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Heterogeneous effects of minimum wage on labor market outcomes: A case study from Turkey

Enes Işık
Enes Işık
, 
Özgür Orhangazi
Özgür Orhangazi
 and 
Hasan Tekgüç
Hasan Tekgüç
   | Nov 22, 2020
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Published Online: Nov 22, 2020
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DOI: https://doi.org/10.2478/izajolp-2020-0016
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© 2020 Enes Işık, Özgür Orhangazi, Hasan Tekgüç, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1

National minimum wage/average wage ratio.Notes: OECD countries in 2016, based on OECD data.
National minimum wage/average wage ratio.Notes: OECD countries in 2016, based on OECD data.

Figure 2

Real minimum wage level in Turkey, 2003–2016.Notes: Based on Turkstat’s CPI and Labor and Social Security Ministry’s minimum wage data.
Real minimum wage level in Turkey, 2003–2016.Notes: Based on Turkstat’s CPI and Labor and Social Security Ministry’s minimum wage data.

Figure 3

Wage groups potentially affected by the minimum wage increase in 2016, by education.Notes: Own calculations based on the 2015 and 2016 waves of Turkish Household Labor Force Survey. The sample includes full-time wage employment in the private sector by education groups (471,271 individuals). Tertiary educated workers are those with 2- or 3-years higher education, or 4 years higher education, or master’s degree (5- or 6-years faculty included) or doctorate. 711TL cutoff is 75% of the lowest minimum wage level in 2015; 1300TL is the minimum wage level in 2016. To read the A panel, for example, around 55% of workers with no high school degree earned less than 1300TL but higher than 711TL in 2015.
Wage groups potentially affected by the minimum wage increase in 2016, by education.Notes: Own calculations based on the 2015 and 2016 waves of Turkish Household Labor Force Survey. The sample includes full-time wage employment in the private sector by education groups (471,271 individuals). Tertiary educated workers are those with 2- or 3-years higher education, or 4 years higher education, or master’s degree (5- or 6-years faculty included) or doctorate. 711TL cutoff is 75% of the lowest minimum wage level in 2015; 1300TL is the minimum wage level in 2016. To read the A panel, for example, around 55% of workers with no high school degree earned less than 1300TL but higher than 711TL in 2015.

Figure 4

Wage groups potentially affected by the minimum wage increase in 2016, by age groups.Notes: Own calculations based on the 2015 and 2016 waves of Turkish Household Labor Force Survey. The sample includes full-time wage employment in the private sector by age groups (471,271 individuals). 711TL cutoff is 75% of the lowest minimum wage level in 2015; 1300TL is the minimum wage level in 2016. To read the A panel, for example, around 60% of workers aged 15–24 earned less than 1300TL but higher than 711TL in 2015.
Wage groups potentially affected by the minimum wage increase in 2016, by age groups.Notes: Own calculations based on the 2015 and 2016 waves of Turkish Household Labor Force Survey. The sample includes full-time wage employment in the private sector by age groups (471,271 individuals). 711TL cutoff is 75% of the lowest minimum wage level in 2015; 1300TL is the minimum wage level in 2016. To read the A panel, for example, around 60% of workers aged 15–24 earned less than 1300TL but higher than 711TL in 2015.

Figure 5

Wage distribution in full-time wage employment in the private sector.Notes: Own calculations based on the 2013 to 2016 waves of Turkish Household Labor Force Survey. The sample includes full-time wage employment in the private sector by informality status. The vertical line shows the minimum wage level in the first half of that year.
Wage distribution in full-time wage employment in the private sector.Notes: Own calculations based on the 2013 to 2016 waves of Turkish Household Labor Force Survey. The sample includes full-time wage employment in the private sector by informality status. The vertical line shows the minimum wage level in the first half of that year.

Figure 6

Fraction of young or less than high school educated workers potentially affected by the increase in the minimum wage in 2016.Notes: Own calculations based on the 2015 wave of the Turkish Household Labor Force Survey. The sample includes full-time wage employment in the private sector. The figures show the regional distribution of young or less than high school educated workers earning higher than 711TL but lower than 1300TL in 2015.
Fraction of young or less than high school educated workers potentially affected by the increase in the minimum wage in 2016.Notes: Own calculations based on the 2015 wave of the Turkish Household Labor Force Survey. The sample includes full-time wage employment in the private sector. The figures show the regional distribution of young or less than high school educated workers earning higher than 711TL but lower than 1300TL in 2015.

