Macro Roundup

Charles Jones paper you asked about (this is the final version of the initial 2013 release), basically what I understand it to shows the centrifugal effect of the allowing women and blacks to compete in the labor market after the 1960s, and shows there were significant gains to women entering (in their absence the models says we would have seen no productivity growth), “…In 1960, 94 percent of doctors and lawyers were white men. By 2010, the fraction was just 62 percent. Similar changes in other highly-skilled occupations have occurred throughout the U.S. economy during the last 50 years. Given that the innate talent for these professions is unlikely to have changed differently across groups, the change in the occupational distribution since 1960 suggests that a substantial pool of innately talented women and black men in 1960 were not pursuing their comparative advantage. We examine the effect on aggregate productivity of the convergence in the occupational distribution between 1960 and 2010 through the prism of a Roy model. Across our various specifications, between20% and 40% of growth in aggregate market output per person can be explained by the improved allocation of talent….”

Chang-Tai Hsieh, Erik Hurst, Charles Jones and Peter Klenow, “The Allocation Of Talent And U.S. Economic Growth,” Econometrica, September 2019, http://klenow.com/HHJK.pdf

“…We apply this framework to measure the changes in barriers to occupational choice facing women and black men in the United States from 1960 to 2010. We find large reductions in these barriers, concentrated in high-skilled occupations. We then use our general equilibrium setup to isolate the aggregate effects of the reduction in occupational barriers facing these groups. Our baseline calculations suggest that falling barriers explain roughly 40% of aggregate growth in market GDP per person. In our baseline model, we assume that individuals are heterogeneous only in their draws of occupational talent. We show that our baseline results are robust to instead assuming that individuals draw idiosyncratic occupational preferences. Even under this polar assumption, we find that one-fifth of U.S. market GDP growth can be explained by falling labor market barriers, falling human capital barriers, and shifting occupational preferences. Much of the productivity gains come from drawing women and black men into high-skilled occupations. Whether women increased their propensity to become lawyers and doctors because of declining labor market frictions or because of changing social norms, the growth implications are similar. That said, we estimate that occupational sorting based on talent draws better fits the data than occupational sorting based on preference draws…”

Given compensation has changed while talent has not they assume the changes have been driven by other barriers to entry, “…First, our model collectively does very well in explaining the average earnings growth of all groups between 1960 and 2010. The model explains about 100% of earnings growth for black men and black women while only slightly overestimating earnings growth for white women and underestimating earnings growth for white men. Second, falling labor market frictions account for 77% of earnings growth for white women, 29% for black men, and 51% for black women….”

Note they make same assumption that you do that there has no been no change in innate talent overtime (they use the same AFQT data we looked at this winter to support that point):“…we make a key assumption that the distribution of innate talent of women and black men—relative to white men—is constant over time.With this assumption, we back out the changes in labor market frictions, human capital frictions, and occupational preferences from synthetic panel data on the occupations and wages of women and black men relative to white men from 1960 to 2010. We infer that preferences changed and/or labor and human capital frictions declined from 1960 to 2010 to jointly explain the convergence in occupations and wages of women and black men relative to white men. When we view these facts through the lens of our general equilibrium model, we find that these shifts account for roughly two fifths of growth in U.S. market GDP per person between 1960 and 2010. They also account for most of the rise in labor force participation over the last five decades….Yet another alternative would be to assume—contrary to our presumption—that women are somehow innately less talented than men, supposedly explaining women’s lower wages and under-representation in skilled occupations in 1960. Rising discrimination in favor of women since 1960 might then account for the closing gaps between men and women. This hypothesis would entail rising misallocation and a drag on aggregate growth. Data on individual test scores suggest women are not less talented than men. The Armed Forces Qualifying Test (AFQT) was administered in both the NLSY 1979 and the NLSY 1997. The NLSY tracks a sample of individuals who were 12-16 years old when the surveys started. The AFQT scores in the NLSY are very similar for men and women in both 1979 and 1997.36 According to these scores, women seem no less talented than men in their early teens. If we condition on working, women likewise have similar scores to men in both 1979 and 1997.37 If one believes the story of rising discrimination in favor of women, one would have expected the relative test scores of working women to fall along with their rising participation rates. AFQT scores do not support the hypothesis that women are innately less talented than men. Collectively, these results suggest that alternate assumptions do not fit aspects of the data as well as our baseline. We therefore prefer our baseline assumption that women and black men faced human capital and labor market frictions in 1960 relative to white men, and that these frictions fell over time…”

I believe this table makes your point that women drove the gains from better matching:

