Figure 1 shows that there is a strong ceiling effect
for the wordsum variable in the white sample.

In the article, figure 1 obviously shows the histogram for the sample of each racial groups (but excluding people aged 70+). In the section "limitation" it is said that in the white sample, the ceiling effect diminishes over time (when looking at each category of cohort6). I attach the pictures here.

The pictures (fig. 1-3) are too large. I can only put one by page. I can put fig 1 exactly where you ask, but for figures 2-3, it's more difficult to put them exactly where I want. That did not look good. That's why I put everything at the end of the article.

For the syntax, I know that lot of people will not even look at the supplementary files, and I want to show the syntax, this can encourage others to do so.

For tobit regression, I would recommend :

The Uses of Tobit Analysis (McDonald & Moffitt 1980)
Roncek, D. W. (1992). Learning more from tobit coefficients: Extending a comparative analysis of political protest. American Sociological Review, 503-507.
Introductory Econometrics: A Modern Approach (Jeffrey M. Wooldridge 2012; pages 596-601) (you can have it in libgen)

By the way, I have contacted Hauser, Huang, Lynn, Flynn, and Dickens, that is, those who have worked on the BW IQ changes over time. They responded quickly, and (almost) the same day (30 sept/1 oct). Lynn said it's interesting. Huang said that their analysis needed an update, and he thanked me for doing so (and hoped the best for my publication). Flynn said it looks fascinating, and we must keep in touch. So, no one has really commented the article. I will try to email other people whose work is close to that topic. I think it's important to have the opinions from scholars.

The histograms are for all years combined. This is obvious since in the "limitation" I said the pattern looks different when we look at specific periods. I will however modify the sentence as follows "Figure 1 plots the histogram of wordsum for all years combined. It shows that there is a strong ceiling effect for the wordsum variable in the white sample.". Would it be ok ?

As for the size of the pictures, I did not ask that big, it's just how Stata has generated them, and i just saved them as png. I will try to reduce the size of the pictures without deteriorating the quality. However, I maintain it would not be possible to put the tables (notably the regressions) where you want me to put them. As you can see, their size is so large that you need (almost) an entire page. That's one of the reason why I put everything at the end. If you want, however, I will put the pictures where you think they should.

For the syntax, i will think about it, because I'm convinced it's beneficial. Of course, most won't care about it, but those who want to go into the details and are familiar with the software (unfortunately, R works badly for me, which is why I decided to use Stata instead) can see it. I have the feelin it's less likely they will examine my syntax if it's only in the supplementary file. They won't bother to go here at OP forums, check the files, etc. That's what I'm afraid about.

I build four models. For model 1, I use cohort (or survey year), race, and the interaction of race with cohort (or survey year). For model 2, I add age and gender variables. For model 3, I add the log of real family income (realinc), degree (degree) and years of school completed (educ), and region of residence at age 16 (reg16). For model 3, I add the number of siblings (sibs) as well as "type of place lived at age 16" (res16) and "living with parents at age 16" (family16).

The variable race "bw1" has a value of 0 for blacks and 1 for whites. Since the year 2000, the GSS begins to ask whether the respondent is hispanic or not. 5 For respondents in survey year 2000+ I have only included the respondents who declared not being hispanic (see appendix). The variable year has values going from 1972 to 2012. The variable cohort has values going from 1883 to 1994. The variable sex has the following values; male=1, female=2. The variable age has values going from 18 to 89. The variable degree has the following values; 0=lower than high school, 1=high school, 2=junior college, 3=bachelor, 4=graduate. The variable educ has values going from 0 to 20. The variable realinc has values going from 245 to 162607, and the respective numbers for log income are 5.5 and 11.99. The variable reg16 has the following values; 0=Foreign, 1=New England, 2=Middle Atlantic, 3=East North Central, 4=West North Central, 5=South Atlantic, 6=East South Atlantic, 7=West South Atlantic, 8=Mountain, 9=Pacific. The variable res16 has the following values; 1= in open country but not on a farm, 2=on a farm, 3=town lower than 50,000, 4=50,000 to 250,000, 5=in a suburb near a big city, 6=city greater than 250,000. The variable family16 has the following values; 0=other arrangement with relatives (e.g., aunt, uncle, grandparents), 1=mother & father, 2=father & stepmother, 3=mother & stepfather, 4=father, 5=mother, 6=male relative, 7=female relative, 8=male & female relatives. The variable sibs has values going from 0 to 37 (apart from two apparent outliers).

which means the gap has been reduced by an half

stronger gain over time in the model 2

It is merely a vocabulary test, and its reliability is not high (0.71).

The wordsum gap has been reduced by approximately 40% or 50% but this is still coherent with their idea that the black-white IQ gap is (at least) 50% genetic and 50% environmental.

The supplementary files are made available at http://openpsych.net/forum/index.php.

One important change is the removal (at least temporarily) of my logistic regression analysis. I know that MacCallum et al. (2002) have already treated the practice of dichotomization of a continuous variable. They say it has problems because it lowers the reliability of the variable and can possibly alter the interpretation of it. One illustration can help to understand. Imagine you have 4 people with different level of fear about spider, A (100%), B (60%), C (40%), D (0%). You dichotomize the variable at the mean or median, so that A and B have value of 1 (fear) while C and D have value of 0 (no fear) and yet B and C are more alike than either A and B or C and D. This labeling is totally arbitrary and not justified. However, these authors applied this criticism to correlational, ANOVA and regression analyses. I was using logistic regression, which attempt to estimate the likelihood of having value of 1 versus 0. But now that I think about it, I'm not so sure about its robustness. One can still argue that my labeling (0-7 vs 8-10) is arbitrary but at the same time, the categories of my dummy variables must also be arbitrary. So, I will email MacCallum and ask him what he thinks about it. Of course, I know lot of people in recent papers conducted such dichotomization for logistic regression, but none of them have cited MacCallum et al. In light of this, I have replaced this analysis by another; I computed the d gap of wordlow and wordhigh, by dividing the black-white difference by the SD given in Table 4.

Generally, I read that people accept the idea that a variable is (can be) thought as continuous when it has at least 5 values. In the variables you mentioned, they have more than 5 values.

The variable reg16 has the following values; 0=Foreign, 1=New England, 2=Middle Atlantic, 3=East North Central, 4=West North Central, 5=South Atlantic, 6=East South Atlantic, 7=West South Atlantic, 8=Mountain, 9=Pacific.

The variable family16 has the following values; 0=other arrangement with relatives (e.g., aunt, uncle, grandparents), 1=mother & father, 2=father & stepmother, 3=mother & stepfather, 4=father, 5=mother, 6=male relative, 7=female relative, 8=male & female relatives.

he variable res16 has the following values; 1= in open country but not on a farm, 2=on a farm, 3=town lower than 50,000, 4=50,000 to 250,000, 5=in a suburb near a big city, 6=city greater than 250,000.

Say, you control for dichotomized race (1;2). In that case, the other coefficients are expressed as if race is equal to 1.5. If race is coded 0;1, then it's 0.5 (note it's also how it works in ANCOVA).