suffer a significant and negative effect on their willingness to participate in the labor market due to violent crime.14 However, estimates show that men do not experience a significant effect of violence on their labor decisions, whether or not they reported feeling fear of victimization. These results suggest that one possible channel through which violent crime affects women’s labor decisions is the fear of being a victim of violent crime.
6.5 Leading-values Test
Given that my empirical strategy uses a fixed-effects approach, the most severe threat to assert causal identification is the existence of some unobserved state or individual-level trends correlated with the increases in violent crime rates. The influence of these unobserved factors would be captured by the coefficients of the independent variables, distorting the estimation of the actual impact of violent crime on labor outcomes. While I assume that this scenario is improbable given the abruptness of the increase in violence and its policy origins, I believe it is relevant to conduct a proper test to support this claim.
To examine if the effects estimated by Equation 1 are biased due to unobserved state or individual-level factors correlated with the increases in violent crime rates, I conduct a leading-values test, similar to what Vel´asquez (2020) does in her work. Namely, I test if the labor outcomes of earlier periods are correlated with the high levels of violent crime of later periods; in the absence of unobserved trends, we should see non-significant effects of violent crime on labor outcomes.
Given that the increases in violence started in early 2007, I estimate Equation 1 lim-iting my observations of labor outcomes to the MxFLS1 (2002) and MxFLS2 (2005);
that is, the two waves before the sudden increase in violent crime. Then, I assigned them the state-level total crime rate of the subsequent waves, MxFLS2 (2005) and MxFLS3 (2009), respectively; this includes a period of low violence and another one of high vi-olence. Table 5 shows the results of this analysis. Assuming that unobserved trends did not cause the violence increase, the change in future crime rate between MxFLS2 and MxFLS3 should not predict decreases in the labor market participation between 2002 and 2005.
Column (1) in Table 7 shows the general results of the leading-values test. Note that the relationship between the total crime rate and the probability to join the labor market in Panel A is positive but insignificant and 32% smaller than the impact found in the
14The effect of the total crime rate on labor outcomes (Columns 7 and 9) is still negative and significant for women who do not report fear of being assaulted during the day or night; however, the magnitude is smaller.
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main specification. Similarly, Panel B and Panel C show non-significant effects of vio-lent crime on the number of weeks or hours worked. Column (2) shows the test results for women. Contrary to the findings in Colum (2) of Table 4, the leading-values test outcomes show insignificant effects of violence on female labor decisions; moreover, the test shows an estimate 15% smaller than that found in Table 4. Accordingly, results for weeks and hours worked by women are also insignificant. Overall, the estimates of the leading-values test are insignificant at the 10% level. These results confirm that the outcomes in Table 3 and Table 4 are not caused by unobserved trends correlated with the increase in violence.
7 Conclusion
This paper intends to examine the impact of the unexpected escalation in violence caused by the Mexican drug war on individuals’ labor decisions. Empirically, I ex-ploit the longitudinal nature of the MxFLS survey and its precise timing encircling the implementation of the Mexican drug war combining it with state-level data on violent crime. This strategy allows me to capture the respondents’ exposure to violent crime before and after the violence onset. By using the MxFLS data combined with an in-dividual fixed-effects strategy, I take into account potentially endogenous state’s and individual’s time-invariant unobserved characteristics that may be correlated with vio-lence exposure and labor decisions.
My results suggest that violent crime has a powerful impact on the labor market outcomes of women. I find that exposure to the violence caused by the Mexican drug war significantly decreases women’s probability to join the labor market and reduces the number of weeks and hours they work. The magnitude of this impact is even greater for women working in the informal economy. Specifically, women experiencing the drug war violence surge would decrease their probability to participate in the labor market by 4.14 percentage points; and women working in the informal economy would reduce their likelihood of being employed by 9.81 percentage points. However, for men, findings suggest that increases in violent crime do not have a significant effect on their labor decisions. Furthermore, young individuals between 15 and 34 are not affected by increases in violence, while 35 and older respondents experienced a significant and negative effect on the labor market behavior due to increases in the total crime rate.
These findings underline how the costs of violence are unequally distributed between women and men, and the old and young.
