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Secular Trends in Macroeconomics and Firm Dynamics: A Conference Recap

Economic Brief
January 2023, No. 23-02

How does declining population growth affect firm dynamics? Is income growth volatility decreasing in the U.S.? How do changes in housing prices affect young businesses? Have investments in artificial intelligence improved productivity? These were among the questions addressed by economists during a recent Richmond Fed research conference.

Economists from the Richmond Fed, research universities and other institutions met in Richmond for a conference in December. Researchers presented papers on a variety of topics, including population growth and firm behavior, U.S. business dynamism, artificial intelligence, and pricing strategies.

Population Growth and Firm-Product Dynamics

Population growth across almost all major world economies has declined in recent decades, regardless of differences in political systems, cultures and levels of development. The United Nations projects that this decline in fertility rates will continue for at least the first half of the 21st century. These demographic changes will likely carry significant implications in many areas, including how firms operate and aggregate economic performance.

Michael Peters of Yale University presented the paper "Population Growth and Firm-Product Dynamics," which aims to shed light on how a declining population growth rate will affect firm dynamics and overall economic output. The paper was co-authored with Conor Walsh of the Columbia University Graduate School of Business.

The authors develop a theoretical model that yields predictions regarding the impact of declining population growth on firm dynamics and economic performance. With respect to firm dynamics, the model finds that lower population growth reduces both creative destruction and the entry of new firms and increases firm size, firm ages and the concentration of production into fewer firms.

With respect to lower population growth's effect on economic performance, the model shows that competition declines with fewer firms and the absence of creative destruction, which leads to both higher prices and lower productivity growth. However, it also leads to increases in the variety of products created.

These results are then tested against data from the U.S. Census Bureau that captures firm entry, size, market power and growth. Using data from 1980 to 2015, the authors find that their model explains almost all the declines in the entry and exit rates of firms, as well as the increases in the average firm size and degrees of concentration. However, it slightly overestimates price markups, and while the model predicts lower long-run economic growth, it also predicts that economic growth remains elevated for almost two decades.

The Role of Nonemployer-to-Employer Flows in U.S. Business Dynamism

Chen Yeh of the Richmond Fed presented research examining the role of nonemployers in the secular decline in business dynamism — or the ongoing process of firm creation, growth, shrinking and eventual closure — within the U.S. over the past several decades. His paper "The Role of Nonemployer-to-Employer Flows in U.S. Business Dynamism" was co-authored with Claudia Macaluso, also of the Richmond Fed.

The authors theorize that nonemployers (or businesses that have no paid employees) play a sizeable role in the decline in business dynamism because fewer are transitioning to employers. They test this theory using two databases. The Integrated Longitudinal Business Database contained 20 million businesses from 1994 to 2016 with a revenue of at least $1,000 and no paid employees. These businesses were then matched with the 5 million employers in the Longitudinal Business Database using the Business Register, which contains the names of businesses and their addresses.

The data show several trends:

  • The nonemployer-to-employer transition rate has been declining.
  • The fraction of employer firms that used to be nonemployers has gone down.
  • Startups — or employer firms that opened in a given year — are less likely to have been a nonemployer.
  • Employer firms that have nonemployer histories grow faster on average, with a nonemployer growth "premium" of 7.32 percent.
  • Employer firms with nonemployer histories are more likely to survive past their first year.

Yeh and Macaluso then conduct a counterfactual analysis of the data using the nonemployer-to-employer transition rate from 1997 and find that, if the rate had remained constant, the employer entry rates and aggregate job creation would have also stayed constant.

Consumption, Saving and the Distribution of Permanent Income

Labor income inequality has increased in the U.S. in recent decades, with the top 10 percent of earners now collecting over 35 percent of all labor income. Research has attributed this trend to an increasing gap in workers' permanent income, that is, their expected long-term average income. This expectation reflects the permanent skills and abilities that workers bring into the workforce. Most models show that this rising inequality in permanent income has no effect on other macroeconomic outcomes, such as rising wealth, rising wealth inequality and falling natural interest rates.

To take another look at this relationship, Ludwig Straub of Harvard University and the National Bureau of Economic Research presented a paper, "Consumption, Savings and the Distribution of Permanent Income."

Straub makes two contributions to the understanding of the relationship between permanent income and aggregate outcomes. First, he proposes a new way to measure the relationship between consumption and permanent income. Existing models predict that the permanent income elasticity of consumption equals 1 (meaning that consumption rises linearly with permanent income). However, using data from the Panel Study of Income Dynamics, he shows that consumption is a concave function of permanent income and that the elasticity of consumption to permanent income is about 0.7.

Second, to identify the sources of this finding in his tests of the data, Straub challenges the assumption in existing models that the permanently rich have the same saving and spending patterns as the permanently poor. Specifically, he notes that the permanently rich will save more for both bequests and additional expenses such as college tuition for kids or expensive medical expenses later in life.

