This review article tries to answer four questions: (i) what are the stylized facts about uncertainty over time; (ii) why does uncertainty vary; (iii) do fluctuations in uncertainty matter; and (iv) did higher uncertainty worsen the Great Recession of 2007-2009? On the first question both macro and micro uncertainty appears to rise sharply in recessions. On the second question the types of exogenous shocks like wars, financial panics and oil price jumps that cause recessions appear to directly increase uncertainty, and uncertainty also appears to endogenously rise further during recessions. On the third question, the evidence suggests uncertainty is damaging for short-run investment and hiring, but there is some evidence it may stimulate longer-run innovation. Finally, in terms of the Great Recession, the large jump in uncertainty in 2008 potentially accounted for about one third of the drop in GDP.

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Fluctuations in Uncertainty

https://www.zora.uzh.ch/id/eprint/189496/1/dp13183.pdf

Inequality in the impact of the coronavirus shock: Evidence from real time surveys

To explore the impact of coronavirus disease 2019 (COVID-19) on small businesses, we conducted a survey of more than 5,800 small businesses between March 28 and April 4, 2020. Several themes emerged. First, mass layoffs and closures had already occurred-just a few weeks into the crisis. Second, the risk of closure was negatively associated with the expected length of the crisis. Moreover, businesses had widely varying beliefs about the likely duration of COVID-related disruptions. Third, many small businesses are financially fragile: The median business with more than $10,000 in monthly expenses had only about 2 wk of cash on hand at the time of the survey. Fourth, the majority of businesses planned to seek funding through the Coronavirus Aid, Relief, and Economic Security (CARES) Act. However, many anticipated problems with accessing the program, such as bureaucratic hassles and difficulties establishing eligibility. Using experimental variation, we also assess take-up rates and business resilience effects for loans relative to grants-based programs.

https://www.pnas.org/content/pnas/117/30/17656.full.pdf

The impact of COVID-19 on small business outcomes and expectations.

