Macro Roundup

Wendy Wang, "The Majority of U.S. Children Still Live in Two-Parent Families," Institute For Family Studies, October 4, 2018, https://ifstudies.org/blog/the-majority-of-us-children-still-live-in-two-parent-families

The Majority of U.S. Children Still Live in Two-Parent Families

After a steady decline since the 1970s, the share of American children who live with two parents has leveled off in the past two decades. Today, nearly 7 in 10 children live with two parents, and this share remains flat since 2000.1 However, unlike their peers growing up a couple of decades ago, children under age 18 today are more likely to live with parents who are living together but not married.

Cohabitation is more common among single parents. In 2018, about 1 in 6 children (16%) living with single parents also live with their mother or father’s partner, up from 11% in 2007. And the share of all children living in this arrangement rose from 2.9% to 4.2% between 2007 and 2018. At the same time, the overall share of children who live with single parents has been steady in the past two decades.

Younger children are more likely to experience this newer living arrangement. More than 10% of children under age 3 live in a cohabiting family in 2018—7% live with two cohabiting parents and 4% live with a single parent and his or her partner. In comparison, among teenagers between ages 12 and 17, about 6% live with cohabiting parents. Only 2% of these teenagers live with their own unmarried mom and dad, while 4% live with a single parent and his or her partner.

The living arrangements of children vary by their racial and ethnic background. A vast majority of Asian children (85%) today live with two married parents, as is the case for most white children (74%) and Hispanic children (61%). The share among black children who live with married parents is markedly lower—36% of black children live with married parents.

Among children who live with single and non-cohabiting parents, most live with their mom. More than 4 in 10 black children live with their mom (44%), and this is the most common living arrangement for black children. The share of children living with a single mom is also relatively higher among Hispanic children (22%), compared with the share among white children (12%) and Asian children (7%).

On the other hand, a small proportion of American children (4%) live in a home without either of their parents. They could be living with grandparents, relatives, or be in foster care. About 8% of black children are in this type of living arrangement, and the share among children of other races and ethnicities is lower, averaging around 3 percent.

Children’s living environment while growing up also varies by parents’ education levels. It is well-documented that a parent’s higher education, especially a mother’s, is associated with better child outcomes. One of the reasons is that highly-educated and economically better-off adults are more likely to marry and stay married, which provides a relatively more stable environment for their children’s development.

Having a college-educated mother is associated with a much higher chance that children live with two married parents. For children whose mother is college educated, 86% live with two married parents in 2018, down slightly from 2007 when it was 88%.2 In contrast, 60% of children with high-school educated mothers live with married parents in 2018.

The growing share of children living in a cohabiting family, whether with their own parents or with one of their parents, adds another layer of complexity to American families today. Children can struggle in adapting to parents’ new living arrangements and navigating the relationships between their parent, his or her partner, and their other parent who does not live with them. Cohabitation is also associated with greater family instability, as most cohabitations in the U.S. are short lived. And family instability, in turn, is strongly linked to poorer child outcomes. Nevertheless, more and more children today find themselves living in a cohabiting family—at least for a time.

Wendy Wang is director of research at the Institute for Family Studies and a former senior researcher at Pew Research Center.

________________________________

1. Based on the Census Bureau’s Current Population Surveys. Parents could be either biological, step, or adoptive parents. Two parents can be either same-sex or opposite-sex couples.

2. Based on children living in households where the mother is present. Children who live with a single dad or who live without their parents (about 9% of children in 2018) are not included in the analysis.

“…After a steady decline since the 1970s, the share of American children who live with two parents has leveled off in the past two decades. Today,nearly 7 in 10 children live with two parents, and this share remains flat since 2000.1 However, unlike their peers growing up a couple of decades ago, children under age 18 today are more likely to live with parents who are living together but not married….”

Daron Acemoğlu and Pascual Restrepo, "Tasks, Automation, and the Rise in US Wage Inequality," National Bureau Of Economic Research, June 2021, https://www.nber.org/papers/w28920

