Citations Needed: Links to Interesting Research
04-19-2019
, 01:22 PM
04-22-2019
, 12:14 PM
Racial disparities in school-based disciplinary actions are associated with county-level rates of racial bias (2019; full text here)
This is a nice example of a really big quantitative research project, I think. Also, I'd like to note that I found this via Kevin Lewis' Findings blog at National Affairs. He curates a pretty impressive collection of links to current research, organized by topics. This one is from the recent post Equal Opportunities. He seems to focus mostly on work in social psychology, but there's a lot of interesting stuff.
The discussion also points towards the kind of argument I've tried to make previously about the complementary relationship between quantitative research on large datasets, like in this study, and qualitative research which tries to dig deeper into more of the details of social processes:
Quote:
Abstract
Black students in the United States are subject to disciplinary action at rates much higher than their white counterparts. These disciplinary actions put students at higher risk for negative life outcomes, including involvement in the criminal justice system. Using federal data covering over 32 million students at nearly 96,000 schools, our research demonstrates that the disciplinary gap between black and white students across five types of disciplinary actions is associated with county-level rates of racial bias. Our work emphasizes the need for policy targeting racial disparities in education and psychological bias.
Black students in the United States are subject to disciplinary action at rates much higher than their white counterparts. These disciplinary actions put students at higher risk for negative life outcomes, including involvement in the criminal justice system. Using federal data covering over 32 million students at nearly 96,000 schools, our research demonstrates that the disciplinary gap between black and white students across five types of disciplinary actions is associated with county-level rates of racial bias. Our work emphasizes the need for policy targeting racial disparities in education and psychological bias.
Quote:
Introduction
In comparison with white Americans, black Americans exhibit poorer educational outcomes across a range of metrics. One outcome of particular concern is the gap in disciplinary actions (1, 2). Research using administrative datasets and longitudinal samples clearly show that black American students are far more likely to be suspended or expelled (3, 4) and, conditional on an office referral, more likely to receive stiffer punishments (5, 6). These disparities are particularly concerning as they are asso- ciated with long-term outcomes, including employment (7) and involvement in the criminal justice system (8).
As complex social phenomena, racial differences in disciplinary outcomes are multiply determined (2). However, racial bias is thought to be one such determinant. For instance, a controlled experiment using hypothetical vignettes found that in comparison with white students, teachers were more likely to view the same behavior from black students as being indicative of a long-term problem and deserving of suspension (9). Similarly, discipline data from an urban high school showed that black students were especially likely to be referred to the office for discipline on the basis of defiant behavior—a relatively subjective category of misbehavior in comparison with others they examined, including truancy or fighting (10). Overall, there is consistent evidence that black students’ behaviors are both perceived as more problematic and are punished more harshly compared with white students. However, to our knowledge, there has been no work assessing whether racial bias is directly associated with disciplinary disparities. Additionally, there has been no work assessing how community-level racial bias is associated with educational disparities.
In comparison with white Americans, black Americans exhibit poorer educational outcomes across a range of metrics. One outcome of particular concern is the gap in disciplinary actions (1, 2). Research using administrative datasets and longitudinal samples clearly show that black American students are far more likely to be suspended or expelled (3, 4) and, conditional on an office referral, more likely to receive stiffer punishments (5, 6). These disparities are particularly concerning as they are asso- ciated with long-term outcomes, including employment (7) and involvement in the criminal justice system (8).
As complex social phenomena, racial differences in disciplinary outcomes are multiply determined (2). However, racial bias is thought to be one such determinant. For instance, a controlled experiment using hypothetical vignettes found that in comparison with white students, teachers were more likely to view the same behavior from black students as being indicative of a long-term problem and deserving of suspension (9). Similarly, discipline data from an urban high school showed that black students were especially likely to be referred to the office for discipline on the basis of defiant behavior—a relatively subjective category of misbehavior in comparison with others they examined, including truancy or fighting (10). Overall, there is consistent evidence that black students’ behaviors are both perceived as more problematic and are punished more harshly compared with white students. However, to our knowledge, there has been no work assessing whether racial bias is directly associated with disciplinary disparities. Additionally, there has been no work assessing how community-level racial bias is associated with educational disparities.
