Macro Roundup

Developed economies are offering increasing amounts in subsidies to encourage citizens to have more children. But there is little evidence that they are having an effect: fertility rates are falling rapidly across the rich world. “Pronatalist policies haven’t really been shown to consistently change fertility levels,” said Bernice Kuang, a demographer at the University of Southampton, in the UK....As evidence in favour of subsidies, demographers cite surveys indicating that couples in some countries are not having as many children as they would like; generally women would like to have around two children, above the actual birth rate in many countries.

“Governments shouldn’t be making explicit incentives to get women to have more babies than they wish just to fulfil some national quota or their desire to create a small baby boom,” he said.

But even so, he questioned whether governments should be trying to intervene in such personal choices.

“Higher equality allows especially better-educated women an easier combination of career and family life,” Sobotka said. “Why not allow some of the men to retreat from work for two or three years to take parental leave and then head back to work?”

Federica Cocco, Edward White, Kang Buseong and Anna Gross, "Baby bust: Can policymakers boost dwindling world fertility rates?"Financial Times, April 20, 2022, https://www.ft.com/content/530cde70-eea2-47ff-8b4e-5efa8b9630d5

Baby bust: Can policymakers boost dwindling world fertility rates?

When Choi Yoon-seo became pregnant in 2020, South Korea paid her $500 — a reward for helping the country address its dire demographic crunch.

That year it hit its lowest fertility rate on record at 0.84 children per woman — the lowest in the world.

The government in Seoul has since increased the one-off pregnancy gift to around $1,700. But Choi said the idea that the payments would encourage her to have another baby was laughable.

“I used up the whole amount on prenatal tests before the halfway point of my pregnancy,” she said.

Since her son was born in January 2021, the 40-year-old has struggled with family, societal and economic pressures. She had to give up her job as a social worker and became overwhelmed with “so much guilt over not being able to be a good mother”.

The state’s childcare subsidies are “unrealistic” given the cost of living and education in the Korean capital, she said.

Seoul’s efforts are not unusual. Developed economies are offering increasing amounts in subsidies to encourage citizens to have more children. But there is little evidence that they are having an effect: fertility rates are falling rapidly across the rich world.

“Pronatalist policies haven’t really been shown to consistently change fertility levels,” said Bernice Kuang, a demographer at the University of Southampton, in the UK.

More recently, other European countries have followed France’s lead. In 2020, Hungary designated fertility clinics a “strategic” sector and made IVF treatment available for all women.

And last year the Italian government introduced incentives aimed at reversing demographic decline, which included longer parental leave for fathers. “An Italy without children is... destined to slowly grow old and disappear,” prime minister Mario Draghi warned.

Despite this, European fertility rates are still falling.

In France, the rate has dropped from above two children per woman in 2010 to 1.83 in 2021, according to data from the country’s statistics office Insee. A recent bill put forward by politicians from the Republicans party blamed cuts to family support by successive presidents François Hollande and Emmanuel Macron.

As evidence in favour of subsidies, demographers cite surveys indicating that couples in some countries are not having as many children as they would like; generally women would like to have around two children, above the actual birth rate in many countries.

Kuang said that lower-income couples needed “financial support and affordable childcare” while higher-income couples “need policies make it easier for working people to balance childcare and, say, school pick-up, with their jobs”.

But Kuang cited South Korea’s situation as evidence that other factors were also at play, “such as rigid gender norms and the unequal division of labour within the household”.

“Governments want to make it so that women can do childcare as well as work, but some women don’t want to work that hard and some families don’t want to operate that way,” said Kuang. “Can you make it so that men can do more? Like find a way for them to work all day and take care of their kids.”