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Business cycles are both less volatile and more synchronized with the world cycle in rich countries than in poor ones. We develop two alternative explanations based on the idea that comparative advantage causes rich countries to specialize in industries that use new technologies operated by skilled workers, while poor countries specialize in industries that use traditional technologies operated by unskilled workers. Since new technologies are difficult to imitate, the industries of rich countries enjoy more market power and face more inelastic product demands than those of poor countries. Since skilled workers are less likely to exit employment as a result of changes in economic conditions, industries in rich countries face more inelastic labour supplies than those of poor countries. We show that either asymmetry in industry characteristics can generate cross-country differences in business cycles that resemble those we observe in the data. We are grateful to Daron Acemoglu and Fabrizio Perri for useful comments. The views expressed here are the authors' and do not necessarily reflect those of The World Bank. Business cycles are not the same in rich and poor countries. A first difference is that fluctuations in per capita income growth are smaller in rich countries than in poor ones, in the top panel of Figure 1 , we plot the standard deviation of per capita income growth against the level of (log) per capita income for a large sample of countries. We refer to this relationship as the volatility graph and note that it slopes downwards. A second difference is that fluctuations in per capita income growth are more synchronized with the world cycle in rich countries than in poor ones. In the bottom panel of Figure 1 , we plot the correlation of per capita income growth rates with world average per capita income growth, excluding the country in question, against the level of (log) per capita income for the same set of countries. We refer to this relationship as the comovement graph and note that it slopes upwards. Table 1 , which is self-explanatory, shows that these facts apply within different sub-samples of countries and years. 1 Why are business cycles less volatile and more synchronized with the world cycle in rich countries than in poor ones? Part of the answer must be that poor countries exhibit more political and policy instability, they are less open or more distant from the geographical center, and they also have a higher share of their economy devoted to the production of agricultural products and the extraction of minerals. Table 1 shows that, in a statistical sense, these factors explain a substantial fraction of the variation in the volatility of income growth, although they do not explain much of the variation in the comovement of income growth. More important for our purposes, the strong relationship between income and the properties of business cycles reported in Table 1 is still present after we control for these variables. In short, there must be other factors behind the strong patterns depicted in Figure 1 beyond differences in political instability, remoteness and the importance of natural resources. With the exception that the comovement graph seems to be driven by differences between rich and poor countries and not within each group. Acemoglu and Zilibotti (1997) also present the volatility graph. They provide an explanation for it based on the observation that rich countries have more diversified production structures. We are unaware of any previous reference to the comovement graph. In this paper, we develop two alternative but non-competing explanations for why business cycles are less volatile and more synchronized with the world in rich countries than in poor ones. Both explanations rely on the idea that comparative advantage causes rich countries to specialize in industries that require new technologies operated by skilled workers, while poor countries specialize in industries that require traditional technologies operated by unskilled workers. This pattern of specialization opens up the possibility that cross-country differences in business cycles are the result of asymmetries between these types of industries. In particular, both of the explanations advanced here predict that industries that use traditional technologies operated by unskilled workers will be more sensitive to country-specific shocks. Ceteris paribus, these industries will not only be more volatile but also less synchronized with the world cycle since the relative importance of global shocks is lower. To the extent that the business cycles of countries reflect those of their industries, differences in industrial structure could potentially explain the patterns in Figure 1 . One explanation of why industries react differently to shocks is based on the idea that firms using new technologies face more inelastic product demands than those using traditional technologies. New technologies are difficult to imitate quickly for technical reasons and also because of legal patents. This difficulty confers a cost advantage on technological leaders that shelters them from potential entrants and gives them monopoly power in world markets. Traditional technologies are easier to imitate because enough time has passed since their adoption and also because patents have expired or have been circumvented. This implies that incumbent firms face tough competition from potential entrants and enjoy little or no monopoly power in world markets. The price-elasticity of product demand affects how industries react to shocks. Consider, for instance, the effects of country-specific shocks that encourage production in all industries. In industries that use new technologies, firms have monopoly power and face inelastic demands for their products. As a result, fluctuations in supply lead to opposing changes in prices that tend to stabilize industry income. In industries that use traditional technologies, firms face stiff competition from abroad and therefore face elastic demands for their products. As a result, fluctuations in supply have little or no effect on their prices and industry income is more volatile. To the extent that this asymmetry in the degree of product-market competition is important, incomes of industries that use new technologies are likely to be less sensitive to country-specific shocks than those of industries that use traditional technologies. Another explanation for why industries react differently to shocks is based on the idea that the supply of unskilled workers is more elastic than the supply of skilled workers. A first reason for this asymmetry is that non-market activities are relatively more attractive to unskilled workers whose market wage is lower than that of skilled ones. Changes in labour demand might induce some unskilled workers to enter or abandon the labour force, but are not likely to affect the participation of skilled workers. A second reason for the asymmetry in labour supply across skill categories is the imposition of a minimum wage. Changes in labour demand might force some unskilled workers in and out of unemployment, but are not likely to affect the employment of skilled workers. The wage-elasticity of the labour supply also has implications for how industries react to shocks. Consider again the effects of country-specific shocks that encourage production in all industries and therefore raise the labour demand. Since the supply of unskilled workers is elastic, these shocks lead to large fluctuations in employment of unskilled workers. In industries that use them, fluctuations in supply are therefore magnified by increases in employment that make industry income more volatile. Since the supply of skilled workers is inelastic, the same shocks have little or no effects on the employment of skilled workers. In industries that use them, fluctuations in supply are not magnified and industry income is less volatile. To the extent that this asymmetry in the elasticity of labour supply is important, incomes of industries that use unskilled workers are likely to be more sensitive to country-specific shocks than those of industries that use skilled workers To study these hypotheses we construct a stylized world equilibrium model of the cross-section of business cycles. Inspired by the work of Davis (1995), we consider in section one a world in which differences in both factor endowments a la Heckscher-Ohlin and industry technologies a la Ricardo combine to determine a country's comparative advantage and, therefore, the patterns of specialization and trade. To generate business cycles, we subject this world economy to the sort of productivity fluctuations that have been emphasized by Kydland and Prescott (1982). 2 In section two, we characterize the cross-section of business cycles and show how asymmetries in the elasticity of product demand and/or labour supply can be used to explain the evidence in Figure 1 . Using available microeconomic estimates of the key parameters, we calibrate the model and find that: (i) The model exhibits slightly less than two-thirds and one-third of the observed cross-country variation in volatility and comovement, respectively; and (ii) The asymmetry in the elasticity of product demand seems to have a quantitatively stronger effect on the slopes of the volatility and comovement graphs, than the elasticity in the labour supply. We explore these results further in sections three and four. In section three, we extend the model to allow for monetary shocks that have real effects since firms face cash-in-advance constraints. We use the model to study how cross-country variation in monetary policy and financial development affect the cross-section of business cycles. Once these factors are considered, the calibrated version of the model exhibits roughly the same cross-country variation in volatility and about 40 percent of the variation in comovement as the data. In section four, we show th