Quote:
Data and Methods
To assess rates of discipline, we used data from the CRDC conducted by the US Department of Education. The dataset comes from the 2015–2016 academic year and has data on “all [local educational agencies] and schools, including long-term secure juvenile justice facilities, charter schools, alternative schools, and schools serving students with disabilities” (ref. 34, p. 6). The CRDC data represent 96,360 institutions enrolling approximately 50.6 million students, of which approximately 24.7 million are white and 7.8 million are black.... We report here on five types of disciplinary actions: in-school suspensions, out-of-school suspensions, law enforcement referrals, school-related arrests, and expulsions.
We used measurements of implicit and explicit bias available from data collected through Project Implicit (25). For a full description of the implicit and explicit bias measures available in these data, refer to (12, 25). We used the Implicit Association Test (IAT) D-score as a measure of implicit bias and the difference between reported warmth toward whites and warmth toward black (both measured from 0 = very cold to 10 = very warm) as a measure of explicit bias, both of which are consistent with previous research on this topic (12). Additionally, we used only respondents who had geographic information that would allow us to place them in a US county, identified as white, and visited the site anytime after it went live in 2002 through the end of 2016. This consisted of approximately 1.6 million total respondents from 3,110 counties; 1.46 million respondents provided data for the IAT, and 1.27 million provided explicit bias ratings.
To assess rates of discipline, we used data from the CRDC conducted by the US Department of Education. The dataset comes from the 2015–2016 academic year and has data on “all [local educational agencies] and schools, including long-term secure juvenile justice facilities, charter schools, alternative schools, and schools serving students with disabilities” (ref. 34, p. 6). The CRDC data represent 96,360 institutions enrolling approximately 50.6 million students, of which approximately 24.7 million are white and 7.8 million are black.... We report here on five types of disciplinary actions: in-school suspensions, out-of-school suspensions, law enforcement referrals, school-related arrests, and expulsions.
We used measurements of implicit and explicit bias available from data collected through Project Implicit (25). For a full description of the implicit and explicit bias measures available in these data, refer to (12, 25). We used the Implicit Association Test (IAT) D-score as a measure of implicit bias and the difference between reported warmth toward whites and warmth toward black (both measured from 0 = very cold to 10 = very warm) as a measure of explicit bias, both of which are consistent with previous research on this topic (12). Additionally, we used only respondents who had geographic information that would allow us to place them in a US county, identified as white, and visited the site anytime after it went live in 2002 through the end of 2016. This consisted of approximately 1.6 million total respondents from 3,110 counties; 1.46 million respondents provided data for the IAT, and 1.27 million provided explicit bias ratings.
Quote:
Quote:
Discussion
These analyses across two separate data collections and five types of disciplinary actions are fully consistent with county-level estimates of racial bias, particularly explicit bias, being associated with racial disciplinary disparities. Specifically, counties with higher rates of explicit biases that favor whites had greater black–white disciplinary disparities across all five outcomes examined. The role of implicit bias is less pronounced. The relationship between implicit bias and disciplinary disparities is also often associated with disciplinary disparities, but the association here is weaker in magnitude and occasionally includes zero as a probable value. In the 2015–2016 data collection, zero is a probable value for the association between implicit bias and expulsions, and in the 2013–2014 data collection, zero is a probable value for all disciplinary actions except for out-of-school suspensions. It should be noted that our analy- ses cover the vast majority of school-aged students in the United States, and our models include a large set of covariates, suggesting that the relationships between bias and discipline are not due to confounds that can often co-occur with racial dispari- ties, such as socioeconomic status or population demographics. (28, 29).
These analyses across two separate data collections and five types of disciplinary actions are fully consistent with county-level estimates of racial bias, particularly explicit bias, being associated with racial disciplinary disparities. Specifically, counties with higher rates of explicit biases that favor whites had greater black–white disciplinary disparities across all five outcomes examined. The role of implicit bias is less pronounced. The relationship between implicit bias and disciplinary disparities is also often associated with disciplinary disparities, but the association here is weaker in magnitude and occasionally includes zero as a probable value. In the 2015–2016 data collection, zero is a probable value for the association between implicit bias and expulsions, and in the 2013–2014 data collection, zero is a probable value for all disciplinary actions except for out-of-school suspensions. It should be noted that our analy- ses cover the vast majority of school-aged students in the United States, and our models include a large set of covariates, suggesting that the relationships between bias and discipline are not due to confounds that can often co-occur with racial dispari- ties, such as socioeconomic status or population demographics. (28, 29).