That is a new and quite interesting paper by Thomas Philippon. Here is the abstract:"Growth theory is based on the assumption of exponential total factor productivity (TFP) growth. Across countries and time periods I find that TFP growth is actually linear. Unlike the exponential model, the additive growth model provides useful medium-term forecasts of TFP. It also explains the TFP slowdown and the volatility puzzle, and predicts falling real interest rates. For the distant future the model predicts ever increasing increments in standards of living but with growth rates that converge to zero. For the distant past the model suggests that the size of TFP increments has changed in the late 1600’s, the early 1800’s, and around 1930."Or consider this presentation:"Initial trend growth is around 2.5%. After 40 years, TFP doubles, and since increments are constant, the trend growth rate is half of what it used to be. After 60 years later, it is only one percent."And:"…the process of US TFP increments has only one break over the past 130 years, around 1930, following the large-scale implementation of the electricity revolution…For the UK, I find two breaks between 1600 and 1914. The first is between 1650 and 1700, when growth becomes positive. The second is around 1830. These breaks are consistent with historical research on the first and second industrial revolutions…"The author argues that linear TFP growth holds for Thailand, Taiwan, and Korea as well, and indeed for all recent countries with data for TFP. As Philippon puts it informally“New ideas add to our stock of knowledge; they do not multiply it.”As stated above a very interesting paper,but I do have some worries. First, his model fits “TFP” better than gdp growth per se, which (at least until recently) does appear to be exponential in advanced economies. If I read the author’spp.21-22 correctly, he is suggesting (speculatively) that 20th century gdp growth received an artificial inflation from improvements in educational achievement that perhaps are unlikely to be replicated. Maybe, but the broader predictions of the theory — including on gdp growth — require further consideration. Second, is TFP even “a real thing”? Or is it a meaning-poor residual, based on arbitrary distinctions between “innovation” and “investment”? Maybe the ongoing trend is simply that more innovation is embodied in concrete investments, thus causing TFP to measure lower? Too much of the paper takes the TFP concept for granted. Nonetheless worth a real ponder.

Tyler Cowen, “Additive Growth,” Marginal Revolutions, April 20, 2022, https://marginalrevolution.com/marginalrevolution/2022/04/additive-growth.html

Thomas Philippon, "Additive Growth,"National Bureau Of Economic Research, April 2022, https://www.nber.org/papers/w29950

“…Model D, unlike model G, appears to have only one break over the period 1890-2019. We can formally test this idea following Bai and Perron (2003). The unconstrained test finds one break in the series around 1930 (the point estimate is 1933). We cantest H0: no breaks versus H1: break in 1933. The W statistic is 21.72 and the p-value is 0.0. I emphasize, however, that while the existence of a break is clear, the date is really an interval between the late 1920s and WW2. The date of the break is consistent with Field (2003)’s argument that “the years 1929- 1941 were, in the aggregate, the most technologically progressive of any comparable period in U.S. economic history.” This period corresponds to the large scale implementation of the discoveries of the second industrial revolution: electric light, electric power, and the internal combustion engine, as discussed in Jovanovic and Rousseau (2005). Gordon (2016) points out that it is somewhat surprising that “much of the progress occurred between 1928 and 1950,” several decades after the discoveries were made. Following David (1990), he explains the paradox by showing that the 1930s were a period of follow-on inventions, such as the perfection of the piston power-powered aircraft and television, and the increasing quality of machinery made possible by the large increases in available horsepowers and kilowatt-hours of electricity. Following these historical insights, figure 5 proposes an interpretation of US TFP from 1890 to 2019, using linear growth with one structural break in 1933 after the electrification revolution. We can summarize this idea in the following remark, keeping in mind that we normalize US TFP to 1 in 1890. Fact 5. From 1890 to 1933, TFP increases by.017 each year until it reaches a level around 1.75 in the early 1930s. From 1933 to 2019 TFP increases by.057 each year (3.3 p.p. of its level in 1933) to reach a level around 8 at the end of the sample….”

The outlier

Core Finding:"....There is no TFP slowdown, or, to put it differently, the perceived TFP slowdown is the result of using a misspecified model as a benchmark. Initial trend growth is around 2.5%. After 40 year, TFP doubles, and since increments are constant, the trend growth rate is half of what it used to be. After 60 years later, it is only one percent...."