Three core findings, "…First, and in contrast to models of SBTC with factor-augmenting technologies, in our framework automation can have a negative effect on workers who are displaced from tasks they used to perform, and such changes can take place with limited increases in total factor productivity (TFP). Hence, real wage declines and slow productivity growth despite rapid automation are not puzzles within this framework. Second, we derive a simple equation linking wage changes of a demographic group to the task displacement it experiences, which forms the basis of our reduced-form analysis. Third, our framework implies that the task displacement experienced by a group can be measured by its employment share in routine tasks in industries undergoing automation. Moreover, the extent of automation in an industry can be inferred from declines in its labor share, thus providing an explanation for the relationship reported in the right panel of Figure 2….The second part of the paper documents a robust and negative (reduced-form) relationship between our measure of task displacement and real wages across groups of workers. Our baseline results use the decline in an industry labor share to infer the overall extent of automation and task displacement taking place in that industry. We show that close to 50% of the variation in labor shares (and measured task displacement) across industries is driven by automation technologies, including the adoption of industrial robots, specialized machinery and software. In line with this result, we find a very similar negative relationship between wage changes and task displacement using an IV strategy that exploits the component of the labor share decline driven by automation. We document that offshoring also contributes to task displacement (and conceptually, its effects on wages should be similar since it also works by displacing workers from their tasks), but quantitatively it accounts for a smaller portion of observed wage changes than automation. These results bolster our confidence that task displacement driven by automation technologies is responsible for the relationships depicted in Figure 2…We find that task displacement—incorporating general equilibrium effects—accounts for about 50% of the observed relative wage changes during this period and explains 80% of the observed increase in the college premium. These sizable distributional effects are accompanied by modest increases in the average wage level, GDP and TFP. For example, technologies causing task displacement only account for a 3.8% increase in TFP between 1980 and 2016. We thus conclude that automation can explain a sizable fraction of changes in wage structure and real wage declines in the data, while having a tiny impact on productivity growth…Relatedly, Figure 9 displays average task displacement and average real wage change by gender and education. It reveals that men without college degree have experienced the highest levels of task displacement as well as substantial real wage declines, while men and women with a post-graduate degree and women with a college degree have been subject to negligible task displacement and have enjoyed robust real wage growth. Once again, these patterns are explained by the component of task displacement driven by automation technologies…”

Impact across demographics groups, “…We now present descriptive statistics for our measure of task displacement at the level of demographic groups and take a preliminary look at its association with real wage changes. Figure 7 shows large differences across demographic groups in terms of task displacement between 1980 and 2016, with some experiencing a 25% reduction in their task shares, while others saw no change in theirs. Importantly, 95% of the variation in task displacement across groups is driven by our index of automation technologies, as can be seen from the left panel of Figure 7, which depicts task displacement by demographic group (computed using equation (12)) against the component driven by automation technologies (computed using equation (13)) The right panel of Figure 7 depicts task displacement by demographic group against a group’s real wage in 1980 and documents that task displacement has been particularly high during this period for groups in the middle of the wage distribution—thus playing both an unequalizing and a polarizing role. Figure 8 provides a first glimpse at the relationship between task displacement and real wage changes across demographic groups. The left panel plots the bivariate correlation between our task displacement measure and real wage changes between 1980 and 2016 (as in the right panel of Figure 2). The figure reveals a powerful negative relationship between task displacement and changes in real wages, with groups experiencing the highest levels of task displacement seeing their real wages fall or stagnate. The right panel displays a falsification exercise demonstrating that the relationship depicted in the left panel is not driven by secular declines in labor market fortunes of some demographic groups. It confirms that there is no correlation between our task displacement measure, which only uses post-1980 information, and real wage changes between 1950 and 1980—a period that predates major advances in automation. All demographic groups, including those who later on experienced adverse task displacement after 1980, enjoyed robust real wage growth, of about 50%, between 1950 and 1980…”

Research from Acemoglu/Restrepo finds that somewhere btw 50-70% of changes in the American wage structure btw 1980 and 2016 were driven by the relative wage declines of worker groups specialized in routine tasks in industries experiencing rapid automation,“…Our reduced-form evidence is based on estimating this equation and reveals a number of striking new facts. Most notably, we documented that between 50% and 70% of the changes in US wage structure between 1980 and 2016 are accounted for by the relative wage declines of worker groups specialized in routine tasks in industries experiencing rapid automation. In our first set of regression models, industry level task displacement is approximated by (the unexplained component of) labor share declines. We also estimate very similar results using explicit measures of industry-level automation and offshoring, confirming that our task displacement variable captures the effects of automation technologies (and to a lesser degree offshoring) rather than increasing markups industry concentration, or import competition. These alternative economic trends themselves do not appear to play a major role in the evolution of the US wage structure….”