Figure 7

Testing parallel trend assumption: Labor market outcomes of workers with no high school degree during the period of 2013–2016.Notes: Own calculations based on the 2013 to 2016 waves of Turkish Household Labor Force Survey. The sample includes full-time wage employment in the private sector. More Impacted regions are ones in which the fraction of workers affected by the increase (workers earning less than 1300TL but higher than 711TL in 2015) in the minimum wage in 2016 is higher than the median fraction. There are 13 NUTS-2 More Impacted and 13 NUTS-2 Less Impacted regions.
Testing parallel trend assumption: Labor market outcomes of workers with no high school degree during the period of 2013–2016.Notes: Own calculations based on the 2013 to 2016 waves of Turkish Household Labor Force Survey. The sample includes full-time wage employment in the private sector. More Impacted regions are ones in which the fraction of workers affected by the increase (workers earning less than 1300TL but higher than 711TL in 2015) in the minimum wage in 2016 is higher than the median fraction. There are 13 NUTS-2 More Impacted and 13 NUTS-2 Less Impacted regions.

Figure 8

Testing parallel trend assumption: Labor market outcomes of young (15–24) workers during the period of 2015–2016.Notes: Own calculations based on the 2013 to 2016 waves of Turkish Household Labor Force Survey. The sample includes full-time wage employment in the private sector. More Impacted regions are ones in which the fraction of workers affected by the increase (workers earning less than 1300TL but higher than 711TL in 2015) in the minimum wage in 2016 is higher than the median fraction. There are 13 NUTS-2 More Impacted and 13 NUTS-2 Less Impacted regions.
Testing parallel trend assumption: Labor market outcomes of young (15–24) workers during the period of 2015–2016.Notes: Own calculations based on the 2013 to 2016 waves of Turkish Household Labor Force Survey. The sample includes full-time wage employment in the private sector. More Impacted regions are ones in which the fraction of workers affected by the increase (workers earning less than 1300TL but higher than 711TL in 2015) in the minimum wage in 2016 is higher than the median fraction. There are 13 NUTS-2 More Impacted and 13 NUTS-2 Less Impacted regions.

Figure A1

Share of minimum wage workers in formal and informal sectors by education groups.Notes: Own calculations based on the 2009 to 2016 waves of Turkish Household Labor Force Survey. The sample includes full-time wage employment in private sector by education groups (of 527,149 individuals 410,463 individuals are working formally, while 116,686 are working informally during the sample period). Informal workers are wage earners working full-time without registering in the Social Security Institution. Taking the minimum wage level in the first half of the year as the minimum threshold and the minimum wage level in the second half of the year as the maximum threshold, and allowing a 5% error margin, we discern minimum wage workers. Generalization of the approach yields the following wage condition for minimum wage workers: MW1t – (MW1t * 0.05) < Yit < MW2t + (MW2t *0.05), where MW1t is the minimum wage level of the first half of the year t, MW2t is the minimum wage level of second half of the year t, Yit. is the wage of the worker i in year t. There are some legitimate reasons for workers in the formal sector to earn less than the minimum wage during the survey month. The question relating to earnings asks respondents their earnings in the previous month and some full-time workers might have worked less than whole month because i) they started a new job in the middle of the previous month, ii) they got an unpaid leave to deal with family emergencies; or iii) the workplace was temporarily closed. Moreover, if the respondent was interviewed in January, she is reporting her wages from December of previous year, which is probably less than the 95% of new minimum wage. As can be seen from the figure, the share of formal sector workers with less than minimum wage is stable over the years for all education groups suggesting that these are idiosyncratic issues independent from macroeconomic trends. We thank İnsan Tunalı for pointing out these data issues.
Share of minimum wage workers in formal and informal sectors by education groups.Notes: Own calculations based on the 2009 to 2016 waves of Turkish Household Labor Force Survey. The sample includes full-time wage employment in private sector by education groups (of 527,149 individuals 410,463 individuals are working formally, while 116,686 are working informally during the sample period). Informal workers are wage earners working full-time without registering in the Social Security Institution. Taking the minimum wage level in the first half of the year as the minimum threshold and the minimum wage level in the second half of the year as the maximum threshold, and allowing a 5% error margin, we discern minimum wage workers. Generalization of the approach yields the following wage condition for minimum wage workers: MW1t – (MW1t * 0.05) < Yit < MW2t + (MW2t *0.05), where MW1t is the minimum wage level of the first half of the year t, MW2t is the minimum wage level of second half of the year t, Yit. is the wage of the worker i in year t. There are some legitimate reasons for workers in the formal sector to earn less than the minimum wage during the survey month. The question relating to earnings asks respondents their earnings in the previous month and some full-time workers might have worked less than whole month because i) they started a new job in the middle of the previous month, ii) they got an unpaid leave to deal with family emergencies; or iii) the workplace was temporarily closed. Moreover, if the respondent was interviewed in January, she is reporting her wages from December of previous year, which is probably less than the 95% of new minimum wage. As can be seen from the figure, the share of formal sector workers with less than minimum wage is stable over the years for all education groups suggesting that these are idiosyncratic issues independent from macroeconomic trends. We thank İnsan Tunalı for pointing out these data issues.