Quote:
The correlational nature of the analyses also presents challenges for interpretation, as it is impossible to definitively establish the causal relationship between bias and disciplinary disparities. The conclusion that explicit biases predict disciplinary disparities is consonant with a great deal of research on disciplinary disparities (30). However, it is possible that living in a region in which black students are disciplined to a greater extent than white students exacerbates and/or reinforces the explicit racial biases of community members or that the relationship between explicit racial biases and disciplinary disparities is bidirectional. It is also possible that some other variable is driving both of these associations (e.g., absence of positive portrayals of African Americans in the media could lead to increased biases in the community and lead teachers to be quicker to discipline black students). Our analyses trade off the ability to ask these more detailed questions about mechanism with the strengths of statistical power and population coverage offered by the large datasets we used here.
04-22-2019
, 01:31 PM
04-22-2019
, 03:47 PM
04-22-2019
, 04:03 PM
04-22-2019
, 05:21 PM
04-24-2019
, 11:13 AM
04-25-2019
, 01:49 PM
The Unifying Moral Dyad: Liberals and Conservatives Share the Same Harm-Based Moral Template. (full text here)
I'm not in love with the methods used here (small sample sizes; convenience samples from Amazon Mechanical Turk) so I would take this with a healthy grain of salt, but it's an interesting piece of research and the conclusion makes intuitive sense to me.
In a nutshell, the authors are arguing with Jonathan Haidt's Moral Foundations Theory, and the idea that there's some fundamental differences in moral cognition between political liberals and conservatives. Haidt classifies moral judgements along different dimensions like purity, care, loyalty, and fairness. He argues that liberals place more emphasis on "care" and "fairness" than conservatives. You end up with a psychological view of political difference in which those differences are taken to be pretty innate.
The authors of this article argue, instead, that both liberals and conservatives understand morality similarly, with both strongly emphasizing that what makes an act immoral is that it is harmful:
Speculating, what this suggests to me is that the differences which emerge in Haidt's foundations between conservatives and liberals reflect more cultural differences than psychological differences, and maybe that is reflected both in differences in language use (liberals are more likely to frame moral issues around care for others or basic fairness; conservatives more likely to use language framed around purity) and the underlying set of culturally constructed concepts people use to think about morality. But fundamentally, both the conservative reacting with disgust to perceived impurity and the liberal reacting with outrage to perceived unfairness are expressing a belief that harm is being done. There is a commonality, which also suggests maybe that there is a way of finding common ground in moral arguments, by trying to draw out how different people understand acts to be either harmful or not.
Quote:
Abstract
Do moral disagreements regarding specific issues (e.g., patriotism, chastity) reflect deep cognitive differences (i.e., distinct cognitive mechanisms) between liberals and conservatives? Dyadic morality suggests that the answer is "no." Despite moral diversity, we reveal that moral cognition--in both liberals and conservatives--is rooted in a harm-based template. A dyadic template suggests that harm should be central within moral cognition, an idea tested--and confirmed--through six specific hypotheses. Studies suggest that moral judgment occurs via dyadic comparison, in which counter-normative acts are compared with a prototype of harm. Dyadic comparison explains why harm is the most accessible and important of moral content, why harm organizes--and overlaps with--diverse moral content, and why harm best translates across moral content. Dyadic morality suggests that various moral content (e.g., loyalty, purity) are varieties of perceived harm and that past research has substantially exaggerated moral differences between liberals and conservatives.
Do moral disagreements regarding specific issues (e.g., patriotism, chastity) reflect deep cognitive differences (i.e., distinct cognitive mechanisms) between liberals and conservatives? Dyadic morality suggests that the answer is "no." Despite moral diversity, we reveal that moral cognition--in both liberals and conservatives--is rooted in a harm-based template. A dyadic template suggests that harm should be central within moral cognition, an idea tested--and confirmed--through six specific hypotheses. Studies suggest that moral judgment occurs via dyadic comparison, in which counter-normative acts are compared with a prototype of harm. Dyadic comparison explains why harm is the most accessible and important of moral content, why harm organizes--and overlaps with--diverse moral content, and why harm best translates across moral content. Dyadic morality suggests that various moral content (e.g., loyalty, purity) are varieties of perceived harm and that past research has substantially exaggerated moral differences between liberals and conservatives.