Evidence from the United States, "...Figure 1 reveals a new fact and makes an important empirical point. The new fact is that there is no TFP slowdown in the US according to model D. The important empirical point is that, with realistic values for TFP growth rates, the distinction between models D and G requires at least 10 years of out-of-sample forecasts....US growth is better described as additive rather than multiplicative. Instead of stating that the average growth rate of TFP is 1.45%, which is correct but not particularly useful, it is more relevant to say that TFP increases by 0.0245 points each year starting from a normalized value of 1 in 1947. For labor productivity, both the additive growth model and the multiplicative growth model predict an increasing size of productivity increments, but at different speeds. The additive model D predicts that labor productivity increments increase with the square of the time horizon, while the geometric model G predicts exponentially increasing increments. Model G does not describe the data with a constant growth rate. Model D describes the data relatively well with year-on-year increments of about $1560 per full time worker ($0.87 per hour, assuming 1800 hours worked in a year) around 2010…”

"...Figure 3 shows the raw and smoothed series....The data is from Bergeaud et al. (2016) and winsorized in the first and last percentiles to remove limit extreme outliers during WW2. The model is initiated over the first 10 observations, 1891 to 1900. As expected the trend growth of the economy changes over this long sample.....Table 1 shows that the volatility of TFP growth rates declines significantly over time...."

TFP growth is not exponential. New ideas add to our stock of knowledge; they do not multiply it. TFP has been growing linearly over the past 90 years in the US and the additive model beats the exponential model for every single country, developed or catching up, where TFP data is available. The TFP frontier appears to grow linearly within broad historical periods: 1650 to 1830, 1830 to 1930, and 1930 until today. Additive TFP growth predicts increasing growth of labor productivity and GDP per capita thanks to capital accumulation. This prediction also appears to be empirically accurate... The additive growth model explains the observed TFP slowdown as a simple side effect of model misspecification. We should not have expected growth rates to be constant in the first place. The additive model does not necessarily solve the research productivity puzzle....... since this puzzle is not about the stochastic process for TFP but rather about the specification of the production function for ideas.Models where ideas are non-rival often imply a tight connection between growth and the quantity of research. These models predict accelerating growth - whether of the linear kind or not - from an increasing number of researchers....I start my investigation with post-war US data. The empirical justification is that this is the most widely used and reliable data. The theoretical justification is that one might expect different TFP dynamics between countries at the frontier and countries catching up to the frontier. The main advantage of post-war US data, then, is that one can reasonably argue that the US was at the technological frontier during the entire period....My primary sources for TFP are Fernald (2012) (Fernald)and Bergeaud et al. (2016) (BCL).... Fernald’s series cover only the US business sector, while BCL include households and the government....The simplest way to start comparing model D and model G is to consider the following experiment. Suppose that two agents, George and Daniela, are asked in the middle of the sample (1983) to predict the level of TFP in the second half of the sample (1984- 2019). The agents have access to data from the end of World War 2 until 1983. The two agents have dogmatic beliefs regarding the correct model of economic growth. George believes in model G from equation (2) while Daniela believes in model D from equation (3). George therefore fits a log linear model over the years 1947: 1983 and predicts future (log) TFP as log Aˆ (G) t = ˆag + ˆgt for t = 1984: 2019. Daniela instead fits a linear model and predicts future TFP as Aˆ (D) t = ˆa + ˆbt. Figure 1 shows that Daniela would have made a much better forecast than George. George is puzzled by the TFP slowdown while Daniela does not perceive an obvious long term break in her model (although there are some meaningful medium term deviations).....Figure 1 reveals a new fact and makes an important empirical point. The new fact is that there is no TFP slowdown in the US according to model D. The important empirical point is that, with realistic values for TFP growth rates, the distinction between models D and G requires at least 10 years of out-of-sample forecasts...Fact 1. There is no TFP slowdown in the US according to model D....US growth is better described as additive rather than multiplicative. Instead of stating that the average growth rate of TFP is 1.45%, which is correct but not particularly useful, it is more relevant to say that TFP increases by 0.0245 points each year starting from a normalized value of 1 in 1947. For labor productivity, both the additive growth model and the multiplicative growth model predict an increasing size of productivity increments, but at different speeds. The additive model D predicts that labor productivity increments increase with the square of the time horizon, while the geometric model G predicts exponentially increasing increments. Model G does not describe the data with a constant growth rate. Model D describes the data relatively well with year-on-year increments of about $1560 per full time worker ($0.87 per hour, assuming 1800 hours worked in a year) around 2010...Fact 2. Postwar US TFP growth is well described by Model D with increments of ∆ = 0.0245 points each year starting from a normalized value of 1 in 1947. Model D also predicts the correct non-linear evolution of labor productivity.

why is the level of violence in the young black male population so high…The murder rates for black men are roughly 10 times higher than the rate for white men in most years…. and how has this level of violence persisted for so long?..The violence in this population is driven by guns. As we outline below, by the end of our sample, 97 percent of murders of young black males are via guns, a rate substantially larger than the rate for the general population. Surveys spanning nearly forty years suggests that young black males carry guns at high rates primarily as protection against others who are also armed...With so many people carrying weapons, disagreements are settled with deadly consequences…To derive specific predictions from our model, we specialize to the case where the two dimensions of a person’s type follow a bivariate normal distribution. If the average instrumental value of carrying a gun is low, gun carry rates are also low. As that value increases, the equilibrium carry rate shifts dramatically. The problem is that returning to a low-gun equilibrium among non-criminals is difficult. The situation can be understood as a coordination failure. Most non-criminals would be better off if everyone stopped carrying guns, but getting there is difficult without a coordinating device. Hence, gun violence persists.The comparative statics are troubling. Once a high gun carry equilibrium is reached, large drops in the benefits of carrying a gun are necessary to revert to a low gun equilibrium. Furthermore, an important asymmetry prevails. The equilibrium gun carry rate, and (by implication in our model) the level of danger and violence, is much more sensitive to changes in the average instrumental value of a gun than its average defensive value, even though the vast majority who arm do so for perceived protection. Our reading of the existing gun policy research suggests these conclusions conflict with real-world policy options in that it has been easier for policy makers to attempt to change the costs of owning guns for non-criminals than criminals. These results suggest the problem may be particularly difficult to solve through legislative means… The homicide rate has ballooned as the COVID-19 pandemic has progressed. In Figure 1, we showed that murder rates increased by 27 percent from 2019 to 2020. Much of this was due to an increase in gun murders, which rose 34 percent compared to 9.4 percent for non-gun murders. Many urban counties have experienced substantially larger increases including Jefferson County, KY, home to Louisville (81.3 percent),Milwaukee County, WI (72.3 percent), Alameda County, CA, home to Oakland (65.2 percent), and Orleans Parish, LA, home to New Orleans (64.2 percent), just to name a few. The groups with the highest murder rates already also had the greatest change in murder rates between 2019 and 2020. Breaking the population into three age groups (15-24, 25-34, 35 and up), four races (white, black, Asian/Pacific Islander, American Indian/Alaskan Native) and 2 genders, there are 24 groups. The three groups that saw the biggest increase in murder rates were white males 15-24 (36.0 percent), black males 25-34 (38.2 percent), and black males 15-24 (39.7 percent). The preliminary data for 2021 looks even worse. Murder counts for the first six months of 2021 are up 13.9 percent over the same time period in 2020.

William Evans and Maciej Kotowski, "The Demand For Protection and the Persistently High Rates of Gun Violence Among Young Black Males,"National Bureau Of Economic Research, April 2022, https://www.nber.org/papers/w29969

“…In Figure 5, the black dashed line reports the fraction of murders of young black males from 1976 to 2019 where an offender is identified in the SHR. This has declined steadily over time from 80 to about 52 percent. In the same graph, the solid line reports the fraction of murders of young black males with known offender where at least one of the offenders is from this same demographic group. This number starts at 44 percent in 1976 rises to 70 percent in 1994 and has been near 60 percent since 2002. When we increase the age range by another decade, the fraction known offenders that are black males ages 15-34 has been hovering around between 77 and 81 percent since 1992…”