Figure A2

Share of minimum wage workers in formal and informal sectors by age groups.Notes: Own calculations based on the 2009 to 2016 waves of Turkish Household Labor Force Survey. The sample includes full-time wage employment in private sector by age groups (of 527,149 individuals 410,463 individuals are working formally, while 116,686 are working informally during the sample period). Informal workers are wage earners working full-time without registering in the Social Security Institution. Taking the minimum wage level in the first half of the year as the minimum threshold and the minimum wage level in the second half of the year as the maximum threshold, and allowing a 5% error margin, we discern possible minimum wage workers. Generalization of the approach yields the following wage condition for minimum wage workers: MW1t – (MW1t * 0.05) < Yit < MW2t + (MW2t * 0.05), where MW1t is the minimum wage level of the first half of the year t, MW2t is the minimum wage level of second half of the year t, Yit is the wage of the worker i in year t. There are some legitimate reasons for workers in the formal sector to earn less than the minimum wage during the survey month. The question relating to earnings asks respondents their earnings in the previous month and some full-time workers might have worked less than whole month because i) they started a new job in the middle of the previous month, ii) they got an unpaid leave to deal with family emergencies; or iii) the workplace was temporarily closed. Moreover, if the respondent was interviewed in January, she is reporting her wages from December of previous year, which is probably less than the 95% of new minimum wage. As can be seen from the figure, the share of formal sector workers with less than minimum wage is stable over the years for all education groups suggesting that these are idiosyncratic issues independent from macroeconomic trends. We thank İnsan Tunalı for pointing out these data issues.
Share of minimum wage workers in formal and informal sectors by age groups.Notes: Own calculations based on the 2009 to 2016 waves of Turkish Household Labor Force Survey. The sample includes full-time wage employment in private sector by age groups (of 527,149 individuals 410,463 individuals are working formally, while 116,686 are working informally during the sample period). Informal workers are wage earners working full-time without registering in the Social Security Institution. Taking the minimum wage level in the first half of the year as the minimum threshold and the minimum wage level in the second half of the year as the maximum threshold, and allowing a 5% error margin, we discern possible minimum wage workers. Generalization of the approach yields the following wage condition for minimum wage workers: MW1t – (MW1t * 0.05) < Yit < MW2t + (MW2t * 0.05), where MW1t is the minimum wage level of the first half of the year t, MW2t is the minimum wage level of second half of the year t, Yit is the wage of the worker i in year t. There are some legitimate reasons for workers in the formal sector to earn less than the minimum wage during the survey month. The question relating to earnings asks respondents their earnings in the previous month and some full-time workers might have worked less than whole month because i) they started a new job in the middle of the previous month, ii) they got an unpaid leave to deal with family emergencies; or iii) the workplace was temporarily closed. Moreover, if the respondent was interviewed in January, she is reporting her wages from December of previous year, which is probably less than the 95% of new minimum wage. As can be seen from the figure, the share of formal sector workers with less than minimum wage is stable over the years for all education groups suggesting that these are idiosyncratic issues independent from macroeconomic trends. We thank İnsan Tunalı for pointing out these data issues.

Figure A3

Wage groups potentially affected by the minimum wage increase in 2016, by informality status.Notes: Own calculations based on the 2015 and 2016 waves of Turkish Household Labor Force Survey. The sample includes full-time wage employment in private sector by education groups (471,271 individuals). Informal workers are wage earners working full-time without registering in the Social Security Institution. 711TL cutoff is 75% of the lowest minimum wage level in 2015, 1300TL is the minimum wage level in 2016. To read the A panel for example, around 50% of workers registered in the Social Security Institution earned lower than 1300TL but higher than 711TL in 2015.
Wage groups potentially affected by the minimum wage increase in 2016, by informality status.Notes: Own calculations based on the 2015 and 2016 waves of Turkish Household Labor Force Survey. The sample includes full-time wage employment in private sector by education groups (471,271 individuals). Informal workers are wage earners working full-time without registering in the Social Security Institution. 711TL cutoff is 75% of the lowest minimum wage level in 2015, 1300TL is the minimum wage level in 2016. To read the A panel for example, around 50% of workers registered in the Social Security Institution earned lower than 1300TL but higher than 711TL in 2015.