This study adds to the growing literature on the labor market consequences of vio-lent crime, further providing evidence of violence’s adverse economic shocks. Though
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many spillover effects of violent crime on the labor market have been identified be-fore, this study shows that the fear of victimization leads certain individuals living in a violent environment to decrease their labor participation and total time worked.
For the Mexican case, increases in violent crime triggered by the Mexican drug war not only affected the lives of DTO members, the army, and police but also dis-proportionally harmed particular sectors of the Mexican population. Specifically, un-derstanding how violent crime asymmetrically affects women employed in the formal and informal economy should be relevant to governments for better resource planning, more informed policymaking, and ultimately improving the labor outcomes of a large proportion of the Mexican workforce.
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Below the national Above the national
Variables violence average violence average P-value
(1) (2) (3)
Labor market variables
Worked last year 0.51 0.50 0.21
Number of weeks
worked last year 24.21 24.96 0.25
Average number of
hours work per week 22.26 22.59 0.01
Informal worker 0.67 0.63 0.00
Crime Variables
Log total crime rate 6.48 7.08 0.00
Log robbery rate 6.13 6.77 0.00
Log intentional injury rate 5.03 5.62 0.00
Intentional homicide rate 3.40 3.07 0.00
Basic characteristics
Age (years) 44.34 42.80 0.00
Female 0.599 0.617 0.00
Married 0.707 0.707 0.94
Years of education 7.500 7.709 0.00
Head of the household 0.400 0.383 0.00
Relatives in U.S. 0.356 0.361 0.34
Macroeconomic variables
GDP per state 2.88 4.86 0.00
Self-perception violence variables
Fear of being assault 0.557 0.559 0.76
Fear of being assault
during the night 0.367 0.405 0.00
Being more unsafe at night
compared to 5 years ago 0.471 0.464 0.20
Note: Descriptive statistics for respondents between 15 and 80 years old during the MxFLS1. The table shows the means of the main variables used.
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Table 2: Impact of Total Crime Rate on Labor Market Outcomes Labor market outcomes
Log total crime rate 12 months before the interview
(1) (2) (3) (4)
Panel A
Worked last 12 months -0.001 -0.021 -0.029** -0.025*
(0.007) (0.018) (0.013) (0.014)
Sample size 32,301 32,301 32,240 31,315
R-squared 0.000 0.003 0.691 0.697
Panel B
Weeks worked last 0.528 -0.284 -0.534 -0.742
12 months (0.281) (0.702) (0.740) (0.743)
Sample size 14,377 14,377 12,413 12,159
R-squared 0.002 0.015 0.461 0.465
Panel C
Hours worked per week 0.811 -0.975 -2.310** -1.774*
last 12 months (0.390) (0.986) (0.954) (0.987)
Sample size 14,390 14,390 12,420 12,167
R-squared 0.000 0.008 0.553 0.554
State fixed effects No Yes Yes Yes
Year effects No Yes Yes Yes
Individual fixed effects No No Yes Yes
Other controls No No No Yes
Note: Data includes individuals between 15 and 80 years old. All the regressions include year fixed-effects, state fixed-effects, and individual fixed-effects. Controls include marital status, household role, years of education, relatives in the U.S., and GDP at the state level. Standard errors in parentheses are clustered at the state level.