In a nutshell, the authors are arguing with Jonathan Haidt's Moral Foundations Theory, and the idea that there's some fundamental differences in moral cognition between political liberals and conservatives. Haidt classifies moral judgements along different dimensions like purity, care, loyalty, and fairness. He argues that liberals place more emphasis on "care" and "fairness" than conservatives. You end up with a psychological view of political difference in which those differences are taken to be pretty innate.
The authors of this article argue, instead, that both liberals and conservatives understand morality similarly, with both strongly emphasizing that what makes an act immoral is that it is harmful:
Speculating, what this suggests to me is that the differences which emerge in Haidt's foundations between conservatives and liberals reflect more cultural differences than psychological differences, and maybe that is reflected both in differences in language use (liberals are more likely to frame moral issues around care for others or basic fairness; conservatives more likely to use language framed around purity) and the underlying set of culturally constructed concepts people use to think about morality. But fundamentally, both the conservative reacting with disgust to perceived impurity and the liberal reacting with outrage to perceived unfairness are expressing a belief that harm is being done. There is a commonality, which also suggests maybe that there is a way of finding common ground in moral arguments, by trying to draw out how different people understand acts to be either harmful or not.
04-27-2019
, 02:09 PM
04-27-2019
, 05:25 PM
Quote:
The Unifying Moral Dyad: Liberals and Conservatives Share the Same Harm-Based Moral Template. (full text here)
I'm not in love with the methods used here (small sample sizes; convenience samples from Amazon Mechanical Turk) so I would take this with a healthy grain of salt, but it's an interesting piece of research and the conclusion makes intuitive sense to me.
In a nutshell, the authors are arguing with Jonathan Haidt's Moral Foundations Theory, and the idea that there's some fundamental differences in moral cognition between political liberals and conservatives. Haidt classifies moral judgements along different dimensions like purity, care, loyalty, and fairness. He argues that liberals place more emphasis on "care" and "fairness" than conservatives. You end up with a psychological view of political difference in which those differences are taken to be pretty innate.
The authors of this article argue, instead, that both liberals and conservatives understand morality similarly, with both strongly emphasizing that what makes an act immoral is that it is harmful:
Speculating, what this suggests to me is that the differences which emerge in Haidt's foundations between conservatives and liberals reflect more cultural differences than psychological differences, and maybe that is reflected both in differences in language use (liberals are more likely to frame moral issues around care for others or basic fairness; conservatives more likely to use language framed around purity) and the underlying set of culturally constructed concepts people use to think about morality. But fundamentally, both the conservative reacting with disgust to perceived impurity and the liberal reacting with outrage to perceived unfairness are expressing a belief that harm is being done. There is a commonality, which also suggests maybe that there is a way of finding common ground in moral arguments, by trying to draw out how different people understand acts to be either harmful or not.
I'm not in love with the methods used here (small sample sizes; convenience samples from Amazon Mechanical Turk) so I would take this with a healthy grain of salt, but it's an interesting piece of research and the conclusion makes intuitive sense to me.
In a nutshell, the authors are arguing with Jonathan Haidt's Moral Foundations Theory, and the idea that there's some fundamental differences in moral cognition between political liberals and conservatives. Haidt classifies moral judgements along different dimensions like purity, care, loyalty, and fairness. He argues that liberals place more emphasis on "care" and "fairness" than conservatives. You end up with a psychological view of political difference in which those differences are taken to be pretty innate.
The authors of this article argue, instead, that both liberals and conservatives understand morality similarly, with both strongly emphasizing that what makes an act immoral is that it is harmful:
Speculating, what this suggests to me is that the differences which emerge in Haidt's foundations between conservatives and liberals reflect more cultural differences than psychological differences, and maybe that is reflected both in differences in language use (liberals are more likely to frame moral issues around care for others or basic fairness; conservatives more likely to use language framed around purity) and the underlying set of culturally constructed concepts people use to think about morality. But fundamentally, both the conservative reacting with disgust to perceived impurity and the liberal reacting with outrage to perceived unfairness are expressing a belief that harm is being done. There is a commonality, which also suggests maybe that there is a way of finding common ground in moral arguments, by trying to draw out how different people understand acts to be either harmful or not.