Figure A4

All monthly wage coefficients for the key variables of interest in the baseline wage estimation.Notes: The wage sample is full time wage employment in private sector in Turkish Household Labor Force Survey 2010–2016. It includes 471,271 individuals (362,845 males; 108,426 females). Dependent variables are natural logarithm of monthly wages. The key variable of interest is the ratio of workers earning higher than 75% of minimum wage level in year t-1 but lower than new minimum wage level in year t. Each row shows the estimates for the interaction of the year t dummy and the fraction of affected workers in year t-1. All coefficients are relative to the interaction of the 2010 dummy and the fraction of affected workers in 2009 variable. In “by education” estimations, age is controlled using dummies for 15–24, 25–34, 35–44, 45–54, and 55–64 age cohorts. Education is controlled using dummies for less than high school, high school, and tertiary education groups in “by age” estimations.
All monthly wage coefficients for the key variables of interest in the baseline wage estimation.Notes: The wage sample is full time wage employment in private sector in Turkish Household Labor Force Survey 2010–2016. It includes 471,271 individuals (362,845 males; 108,426 females). Dependent variables are natural logarithm of monthly wages. The key variable of interest is the ratio of workers earning higher than 75% of minimum wage level in year t-1 but lower than new minimum wage level in year t. Each row shows the estimates for the interaction of the year t dummy and the fraction of affected workers in year t-1. All coefficients are relative to the interaction of the 2010 dummy and the fraction of affected workers in 2009 variable. In “by education” estimations, age is controlled using dummies for 15–24, 25–34, 35–44, 45–54, and 55–64 age cohorts. Education is controlled using dummies for less than high school, high school, and tertiary education groups in “by age” estimations.

Figure A5

All employment coefficients for the key variables of interest in the baseline employment estimation.Notes: The employment sample is working age population in Turkish Household Labor Force Survey 2010–2016. It includes 2,361,071 individuals (1,152,475 males; 1,208,596 females). Dependent variable is a dummy variable taking 1 if an individual is a wage earner and working full-time in private sector. The key variable of interest is the ratio of workers earning higher than 75% of minimum wage level in year t-1 but lower than new minimum wage level in year t. Each row shows the estimates for the interaction of the year t dummy and the fraction of affected workers in year t-1. All coefficients are relative to the interaction of the 2010 dummy and the fraction of affected workers in 2009 variable. In “by education” estimations, age is controlled using dummies for 15–24, 25–34, 35–44, 45–54, and 55–64 age cohorts. Education is controlled using dummies for less than high school, high school, and tertiary education groups in “by age” estimations.
All employment coefficients for the key variables of interest in the baseline employment estimation.Notes: The employment sample is working age population in Turkish Household Labor Force Survey 2010–2016. It includes 2,361,071 individuals (1,152,475 males; 1,208,596 females). Dependent variable is a dummy variable taking 1 if an individual is a wage earner and working full-time in private sector. The key variable of interest is the ratio of workers earning higher than 75% of minimum wage level in year t-1 but lower than new minimum wage level in year t. Each row shows the estimates for the interaction of the year t dummy and the fraction of affected workers in year t-1. All coefficients are relative to the interaction of the 2010 dummy and the fraction of affected workers in 2009 variable. In “by education” estimations, age is controlled using dummies for 15–24, 25–34, 35–44, 45–54, and 55–64 age cohorts. Education is controlled using dummies for less than high school, high school, and tertiary education groups in “by age” estimations.