*** indicates significance at 1%, ** indicates significance at 5% and * significance at the 10%.
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Table 3: Impact of Violent Crime on Labor Market Outcomes: By crime type
Labor market outcomes
Violent crime variables
Log homicide rate last 12 months Log robbery rate last 12 months Log assault rate last 12 months
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Panel A
Worked last 12 months -0.006 -0.003 -0.002 -0.012 -0.020 -0.016 -0.024 *** -0.027*** -0.027***
(0.007) (0.007) (0.007) (0.013) (0.012) (0.013) (0.010)) (0.012) (0.014)
Sample size 32,301 32,240 31,315 32,301 32240 31,315 32,301 32,298 32,298
R-squared 0.003 0.691 0.697 0.003 0.691 0.697 0.003 0.002 0.003
Panel B
Weeks worked last 12 months -0.162 0.077 -0.03 -0.221 -0.598 -0.866 -0.051 0.000 0.023
(0.385) (0.405) (0.409) (0.620) (0.680) (0.706) (0.478) (0.533) (0.545)
Sample size 14,377 12,413 12,159 14,377 12,413 12,159 14,377 12,413 12,159
R-squared 0.015 0.461 0.465 0.015 0.461 0.465 0.016 0.461 0.465
Panel C
Hours worked per week -0.468 -0.297 -0.019 -1.391 -2.614 *** -1.890** 0.377 -0.621 -0.880
last 12 months (0.519) (0.538) (0.549) (0.865) (0.906) (0.947) (0.649) (0.668) (0.687)
Sample size 14,390 12,420 12,167 14,390 12,420 12,167 14,390 12,420 12,167
R-squared 0.007 0.552 0.553 0.007 0.553 0.554 0.007 0.552 0.554
State fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes
Year effects Yes Yes Yes Yes Yes Yes Yes Yes Yes
Individual fixed effects No Yes Yes No Yes Yes No Yes Yes
Controls No No Yes No No Yes No No Yes
Note: Data includes individuals between 15 and 80 years old. Controls include marital status, household role, years of education, relatives in the U.S. and GDP at state level.
*** indicates significance at 1% , ** indicates significance at 5% and * significance at the 10%. Standard errors clustered at the state level in parentheses.
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Table 4: Impacts of Total Crime Rate on Labor Market Outcomes: By gender an formality status
Log total crime rate12 months before the interview Labor market outcomes Men Woman Informal Informal Formal Formal
Men Women Men Women
(1) (2) (3) (4) (5) (6)
Panel A
Worked last 12 months 0.010 -0.046 ** -0.008 -0.109 ** -0.030 -0.058 (0.020) (0.018) (0.025) (0.049) (0.023) (0.039)
Sample size 12,201 19,091 5,962 3,426 3,196 1,690
R-squared 0.397 0.615 0.492 0.516 0.459 0.517
Panel B
Weeks worked last 0.132 -2.733 * -0.215 -2.858 -1.464 -0.885 12 months (0.903) (1.312) (1.444) (2.335) (1.171) (1.531)
Sample size 8,517 3,632 4,723 1,728 2,854 1,461
R-squared 0.467 0.463 0.4793 0.504 0.488 0.444
Panel C
Hours worked per week -0.923 -3.839 * -1.316 -2.781 1.861 -2.386 last 12 months (1.132) (1.992) 1.759 3.795 1.543 2.081
Sample size 8,509 3,648 4,735 1,729 2,841 1,474
R-squared 0.520 0.583 0.523 0.599 0.607 0.670
Note: Data includes individuals between 15 and 80 years old. All the regressions include year fixed-effects, state fixed-fixed-effects, and individual fixed-effects. Controls include marital status, household role, years of education, relatives in the U.S., and GDP at the state level. Standard errors in parentheses are clustered at the state level. *** indicates significance at 1%, ** indicates significance at 5% and * significance at the 10%.
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Table 5: Impacts of Total Crime Rate on Labor Market Outcomes: By age group Labor market outcomes
Log total crime rate12 months before the interview
15-35 35-50 50-65 65-80
(1) (2) (3) (4)
Panel A
Worked last 12 months -0.001 -0.049 ** -0.097 *** -0.106 **
(0.028) (0.023) (0.034) (0.051)
Sample size 10,093 9,553 5,973 2,943
R-squared 0.650 0.780 0.756 0.680
Panel B
Weeks worked last 0.264 -0.791 0.152 -3.133
12 months (1.727) (1.165) (1.907) (4.004)
Sample size 3,340 4,605 2,184 512
R-squared 0.475 0.497 0.525 0.517
Panel C
Hours worked per week -1.776 -1.803 -3.876 -3.649
last 12 months (1.819) (1.555) (2.950) -6.347
Sample size 3,356 4,617 2,168 515
R-squared 0.571 0.596 0.558 0.609
Note: Data includes individuals between 15 and 80 years old. All the regressions include year fixed-effects, state fixed-fixed-effects, and individual fixed-effects. Controls include marital status, household role, years of education, relatives in the U.S., and GDP at the state level. Standard errors in parentheses are clustered at the state level. *** indicates significance at 1%, ** indicates significance at 5% and * significance at the 10%.