Finally, Straub studies the implications of rising permanent income inequality on outcomes. He extends a standard precautionary savings model to show that the rising income inequality since the 1970s leads to a 1 percent decline in interest rates, an increase of private wealth's share of GDP of around 30 percent and a rapid rise in wealth inequality.

The Great Micro Moderation

Conventional wisdom holds that individual-level income volatility has increased over the past 40 years. This finding contrasts with trends at the macro level, where observers have documented "The Great Moderation," a secular decline in GDP growth volatility over the past several decades. What accounts for these contrasting findings?

Sergio Salgado of the Wharton School of Business at the University of Pennsylvania presented a paper, "The Great Micro Moderation." The paper was co-authored with Nicholas Bloom and Luigi Pistaferri of Stanford University, Fatih Guvenen of the University of Minnesota, John Sabelhaus of the Brookings Institution, and Jae Song of the Social Security Administration.

The authors argue that the contrasting findings may be due to the type of data being used. They note that the finding of increased individual-level income volatility is based on self-reported survey data, which they suggest are plagued by sample attrition and measurement error.

Instead, the researchers use the Social Security Administration's master earnings file for all U.S. employees between 1978 and 2013. In addition to being largely free of measurement error, these data allow researchers to study the evolution of earnings growth dispersion across a much larger sample of individuals, as well as within specific population groups. Because the data match workers to firms, the authors can also examine the relationship between individual-level and firm-level outcomes.

Salgado and his colleagues find evidence of moderation at the micro level. Specifically, they find that earnings volatility of individuals has decreased by about one-third since the late 1970s, and these results are consistent within different population breakdowns (such as by age, sex, industry and income level). The authors attribute this decline in volatility to a decrease in the proportion of individuals switching jobs. At the same time, firm employment growth volatility has also decreased by about one-third. Finally, they demonstrate that these decreases in wage and firm employment growth instability are connected: A 10 percent decrease in the dispersion of firm employment growth within a sector is associated with a 4.2 percent decrease in the dispersion of the growth rate of earnings of workers in that sector.

Dynamism Diminished: The Role of Housing Markets and Credit Conditions

The share of U.S. workers employed by young businesses (those in operation for less than six years since adding their first paid employee) has steadily fallen in recent decades, dropping from about 20 percent in the late 1980s to about half that in 2018. Employment at young firms has also historically been correlated with changes in the business cycle. For example, employment at young firms fell sharply during the 2007-09 recession, then recovered slowly.

John Haltiwanger of the University of Maryland presented a paper examining the impact of the Great Recession on young businesses, "Dynamism Diminished: The Role of Housing Markets and Credit Conditions (PDF)," co-authored with Steven Davis of the University of Chicago. Entrepreneurs frequently depend on housing wealth (in the form of home equity loans, for instance) to fund new businesses. When housing prices collapsed in 2006, this channel of funding greatly diminished. Additionally, many banks reduced their lending during the recession, constraining another potential source of funding for new firms.

Haltiwanger and Davis use employment and credit data across metropolitan areas to test how the change in housing prices and decline in bank lending affected young firms. They take advantage of the fact that the 2007-09 recession housing bust did not affect all localities equally, which allowed the authors to isolate local house price shifts and estimate the effects of the recession on young firms and local economies.

They find that local house price swings had a large effect on the local employment share of young firms. Changes in the supply of credit from banks also affected the employment share at young firms during the 2007-09 recession. Together with the change in house prices, this accounted for more than 100 percent of the decline in young-firm employment during that period. Because young firms tend to hire younger and less-educated employees, the housing bust had an outsized effect on those workers. (For more on this finding, see the 2022 interview with Steven Davis in the Richmond Fed's Econ Focus.)

The Impact of Artificial Intelligence on Firms and Workers

One of the main drivers of long-run economic growth is the discovery of new technology, which can reduce costs by making workers more productive or replacing them altogether. Over the past decade, there has been growing investment in artificial intelligence (AI), leveraging machine learning to quickly analyze big datasets. In theory, AI could improve firms' efficiency and help them more quickly innovate new products and processes.

Has AI produced any of these productivity benefits so far? Tania Babina of Columbia University presented two papers aimed at answering this question. The first, "Artificial Intelligence, Firm Growth, and Product Innovation," looks at the impact AI has had on firms. The second, "Firm Investments in Artificial Intelligence Technologies and Changes in Workforce Composition," examines how the workforce has been affected by the adoption of AI. Both papers were co-authored with Anastassia Fedyk of the University of California, Berkeley, Alex He of the University of Maryland, and James Hodson of the AI for Good Foundation.

For both studies, Babina and her co-authors develop a new way of measuring firms' investments in AI by analyzing how many AI-skilled workers they have or are planning to hire. To measure the current stock of AI-skilled workers at each firm, they used resume data from Cognism, an aggregator of employment profiles. For data on AI worker demand, they used online job posting aggregator Burning Glass Technologies. They identified words and phrases in the resumes and job postings that are highly correlated with core AI skills. Both measures show increased investment in AI-skilled workers between 2007 and 2018.

