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The effects of a place-based tax cut and minimum wage increase on labor market outcomes

Raymundo M. Campos-Vazquez
Raymundo M. Campos-Vazquez
, 
Victor Delgado
Victor Delgado
 oraz 
Alexis Rodas
Alexis Rodas
   | 02 paź 2020
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Data publikacji: 02 paź 2020
Zakres stron: -
DOI: https://doi.org/10.2478/izajolp-2020-0012
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© 2020 Raymundo M. Campos-Vazquez, Victor Delgado, Alexis Rodas, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1

Employment and labor income between border and non-border regions.Notes: Authors’ calculations using IMSS data restricted to municipalities with at least 50,000 inhabitants. Border refers to the 18 municipalities in the treatment group. Labor income is deflated using the same Consumer Price Index for both regions.
Employment and labor income between border and non-border regions.Notes: Authors’ calculations using IMSS data restricted to municipalities with at least 50,000 inhabitants. Border refers to the 18 municipalities in the treatment group. Labor income is deflated using the same Consumer Price Index for both regions.

Figure 2

Synthetic control method (SCM) results, 2015–2019.Notes: Authors’ calculations. The SCM is applied to each of the 18 municipalities in the treated group. The trends for the treatment and synthetic group are then obtained using the 2015 population as weights. The base period is September 2018; the policy was implemented in January 2019.
Synthetic control method (SCM) results, 2015–2019.Notes: Authors’ calculations. The SCM is applied to each of the 18 municipalities in the treated group. The trends for the treatment and synthetic group are then obtained using the 2015 population as weights. The base period is September 2018; the policy was implemented in January 2019.

Figure 3

Heterogeneity results, 2015–2019.Notes: Authors’ calculations. The synthetic control method (SCM) is implemented for each outcome variable on the y-axis for the 18 municipalities in the treated group. The 95% confidence intervals (robust and clustered at the municipality level) are obtained from a regression with fixed effects for time and treatment group (18) using the weights obtained in the SCM. The p-values from the placebo tests are shown next to the triangles. Placebos with a RMSPE larger than 2.5 times the treated unit are omitted.
Heterogeneity results, 2015–2019.Notes: Authors’ calculations. The synthetic control method (SCM) is implemented for each outcome variable on the y-axis for the 18 municipalities in the treated group. The 95% confidence intervals (robust and clustered at the municipality level) are obtained from a regression with fixed effects for time and treatment group (18) using the weights obtained in the SCM. The p-values from the placebo tests are shown next to the triangles. Placebos with a RMSPE larger than 2.5 times the treated unit are omitted.

Figure 4

Heterogeneity results by economic sector, 2015–2019.Notes: Authors’ calculations. The synthetic control method (SCM) is implemented for each outcome variable on the y-axis for the 18 municipalities in the treated group. The 95% confidence intervals (robust and clustered at the municipality level) are obtained from a regression with fixed effects for time and treatment group (18), using the weights obtained in the SCM. The p-values from the placebo tests are shown besides the triangles. Placebos with a RMSPE larger than 2.5 times the treated unit are omitted.
Heterogeneity results by economic sector, 2015–2019.Notes: Authors’ calculations. The synthetic control method (SCM) is implemented for each outcome variable on the y-axis for the 18 municipalities in the treated group. The 95% confidence intervals (robust and clustered at the municipality level) are obtained from a regression with fixed effects for time and treatment group (18), using the weights obtained in the SCM. The p-values from the placebo tests are shown besides the triangles. Placebos with a RMSPE larger than 2.5 times the treated unit are omitted.

Figure 5

Robustness test using metropolitan areas in the United States as controls, 2015–2019.Notes: Authors’ calculations. The synthetic control method is implemented for each outcome variable on the x-axis for the 18 municipalities in the treated group. Control group in panel A constructed from metropolitan areas in the US control group in panel B constructed from municipalities in Mexico and all metropolitan areas in US border states. The difference is not statistically significant.
Robustness test using metropolitan areas in the United States as controls, 2015–2019.Notes: Authors’ calculations. The synthetic control method is implemented for each outcome variable on the x-axis for the 18 municipalities in the treated group. Control group in panel A constructed from metropolitan areas in the US control group in panel B constructed from municipalities in Mexico and all metropolitan areas in US border states. The difference is not statistically significant.