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Table 6: Impact of Total Crime Rate on Labor Market Outcomes: By Subjective Measures of Fear
Log total crime rate 12 months before the interview
Report fear of Report fear of being Not going out Not fear of Not fear of being Going out Labor market outcomes being assaulted assaulted during the night during the night being assaulted assaulted during the night during the night
Women Men Women Men Women Men Women Men Women Men Women Men
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
Panel A
Worked last 12 months -0.061* 0.040 -0.073** 0.035 -0.062* -0.022 -0.020 -0.036 -0.048* 0.004 0.020 0.049 (0.032) (0.032) (0.036) (0.049) (0.032) (0.033) (0.034) (0.037) (0.028) (0.027) (0.037) (0.042)
Sample size 5,259 5,259 5,842 2,286 6,856 4,464 5,969 4,084 9,175 7,606 5,847 3,242
R-squared 0.657 0.657 0.676 0.657 0.688 0.645 0.668 0.649 0.660 0.639 0.678 0.679
Panel B
Weeks worked -1.735 -1.516 -0.165 -0.171 -3.682 -2.166 -1.164 -1.110 -3.465 0.988 0.713 2.599
last 12 months (2.016) (1.407) ( 2.419 ) ( 2.050) (2.507) (1.529) (3.249) (1.805) (2.488) (1.277) (2.775) 1.953
Sample size 1,766 3,588 1,159 1,586 1,138 3,180 741 2,607 1,415 5,010 872 2,069
R-squared 0.517 0.516 0.500 0.551 0.555 0.516 0.512 0.531 0.4905 0.4955 0.533 0.576
Panel C
Hours worked per week -13.613*** -2.089 -8.415** 1.611 -8.196* -3.132 -6.06** -1.549 1.741 -2.879 -5.110 -1.628 last 12 months (4.499) (2.217) ( 3.408) (1.599) ( 4.238) (2.487) (2.866) 1.876 (3.317) (2.845) (3.438) (2.012)
Sample size 749 2,629 1,423 5,028 885 2,070 1,755 3,559 1,166 1,566 1,142 3,164
R-squared 0.6267 0.5615 0.5985 0.5433 0.5843 0.5958 0.604 0.588 0.598 0.596 0.657 0.566
Note: Data includes individuals between 15 and 80 years old. All the regressions include year fixed-effects, state fixed-effects, and individual fixed-effects. Controls include marital status, household role, years of education, relatives in the U.S., and GDP at the state level. Standard errors in parentheses are clustered at the state level. *** indicates significance at 1%, ** indicates significance at 5% and * significance at the 10%.
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Table 7: Impact of Total Crime Rate on Labor Market Outcomes comparing the same individual in MxFLS1 and MxFLS2, assinging the Total Crime Rate from the
subsequent wave
Log Total Crime Rate
Labor market outcomes All Women Men
(1) (2) (3)
Panel A
Worked last 12 months 0.017 -0.023 0.038 (0.020) (0.027) (0.287)
Sample size 20,792 8,162 12,596
R-squared 0.798 0.750 0.728
Panel B
Weeks worked last 0.528 0.029 0.077
12 months (1.129) (1.371) (1.874)
Sample size 7,182 5,290 1,880
R-squared 0.554 0.5631 0.530
Panel C
Hours worked per week -0.254 0.576 -2.341 last 12 months (1.547) (1.807) (3.052)
Sample size 7,240 5,308 1,920
R-squared 0.638 0.615 0.657
Note: Data includes individuals between 15 and 80 years old. All the regressions include year fixed-effects, state fixed-effects, and individual fixeffects. Controls include marital status, household role, years of ed-ucation, relatives in the U.S., and GDP at the state level. Standard errors in parentheses are clustered at the state level. *** indicates significance at 1%, ** indicates significance at 5% and * significance at the 10%.
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Figure 1: Victims of intentional homicide, rate per 100,000 population
Notes: Data from INEGI and UNODC homicide statistics.
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Figure 2: Monthly homicide rate per 100,000 population and MxFLS application periods
Notes: Data from INEGI monthly homicide statistics.