Factoid from GS via Pethokoukis“…“The world’s top 100 companies in R&D spending include 37 from the United States, 15 from Japan, 12 from Germany, 9 from China, and 4 from both France and South Korea. The number of Chinese companies in the top 100 increased significantly in recent years, from two in 2013 to nine in 2018. Five of the companies from China are state-owned enterprises.”…”

James Pethokoukis, “Does America Need To Invest More In Science? 5 Charts To Consider,” American Enterprise Institute, August 6, 2020, https://www.aei.org/economics/does-america-need-to-invest-more-in-science-5-charts-to-consider/

Will Dobbie and Ronald Fryer, "Are High Quality Schools Enough to Close the Achievement Gap? Evidence from a Social Experiment in Harlem," National Bureau Of Economic Research, November 2009, https://www.nber.org/papers/w15473

“…Harlem Children’s Zone is effective at increasing the achievement of the poorest minority children. Students enrolled in the sixth grade gain more than four fifths of a standard deviation in math and between one-quarter and one-third of a standard deviation in English Language Arts (ELA) by eighth grade. Taken at face value, these effects are enough to close the black-white achievement gap in mathematics and reduce it by about half in ELA. Students in the HCZ elementary school gain approximately four fifths to one and a half a standard deviation in both math and ELA by third grade, closing the racial achievement gap in both subjects. HCZ students are also less likely to be absent. These results are robust across identification strategies, model specifications, and subsamples of the data. Surprisingly, students of all ability levels obtain roughly the same benefit from attending HCZ charters. ….HCZ is enormously successful at boosting achievement in math and ELA in elementary school and math in middle school. The impact of being offered admission into the HCZ middle school on ELA achievement is positive, but less dramatic..Figures 3-5 provide a visual representation of our basic results from HCZ Promise Academy - Middle School. Figure 3A plots yearly, raw, mean state math test scores, from fourth to eighth grade, for four subgroups: lottery winners, lottery losers, white students in New York City public schools and black students in New York City public schools. Lottery winners are comprised of students who either won the lottery or who had a sibling that is already enrolled in the HCZ Promise Academy. Lottery losers are individuals who lost the lottery and did not have a sibling already enrolled. Technically, these represent ITT estimates. In fourth and fifth grade, before they enter the middle school, math test scores for lottery winners, losers, and the typical black student in New York City are virtually identical, and roughly 0.75 standard deviations behind the typical white student. Lottery winners have a modest increase in sixth grade, followed by a more substantial increase in seventh grade and even larger gains by their eighth-grade year. The TOT estimate, which is the effect of actually attending the HCZ Promise Academy Middle School, is depicted in panel B of Figure 3. As we directly observe test scores of lottery winners who enroll and we have a TOT estimate, we can estimate the mean test score for those in the control group who would have enrolled if they had won the lottery (Kling et al., 2007). The TOT results follow a similar pattern, showing remarkable convergence between children in the middle school and the average white student in New York City. After three years of “treatment,” HCZ Promise Academy students have nearly closed the achievement gap in math - they are behind their white counterparts by 0.121 standard deviations (p-value = 0.113). If one adjusts for gender and free lunch, the typical eighth grader enrolled in the HCZ middle school outscores the typical white eighth grader in New York City public schools by 0.087 standard deviations though the difference is not statistically significant (p-value = 0.238). Put differently, the typical student before entering the HCZ public charter school was at the 20th percentile of the white distribution. After three years, the typical student is at the 45th percentile before adjusting for gender and free lunch and the 54th percentile after adjusting for gender and free lunch…”