For example, in our current political climate it has become in vogue to call someone a "Nazi" for not adhering to the leftist zeitgeist of the day. However, if somehow we were all transported to Germany circa 1930s I think we would be surprised which individuals actually became enthusiastic Nazis and who didn't. I think it might be the exact opposite of who you might expect.
I listen to Jordan Peterson. Most of the critiques of him in the popular zeitgeist are some combination of ad hominem attacks and straw manning. That being said, I personally am skeptical of his willingness to try to assign personality characteristics to people based on their political beliefs. I have read some of the literature he cites to support his arguments, and to me it seems his arguments and inferences go way beyond what the data suggests.
04-28-2019
, 11:10 AM
The Effect of Minimum Wages on Low-Wage Jobs (2019) (full text here)
Forthcoming from the Quarterly Journal of Economics
I really like the methods in this study. The TL;DR takeaway is basically just "historical minimum wage increases have been too small to negatively impact employment"
Forthcoming from the Quarterly Journal of Economics
Quote:
Abstract
We estimate the effect of minimum wages on low-wage jobs using 138 prominent state-level minimum wage changes between 1979 and 2016 in the U.S using a difference-in-differences approach. We first estimate the effect of the minimum wage increase on employment changes by wage bins throughout the hourly wage distribution. We then focus on the bottom part of the wage distribution and compare the number of excess jobs paying at or slightly above the new minimum wage to the missing jobs paying below it to infer the employment effect. We find that the overall number of low-wage jobs remained essentially unchanged over the five years following the increase. At the same time, the direct effect of the minimum wage on average earnings was amplified by modest wage spillovers at the bottom of the wage distribution. Our estimates by detailed demographic groups show that the lack of job loss is not explained by labor-labor substitution at the bottom of the wage distribution. We also find no evidence of disemployment when we consider higher levels of minimum wages. However, we do find some evidence of reduced employment in tradable sectors. We also show how decomposing the overall employment effect by wage bins allows a transparent way of assessing the plausibility of estimates.
We estimate the effect of minimum wages on low-wage jobs using 138 prominent state-level minimum wage changes between 1979 and 2016 in the U.S using a difference-in-differences approach. We first estimate the effect of the minimum wage increase on employment changes by wage bins throughout the hourly wage distribution. We then focus on the bottom part of the wage distribution and compare the number of excess jobs paying at or slightly above the new minimum wage to the missing jobs paying below it to infer the employment effect. We find that the overall number of low-wage jobs remained essentially unchanged over the five years following the increase. At the same time, the direct effect of the minimum wage on average earnings was amplified by modest wage spillovers at the bottom of the wage distribution. Our estimates by detailed demographic groups show that the lack of job loss is not explained by labor-labor substitution at the bottom of the wage distribution. We also find no evidence of disemployment when we consider higher levels of minimum wages. However, we do find some evidence of reduced employment in tradable sectors. We also show how decomposing the overall employment effect by wage bins allows a transparent way of assessing the plausibility of estimates.
Quote:
Introduction
Even though nearly three decades have passed since the advent of “new minimum wage research” (see e.g. Card and Krueger 1995; Neumark and Wascher 2008), there is surprisingly little research on the effect of the policy on overall employment. This shortcoming is particularly acute given the importance policymakers place on understanding overall responses. For example, in its attempt to arrive at such an estimate, the 2014 Congressional Budget Office (CBO) report noted the paucity of relevant research and then used estimates for teen minimum wage elasticities to extrapolate the total impact on low-wage jobs.
In this paper we use a difference-in-differences design to estimate the impact of minimum wage increases on the entire frequency distribution of wages; and subsequently focus on changes at the bottom of the distribution to estimate the impact on employment and wages of affected workers.
Our approach relies on the idea that the overall employment and wage effects of the policy can be inferred from the localized employment changes around the minimum wage. An increase in the minimum wage will directly affect jobs that were previously paying below the new minimum wage. The jobs shifted into compliance create a “bunching,” and show up as “excess jobs” at and slightly above the minimum. The effect of the minimum wage on the wage distribution fades out and becomes negligible beyond a certain point. Therefore, the overall employment and wage effects of the policy can be inferred from the localized employment changes around the minimum wage. For instance, we can assess the changes in employment from the difference between the number of excess jobs at and slightly above the minimum wage and the number of missing jobs below the minimum.
To identify the effect of the minimum wage on the frequency distribution of wages, we implement an event study analysis that exploits 138 prominent state-level minimum wage increases between 1979 and 2016. We estimate employment changes in each dollar wage bin relative to the new minimum wage for three years prior to and five years following an event. Our empirical approach, therefore, disaggregates the total employment effect of the policy into constituent wage bins, and we use these bin-by-bin estimates locally around the minimum wage to assess the effect of the policy.
Even though nearly three decades have passed since the advent of “new minimum wage research” (see e.g. Card and Krueger 1995; Neumark and Wascher 2008), there is surprisingly little research on the effect of the policy on overall employment. This shortcoming is particularly acute given the importance policymakers place on understanding overall responses. For example, in its attempt to arrive at such an estimate, the 2014 Congressional Budget Office (CBO) report noted the paucity of relevant research and then used estimates for teen minimum wage elasticities to extrapolate the total impact on low-wage jobs.
In this paper we use a difference-in-differences design to estimate the impact of minimum wage increases on the entire frequency distribution of wages; and subsequently focus on changes at the bottom of the distribution to estimate the impact on employment and wages of affected workers.
Our approach relies on the idea that the overall employment and wage effects of the policy can be inferred from the localized employment changes around the minimum wage. An increase in the minimum wage will directly affect jobs that were previously paying below the new minimum wage. The jobs shifted into compliance create a “bunching,” and show up as “excess jobs” at and slightly above the minimum. The effect of the minimum wage on the wage distribution fades out and becomes negligible beyond a certain point. Therefore, the overall employment and wage effects of the policy can be inferred from the localized employment changes around the minimum wage. For instance, we can assess the changes in employment from the difference between the number of excess jobs at and slightly above the minimum wage and the number of missing jobs below the minimum.
To identify the effect of the minimum wage on the frequency distribution of wages, we implement an event study analysis that exploits 138 prominent state-level minimum wage increases between 1979 and 2016. We estimate employment changes in each dollar wage bin relative to the new minimum wage for three years prior to and five years following an event. Our empirical approach, therefore, disaggregates the total employment effect of the policy into constituent wage bins, and we use these bin-by-bin estimates locally around the minimum wage to assess the effect of the policy.
Quote:
Results
Our main estimates show that the number of excess jobs at and slightly above the minimum wage closely matches the number of missing jobs just below the minimum wage, while we find no evidence for employment changes at or more than $4 above the minimum wage. A similar pattern obtains for low-skilled workers, suggesting labor-labor substitution is unlikely to be a factor in our setting. Moreover, we find that the level of the minimum wages that we study—which range between 37% and 59% of the median wage—have yet to reach a point where the job losses become sizable. However, the employment consequences of a minimum wage that surpass the ones studied here remain an open question. Furthermore, if minimum wage increases affect tradable sectors more, our findings suggest employment effects may be more pronounced.
Our main estimates show that the number of excess jobs at and slightly above the minimum wage closely matches the number of missing jobs just below the minimum wage, while we find no evidence for employment changes at or more than $4 above the minimum wage. A similar pattern obtains for low-skilled workers, suggesting labor-labor substitution is unlikely to be a factor in our setting. Moreover, we find that the level of the minimum wages that we study—which range between 37% and 59% of the median wage—have yet to reach a point where the job losses become sizable. However, the employment consequences of a minimum wage that surpass the ones studied here remain an open question. Furthermore, if minimum wage increases affect tradable sectors more, our findings suggest employment effects may be more pronounced.
05-03-2019
, 11:08 AM
Average annual hours actually worked per worker:
https://stats.oecd.org/Index.aspx?DataSetCode=ANHRS
2016
1 Mexico 2,250
14 United States 1,789
https://en.wikipedia.org/wiki/List_o...mployment_rate
Mexico 3.39 2018 (June)
United States 3.8 2019 (March)
Mexicans work more hours and have less unemployment than USA.
These mexicans sure looks lazy as hell
https://stats.oecd.org/Index.aspx?DataSetCode=ANHRS
2016
1 Mexico 2,250
14 United States 1,789
https://en.wikipedia.org/wiki/List_o...mployment_rate
Mexico 3.39 2018 (June)
United States 3.8 2019 (March)
Mexicans work more hours and have less unemployment than USA.
These mexicans sure looks lazy as hell
05-04-2019
, 06:08 PM
05-08-2019
, 11:23 AM
05-18-2019
, 08:55 AM
05-21-2019
, 10:46 AM