1. Erik Brynjolfsson, Chad Syverson, and Daniel Rock find a productivity J-curve as General Purpose Technologies (GPTs) evolve,
General purpose technologies (GPTs) such as AI enable and require significant complementary investments, including business process redesign, co-invention of new products and business models, and investments in human capital. These complementary investments are often intangible and poorly measured in the national accounts, even if they create valuable assets for the firm. We develop a model that shows how this leads to an underestimation of output and productivity in the early years of a new GPT, and how later, when the benefits of intangible investments are harvested, productivity will be overestimated... The error in measured total factor productivity therefore follows a J-curve shape, initially dipping while the investment rate in unmeasured capital is larger than the investment rate in other types of capital, then rising as growing intangible stocks begin to affect measured production...
This period can be of considerable length. For example, the technologies driving the British industrial revolution led to “Engels’ Pause,” a half-century-long period of capital accumulation, industrial innovation, and wage stagnation. In the later GPT case of electrification, it took a generation as the nature of factory layouts was re-invented.
2. Stephan Heblich, Daniel M Sturm, and Stephen J Redding seek to quantify the impact of transport technologies on the urban economy. Their model uses data for London from 1801-1921 and the introduction of steam railways and finds for the period,
... that removing the entire railway network reduces the population and the value of land and buildings in Greater London by 20 percent or more, and brings down commuting into the City of London from more than 370,000 to less than 60,000 workers.
3. Stephen Cecchetti and Enisse Kharroubi find evidence of the adverse impact of credit growth on productivity,
We examine the negative relationship between the rate of growth in credit and the rate of growth in output per worker. Using a panel of 20 countries over 25 years, we establish that there is a robust correlation: the higher the growth rate of credit, the lower the growth rate of output per worker. We then proceed to build a model in which this relationship arises from the fact that investment projects that are more risky have a higher return. As their borrowing grows more quickly over time, entrepreneurs turn to safer, hence lower return projects, thereby reducing aggregate productivity growth. We take this theoretical prediction to industry-level data and find that credit growth disproportionately harms output per worker growth in industries that have either less tangible assets or are more R&D intensive.
And their conclusions on financial sector growth are very important,
First, the growth of a country's financial system is a drag on productivity growth. That is, higher growth in the financial sector reduces real growth. Financial booms are not, in general, growth-enhancing. Second, using sectoral data, we examine the distributional nature of this effect and find that credit booms harm what we normally think of as the engines for growth – those industries that have either lower asset tangibility or high R&D-intensity. This evidence, together with recent experience during the financial crisis, leads us to conclude that there is a pressing need to reassess the relationship of finance and real growth in modern economic systems.
This graphic on the R&D intensity of various manufacturing industries is interesting
4. Falk Brauning and Victoria Ivashina find significant spillovers from US monetary policy on emerging market economies through the foreign banks' lending channel.
Foreign banks’ lending to firms in emerging market economies (EMEs) is large and denominated predominantly in U.S. dollars... Outstanding shares of foreign banks’ dollar credit for African, American, and Asian emerging economies are over 90 percent. Even for emerging Europe, this number is 60 percent... This creates a direct connection between U.S. monetary policy and EME credit cycles. We estimate that over a typical U.S. monetary easing cycle, EME borrowers experience a 32-percentage-point greater increase in the volume of loans issued by foreign banks than do borrowers from developed markets, followed by a fast credit contraction of a similar magnitude upon reversal of the U.S. monetary policy stance. This result is robust across different geographies and industries, and holds for U.S. and non-U.S. lenders, including those with little direct exposure to the U.S. economy. EME local lenders do not offset the foreign bank capital flows, and U.S. monetary policy affects credit conditions for EME firms, both at the extensive and intensive margin. Consistent with a risk-driven credit-supply adjustment, we show that the spillover is stronger for riskier EMEs, and, within countries, for higher-risk firms.
In case of EM's loans are the dominant form of external liability compared with bonds for developed markets, and foreign banks share of all external liability is for EMs is double that of developed countries.
5. Raj Chetty et al map children's adult life outcomes based on their childhood circumstances.
We construct a publicly available atlas of children's outcomes in adulthood by Census tract using anonymized longitudinal data covering nearly the entire U.S. population. For each tract, we estimate children's earnings distributions, incarceration rates, and other outcomes in adulthood by parental income, race, and gender. These estimates allow us to trace the roots of outcomes such as poverty and incarceration back to the neighborhoods in which children grew up. We find that children's outcomes vary sharply across nearby areas: for children of parents at the 25th percentile of the income distribution, the standard deviation of mean household income at age 35 is $5,000 across tracts within counties. We illustrate how these tract-level data can provide insight into how neighborhoods shape the development of human capital and support local economic policy using two applications. First, the estimates permit precise targeting of policies to improve economic opportunity by uncovering specific neighborhoods where certain subgroups of children grow up to have poor outcomes. Neighborhoods matter at a very granular level: conditional on characteristics such as poverty rates in a child's own Census tract, characteristics of tracts that are one mile away have little predictive power for a child's outcomes. Our historical estimates are informative predictors of outcomes even for children growing up today because neighborhood conditions are relatively stable over time. Second, we show that the observational estimates are highly predictive of neighborhoods' causal effects, based on a comparison to data from the Moving to Opportunity experiment and a quasi-experimental research design analyzing movers' outcomes. We then identify high-opportunity neighborhoods that are affordable to low- income families, providing an input into the design of affordable housing policies. Our measures of children's long-term outcomes are only weakly correlated with traditional proxies for local economic success such as rates of job growth, showing that the conditions that create greater upward mobility are not necessarily the same as those that lead to productive labor markets.
6. Finally, Tyler Watts, Greg Duncan, and Haonan Quan, have a 900 student sample study which questions the findings of the famous Marshmallow test which appeared to show that children who were able to exercise self-control and resist marshmallows placed before them did better in life. They find limited support for delayed gratification leading to better outcomes and claims that circumstances matter more,
Instead, it suggests that the capacity to hold out for a second marshmallow is shaped in large part by a child’s social and economic background—and, in turn, that that background, not the ability to delay gratification, is what’s behind kids’ long-term success... This new paper found that among kids whose mothers had a college degree, those who waited for a second marshmallow did no better in the long run—in terms of standardized test scores and mothers’ reports of their children’s behavior—than those who dug right in. Similarly, among kids whose mothers did not have college degrees, those who waited did no better than those who gave in to temptation, once other factors like household income and the child’s home environment at age 3 (evaluated according to a standard research measure that notes, for instance, the number of books that researchers observed in the home and how responsive mothers were to their children in the researchers’ presence) were taken into account. For those kids, self-control alone couldn’t overcome economic and social disadvantages.
The failed replication of the marshmallow test does more than just debunk the earlier notion; it suggests other possible explanations for why poorer kids would be less motivated to wait for that second marshmallow. For them, daily life holds fewer guarantees: There might be food in the pantry today, but there might not be tomorrow, so there is a risk that comes with waiting. And even if their parents promise to buy more of a certain food, sometimes that promise gets broken out of financial necessity. Meanwhile, for kids who come from households headed by parents who are better educated and earn more money, it’s typically easier to delay gratification: Experience tends to tell them that adults have the resources and financial stability to keep the pantry well stocked. And even if these children don’t delay gratification, they can trust that things will all work out in the end—that even if they don’t get the second marshmallow, they can probably count on their parents to take them out for ice cream instead.
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