Figure A6

All informality coefficients for the key variables of interest in the baseline informality estimation.Notes: The informality sample is working age population in Turkish Household Labor Force Survey 2010–2016. It includes 2,361,071 individuals (1,152,475 males; 1,208,596 females). Dependent variable is a dummy variable taking 1 if an individual is a wage earner and working without registered in the Social Security Institution in full-time in private sector. The key variable of interest is the fraction of affected workers (the ratio of workers earning higher than 75% of minimum wage level in year t-1 but lower than new minimum wage level in year t). Each row shows the estimates for the interaction of the year t dummy and the fraction of affected workers in year t-1. All coefficients are relative to the interaction of the 2010 dummy and the fraction of affected workers in 2009 variable. In “by education” estimations, age is controlled using dummies for 15–24, 25–34, 35–44, 45–54, and 55–64 age cohorts. Education is controlled using dummies for less than high school, high school, and tertiary education groups in “by age” estimations
All informality coefficients for the key variables of interest in the baseline informality estimation.Notes: The informality sample is working age population in Turkish Household Labor Force Survey 2010–2016. It includes 2,361,071 individuals (1,152,475 males; 1,208,596 females). Dependent variable is a dummy variable taking 1 if an individual is a wage earner and working without registered in the Social Security Institution in full-time in private sector. The key variable of interest is the fraction of affected workers (the ratio of workers earning higher than 75% of minimum wage level in year t-1 but lower than new minimum wage level in year t). Each row shows the estimates for the interaction of the year t dummy and the fraction of affected workers in year t-1. All coefficients are relative to the interaction of the 2010 dummy and the fraction of affected workers in 2009 variable. In “by education” estimations, age is controlled using dummies for 15–24, 25–34, 35–44, 45–54, and 55–64 age cohorts. Education is controlled using dummies for less than high school, high school, and tertiary education groups in “by age” estimations

Figure A7

The regional fraction of workers earned less than 711 Turkish liras and the regional informality ratio.Notes: Own calculations based on the 2015 wave of Turkish Household Labor Force Survey. The sample includes full-time wage employment in private sector (68,649 individuals). 711TL cutoff is 75% of the lowest minimum wage level in 2015. To read the figure for example, around 70% of workers earned less than 711TL and approximately 20% of workers worked without social security in TRC3 region in 2015.
The regional fraction of workers earned less than 711 Turkish liras and the regional informality ratio.Notes: Own calculations based on the 2015 wave of Turkish Household Labor Force Survey. The sample includes full-time wage employment in private sector (68,649 individuals). 711TL cutoff is 75% of the lowest minimum wage level in 2015. To read the figure for example, around 70% of workers earned less than 711TL and approximately 20% of workers worked without social security in TRC3 region in 2015.

Figure B1

Alternative wage estimations with different sample period choice.Notes: The wage sample is full time wage employment in private sector in Turkish Household Labor Force Survey 2010–2016. It includes 471,271 individuals (362,845 males; 108,426 females). Dependent variables are natural logarithm of monthly wages. The key variable of interest is the ratio of workers earning higher than 75% of minimum wage level in year t-1 but lower than new minimum wage level in year t. Each row shows the estimates for the interaction of the year t dummy and the fraction of affected workers in year t-1. All coefficients are relative to the interaction of the 2010 dummy and the fraction of affected workers in 2009 variable. Age is controlled using dummies for 15–24, 25–34, 35–44, 45–54, and 55–64 age cohorts.
Alternative wage estimations with different sample period choice.Notes: The wage sample is full time wage employment in private sector in Turkish Household Labor Force Survey 2010–2016. It includes 471,271 individuals (362,845 males; 108,426 females). Dependent variables are natural logarithm of monthly wages. The key variable of interest is the ratio of workers earning higher than 75% of minimum wage level in year t-1 but lower than new minimum wage level in year t. Each row shows the estimates for the interaction of the year t dummy and the fraction of affected workers in year t-1. All coefficients are relative to the interaction of the 2010 dummy and the fraction of affected workers in 2009 variable. Age is controlled using dummies for 15–24, 25–34, 35–44, 45–54, and 55–64 age cohorts.

Figure B2

Wage growth in full time employment, 2005–2016.
Wage growth in full time employment, 2005–2016.

Figure B3

The unconditional relationship between the key variable of interest in 2015 and wage growth in 2016.Notes: Own calculations based on the 2015 and 2016 waves of Turkish Household Labor Force Survey. The sample includes full-time wage employment in private sector. 711TL cutoff is 75% of the lowest minimum wage level in 2015. To read the figure for example, around 60% of men with no high school degree earned higher than 711TL but lower than 1300TL in 2015 and wage growth from 2015 to 2016 for this group was around 25%.
The unconditional relationship between the key variable of interest in 2015 and wage growth in 2016.Notes: Own calculations based on the 2015 and 2016 waves of Turkish Household Labor Force Survey. The sample includes full-time wage employment in private sector. 711TL cutoff is 75% of the lowest minimum wage level in 2015. To read the figure for example, around 60% of men with no high school degree earned higher than 711TL but lower than 1300TL in 2015 and wage growth from 2015 to 2016 for this group was around 25%.

Informality effects, baseline estimation using the baseline fraction affected exposure variable

Informality
All firmsFirms with less than 10 employees
OverallMaleFemaleOverallMaleFemale
By education(1)(2)(3)(4)(5)(6)
No high school degree0.093**0.0180.0040.172**0.0880.112
(0.041)(0.038)(0.016)(0.066)(0.068)(0.122)
High school degree0.037**0.011−0.003−0.0030.103−0.087
(0.015)(0.033)(0.016)(0.043)(0.074)(0.093)
Higher degree0.0100.001−0.002−0.0000.054−0.076
(0.014)(0.029)(0.012)(0.055)(0.107)(0.064)
By age
15–240.1720.0520.0210.4440.1470.337
(0.125)(0.063)(0.024)(0.264)(0.205)(0.224)
25–340.038−0.022−0.0030.063−0.0110.074
(0.038)(0.046)(0.009)(0.085)(0.070)(0.058)
35–440.036−0.035−0.0060.0940.0410.062
(0.043)(0.029)(0.014)(0.073)(0.046)(0.062)
45–540.0220.010−0.0000.078*0.128**0.049
(0.038)(0.034)(0.016)(0.043)(0.055)(0.043)
55–640.020−0.032−0.0020.0230.009−0.007
(0.021)(0.034)(0.003)(0.029)(0.037)(0.019)
Individual-level controlYESYESYESYESYESYES
Standard errors clustered at NUT2-levelYESYESYESYESYESYES
Year and region fixed effectsYESYESYESYESYESYES
5 regions x year effectsYESYESYESYESYESYES

Lighthouse effects, effects of the minimum wage increase on informal wages, the baseline fraction affected exposure variable

Monthly informal wageHourly informal wage
OverallMaleFemaleOverallMaleFemale
By education(1)(2)(3)(4)(5)(6)
No high school degree0.2320.539**−0.1280.2050.670***−0.187
(0.195)(0.196)(0.295)(0.203)(0.230)(0.370)
High school degree0.3310.445**−0.0310.3120.517**0.193
(0.221)(0.203)(0.461)(0.195)(0.199)(0.488)
Higher degree0.5271.990***0.909*0.841**2.336***1.472**
(0.339)(0.551)(0.473)(0.357)(0.484)(0.591)
By age
15–24−0.0180.185−0.4650.2890.607−0.051
(0.345)(0.352)(0.546)(0.441)(0.489)(0.627)
25–340.582***0.430***0.622**0.568**0.508***0.620*
(0.205)(0.148)(0.256)(0.207)(0.164)(0.306)
35–440.3030.539***0.606*0.2680.645***0.295
(0.308)(0.139)(0.296)(0.245)(0.144)(0.355)
45–540.5060.492**0.593*0.3470.536***0.488
(0.304)(0.210)(0.294)(0.287)(0.168)(0.334)
55–640.700**0.779*−0.3080.672**0.779*−0.150
(0.297)(0.396)(0.463)(0.264)(0.387)(0.414)
Individual level controlYESYESYESYESYESYES
Standard errors clustered at NUT2-levelYESYESYESYESYESYES
Year and region fixed effectsYESYESYESYESYESYES
5 regions x year effectsYESYESYESYESYESYES

Informality effects, alternative estimation using the alternative exposure variables or excluding outlier regions

Fraction of formal affectedFraction at minimumExcluding outlier regions
MaleFemaleMaleFemaleMaleFemale
By education(1)(2)(3)(4)(5)(6)
No high school degree−0.002−0.0060.012−0.0050.0070.012
(0.053)(0.014)(0.045)(0.020)(0.038)(0.014)
High school degree0.018−0.0120.006−0.0100.015−0.002
(0.036)(0.016)(0.038)(0.016)(0.033)(0.018)
Higher degree−0.019−0.0020.015−0.001−0.012−0.003
(0.032)(0.015)(0.035)(0.016)(0.031)(0.013)
By age
15–240.007−0.0120.0540.0400.0890.034
(0.060)(0.032)(0.063)(0.029)(0.087)(0.023)
25–34−0.0490.001−0.0310.001−0.019−0.001
(0.053)(0.009)(0.054)(0.011)(0.047)(0.009)
35–44−0.006−0.022−0.030−0.015−0.032−0.016
(0.036)(0.016)(0.035)(0.020)(0.029)(0.020)
45–540.016−0.0020.0100.0010.0070.001
(0.052)(0.013)(0.044)(0.012)(0.034)(0.023)
55–64−0.0630.000−0.0080.004−0.045−0.001
(0.047)(0.005)(0.038)(0.004)(0.036)(0.003)
Individual level controlYESYESYESYESYESYES
Standard errors clustered at NUT2-levelYESYESYESYESYESYES
Year and region fixed effectsYESYESYESYESYESYES
5 regions x year effectsYESYESYESYESYESYES

Wage effects, baseline estimation using the baseline fraction affected exposure variable

Monthly wagesHourly wages
OverallMaleFemaleOverallMaleFemale
By education(1)(2)(3)(4)(5)(6)
No high school degree0.321***0.475***−0.1400.327***0.616***−0.205
(0.091)(0.074)(0.206)(0.085)(0.111)(0.250)
High school degree0.353***0.316***0.443***0.338***0.362***0.504***
(0.054)(0.051)(0.087)(0.060)(0.070)(0.117)
Higher degree0.471***1.013***0.468***0.512***1.170***0.551***
(0.151)(0.186)(0.095)(0.137)(0.187)(0.101)
By age
15–240.1100.364*−0.0550.2150.668**0.035
(0.155)(0.194)(0.251)(0.202)(0.271)(0.297)
25–340.410***0.299***0.535***0.403***0.345***0.527***
(0.069)(0.052)(0.073)(0.059)(0.050)(0.081)
35–440.372***0.287***0.617***0.304***0.337***0.559***
(0.109)(0.064)(0.109)(0.088)(0.065)(0.113)
45–540.508***0.589***0.5760.448***0.658***0.596
(0.165)(0.081)(0.465)(0.147)(0.076)(0.510)
55–640.640**0.829**−0.1410.674***0.880**−0.052
(0.276)(0.327)(0.336)(0.241)(0.332)(0.327)
Individual level controlYESYESYESYESYESYES
Standard errors clustered at NUT2-levelYESYESYESYESYESYES
Year and region fixed effectsYESYESYESYESYESYES
5 regions x year effectsYESYESYESYESYESYES

Employment effects, baseline estimation using the baseline fraction affected exposure variable

Employment
OverallMaleFemale
By education(1)(2)(3)
No high school degree0.041−0.0400.055**
(0.128)(0.040)(0.024)
High school degree−0.0330.046−0.008
(0.079)(0.058)(0.023)
Higher degree−0.0150.1500.021
(0.077)(0.092)(0.043)
By age
15–240.153−0.036−0.008
(0.209)(0.074)(0.040)
25–340.0720.043−0.020
(0.169)(0.050)(0.019)
35–44−0.215−0.029−0.017
(0.235)(0.048)(0.027)
45–54−0.166−0.021−0.024
(0.142)(0.034)(0.029)
55–640.023−0.0310.000
(0.039)(0.070)(0.006)
Individual-level controlYESYESYES
Standard errors clustered at NUT2-levelYESYESYES
Year and region fixed effectsYESYESYES
5 regions x year effectsYESYESYES

Employment effects, alternative estimation using the alternative exposure variables or excluding outlier regions

Fraction of formal affectedFraction at minimumExcluding outlier regions
MaleFemaleMaleFemaleMaleFemale
By education(1)(2)(3)(4)(5)(6)
No high school degree−0.0010.033−0.0680.042−0.078**0.073***
(0.046)(0.022)(0.050)(0.027)(0.035)(0.025)
High school degree0.0880.0090.0600.0220.058−0.007
(0.068)(0.028)(0.066)(0.035)(0.056)(0.024)
Higher degree0.1560.0210.2110.0440.1190.024
(0.106)(0.046)(0.125)(0.044)(0.104)(0.044)
By age
15–24−0.070−0.0460.0280.0030.032−0.002
(0.062)(0.029)(0.075)(0.047)(0.094)(0.046)
25–340.058−0.0310.060−0.0000.051−0.016
(0.049)(0.022)(0.063)(0.023)(0.051)(0.021)
35–440.047−0.015−0.055−0.031−0.035−0.039
(0.067)(0.036)(0.055)(0.037)(0.043)(0.043)
45–540.065−0.001−0.0100.002−0.061*−0.031
(0.062)(0.029)(0.041)(0.021)(0.032)(0.042)
55–64−0.229***−0.011−0.0330.018**−0.0520.003
(0.049)(0.010)(0.068)(0.007)(0.080)(0.007)
Individual level controlYESYESYESYESYESYES
Standard errors clustered at NUT2-levelYESYESYESYESYESYES
Year and region fixed effectsYESYESYESYESYESYES
5 regions x year effectsYESYESYESYESYESYES