“…. We use two separate statistical strategies to account for the fact that students who attend HCZ schools are not likely to be a random sample. First, we exploit the fact that HCZ charter schools are required to select students by lottery when the number of applicants exceeds the number of available slots for admission. In this scenario, the treatment group is composed of students who are lottery winners and the control group consists of students who are lottery losers. This allows us to provide a set of causal estimates of the effect of being offered admission into the HCZ charter schools on a range of outcomes, including test scores, attendance, and grade completion. There are two important caveats to our lottery identification strategy. First, the HCZ middle school was not significantly oversubscribed in its first year of operation, and the HCZ elementary schools have never been significantly oversubscribed, making it more difficult to estimate the effect of being offered admission for these groups. Second, results from any lottery sample may lack external validity. The counterfactual we identify is for students who are already interested in charter schools. The effect of being offered admission to HCZ for these students may be different than for other types of students. To complement the lotteries, our second identification strategy uses the interaction between a student’s home address and her cohort year as an instrumental variable. This approach takes advantage of two important features of the HCZ charter schools: (1) anyone is eligible to enroll in HCZ’s schools, but only students living inside the Zone are actively recruited by HCZ staff; and (2) there are cohorts of children that are ineligible due to the timing of the schools’ opening and their age. Our identification is driven by the between-cohort comparison of outcomes within the Zone, using the outcomes of children outside the Zone to control for natural year-to-year variation in test scores. If the interaction between a student’s address and cohort only affects his or her outcome through its effect on enrollment in the charter school, this provides another set of causal estimates. Both statistical approaches lead us to the same basic story. Harlem Children’s Zone is effective at increasing the achievement of the poorest minority children. Students enrolled in the sixth grade gain more than four fifths of a standard deviation in math and between one-quarter and one-third of a standard deviation in English Language Arts (ELA) by eighth grade. Taken at face value, these effects are enough to close the black-white achievement gap in mathematics and reduce it by about half in ELA. Students in the HCZ elementary school gain approximately four fifths to one and a half a standard deviation in both math and ELA by third grade, closing the racial achievement gap in both subjects. HCZ students are also less likely to be absent. These results are robust across identification strategies, model specifications, and subsamples of the data. Surprisingly, students of all ability levels obtain roughly the same benefit from attending HCZ charters. ….HCZ is enormously successful at boosting achievement in math and ELA in elementary school and math in middle school. The impact of being offered admission into the HCZ middle school on ELA achievement is positive, but less dramatic. High-quality schools or community investments coupled with high-quality schools drive these results, but community investments alone cannot....Taken at face value, the achievement gains of HCZ students will translate into dramatically improved life trajectories. Our middle school lottery estimates - the most modest estimated - suggest a 9.7 to 12.5 percent increase in….a 2.8 to 3.7 percent decrease in the probability of committing a property or violent crime….and a 12.5 to 18.8 decrease in the probability of having a health disability…..If HCZ affects educational attainment as dramatically as achievement, the implied benefits are enormous….The public benefits alone from converting a high school dropout to graduate are more than $250,000.54...”

However in2013he did look at the impact of the same school on Non-Test outcomes and found HCZ had additional positive effects, “…Six years after the random admissions lottery, youth offered admission to the Promise Academy middle school score 0.283 standard deviations higher on a nationally-normed math achievement test and are 14.1 percentage points more likely to enroll in college. Admitted females are 12.1 percentage points less likely to be pregnant in their teens, and males are 4.3 percentage points less likely to be incarcerated....”

Fryer found (after attempting to control for selection bias) that Harlem Children's Zone has been effective in closing black-white achievement gap, note he hasn’t undated this since 2009 to see how robust the results remain (it came out in 2009, was published in 2011)

“….It appears that cognitive skills during adolescence, as measured by scores on the Armed Forces Qualification Test (AFQT), are strongly associated with these gaps. For example, conditional on having the median AFQT score, the racial gaps in both upward and downward mobility are relatively small. Consistent with previous studies linking AFQT scores to racial differences in adult outcomes (for example, Neal and Johnson, 1996; Cameron and Heckman, 2001), I do not interpret these scores as measuring innate endowments but rather as reflecting the accumulated differences in family background and other influences that are manifested in test scores.6 If these results are given a causal interpretation, they suggest that actions that reduce the racial gap in test scores could also reduce the racial gap in intergenerational mobility…. The effects of including one’s AFQT score on rates of upward mobility are shown in panel C of figure 8. Here, the results provide a relatively clean and compelling story. For both blacks and whites, upward mobility rises with AFQT scores in a fairly similar fashion. There are especially sharp gains in upward mobility associated with increases in test scores at the low end of the AFQT distribution. Upward mobility continues to rise at a somewhat slower but still strong rate in the middle and in the upper half of the AFQT distribution. Remarkably, the lines for blacks and whites are relatively close throughout the AFQT distribution. For example, the black-white gap in moving out of the bottom quintile is only 5.2 percentage points for those with median AFQT scores, compared with the unconditional gap of 27 percentage points. This suggests that cognitive skills measured at adolescence can “account” for much of the black-white difference in upward mobility. This result echoes previous findings by Neal and Johnson (1996) and Cameron and Heckman (2001), who have also found that AFQT scores can account for much of the racial gap in adult earnings and college enrollment rates. As with these aforementioned studies, I interpret this finding as reflecting the cumulative effect of a broad range of family background influences rather than reflecting only innate differences.28 The effects of AFQT scores on downward mobility (panel D of figure 8) are also quite striking. The lines for whites and blacks converge quite a bit and for a broad swath of the AFQT distribution, the racial gap is below 10 percentage points and is not statistically different from 0. Therefore, as was the case with upward mobility, test scores during adolescence are strongly associated with rates of downward mobility….”

