 4.134 (0.004)
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