Core idea, “..Figure 1 depicts some salient aspects of US developments: while the real wages of workers with a post-graduate degree rose, the real wages of low-education workers declined significantly. The real earnings of men without a high-school degree are now 15% lower than they were in 1980....One of the most powerful pieces of evidence offered in favor of SBTC (skill-biased technological change) in the 1990s was that increases in the demand for skills were pervasive across industries. This evidence also motivated the interpretation that industry-level differences in technology played a limited role in the transformation of US wage structureThis paper proposes an alternative approach for thinking about wage inequality. We argue that much of the changes in US wage structure are driven by the automation of tasks previously performed by certain types of workers in some industries (e.g., numerically-controlled machinery or industrial robots replacing blue-collar workers in manufacturing or specialized software replacing clerical workers). Workers who are not displaced from the tasks in which they have a comparative advantage, such as those with a postgraduate degree or women with a college degree, enjoyed real wage gains, while those, including low-education men, who used to specialize in tasks and industries undergoing rapid automation, experienced stagnant or declining real wage…”

“According to this framework, the demand for different types of workers comes from an aggregate production function of the form F(A H H,A L L), where H and L are employment levels of high-skill and low-skill workers, and A H and A L represent technologies (or equipment) augmenting these two types of workers. SBTC corresponds to technology becoming more favorable to high-skill workers (e.g., a greater increase in A H than in A L, provided that F has an elasticity of substitution greater than one)” It’s Ed’s smart guys working for smart guys, but formally modeled. Worth highlighting that at least some people are thinking along these lines.They claim declining labor share is a telltale sign of automation but this is not true. Note that a baseline assumption of their modeling is that displaced workers cannot compete with non-displaced workers. This is odd. I think this effect would actually exacerbate their results (since it would deepen the competition for low skill labor). Really useful to compare this paper to the Demings paper. They’re sort of 2 sides of the same coin, right? Demings is saying managerial and non-routine tasks are driving high-end wages up. Here, they’re saying routine/automatable tasks are sending low-end wages down. Good to put those together.”

Ben Comments:Look at figure 1 - 1980 is the highest real wages for working class people. Odd year to pick since there was a big run up in the 10 years before that. In fact, for men, only post-graduate degree is earning more than the 1980 group. It’s impossible to tell, from their graph, if 1980 is the anomaly or if the low (or negative) growth since then is the anomaly. It’s possible that substitutable low-skill labor just earns a low wage that’s relatively constant. This is Ed’s notion of the ratio of high skill to low skill.

I am surprised wages declined but I’m also suspicious for 4 reasons

• Does it include healthcare, pension, etc? if not, that’s cynical or stupid.
• the deflator (which one did they use?)
• the index year, especially given the extreme rise and fall of wage
• and, perhaps more importantly, the changing composition of workers in education categories. Today, only the dredges are dropouts for example.

New paper from Science Advances highlights the replication problem in science, “….We use publicly available data to show that published papers in top psychology, economics, and general interest journals that fail to replicate are cited more than those that replicate. This difference in citation does not change after the publication of the failure to replicate. Only 12% of post replication citations of nonreplicable findings acknowledge the replication failure. Existing evidence also shows that experts predict well which papers will be replicated. Given this prediction, why are nonreplicable papers accepted for publication in the first place? A possible answer is that the review team faces a trade-off. When the results are more “interesting,” they apply lower standards regarding their reproducibility….”

Core result, “…Our main finding is that papers that fail to replicate in (5-7) are cited more than those that are replicable. We find no significant change in citation trends, even after the publication of the failed replication. Notably, only a minority of publications after the failed replications were published acknowledge the failure. The “quality” of citations of papers that failed to replicate is similar to that of papers that were replicated: We do not find a difference in how often the citations of nonreplicable publications are cited by others or in the impact factor of the journals in which citations are published…”

The evidence, "...Figure 1 shows the distribution of total citation counts by the end of 2019 of the papers included in the replication projects, separately depending on replicability for Nature/Science, economics journals, and psychology journals. Replicability is measured according to the criterion that the replication project featured a P value of 0.05 or lower in a two-sided test, with an effect in the same direction as the original test….”

Scott Winship uses PSID data to track families across generations, core finding is that 20% of blacks versus 1% of whites have at least three consecutive generations living in poverty, “…We find that Black families are over 16 times more likely than White families are to experience three generations of poverty (defined as the bottom fifth of the income distribution). Three-generation poverty occurs among one in 100 Whites, but it describes the experience of one in five Black adults. Black Americans are 41 percent more likely to be in third-generation poverty than White adults are to be poor. The grandparents of Black adults had much lower incomes than the grandparents of their White counterparts had; this initial inequality has been compounded by lower rates of subsequent Black upward mobility out of poverty and by greater Black downward mobility. These patterns mean that poor Black and White adults today have dramatically different family poverty trajectories. Half of Blacks in the bottom fifth of the income distribution have parents and grandparents who were also poor, compared to just 8 percent of poor Whites. We show that the longer the time frame, the starker the racial gaps…”

Scott Winship, Christopher Pulliam, Ariel Gelrud Shiro, Richard V. Reeves, and Santiago Deambrosi, "Long Shadows: The Black White Gap In Multigenerational Poverty," American Enterprise Institute, June 2021, https://www.aei.org/wp-content/uploads/2021/06/Long-Shadows.pdf

The Data: