Macro Roundup

Xavier Giroud, Simone Lenzu, Quinn Maingi and Holger Mueller report on their research (attached) that showed productivity spillovers diffuse through the economy though firms with dispersed geographic footprints, "....We first assess the effects of an increase in within-firm, across-location knowledge sharing on the distribution of economic activity. Intuitively, an increase in knowledge sharing between plants of multi-region (‘multi-county’, or MC) firms makes these plants more productive. Given a reduction in marginal costs, the plants increase labour demand, pushing up nominal wages (but not enough to offset the reduction in marginal costs). As a result, goods prices decline, which pushes up real wages. At the macroeconomic level, this influences the spatial distribution of economic activity. Surprisingly, knowledge sharing between plants widens economic disparities across regions. In relative terms, highly populous regions with high real GDP become richer, while rural regions with low real GDP become poorer (Figure 1, panel A).The fundamental economic force driving this result is the sorting across locations by multi-county plants. In equilibrium, counties with more multi-county plants experience relatively large average real wage increases, as well as labour inflows, while counties with fewer multi-county plants experience smaller average real wage increases and thus labour outflows. In our model economy, as well as in the data, 60% of all multi-county plants are in the 10% most populous counties (Figure 1, panel B). Hence, the productivity gains from an increase in knowledge sharing between plants are disproportionately concentrated in the richest, most populous counties....we examine the welfare implications of place-based policies in environments with and without knowledge sharing between plants. In each scenario, we simulate local productivity shocks - such as those arising from place-based policies - and measure the resulting changes in worker-level utility. While, on average, the welfare gains are much higher with knowledge sharing (64%), they are unevenly distributed. In particular, workers in other regions working for plants that are connected (through plant-level networks) to the region with the productivity shock experience utility gains that are five times larger than workers at non-connected plants. At the regional level, the welfare gains are disproportionately concentrated in highly populous regions, as those tend to have the most connected plants. Hence, knowledge sharing through plant-level networks leads to a highly uneven distribution of the welfare gains from local productivity shocks. These results complement the ones in Gaubert (2018a, 2018b), proving an additional explanation for why place-based policies may increase spatial disparities across regions....."

Xavier Giroud, Simone Lenzu, Quinn Maingi, Holger Mueller, "Propagation and amplification of local productivity spillovers," Center For Economic And Policy Research, December 1, 2021, https://voxeu.org/article/propagation-and-amplification-local-productivity-spillovers

Propagation and amplification of local productivity spillovers

Local governments spend billions of dollars every year on subsidies for companies to locate within their jurisdictions (Story 2012, Bartik 2020). From an efficiency standpoint, a common rationale for such place-based incentives is the existence of ‘Marshallian agglomeration economies’ - spillovers to the local economy in the form of input sharing, labour market pooling, and knowledge externalities - that raise the productivity of local workers and businesses (Moretti 2010, Kline and Moretti 2014, Neumark and Simpson 2015, Gaubert 2018a, 2018b). While the success of each individual place-based policy ultimately depends on how the policy affects the equilibrium balance of agglomeration and congestion forces (Redding and Esteban 2016), it is widely believed that productivity gains are highly localised and confined to the region in which the policy is implemented. In a recent paper (Giroud et al. 2021), we challenge this view, providing both reduced-form and model-based evidence showing that local productivity spillovers may propagate throughout the economy through the plant-level networks of multi-region firms.

Local versus global productivity spillovers

To identify local productivity spillovers, our empirical analysis builds on the natural experiments in Greenstone et al (2010), who study the effects of large plant openings (‘million dollar plants’, or MDPs) on the productivity of incumbent plants. In this setting, plants in ‘runner-up’ counties, which narrowly lost the competition to attract an MDP, provide a counterfactual for plants in the ‘winner’ county, where the plant was ultimately located.

Using confidential plant-level data from the US Census Bureau, we show that the MDP openings raise the productivity of incumbent plants by 4%. This local productivity spillover is strong within a 50-mile radius around the plant, weaker within a 100-mile radius, and insignificant beyond. Hence, and consistent with a large empirical literature, the productivity spillovers between (plants of) different firms decay rapidly with geographical distance.

A different picture emerges when one examines how productivity spillovers spread spatially within firm boundaries. We consider large multi-plant, multi-region firms which are exposed to the MDP openings (and thus to the local productivity spillover), by having a plant in the winner county. Comparing plants in the same county, industry, and year that belong to firms which either own plants in the winner county (‘treated plants’) or in the runner-up counties (control), our research finds that in response to the MDP openings, treated plants outside the winner county experience productivity gains of 1.8%, along with employment gains of 1.6%. Unlike the local productivity spillover, which takes place across plants of different firms, this global productivity spillover, which takes place across different plants of the same firm, does not decay with geographical distance. Indeed, the estimated global spillover effect remains virtually unchanged if we exclude all plants within a 500-mile radius, or within the same state or census division as the MDP in question.

In A survey article, Rosenthal and Strange (2020) write:

“Implicit in the idea that spatial concentration increases productivity is another idea: the degree of proximity matters. Agglomeration economies must decay with distance. How close, then, do firms and workers need to be to each other to benefit from agglomeration economies? Or more colloquially, how close is close?”

We provide a nuanced answer to this question. On the one hand, firms must have a nearby plant to benefit from local knowledge spillovers, as the local agglomeration economy is strongly significant only within a 50-mile radius around the MDP. On the other hand, not all of a firm’s plants need to be located nearby. In fact, it may suffice if only one of the firm’s plants is near the MDP. Once the knowledge spills over to that plant, it can be passed on to other plants inside the firm’s boundaries, increasing the productivity of distant plants hundreds of miles away.

Knowledge sharing across regions within firm boundaries

What explains the global productivity spillover? The evidence presented in our latest paper is consistent with knowledge sharing between plants of the same firm across different regions. While the local productivity spillover may be due to either labour market pooling or knowledge externalities, it is unlikely that a thicker labour market in the winner county would affect the productivity of treated plants hundreds of miles away. Knowledge, on the other hand, can be used in local and distant plants alike. Indeed, once it spills over to the firm’s local plant, it can be freely shared with other plants inside the firm’s boundaries (Markusen 1984). Consistent with this notion, the global productivity spillover does not decay with geographical distance. Further, and consistent with knowledge sharing, the global productivity spillover is much stronger if the distant plant and either the MDP or the firm’s plant in the winner county are in the same industry or in knowledge-based industries characterised by mutual R&D flows or patent citations.

Does knowledge sharing between plants reduce regional disparities?

While the reduced-form evidence points to sizeable spillover effects on distant regions, the importance of the significance of plant-level networks for regional and aggregate outcomes requires a meaningful quantitative framework. Since all plants in the economy are affected through general equilibrium effects (labour flows, goods trade), non-treated plants in distant counties provide an imperfect counterfactual to quantify the significance of knowledge sharing for the amplification of local productivity shocks. For example, as (manufacturing) goods are tradable, changes in goods prices affect real wages across all plants in the economy, including non-treated plants.

To quantify the significance of plant-level networks for the propagation and amplification of local productivity shocks, we develop and estimate a quantitative spatial model with goods trade, labour mobility, plant-level networks, and a rich geography - where plants of the same firm, across regions, are linked through shared knowledge. We estimate the model via indirect inference by exploiting the close link between structural parameters and reduced-form regression coefficients, and then use the structure of the model to undertake counterfactual analyses.

We first assess the effects of an increase in within-firm, across-location knowledge sharing on the distribution of economic activity. Intuitively, an increase in knowledge sharing between plants of multi-region (‘multi-county’, or MC) firms makes these plants more productive. Given a reduction in marginal costs, the plants increase labour demand, pushing up nominal wages (but not enough to offset the reduction in marginal costs). As a result, goods prices decline, which pushes up real wages. At the macroeconomic level, this influences the spatial distribution of economic activity. Surprisingly, knowledge sharing between plants widens economic disparities across regions. In relative terms, highly populous regions with high real GDP become richer, while rural regions with low real GDP become poorer (Figure 1, panel A).

In our second counterfactual, we examine the welfare implications of place-based policies in environments with and without knowledge sharing between plants. In each scenario, we simulate local productivity shocks - such as those arising from place-based policies - and measure the resulting changes in worker-level utility. While, on average, the welfare gains are much higher with knowledge sharing (64%), they are unevenly distributed. In particular, workers in other regions working for plants that are connected (through plant-level networks) to the region with the productivity shock experience utility gains that are five times larger than workers at non-connected plants. At the regional level, the welfare gains are disproportionately concentrated in highly populous regions, as those tend to have the most connected plants. Hence, knowledge sharing through plant-level networks leads to a highly uneven distribution of the welfare gains from local productivity shocks. These results complement the ones in Gaubert (2018a, 2018b), proving an additional explanation for why place-based policies may increase spatial disparities across regions.

Over the past 30 years the age of peak earnings has been almost constant in the US (40-50) but had a major decline in the PRC from ~ 55 to 35. China has seen lower returns to human capital then the US. Three core findings, “….In this paper, we first document stark differences in the cross-sectional age-earnings profiles between the U.S. and China, the two largest economies in the world, during the past thirty years. We find that, first, the peak age in cross-sectional age-earnings profiles, which we refer to as the “golden age,” stayed almost constant at around45-50years old in the U.S., but decreased sharply from55to around35years old in China; second,the age-specific real earnings grew drastically in China, but stayed almost stagnant in the U.S.; andthird, the cross-sectional and life-cycle age-earnings profiles looked remarkably similar in the U.S., but differed substantially in China….”

Hanming Fang and Xincheng Qiu, "“Golden Ages”: A Tale of the Labor Markets in China and the United States," National Bureau Of Economic Research, November 2021, https://www.nber.org/papers/w29523

Korea’s path“…Is this a realistic prediction? Only history will tell for sure, but interestingly, Figure11shows that such a pattern of increasing “golden ages” actually happened in Korea during the past ten years, using data from the Korean Labor and Income Panel Study (KLIPS). Korea experienced its fastest growth during the1960s to1990s. After that, it began to slowdown. Appendix Figure A.6depicts the decomposition for Korea, together with the decomposition for U.S. and China. It is worth noting that the cohort effects are particularly large from cohort1945to cohort1960, but starts to decelerate afterwards. This is consistent with our explanation of the race between inter-cohort productivity growth and returns to experience. As inter-cohort productivity growth starts to give its way to experience in Korea, the “golden age” comes back to older ages, as in our hypothetical scenario in Figure10…”

Potential implications, “…We also use the inferred components, particularly the series of the quantities of human capital, to revisit several important and classical applications in macroeconomics and labor economics, including the growth accounting and the estimation of the TFP growth, and the college wage premium and the skill-biased technical change. We find that once we adjust for the changes in the quantities of human capital, the estimated contribution of the TFP to GDP per capita growth is smaller than the previous estimates in the literature. We also find that the skill-biased technical change played an important role in the rising college premium to ensure that the relative price of college human capital does not drop as much as it would otherwise do when there is a large increase in the quantity of college human capital. A simple simulation exercise using our framework also suggests that, as the Chinese economy slows down to a “new normal” growth rate similar to that in the U.S., the golden ages of the cross-sectional age-earnings profile in China will start to increase to older ages, similar to what has happened in Korea in the last ten years….”

Theory, “…To explain these striking differences, we propose and empirically implement a unified decomposition framework to infer from the repeated cross-sectional earnings data the life-cycle human capital accumulation (the experience effect), the inter-cohort productivity growth (the cohort effect), and the human capital price changes over time (the time effect), under an identifying assumption that the growth of the experience effect stops at the end of one’s working career. The decomposition suggests that China has experienced a much larger inter-cohort productivity growth and higher increase in the rental price to human capital compared to the U.S.; but the return to experience is higher in the U.S….”

Datapoint suggesting underlying talent might be constrained, “…The contributions of relative labor supply, relative human capital per worker, and skill-biased technical change to the evolution of relative human capital prices are depicted in Figure9. Itshows clearly that in both U.S. and China, the relative quantity of college human capital grows rapidly, which would have led to sharp declines in the price of college human capital relative to non-college human capital. Due to skill-biased technical changes, the relative price of collegehuman capital did not decline even more in the U.S. and actually increased in China in the last thirty years….”

“…For example, college and high school graduates may possess different types of skills that are not perfect substitutes. To do so, we perform the decomposition as discussed in Section4separately for college workers and high school workers. College and high school workers are allowed to have different paths of life-cycle human capital growth, different inter-cohort human capital growth, and different time series of human capital price changes. The only restriction is that for both college workers and high school workers, there is no additional skill accumulation from experience in the last two experience bins towards the end of working life. Since our imputation of potential experience assumes that college graduates start to gain experience from22years old and high school graduates start to gain experience from18years old, effectively it is assumed that college graduates do not have additional returns to experience in52-61years old and high school graduates in48-57years old. This is largely overlapped with the “flat spot” proposed by Bowlus and Robinson (2012). After detailed investigation of the U.S. data, they conclude that a reasonable choice for the flat spot of the experience effect is around50-59for college graduates and46-55for high school graduates The results are presented in Figure7. First, within an education group, the returns to experience are still higher in the U.S. than in China. Within a country, the experience effects are larger for college workers than high school workers. This is consistent with findings documented by the previous literature that life-cycle wage growth tends to be faster for workers with more education (see Bagger et al., 2014, for example). The difference between the two education groups in their experience effect profiles, however, is much smaller compared to the difference in the cohort effects that we are turning to….”

Controlling For Education The Returns To Experience Are Still Higher In US Relative TO PRC

“…We present the contribution of each source — physical capital per worker, human capital per worker, and the residual — to the growth of GDP per worker in Figure5. We find that all three sources contribute almost equally to the U.S. growth, with the contribution of human capital slightly exceeding the other two sources. The picture is quite different in China. Although the absolute level of the growth in human capital is larger in China than in the U.S., the relative contribution of human capital turns out be the least important to China’s growth. But this is merely a result of an even faster speed at which the physical capital and TFP grow in China. In fact, physical capital is responsible for almost60% of the growth in GDP per worker, and TFP for almost another30% in China….”

Chinese GDP Per Worker Driven By Physical Investment, Not Human Capital Or TFP

“…Conceptually, across-sectionalage-earnings profile, which summarizes earnings of workers of different ages at a given point of time, is a different notion to thelife-cycleearnings profile, which tracks the earnings of a typical person over his life course. Thus one should not expect the crosssectional age-earnings profiles to coincide with the life-cycle ones. In Figure3, we reproduce the cross-sectional profiles from Figure1on the left, and plot the life-cycle earnings path of various birth cohorts on the right, with each curve representing a10-year cohort bin. The top panel is for the U.S., and the bottom panel for China. In the U.S. (Figure3a), cohorts with year of birth expanding half a century share remarkably similar life-cycle earnings paths. Furthermore, life-cycle profiles on the right of Figure3a closely resemble the cross-sectional profiles on the left (which is reproduced from the right panel of Figure1a), in both its shape and level. In a stationary environment where the life-cycle profile does not vary across cohorts, the cross-sectional profiles and the life-cycle profiles essentially coincide with each other. In such an economy, a30-year-old worker who wants to predict his (real) earnings10years later can simply take a look at the contemporary earnings of a40-year-old worker. This provides a justification for the voluminous prior literature that use cross-sectional profiles as approximations to life-cycle patterns. Although conceptually it is not correct to interpret crosssectional age-earnings profiles as life-cycle patterns, in practice they are close to each other for the U.S. case. In other words, stationarity is an reasonable assumption when studying the U.S. earnings profiles. However, as shown in Figure3b, the life-cycle patterns of different cohorts differ drasticallyfor China. More recent cohorts enjoy both much higher earnings and steeper life-cycle earnings growth. These life-cycle profiles also demonstrate no resemblance at all to the cross-sectional profiles, although they are actually linked to each other. Note that both the left and the right panels are just different ways to visualize the same underlying data.13It is perhaps not surprising that in a fast-growing economy such as China, stationarity is not a valid approximation…”

Cross Section versus Life Earnings Age-Earnings Profiles

“…To sum up, Figure2plots the evolution of the cross-sectional “golden ages” in the U.S. and China during1986-2012. For each country and each year, we run a kernel regression of log earnings on age to predict age-specific earnings, and obtain an estimated golden age in that year as the age achieving the maximal predicted earnings. Furthermore, we fit a linear time trend of the estimated golden age for each country. Figure2shows clearly that in the U.S., the golden age has stayed constant at around48years old in the past thirty years, while in China there exhibits a strong downward trend in the golden ages from1986to2012, decreasing from more than55years old to around35years old….”

PRC, “…In Figure1b, we plot the cross-sectional age-earnings profile for Chinese male workers, using the same procedure as discussed before. There are several striking contrasts between Figure1a and Figure1b. First, Chinese workers have experienced a dramatic increase in real earnings in the past30years for all age groups. It is reflected in the large vertical upward shifts of the ageearnings profiles for later cross sections. The earnings of Chinese urban male workers increased by nearly six folds. This is in marked contrast to the earnings stagnation in the U.S. Second, while the shape of the cross-sectional age-earnings profiles and hence the corresponding “golden ages” have stayed more or less constant in the U.S., the “golden age” in China is continuously evolving to younger ages. Prior to2000, the age-earnings profiles of China had a familiar hump-shape with the “golden age” at around55, although there already were some signs of a declining “golden age” in1996-2000. Between2001and2004, the age-earnings profile is almost flat and humps at around age40-45. After2005, the “golden age” is35years old…”

US, “…Figure1a depicts the cross-sectional age-earnings profiles for male workers in the U.S. Each curve represents a cross section that pools five or four adjacent years. In the construction of each curve, we first perform a nonparametric kernel regression of annual labor earnings on age separately for each cross section, where the Epanechnikov kernel function and rule-of-thumb bandwidth estimator are applied, and then display the smoothed values with the95% confidence intervals. To avoid potential impacts of extreme values, we drop outliers defined as earnings in the top2.5% and bottom2.5% in each year. We normalize all earnings to the2015year using CPI. Individuals are weighted by the person-level ASEC weight. Figure1a reveals that, first, the “golden age” in the U.S. is relatively stable at around50years old during the past three decades;second, the U.S. has witnessed little growth in age-specific mean real earnings. That is, both the shape and the level of the age-earnings profiles are largely unchanged…”

The Evidence

Scott Winship, "Economic mobility in America: A state-of-the-art primer," Archbridge Institute, December 1, 2021, https://www.aei.org/research-products/report/economic-mobility-in-america-a-state-of-the-art-primer/

"...The PSID and NLS yield conflicting results. In the PSID, the intergenerational elasticity rises over time, sometimes substantially, indicating falling mobility. In the NLS, the intergenerational elasticity is flat over time. For example, in the PSID, comparing parent family income to daughters’ earnings, the IGE rose from 0.25 for cohorts born 1952-59 to 0.43 for those born 1976-83. In the NLS, the elasticity was 0.26 for those born 1951-53, 0.23 for those born 1962-64, and 0.26 for those born 1981-83...."...Outpacing one’s father’s earnings has increased among daughters, but fewer daughters exceed their mothers’ earnings or, perhaps, their parents’ family income...."

Absolute Mobility Among Women

“…The red line in Figure 16, below, displays estimates of absolute family income mobility for the four sets of PSID cohorts. For cohorts born 1952-59, 47 percent of sons had higher earnings in their early 30s than their fathers had when the sons were between the ages of 8 and 21. The figure for sons born 1976-83 was 50 percent—no differentin the sense of being statistically significant. In contrast, the blue line in the chart comes from the Chetty et al. data and shows the trend for 1949-84 birth cohorts comparing the individual incomes of fathers and sons. (Individual income includes non-earnings income, so the trend is not strictly comparable to the PSID trend.) The four blue dots plot the eight-year averages for the same sets of birth cohorts that are combined in the PSID. The Chetty estimates indicate that absolute mobility fell dramatically over time. Across the cohorts covered in the PSID, the decline was from 61 percent to 42 percent…. If absolute mobility has, in fact, been flat in recent decades, that would suggest that two-thirds to 80 percent of today’s forty-somethings are better off than their parents were at the same age—much higher than the 50 percent suggested by Chetty et al. Similarly, Part One of this primer reported estimates from the PSID, using size-adjusted family income and pooling sons and daughters. The range of those estimates ran from 63 percent to 77 percent for recent cohorts of adults…”

Relative Mobility Among Women

“…Daughters’ relative mobility also likely declined, whether comparing father earnings, mother earnings, or parental income to daughters’ earnings or family income. Whether fathers’ or mothers’ earnings are the baseline, the decline in mobility was modest, except comparing mothers’ and daughters’ earnings when women without any earnings are included. The declines using parents’ family income as a baseline are modest in the NLS but sizable in the PSID. In the PSID the adulthood gap between the richest and poorest children rose by 14 to 19 percentiles over the long run. Lower mobility for daughters primarily reflects diminished upward mobility from the bottom….”

Absolute Mobility Among Men

“…Absolute mobility is concerned with changes in real (inflation-adjusted) income between generations. If poor children stay poor as adults but see large income gains over what their parents received, they experience upward absolute mobility. Using a summary measure often described incorrectly as an indicator of relative mobility (the “intergenerational elasticity”), mobility comparing fathers’ and sons’ earnings changed minimally, possibly increasing between the 1952-59 and 1976-83 birth cohorts. Using parents’ family income as a benchmark, the change in mobility over 30 years is minimal, though the shortterm trends in the PSID and NLS look very different. Mobility was the same in 2014 (among men born 1982-84) as in 1981 (among men born 1949-51) in the sense that the absolute gaps between sons in childhood were similarly narrowed by adulthood in both years.For example, a 10 percentage-point gap between sons in childhood typically narrowed to a 2.0-point gap in adulthood in 1981. In 2014, it only narrowed to a 2.5-point gap. (The elasticity rose from 0.21 to 0.26.) This estimated change in the NLS samples was small enough that we cannot discount the possibility that no change occurred in the real world represented by these samples. Turning to the likelihood that sons will do better than their parents, I find that there has been little change in the share of men who have higher earnings than their fathers.It appears that there was little change in the share of sons whose family income exceeds that of their parents, though that share falls in the PSID when incomes are adjusted for family size. In the NLS, for instance, the share was 51 percent among sons born 1949-51 and 53 percent among sons born 1982-84. However, the change in the PSID (using size-adjusted incomes) was from 66 percent to 56 percent, comparing cohorts born 1952-59 to those born 1976-83….”

Contrast With Chetty,"...The finding of relatively flat absolute mobility is in contrast to widely publicized results from Raj Chetty and his colleagues showing a large decline. I argue that an ideal measure, capturing family income over all of childhood rather than in a single year, would show a decline in absolute mobility, though a smaller one than is reported by Chetty’s study and others. The difference between my results and those other studies is that I measure parent income in adolescence rather than near birth. Because income growth was so fast during the 1950s and early 1960s, it is more difficult for sons born mid-century to exceed parental income when the bar is set later in childhood rather than earlier in childhood...."

that compare fathers’ and sons’ earnings…”

among sons born 1976-83. Due to imprecision in these estimates (which come from samples intended to represent the broader population of men), we cannot conclude that every cohort difference in Figure 2 would be found if we could observe the mobility of the entire population of men in both cohorts. Given some simplifying assumptions, statistical techniques allow us to say that if there were no true difference in mobility between two cohorts of men, we would be very unlikely to find a difference of a given magnitude in the PSID data. These techniques suggest that only one of the trends in Figure 2 is likely to be “real” in this “statistically significant” sense.13 Sons raised in the top fourth of father earnings became much less likely to end up in the top fourth themselves (right-most set of bars).14 Despite this evidence of increasing downward mobility, the IRA in Figure 1 does not reflect an increase, because it appears that upward mobility from the bottom fourth fell (though none of the changes in the left-most set of bars are statistically significant). No previous research has examined long-term trends in relative mobility in the US looking at transition probabilities

“…Figure 2 displays four sets of bars, each of them depicting outcomes for men who grew up with fathers in a different part of the male earnings distribution. The left-most bars show results for the 25 percent of sons with the lowest-earning fathers, the next set does the same for the sons of the next-poorest quarter of fathers, the next bars apply to sons with fathers in the third quartile of earnings, and the right-most bars depict the 25 percent of sons raised by the highest-earning fathers. Within each set are two bars, one for the earliest and most recent set of PSID birth cohorts. Each bar, in turn, is divided into four portions, showing where in the distribution of male earnings sons from a given cohort and quartile of the father earning distribution end up. In a world where the ranking of fathers on the earnings ladder had no relationship to the ranking of sons, each of the bars in Figure 2 would be divided into four equal sections. For instance, 25 percent of men starting in the bottom fourth would end up in the bottom fourth, 25 percent would end up in the second fourth, 25 percent in the third fourth, and 25 percent at the top. The analyses here are primarily concerned with the trend in mobility, so the important comparisons are within each set of bars in Figure 2. For instance, the left-most set of bars indicates that 40 percent of sons born 1952-59 and raised in the bottom fourth of father earnings were in the bottom fourth of male earnings themselves when observed as adults. In the subsequent set of birth cohorts, upward mobility out of the bottom fourth became less common over time: 49 percent remained in the bottom fourth. Men who started out in the bottom fourth were also slightly less likely to rise to the top fourth of male earnings over time; that share was 5 percent among sons born 1952-59 and 4 percent

“….In Figure 1, the PSID estimates for four cohorts and NLS estimates for three are displayed as lighter lines, whilethe dark lines display the linear trends through the four (or three) data points. The red lines display the IRA trend for male earnings, from the PSID. The trend shown by the light red line compares sons born 1952-59, 1960-67, 1968-75, and 1976-83, displaying the data points at 1955.5, 1963.5, 1971.5, and 1979.5. Relative mobility was unchanged over this period. The IRA for the earliest set of cohorts was 0.36, and for the most recent cohorts it was 0.32. (Appendix 2 provides estimates for all cohorts for all trends.) The change is far too imprecise to have confidence an actual decline in the IRA (an increase in mobility) occurred. Given the magnitude of the change and the number of men in the PSID samples, statistical methods allow us to assess how likely it would be to find a change in the data even if there were no true change in the American population. The decline in the IRA fails to achieve “statistical significance” in this sense.11 The linear trend line closely resembles the trend between the earliest and most recent cohorts….”

Relative Mobility Among Men

“….Relative mobility is concerned with the extent to which adults transcend their parents’ rankings by moving up o down in ranks. If everyone gets richer, but the poorest children still end up as the poorest adults, there is limited relative mobility. Relative earnings mobility, comparing fathers and sons, may have increased slightly over time. To the extent that it did, it likely reflected increased downward mobility from the top.Relative mobility trends are similar whether parental family income in adolescence is compared with men’s subsequent earnings or with their own family income in adulthood. Relative mobility may have declined, but any change was modest. According to the NLS data, for example, rather than the richest and poorest adolescent sons being separated by just under 25 percentiles in adulthood, as in 1981 (for cohorts born around 1950), they were separated by around 30 percentiles in adulthood in 2014 (for cohorts born in the early 1980s). There is evidence that both upward mobility from the bottom and downward mobility from the top declined. When sons’ earnings are the outcome, both the PSID and NLS indicate reduced downward mobility, and both indicate reduced upward mobility when considering sons’ family income….”

Evidence, “…The red line in Figure 16, below, displays estimates of absolute family income mobility for the four sets of PSID cohorts. For cohorts born 1952-59, 47 percent of sons had higher earnings in their early 30s than their fathers had when the sons were between the ages of 8 and 21. The figure for sons born 1976-83 was 50 percent—no different in the sense of being statistically significant. In contrast, the blue line in the chart comes from the Chetty et al. data and shows the trend for 1949-84 birth cohorts comparing the individual incomes of fathers and sons. (Individual income includes non-earnings income, so the trend is not strictly comparable to the PSID trend.) The four blue dots plot the eight-year averages for the same sets of birth cohorts that are combined in the PSID. The Chetty estimates indicate that absolute mobility fell dramatically over time. Across the cohorts covered in the PSID, the decline was from 61 percent to 42 percent…. If absolute mobility has, in fact, been flat in recent decades, that would suggest that two-thirds to 80 percent oftoday’s forty-somethings are better off than their parents were at the same age—much higher than the 50 percentsuggested by Chetty et al. Similarly, Part One of this primer reported estimates from the PSID, using size-adjustedfamily income and pooling sons and daughters. The range of those estimates ran from 63 percent to 77 percent forrecent cohorts of adults…”

Note Contrast With Chetty,"...The finding of relatively flat absolute mobility is in contrast to widely publicized results from Raj Chetty and his colleagues showing a large decline. I argue that an ideal measure, capturing family income over all of childhood rather than in a single year, would show a decline in absolute mobility, though a smaller one than is reported by Chetty’s study and others. The difference between my results and those other studies is that I measure parent income in adolescence rather than near birth. Because income growth was so fast during the 1950s and early 1960s, it is more difficult for sons born mid-century to exceed parental income when the bar is set later in childhood rather than earlier in childhood...."

"...Europe has overtaken China in creating billion-dollar tech startups, according to a Analysis that points to Europe’s rising status in the field and the effect of Beijing’s crackdown on capitalist entrepreneurialism. Both economies still lag behind the U.S. in creating so-called unicorns....While China still has far more large tech companies than Europe, and a larger internal market for them, trends have shifted over the past year according to research by Atomico, a London-based venture-capital firm that produces an annual analysis, the State of European Tech.... This year, China has added 26 tech companies with valuations above $1 billion, known as unicorns, bringing its total to 300, according to Atomico and data from analysts Dealroom.co. Europe has 98 new unicorns this year, bringing its total to 321, according to Atomico. It defines Europe as all 27 European Union countries and 18 others, including the U.K., Norway and Switzerland... The U.S. still leads Europe and China with 1,178 unicorns, of which 384 were added this year, according to Atomico. Europe has also leapfrogged China on venture capital investment this year, according to Atomico. In the first nine months of 2021, China drew $45 billion in venture funding compared with $52 billion for all of 2020, while Europe has already surpassed last year’s total, Atomico said. Through September, venture capital funds invested $77 billion in Europe, up from $48 billion for the whole of last year....European startups also generally carry lower valuations than their U.S. counterparts, attracting deal-hungry American investors. Europe this year has accounted for 33% of early-stage investments of less than $5 million, compared with 35% in the U.S. and 17% in Asia, according to Atomico. Europe’s share falls and the U.S. share grows with the size of funding rounds, Atomico says, offering a reminder that more money is chasing bigger deals in the U.S. than Europe...."

Daniel Michaels, “Europe Tops China in Spawning $1 Billion Tech Startups,” Wall Street Journal, December 6, 2021,
https://www.wsj.com/articles/europe-tops-china-in-spawning-1-billion-tech-startups-11638869014

Europe Tops China in Spawning $1 Billion Tech Startups

BRUSSELS—Europe has overtaken China in creating billion-dollar tech startups, according to a Analysis that points to Europe’s rising status in the field and the effect of Beijing’s crackdown on capitalist entrepreneurialism. Both economies still lag behind the U.S. in creating so-called unicorns.

Over the past year, China has reined in its most successful and freewheeling tech giants, including Alibaba Group Holding, Tencent Holdings and Didi Global Inc., leading to a loss of market value that has cast a chill across the whole sector. Europe, meanwhile, has attracted further investment in its expanding tech industry, particularly from U.S. funds.

While China still has far more large tech companies than Europe, and a larger internal market for them, trends have shifted over the past year according to research by Atomico, a London-based venture-capital firm that produces an annual analysis, the State of European Tech.

This year, China has added 26 tech companies with valuations above $1 billion, known as unicorns, bringing its total to 300, according to Atomico and data from analysts Dealroom.co. Europe has 98 new unicorns this year, bringing its total to 321, according to Atomico. It defines Europe as all 27 European Union countries and 18 others, including the U.K., Norway and Switzerland.

The U.S. still leads Europe and China with 1,178 unicorns, of which 384 were added this year, according to Atomico.

Europe has also leapfrogged China on venture capital investment this year, according to Atomico. In the first nine months of 2021, China drew $45 billion in venture funding compared with $52 billion for all of 2020, while Europe has already surpassed last year’s total, Atomico said. Through September, venture capital funds invested $77 billion in Europe, up from $48 billion for the whole of last year.

Both Europe’s number of unicorns and venture capital inflows are records, according to Atomico partner Tom Wehmeier, who leads the preparation of the report.

Europe’s venture funding total is near that of Asia overall, which saw venture capital investments of $110 billion through September, versus $87 billion in all of last year, Atomico said.

Investors, particularly from the U.S., have been drawn to Europe by an expanding number of tech startups launched by a growing population of entrepreneurs, many of whom have already worked in other tech companies. Over recent years, European governments and the EU have tried to encourage people to start businesses and embrace a degree of risk, particularly as the continent’s traditional social safety nets have strained under economic pressure and demographic shifts.

European universities’ strong science and engineering programs have long fed into large industrial enterprises but increasingly send graduates to the tech industry.

Tech-sector ties across the Atlantic have deepened recently, particularly as China and other parts of the world have become less welcoming. U.S. tech companies including Amazon.com Inc., Alphabet Inc. and Facebook parent Meta Platforms Inc. have established research centers in Europe or partnerships with universities to tap into the continent’s talent pool. Specialists can be less expensive to employ in Europe than in U.S. high-tech centers such as around San Francisco or Seattle.

European startups also generally carry lower valuations than their U.S. counterparts, attracting deal-hungry American investors. Europe this year has accounted for 33% of early-stage investments of less than $5 million, compared with 35% in the U.S. and 17% in Asia, according to Atomico. Europe’s share falls and the U.S. share grows with the size of funding rounds, Atomico says, offering a reminder that more money is chasing bigger deals in the U.S. than Europe.

China, meanwhile, has pushed to bring greater state control over its private sector. President Xi Jinping has said companies should do more to serve the Communist Party’s economic, social and national-security concerns. Along the way, the government has launched probes, derailed public offerings and clipped the wings of recently highflying tech entrepreneurs, erasing hundreds of billions of dollars in market value in the process.

Atomico’s Mr. Wehmeier said Europe’s relative predictability—which many people criticize as inertia or stodginess—has become an advantage relative to the situation in China now and to the unpredictability in the U.S. in recent years.

“Investors struggle with uncertainty,” Mr. Wehmeier said. “Europe’s stability is a great foundation for building a healthy tech ecosystem, and that has become more apparent recently.”

Cathy Rampell argues that the slowdown in immigration, relative to the pre-Trump trend, and a backlog of employment authorization applications are likely having a significant impact on labor supply, "...But there’s one underappreciated factor contributing to labor shortfalls that the Biden administration could alleviate almost immediately: the “missing” immigrant workers. Immigration inflows slowed sharply during the Trump administration and then collapsed under the combination of Trump-era policies and pandemic-driven closures. The number of visas issued by the State Department’s Foreign Service posts, for example, fell by more than 60 percent between fiscal years 2016 and 2020. There are millions fewer immigrants here today than would have been the case if pre-Trump trends in immigration had continued. But the labor force is also losing immigrants already here legally, whose work permits are expiring because the Biden administration hasn’t gotten its act together....But the queue to get any specific application reviewed is long — and growing. As of June 30, nearly 1.4 million employment authorization applications were pending. That’s double the size of the backlog that existed right before covid-19 reached the United States. It’s triple the number from the quarter just before Donald Trump took office...."

Cathy Rampell, "The missing immigrant workers,"Washington Post, November 22, 2021, https://www.washingtonpost.com/opinions/2021/11/22/legal-immigrant-workers-paperwork-renewal-backlog/

The missing immigrant workers

Helen Muradyan, a second-year resident physician, stopped working last month.

Not because her skills aren’t needed. To the contrary: The Southern California community hospital and health clinic that employed Muradyan struggle to find staff even during normal times. The pandemic worsened their staffing shortages.

“At one point we were operating at 150 percent of capacity,” Muradyan told me. “We worked day, night. We worked without breaks or anything, without seeing our loved ones, without seeing our family.”

But Muradyan, an immigrant from Armenia, had to stop working — because the U.S. government couldn’t be bothered to process her application to renew her work permit. Eventually, her existing work permit expired, and her employers had to terminate her.

Many factors contribute to our nationwide labor shortages, which are, in turn, driving supply-chain problems and inflation. Most of those issues — lack of child care, early retirements, fear of getting ill, burnout — would be difficult for employers or policymakers to resolve even if wages rise. But there’s one underappreciated factor contributing to labor shortfalls that the Biden administration could alleviate almost immediately: the “missing” immigrant workers.

Immigration inflows slowed sharply during the Trump administration and then collapsed under the combination of Trump-era policies and pandemic-driven closures. The number of visas issued by the State Department’s Foreign Service posts, for example, fell by more than 60 percent between fiscal years 2016 and 2020. There are millions fewer immigrants here today than would have been the case if pre-Trump trends in immigration had continued.

But the labor force is also losing immigrants already here legally, whose work permits are expiring because the Biden administration hasn’t gotten its act together.

For immigrants in the country lawfully, renewing an existing work permit was once relatively straightforward. When Muradyan applied for a renewal in 2019, she recalls the process took two to three months. That’s close to the average processing time such applications required in recent years.

Her experience this year has been very different.

U.S. Citizenship and Immigration Services (USCIS) received her renewal application in April; then, for months, Muradyan heard almost nothing. Requests to expedite processing were rejected twice, then ignored. On Oct. 13, her existing permit expired. It might be months more before USCIS reviews her pending application: The agency field office handling her case estimates that similar applications are taking 9.5 to 11 months to process.

Terminations that result from these delays affect more than workers. Muradyan’s employers, for instance, have had difficulty covering her scheduled shifts.

“Sometimes, my colleagues call me asking like, ‘Okay, your patient came — what’s supposed to happen for her exactly?’” Muradyan says. “My hospital is struggling, my colleagues are struggling, my patients are struggling.”

Muradyan is struggling, too.

An asylum seeker, Muradyan lives with her elderly parents and her 10-year-old U.S.-born son, who has started asking whether they will be able pay their rent. She lost her health insurance, which was tied to her employment. The longer it takes USCIS to process her paperwork, the worse the possible consequences get: Under her residency program’s rules, if she misses 12 weeks of work, she has to redo an entire year of training (and would have to wait until July 2022 to restart). Her license to prescribe medication will expire if she is out of work too long.

She is hardly the only person hurt by USCIS’s backlog.

Muradyan is a plaintiff in one of several national class-action lawsuits against USCIS over processing delays. A nonprofit representing the co-plaintiffs in her suit, the Asylum Seeker Advocacy Project, says at least 2,000 of its members have either recently lost their jobs because of work-permit processing delays or are on the verge of having their documents expire.

These asylum seekers have been employed in industries experiencing high-profile labor shortages: truck driving, food services, nursing, tech. And some firms that employ these unlucky immigrants are losing multiple workers.

Abelardo Rios is a telecommunications field technician who has been installing 5G equipment in Florida. His employer sometimes struggled to recruit workers even before covid-19, he says, “because not everybody is willing to go up a tower that’s 400 feet.”

Nonetheless, he and his direct supervisor are being let go this month because their work-authorization renewal applications are stuck in the USCIS backlog.

“My employer basically has told us that if they keep us working, they would be breaking the law, and they can’t do that,” Rios said. Because he, too, wants to stay on the right side of the law, he’s not sure how he’ll make his next mortgage payment. Like Muradyan, Rios is his family’s sole breadwinner.

Cases such as Rios’s or Muradyan’s shouldn’t take long to adjudicate: On average, USCIS employees spend only 12 minutes before rendering a decision on each employment authorization application, according to a 2019 agency estimate.

But the queue to get any specific application reviewed is long — and growing. As of June 30, nearly 1.4 million employment authorization applications were pending. That’s double the size of the backlog that existed right before covid-19 reached the United States. It’s triple the number from the quarter just before Donald Trump took office.

Multiple factors have expanded the backlog: Pre-pandemic, Trump officials had deliberately slowed down processing of nearly all immigration-related applications. The red tape and other obstacles they added increased costs not just for applicants but also USCIS. The agency had a budget crisis last year and ultimately froze the hiring of new employees and laid off contractors.

Covid safety measures then further slowed processing.

The Biden administration has reversed many Trump-era policies but the agency remains understaffed. And shrinking the work-permit backlog does not appear to be a Biden administration priority, even as it touts a “whole-of-government” approach to unblocking supply chains and reducing inflationary pressures.

When asked what role the constricted immigrant labor force might play in today’s economic bottlenecks, White House officials often change the subject. When I asked USCIS what it is doing to shorten wait times for employment documents, the agency said it was “committed to using all available policy and operational improvements to reduce both the number of pending cases and overall processing times,” and cited several changes it has made to expedite processing. (Some of those were the result of a recent settlement in a different class-action case.)

The problem, says Cato Institute research fellow David Bier, is that the administration has "done a lot of the reversing of the bad stuff that Trump did to slow things down even more, but not enough to go above and beyond that necessary to get rid of the backlogs and added delays that the Trump policies originally created.”

So what could USCIS do, while it rebuilds its staff?

Eliminating some redundancies from the process is a good start. The agency could also automatically extend more existing work permits while applicants wait for their renewals to be processed. Some categories of noncitizens already get this courtesy, but even those grace periods are too short. Asylum seekers, for instance, have their existing work permits auto-extended for up to 180 days while the renewal application awaits processing. But as Muradyan’s case and others show, that’s not enough time to avoid a lapse in employment.

“It doesn’t make sense, that if someone really wants to work, they can’t go back to work because of these delays,” she said. “It’s like they just don’t care.”

