Reply to the Tran et al. response regarding "Revisiting the Asian second-generation advantage"

Ethnic and Racial Studies recently published "Revisiting the Asian Second-Generation Advantage", by Van C. Tran, Jennifer Lee, and Tiffany J. Huang, which I will refer to below as Tran et al. 2019. Ethnic and Racial Studies has also published my comment, and a Tran et al. response. I'll reply to their response below...

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Here are three findings from Tran et al. 2019 important for the discussion below:

1. Table 2 indicates that U.S. second-generation Chinese, Indians, Filipinos, Vietnamese, and Koreans are more likely than native Whites to hold a college degree.

2. Table 2 indicates that U.S. second-generation Chinese, Indians, Filipinos, Vietnamese, and Koreans are more likely than native Whites to report being in a managerial or professional position.

3. Table 4 Model 1 does not provide evidence at p<.05 that U.S. second-generation Chinese, Indians, Filipinos, Vietnamese, or Koreans are less likely than native Whites to report being in a managerial or professional position, controlling for age, age squared, gender, region, survey year, and educational attainment.

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Below, I'll respond to what I think are the two key errors in the Tran et al. reply.

1.

From the first paragraph of the Tran et al. reply:

Given this Asian educational advantage, we hypothesized that second-generation Asians would also report an occupational advantage over whites, measured by their likelihood to be in a professional or managerial occupation.

It makes sense to expect the second-generation Asian educational advantage to translate to a second-generation Asian occupational advantage. And that is what Tran et al. 2019 Table 2 reported: 45% of native Whites reported being in a professional or managerial position, compared to 73% of second-generation Chinese, 79% of second-generation Indians, 52% of second-generation Filipinos, 53% of second-generation Vietnamese, and 60% of second-generation Koreans. Tran et al. 2019 even commented on this occupational advantage: "Yet despite variation among the second-generation Asian groups, each exhibits higher rates of professional attainment than native-born whites and blacks" (p. 2260). But here is the Tran et al. reply following immediately from the prior block quote:

Contrary to our expectation, however, we found that, with the exception of second-generation Chinese, the other four Asian ethnic groups in our study – Indians, Filipinos, Vietnamese and Koreans – report no such advantage in professional or managerial attainment over whites (Tran, Lee, and Huang 2019: Table 4, Model 1). More precisely, the four Asian ethnic groups are only as likely as whites to be in a managerial or professional occupation, controlling for age, the quadratic term of age, gender, education, and region of the country.

The finding contrary to the Tran et al. expectation (from Tran et al. 2019 Table 4 Model 1) was not from what the other four Asian ethnic groups reported but was from a model predicting what was reported controlling for educational attainment and other factors. Tran et al. therefore expected an educational advantage to cause an occupational advantage that remained after controlling for the educational advantage. The Tran et al. reply states this expressly (p. 2274, emphasis in the original):

Because second-generation Asians hold such a significant educational advantage over whites, we had expected that second-generation Asians would also report an occupational advantage over whites, even after controlling for respondents' education.

Properly controlling for a factor means to eliminate the factor as an explanation. For instance, men having a higher average annual salary than women have might be due to men working more hours on average per year than women work. Comparing the average hourly salary for men to the average hourly salary for women controls for hours worked and eliminates the explanation that the any residual gender difference in average annual salary is due to a gender difference in hours worked per year. The logic of the Tran et al. expectation applied to the gender salary gap would produce expectations such as: Because men work more hours on average than women work, we expected that men would have a higher average annual salary than women have, even after controlling for the fact that men work more hours on average than women work.

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2.

From the Tran et al. reply (p. 2274, emphasis added):

Given that second-generation Asians are more likely to have graduated from college than whites, we hypothesized that they would evince a greater likelihood of attaining a professional or managerial position than whites, as is the case for the Chinese. Instead, we found that second-generation Chinese are the exception, rather than the norm, among second-generation Asians. Hence, we concluded that second-generation Asians are over-credentialed in education in order to achieve parity with whites in the labor market.

I think that there are two ways that labor market parity can be properly conceptualized in the context of this analysis. The first is for labor market outcomes for second-generation Asians to equal labor market outcomes for native Whites, without controlling for any factors; the second is for labor market outcomes for second-generation Asians to equal to labor market outcomes for native Whites, controlling for particular factors. Tran et al. appear to be using the "controlling for" conceptualization of parity. Now to the bolded statement...

Ignoring the advantage for second-generation Chinese, and interpreting as parity insufficient evidence of a difference in the presence of statistical control, Tran et al. 2019 provided evidence that second-generation Asians are over-credentialed in education relative to native Whites *and* that second-generation Asians have achieved labor market parity with native Whites. But I do not see anything in the Tran et al. 2019 analysis or reply that indicates that second-generation Asians need to be over-credentialed in education "in order to achieve" this labor market parity with native Whites.

Returning to the gender salary gap example, imagine that men have a higher average annual salary than women have, but that this salary advantage disappears when controlling for hours worked, so that men have salary parity with women; nothing in that analysis indicates that men need to overwork in order to achieve salary parity with women.

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So I think that the two key errors in the Tran et al. reply are:

1. The expectation that the effect of education will remain after controlling for education.

2. The inference from their reported results that second-generation Asians need to be over-credentialed in order to achieve labor market parity with natives Whites.

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4 Comments on “Reply to the Tran et al. response regarding "Revisiting the Asian second-generation advantage"

  1. For instance, men having a higher average annual salary than women have might be due to men working more hours on average per year than women work. Comparing the average hourly salary for men to the average hourly salary for women controls for hours worked and eliminates the explanation that the any residual gender difference in average annual salary is due to a gender difference in hours worked per year.

    While I agree with what I take to be your point, this example seems confused. If the gender gap in annual salary is due to men working more hours, controlling for hourly wages will not eliminate the gap in, say, a regression model.

    • Thanks for the comment, AH. But I don't think that the passage that you quoted suggested controlling for hourly wages; the passage instead referred to "comparing the average hourly salary for men to the average hourly salary for women" because that comparison "controls for hours worked".

      For example, if five women respectively worked 1, 2, 3, 4, and 5 hours per year and five men respectively worked 11, 12, 13, 14, and 15 hours per year and each worker were paid $10 per hour, the gender gap in average annual salary would be $100 (average annual salaries of $30 for women and $130 for men). Running a regression predicting annual salary using predictors of sex and hours worked would produce a coefficient on the sex predictor of $0.00 (rounded), thus eliminating the gender gap in that regression.

      R code below; output here.

      options(scipen = 999)
      sex < - c(0,0,0,0,0,1,1,1,1,1) hours <- c(1,2,3,4,5,11,12,13,14,15) annual.salary <- hours * 10 data <- data.frame(sex, hours, annual.salary) lm(annual.salary ~ sex, data = data) lm(annual.salary ~ sex + hours, data = data) Comparison of average hourly salaries would also eliminate the gender gap in that comparison because the comparison would be of a $10 average hourly salary for men to a $10 average hourly salary for women.

      • Yes, regressing annual salary on sex and hours worked would show sex to have no effect, but from your description I understood that your model was that you would regress annual salary on sex and hourly wages. In this latter regression, sex would have the same effect as in a regression of annual salary on sex only.

        • Yes, the hourly rate predictor would not eliminate the gap.
          Thanks for reading and for the comments, AH!

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