They find that AI investments and firm size are complements. Large firms invest more in AI, and in doing so, they grow larger in terms of sales, employment and market share. Firms that initially had a more educated workforce were also more likely to invest in AI. The workforce at AI-investing firms tend to exhibit a flattening hierarchical structure and more upskilling. Regarding the productivity benefits of AI investment, Babina and her co-authors find that firms that invest more in AI exhibit greater product innovation, but the authors find no evidence of reduced costs from general productivity improvements.

Nonlinear Pricing and Misallocation

Businesses frequently charge different prices for the same goods or services, such as offering discounts for buying goods in bulk quantities. However, most macro models still presume that firms only charge a single price for their good or service regardless of quantity, engaging in what economists call "linear pricing." Whether firms use linear pricing in practice has important implications for the allocation of goods and services across the economy.

On this topic, Alessandra Peter of New York University presented the paper "Nonlinear Pricing and Misallocation (PDF)," co-authored with Gideon Bornstein of the University of Pennsylvania. Using price data on consumer goods from the Nielsen Retail Scanner dataset, they establish that about 90 percent of products are sold in various quantities, and prices typically do not increase linearly with quantity. On average, they find that a 10 percent increase in package size corresponds to only a 4 percent increase in price.

Using this information, they construct a model in which firms can charge nonlinear prices for various quantities of the good they produce, although they must offer the same pricing schedule to all consumers. Consumers in the model have different preferences for the good produced by the firm:

  • High-taste consumers have a strong preference for that good.
  • Low-taste consumers have a weak preference for that good.

In a model with linear pricing, high-productivity firms charge higher markups and underproduce relative to low-productivity firms. In contrast, under nonlinear pricing, high-productivity firms still charge higher markups, but underproduction does not happen across firms. Instead, production is misallocated within those firms: They overproduce goods for high-taste consumers and underproduce for low-taste consumers.

Peter and Bornstein estimate that the welfare losses from this new type of misallocation are twice as large as those that occur under linear pricing. Moreover, this difference is important when considering policy responses to improve market efficiency. Under linear pricing, an appropriate response would be to subsidize firms with higher markups to improve their production. But in a world where firms can charge nonlinear prices, this policy would make welfare losses worse, because it would do nothing to address misallocation across consumers within firms. It would merely benefit larger firms at the expense of smaller ones.

Measuring Growth in Consumer Welfare With Income-Dependent Preferences

Do living standards rise over time? Economists have typically tried to answer this question by constructing indexes that track aggregate real (inflation-adjusted) consumption over time. But this approach assumes that consumers demand the same types of goods regardless of income, despite evidence to the contrary. It also assigns the same inflation rate to all households, despite growing evidence in the U.S. that high-income households tend to experience lower inflation rates.

Danial Lashkari of Boston College presented the paper "Measuring Growth in Consumer Welfare with Income-Dependent Preferences," co-authored with Xavier Jaravel of the London School of Economics. They construct a more flexible measure of welfare changes over time using data on spending patterns for households with different incomes. They use this new approach to "correct" standard measures of changes in living standards in the U.S. from 1955 to 2019. This work also provides additional evidence of inflation inequality across households. Lashkari and Jaravel find that cumulative inflation from 1955 to 2019 was 700 percent for top earners versus 875 percent at the bottom of the income distribution.

Lashkari and Jaravel account for these differences in inflation rates and consumption preferences by household income when re-evaluating changes in living standards over time. From the perspective of 2019 prices, households in 1955 were poorer on average, meaning they had stronger preferences for necessity goods, which were cheaper. Without correcting for these facts, living standards in 1955 would appear lower. Likewise, living standards in 2019 would appear lower without correcting for the facts that households have become richer on average since 1955 and that luxury goods have become cheaper.

Correcting for these facts and taking prices in 2019 as a reference point, Lashkari and Jaravel find that real consumption in 1955 has been underestimated by about 11.5 percent. This means that the yearly growth rate of consumption from 1955 to 2019 was smaller than previously estimated: 1.89 percent instead of 2.07 percent. This suggests that consumer welfare grew more slowly in the post-World War II U.S. than uncorrected measures would indicate.

Tim Sablik and Matthew Wells are senior economics writers in the Research Department of the Federal Reserve Bank of Richmond.

To cite this Economic Brief, please use the following format: Sablik, Tim; and Wells, Matthew. (January 2023) "Secular Trends in Macroeconomics and Firm Dynamics: A Conference Recap." Federal Reserve Bank of Richmond Economic Brief, No. 23-02.

This article may be photocopied or reprinted in its entirety. Please credit the authors, source, and the Federal Reserve Bank of Richmond and include the italicized statement below.

Views expressed in this article are those of the authors and not necessarily those of the Federal Reserve Bank of Richmond or the Federal Reserve System.

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