“…Third, during the height of SQF use in New York City, murders of young black males fell dramatically. In Figure 15a, we report in solid lines the murder rate for black males 15-24 and 25-34 from New York City while the dotted lines of the same color represent the same numbers for the rest of the country. The police reforms that started in New York City in 1994 occur at a time when the crack epidemic was receding and murder rates for young black males were falling. This makes it difficult to assign how much of the decline is due to SQF or other factors, like mean reversion. That said, the rates for young black males in the rest of the country retreat to their pre-crack levels and do not decline, whereas the decline is dramatic in New York City. In Figure 15b, we report the time series of murder rates in New York City and the rest of the country for everyone who is not a black male 15-34. Here, there is conflicting data on the impact of the SQF program. A result consistent with those favorable to SQF is that New York City had persistently higher murder rates than the rest of the country and starting in 1994, these murder rates drop quickly and are now below the rest of the country. The percent drop in murder rates in New York City for those that are not black males 15-34 is comparable to the drop for younger black males, the primary targets of SQF. This could mean some other policy was impacting murder rates... The model developed in section 3 suggests that once a high-gun carry equilibrium is obtained, it would take a massive reduction in the protective value of guns to return the world to a low gun equilibrium. There may have been no more targeted policing strategy for gun violence among young black males that SQF in New York City. The numbers above suggest it was especially targeted to young black males, but the low yield of arrests, guns and contraband suggest the program was not targeted to likely criminals but to a much larger group. The increased risk of sanction for carrying aweapon along with improved in community safety reduce the attractiveness of guns as a protective means to non-criminals. If we were to make the generous assumption that all of the decline in murders in New York City were attributable to SQF, then this program illustrates the degree to which jurisdictions must police in order to obtain large reduction in the protective value of guns and hence a large reduction in murders. This conclusion is consistent with the relative rigidity of equilibria with respect to changes in… Large and costly interventions along this dimension are necessary to shift outcomes…

“…For completeness, we report in Figure 3 the same graph for white male. The basic time series patterns are quite similar to those of black males. The startling distinction between Figures 2 and 3 is the scale.The murder rates for black men are roughly 10 times higher than the rate for white men in most years. The time series movement in the murder rate for young black males is driven almost exclusively by gun violence. In Figure 4, we report the murder rate, the gun murder rate and the non-gun murder rate for this group since 1968. The non-gun murder rate has fallen steadily since 1968. In contrast, the overall murder rate essentially follows the gun murder rate. Moreover, the fraction of murders attributable to guns is now much higher than 50 years ago, climbing from about three-quarters of murders in 1968 to 98 percent in 2020..”

“…In Figure 2, we report the national time series from 1968 through 2020 for murder rates among black males in three age groups: 15-24, 25-34, and 35-plus. The murder rate for older black men has fallen from 98.3 in 1972 to a low of 21.2 in 2010, a drop of 78 percent. Since 2010, the murder rate in this group has edged up, but is still 63 percent below its peak and 55 percent lower than the 1968 value. The murder rate for black men in the 25-34 age category has been much more volatile, seeing peaks almost every decade. The long term trend is decidedly negative. Between 1968 and 2020, murder rates fell 26 percent. The peak (176.6 in 1972) to trough (67.7 in 2014) changes represents a 62 percent decline. The numbers for young black males follow a completely different series. Between 1978 and 1994 there was a 137 percent increase in murders, then over the next 21 years, murder rates fell by 60 percent. However, the murder rate in 2019 (77) was at about the same as it was in 1968 (81) and the rate and the rate in 2020 (108) is 33 percent higher than in 1968. This is a staggering difference compared to the rest of the country. The rate in 2020 is 17 times what it is for people not in that demographic. Although young black males are about 1 percent of the population, the fraction of murders in this group represented 17 percent of murders in 1994 and 17 percent in 2020…”

Trends in Murder Rates

“…In Figure 1, we graph the national murder rate as reported by the National Vital StatisticsData (NVSD) from 1968 through 2020.The time series was volatile for the first 23 years with peaks in 1974, 1980, and 1992. The national murder rate declined consistently from 1992 through 2015, with a substantial increase after that, especially in 2020 when murder rates for the nation rose by 27 percent, more than twice the second largest annual percent increase in the murder rate in the series. The rate in 2020 is about the same as it was in 1968 and the number for 2019 was 20 percent lower than the value in 1968. In the same graph, we also report the gun and non-gun murder rates. The variation in murders over time is driven mainly by changes in gun murders. Of the increase between 1968 and 1992, 81 percent was attributable to gun violence and of the decline from 1992 to 2015, 65 percent was due to a drop in gun murders. The murder rate for black males differs fundamentally in level and time series than the rest of the country…”