The FRBNY's Dean Parker and Moritz Schularick find that term spread is a useful indicator of financial crisis both domestically and overseas The effect is grounded in an uptick at the short end of the yield curve likely driven by higher risk taking by financial intermediaries in the run up to a crisis, "...It is clear that theterm spread is significantly lower in the years before a financial crisis, both in the international sample and in the U.S. case. When all countries are considered, the term spread is approximately one percentage point lower on average in the two years before the onset of a crisis. For the United States specifically, this effect is almost doubled in the year before an onset, reaching 2 percentage points lower than usual on average.For a concrete example, in the run-up to 2007-08 financial crisis in the United States, long-term rates declined despite increasingly tight monetary policy, giving rise to Alan Greenspan’s famous description of the development as a “conundrum.” As a result, the term spread fell by 3 percentage points at the time. Similarly, though not as widely discussed contemporarily, the term spread declined a total of 3.3 percentage points in the United States between 1924 and 1928 in the lead-up to the 1929 crash and Great Depression. In the run-up to normal recessions, however, we observe only limited movement.... There are various possible explanations for the predictive ability that depend on whether the decrease of the term spread is driven by the short-term rate rising or the long-term rate falling. As seen in the panel chart below, when taken in isolation, rising short-term rates seem to play the dominant role for the narrowing of the term spread. This is because the long-term rate does not seem to behave differently in the run-up to a crisis. Such an increase in short-term funding rates relative to the yield on long-term investments is indicative of declining interest margins and higher risk taking by financial intermediaries. Our findings hence echo the evidence for rising financial risk taking in financial markets before financial crises that is reflected both in quantities of credit growth and a depressed price of risk..."

Dean Parker and Moritz Schularick, "The Term Spread as a Predictor of Financial Instability," Liberty Street Economics, November 24, 2021,
https://libertystreeteconomics.newyorkfed.org/2021/11/the-term-spread-as-a-predictor-of-financial-instability/

The Term Spread as a Predictor of Financial Instability

The term spread is the difference between interest rates on short- and long-dated government securities. It is often referred to as a predictor of the business cycle. In particular, inversions of the yield curve—a negative term spread—are considered an early warning sign. Such inversions typically receive a lot of attention in policy debates when they occur. In this post, we point to another property of the term spread, namely its predictive ability for financial crisis events, both internationally and in historical U.S. data. We study the predictive power of the term spread for financial instability events in the United States and internationally over the past 150 years.

The Term Spread as a Crisis Predictor

The behavior and slope of the yield curve are longstanding tools for predicting the onset of economic recessions within the next year, or future employment growth. Consideration of the yield curve as a predictor for recessions rose to prominence with Estrella and Mishkin’s (1996) paper. Rudebusch and Williams (2008) also highlighted the power of the term spread in predicting aggregate economic downturns. While there are many academic studies and other articles on the efficacy of the term spread and its inversion for predicting the onset of normal recessions, little is known about its forecasting performance for financial crises. Our study aims to close this gap.

In normal times, a bond with a longer maturity fetches a higher rate on the market due to increased duration risk. However, in the lead up to an economic downturn, various factors can alter the shape of the yield curve. Changes in risk attitudes and search for yield, central bank policy actions, and international demand for safe assets can all influence the term spread and drive the yield for short-term debt closer to or even above the yield for long-term debt. Are such changes in the term spread predictive of a higher risk of financial instability?

The Behavior of the Term Spread around Financial Crises

For our analysis, we use the Macrohistory Database compiled by Jordà, Schularick, and Taylor (2017). The data cover eighteen advanced economies around the world from 1870 to 2017 and contains country specific short- and long-term rates. For the United States specifically, these are the three-month Treasury bill and ten-year Treasury bond rates, respectively. The main benefit of the Macrohistory Database is that it enables examining financial crises in a variety of economic and temporal contexts. Our international sample consists of eight-three crises out of 2,304 total observations, or approximately four percent of the sample. The United States has six years marked as crisis years over the past 128 years: 1873, 1893, 1907, 1930, 1984, and 2007. A financial crisis is defined by Jordà, Schularick, and Taylor as a period of banking distress characterized by major bank failures, exceptional losses in the banking sector, and/or significant government intervention. Briefly, the 1873 and 1893 crises were bank runs precipitated by railroad failures, 1907 was brought on by the collapse of Knickerbocker Trust, 1984 marks the beginning of the Savings & Loan crisis, and 1930 and 2007 saw the Great Depression and Global Financial Crisis, respectively. We also corroborate our results using a different definition of financial instability, namely large declines in the market value of bank equity (Baron, Verner, and Xiong 2020).

The chart below plots the term spreads in the five years before and after a financial crisis or recession. We test, using regression analysis, whether the level of the term spread is above or below normal levels in these windows. The blue dots and bars in the chart represent point estimates and confidence intervals, respectively. They can be read as the average levels of the term spread in the lead-up to and aftermath of an event compared to periods where no event occurred. Positive values imply a higher level of the term spread than usual and negative values imply a lower level than usual.

Using the Term Spread as a Forecasting Tool

With the knowledge that something abnormal occurs in the term spread in the years preceding a financial crisis, the natural next step is to examine its efficacy as a predictive tool and how it functions when used as part of a larger model including other known predictors of crises. Schularick and Taylor (2012) show that real bank-loan growth is a strong predictor of impending crisis and that model serves as the base for our later models. As for definitions of financial crises, we use both the Jordà-Schularick-Taylor narrative and the bank equity declines from Baron, Verner, and Xiong (2020).

We run logistic regressions to predict the two types of crises and compare models using real bank-loan growth and term spread as predictors. We also test the inclusion of an inverted yield curve (a negative term spread). The results are reported in the table below with each column reporting a separate regression.

Our main finding is that, for both crisis definitions, including the term spread alongside standard predictors yields a significant improvement in forecasting power. A smaller term spread increases the risk of a financial crisis. Using the inverted yield curve as a predictor results in a slightly worse predictive ability of the model.

There are various possible explanations for the predictive ability that depend on whether the decrease of the term spread is driven by the short-term rate rising or the long-term rate falling. As seen in the panel chart below, when taken in isolation, rising short-term rates seem to play the dominant role for the narrowing of the term spread. This is because the long-term rate does not seem to behave differently in the run-up to a crisis. Such an increase in short-term funding rates relative to the yield on long-term investments is indicative of declining interest margins and higher risk taking by financial intermediaries. Our findings hence echo the evidence for rising financial risk taking in financial markets before financial crises that is reflected both in quantities of credit growth and a depressed price of risk (Krishnamurty and Muir 2017; Baron and Xiong 2017).

The term spread is often used as an early warning indicator for recessions. In this post, we showed that it performs well as a predictor of financial crises, both internationally and in the United States alone. There is a significant benefit to including the term spread as a predictor for two separate crisis definitions. We identified that this effect is driven by the short end of the yield curve rising and offered a potential explanation based in higher risk taking by financial intermediaries in the time before a crisis.