Figure 6

Instrumental variable results, 2015–2019.Notes: Authors’ calculations. 95% confidence intervals. “All control units” is a regression for all units in the treatment and control groups; the dependent variable is the employment index and the independent variable is the labor income index in 2019, instrumented with a dummy variable for the municipalities in the border region. The regression includes fixed effects of time and municipality as well as the control variables summarized in Table 1 interacted with a trend and state trends, and the fraction of workers in the municipality with daily earnings less than MXN 180 interacted with a trend. Regression is weighted by the population in 2015. The results for “synthetic control units” uses only fixed effects for time and the 18 treatment groups, but weighted by the synthetic control weights interacted with population in 2015.
Instrumental variable results, 2015–2019.Notes: Authors’ calculations. 95% confidence intervals. “All control units” is a regression for all units in the treatment and control groups; the dependent variable is the employment index and the independent variable is the labor income index in 2019, instrumented with a dummy variable for the municipalities in the border region. The regression includes fixed effects of time and municipality as well as the control variables summarized in Table 1 interacted with a trend and state trends, and the fraction of workers in the municipality with daily earnings less than MXN 180 interacted with a trend. Regression is weighted by the population in 2015. The results for “synthetic control units” uses only fixed effects for time and the 18 treatment groups, but weighted by the synthetic control weights interacted with population in 2015.

Figure 7

Labor force survey results using repeated cross-sections, 2015–2019.Notes: Authors’ calculations using ENOE data. Each dot is the coefficient for the border region in 2019 for different regressions; the outcome variables are shown on the y-axis. The regression is at the individual level. Control variables include fixed effects by time and municipality, individual age, gender, schooling, and marital status. Municipality Control X Trends: Trends are interacted with controls at the municipality level. Municipality Trends: municipality fixed effects are interacted with a trend.
Labor force survey results using repeated cross-sections, 2015–2019.Notes: Authors’ calculations using ENOE data. Each dot is the coefficient for the border region in 2019 for different regressions; the outcome variables are shown on the y-axis. The regression is at the individual level. Control variables include fixed effects by time and municipality, individual age, gender, schooling, and marital status. Municipality Control X Trends: Trends are interacted with controls at the municipality level. Municipality Trends: municipality fixed effects are interacted with a trend.

Descriptive statistics using IMSS data (September 2018)

VariablesAllBorder regionNon-border region
No. of municipalities41318395
Employment: All19,035,2802,050,74416,984,536
% Employment: Men62.0959.8562.37
% Employment: Women37.9140.1537.63
% Employment: Firm size 1–56.775.316.95
% Employment: Firm size 6–5020.8915.9621.48
% Employment: Firm size 51+72.3478.7371.57
% Employment: Age < 3033.3735.8333.07
% Employment: Age 30–5963.7561.9163.97
% Employment: Age 60+2.882.262.95
% Earning < MXN 180 daily39.8330.6440.94
Avg. daily income < MXN 180 dailyUSD 121.6USD 129.4USD 120.9
Avg. monthly income: All9,42710,1319,365
Avg. monthly income: Men9,94911,0069,856
Avg. monthly income: Women8,4708,8198,440
Avg. monthly income: Firm size 1–54,8975,2814,863
Avg. monthly income: Firm size 6–506,9927,7696,924
Avg. monthly income: Firm size 51+10,89011,01810,878
Avg. monthly income: Age < 307,1167,7167,063
Avg. monthly income: Age 30–5910,62611,54710,545
Avg. monthly income: Age 60+9,1929,1969,191
Avg. population1,351,532927,0281,388,767
% People in poverty38.130.838.8
# Social needs (poverty measure)2.01.82.0
% People in extreme poverty5.22.35.5
% People in income poverty8.810.08.7
% People behind in school15.014.315.1
% People attending school (15–24 years old)46.944.647.1
% People (25+ years old) with at least college degree15.712.516.0
% Households receiving remittances9.510.49.4
% Women who work (20–65 years old)44.649.044.2
% Men who work (20–65)82.583.682.4
% Employment in manufacturing (20–65)16.431.215.1
% Informal employment (20–65)45.532.246.7
eISSN:
2193-9004
Język:
Angielski
Częstotliwość wydawania:
Volume Open
Dziedziny czasopisma:
Business and Economics, Political Economics, Microeconomics, Macroecomics, Economic Policy, Mathematics and Statistics for Economists, Econometrics
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