Descriptive statistics

Less impacted regionsMore impacted regions
201420152016201420152016
Overall labor force statisticsMFMFMFMFMFMF
Working-age population (000)157601556715861157351611115935977498779981100041009810106
Full-time wage employment (FTWE) (000)745328897647305777083211384110933999124840001311
Full time wage employment ratio0.470.190.480.190.480.200.390.110.400.120.400.13
Informal (FTWE) (000)806348761336736337793308791316747311
Informality (FTWE)0.110.120.100.110.100.100.210.280.200.250.190.24
Average wages (FTWE) (TL)169715751867170921632035142412731557139418321693
Change in average wages10.02%8.58%15.80%19.02%9.27%9.43%17.74%21.46%
Average informal wages (FTWE) (TL)10027721134856130610118416659137201072837
Change in average informal wages13.24%10.90%15.12%18.15%8.50%8.24%17.48%16.30%
Labor force statistics by selected demographic groups
Less than high school emp. (FTWE)0.400.100.400.100.400.110.320.060.330.070.320.07
Tertiary educated emp. (FTWE)0.670.540.680.530.670.530.660.470.670.490.650.48
15-24 employment (FTWE)0.320.160.320.170.300.170.260.090.270.100.250.10
35-44 employment (FTWE)0.620.230.630.260.620.270.530.150.550.170.540.18
Less than high school informality (FTWE)0.180.280.160.260.160.260.310.530.310.490.290.48
Tertiary educated informality (FTWE)0.020.020.020.020.020.010.030.030.030.030.030.02
15-24 informality (FTWE)0.240.160.230.160.200.140.440.380.440.350.420.30
35-44 informality (FTWE)0.060.120.060.100.060.090.140.260.130.240.110.22
Less than high school average wage (FTWE) (TL)122890913531007158812461057771116085013981060
Tertiary educated average wage (FTWE) (TL)281922903010246833622798246820572603215428952518
15-24 average wage (FTWE) (TL)1078106211951159146714499169071015100412151256
35-44 average wage (FTWE) (TL)189217282086184423892208160714081729148220291777

Wage effects, alternative estimation using the alternative exposure variables or excluding outlier regions

Fraction of formal affectedFraction at minimumExcluding outlier regions
MaleFemaleMaleFemaleMaleFemale
By education(1)(2)(3)(4)(5)(6)
No high school degree0.482***0.1840.566***0.1030.535***−0.249
(0.114)(0.218)(0.096)(0.283)(0.068)(0.200)
High school degree0.392***0.540***0.328***0.587***0.318***0.467***
(0.071)(0.096)(0.079)(0.100)(0.050)(0.089)
Higher degree1.283***0.549***1.286***0.504***1.069***0.469***
(0.240)(0.112)(0.239)(0.130)(0.195)(0.092)
By age
15–240.336*0.2460.426**0.1440.522**−0.201
(0.196)(0.196)(0.196)(0.274)(0.206)(0.258)
25–340.321***0.522***0.311***0.578***0.296***0.521***
(0.068)(0.087)(0.069)(0.113)(0.052)(0.075)
35–440.277***0.463**0.347***0.603***0.279***0.582***
(0.081)(0.197)(0.081)(0.161)(0.065)(0.126)
45–540.852***0.3210.733***0.3180.631***0.594
(0.174)(0.428)(0.117)(0.437)(0.076)(0.488)
55–641.927***−0.4670.933**−0.2191.005***−0.154
(0.195)(0.564)(0.391)(0.342)(0.328)(0.342)
Individual level controlYESYESYESYESYESYES
Standard errors clustered at NUT2-levelYESYESYESYESYESYES
Year and region fixed effectsYESYESYESYESYESYES
5 regions x year effectsYESYESYESYESYESYES
eISSN:
2193-9004
Language:
English
Publication timeframe:
Volume Open
Journal Subjects:
Business and Economics, Political Economics, Microeconomics, Macroecomics, Economic Policy, Mathematics and Statistics for Economists, Econometrics
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