“…Our most striking finding, however, is that when we (nonparametrically) control for AFQT scores, most of the racial gap in IGM disappears—whether measured via transition probability or upward mobility. We are careful in our interpretation of this finding. First, existing studies suggest that these scores largely reflect accumulated differences in family background and other influences, and are not primarily a measure of innate endowments; indeed, there is evidence that black-white differences in tests scores can be strongly affected by environmental influences….Second, like most of the existing IGM literature, our analysis is essentially descriptive in nature and one should be cautious in attaching causal interpretations to them. Third, all measures used in the present paper are based on relative positions and do not pertain to movements out of absolute thresholds such as from under the poverty line. However, our finding that there is no racial gap in mobility, conditional on cognitive test scores in adolescence, is new and merits further analysis to establish possible policy implications, such as the importance of early-life interventions relative to, say, access to affirmative action later in life…. We estimate the effect of AFQT scores on upward transition probabilities separately by race. Our dependent variable is the probability of leaving the bottom quintile. Figure 6 shows the result of this exercise. We find that conditional on AFQT scores, whites have only slightly higher likelihood of exiting the bottom quintile and that this gap does not vary a great deal across the AFQT distribution. For example, at the 25th interval of our normalized AFQT scores, the transition probability for whites is 0.63 and for blacks is 0.61, or a difference of just 2 percentage points. At the 10th interval, the gap is about 7 percentage points and at the 75th interval, the gap is about 15 percentage points. At no point in the AFQT distribution can we reject the hypothesis that the transition probabilities are the same….”

Debopam Bhattacharya and Bhashkar Mazumder, " A nonparametric analysis of black-white differences in intergenerational income mobility in the United States,"Quantitative Economics, 2011, https://onlinelibrary.wiley.com/doi/epdf/10.3982/QE69

Moffitt took a look at the research looking at male earnings volatility and attempted to adjudicate it, finds little change in male earnings volatility in the past 35 years

Note attached copy is from a version of the paper from July 2020

"In this paper we compare estimates of earnings volatility from SIPP survey data and the SIPP GSF, which links SIPP survey respondents to their respective administrative earnings records. We show that the trends in the SIPP survey and the SIPP GSF are similar. Volatility increases modestly in the SIPP GSF and declines modestly in the SIPP survey data between the mid-1980s and the early 2010s. Levels in the SIPP GSF are considerably higher than in the SIPP survey data. The results are robust to age-adjusting, more aggressively trimming low earnings, the use of imputations, and different methods of handling population representativeness. We also show that differences in the underlying earnings distribution explain the differences in the trend and level of volatility in the SIPP GSF and SIPP survey data. When we force the earnings distributions in the two datasets to be the same, we find that the differences in levels of volatility largely disappear, and the minor differences in trends that exist are reduced. This paper is part of a larger project designed to consider estimates of earnings volatility across multiple datasets. As part of this project, we also compare volatility in the SIPP GSF and SIPP survey data to that in PSID. We compare volatility using the same sample selection criteria and methods and find that there remain differences in both the level and trend in volatility. Volatility in the SIPP GSF is higher than the PSID, while volatility in the SIPP survey is lower, and both shower smaller relative increases between 1999 and 2010. Even when the earnings distributions are forced to be the same across the three datasets, there remain differences in both level and trend in volatility between the SIPP and the PSID. We find that, for both the SIPP GSF and the SIPP, survey volatility falls to a lower level than the PSID when weighting the cross-sectional earning distribution to the PSID, implying that volatility is higher in the PSID at a fixed level of inequality. More broadly, the results of this paper and the other papers in this group have important implications for the broader literature on volatility. Using 3 different survey data sets and 3 different administrative data sets, we find broad agreement that volatility has shown no major trend upward or downward in the past 35 years. Thus our paper, along with the others, finds no difference in the evidence obtained from survey and administration data. Another finding that has broad applicability is that, in some cases, differences in the underlying cross-sectional earnings distribution in different data sets plays a role in explaining differences in both levels and trends in volatility. The substantial differences across data sources in the level of inequality, across methods used to estimate volatility in the weight placed on different portions of the earnings distribution, and across sample definitions that may themselves alter the level and trend in inequality, could contribute to some of the disagreement that has developed in this literature...."