http://link.springer.com/content/pdf/10.1007/BF01322868.pdf

Business cycles: A methodological approach

The United States has long been viewed as having among the world’s most entrepreneurial, dynamic, and flexible economies. It is often argued that this dynamism and flexibility has enabled the US economy to adapt to changing economic circumstances and recover from recessions in a robust manner. While the evidence provides broad support for this view, the outcomes of entrepreneurship are more heterogeneous than commonly appreciated and appear to be evolving in ways that could raise concern. Evidence along a number of dimensions and a variety of sources points to a US economy that is becoming less dynamic. Of particular interest are declining business startup rates and the resulting diminished role for dynamic young businesses in the economy. We begin by describing how the concept of entrepreneurship is reflected in existing data on firm age and size. The recent addition of firm age to official statistics represents a dramatic improvement in the information available to entrepreneurship researchers. We then turn to a discussion of the role of startup firms in job creation. Business startups account for about 20 percent of US gross (total) job creation while high-growth businesses (which are disproportionately young)

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The Role of Entrepreneurship in US Job Creation and Economic Dynamism

https://www.nber.org/system/files/working_papers/w21776/w21776.pdf

Where has all the skewness gone? The decline in high-growth (young) firms in the U.S.

From the Abstract: Using weekly, anonymized administrative payroll data from the largest U S payroll processing company, we measure the deterioration of the U S labor market during the first two months of the global COVID-19 [coronavirus disease 2019] pandemic We find that U S private-sector employment contracted by about 22 percent between mid-February and mid-April Businesses suspending operations--perhaps temporarily--account for a significant share of employment losses, particularly among smaller businesses Hours worked for continuing workers fell by 4 5 percent We highlight large differences in employment declines by industry, business size, state of residence, and demographic group Workers in the bottom quintile of the wage distribution experienced a 35 percent employment decline while those in the top quintile experienced only a 9 percent decline Large differences across the wage distribution persist even after conditioning on worker age, business industry, business size, and worker location As a result, average base wages increased by over 5 percent, though this increase arose entirely through a composition effect Overall, we document that the speed and magnitude of labor market deterioration during the early parts of the pandemic were unprecedented in the postwar period, particularly for the bottom of the earnings distribution COVID-19 (Disease);Epidemics;Public health;Social Distancing;Labor market

/pdf/the-u-s-labor-market-during-the-beginning-of-the-pandemic-5b1vaypn2r.pdf

The U.S. Labor Market during the Beginning of the Pandemic Recession

The pace of job reallocation has declined in all U.S. sectors since 2000. In standard models, aggregate job reallocation depends on (a) the dispersion of idiosyncratic productivity shocks faced by businesses and (b) the marginal responsiveness of businesses to those shocks. Using several novel empirical facts from business microdata, we infer that the pervasive post-2000 decline in reallocation reflects weaker responsiveness in a manner consistent with rising adjustment frictions and not lower dispersion of shocks. The within-industry dispersion of TFP and output per worker has risen, while the marginal responsiveness of employment growth to business-level productivity has weakened. The responsiveness in the post-2000 period for young firms in the high-tech sector is only about half (in manufacturing) to two thirds (economy wide) of the peak in the 1990s. Counterfactuals show that weakening productivity responsiveness since 2000 accounts for a significant drag on a ggregate productivity.

/pdf/changing-business-dynamism-and-productivity-shocks-vs-33uklk2xzn.pdf

Changing Business Dynamism and Productivity: Shocks vs. Responsiveness

Using weekly, anonymized administrative payroll data from the largest U.S. payroll processing company, we measure the deterioration of the U.S. labor market during the first two months of the global COVID-19 pandemic. We find that U.S. private-sector employment contracted by about 22 percent between mid-February and mid-April. Businesses suspending operationsâ€”perhaps temporarilyâ€”account for a significant share of employment losses, particularly among smaller businesses. Hours worked for continuing workers fell by 4.5 percent. We highlight large differences in employment declines by industry, business size, state of residence, and demographic group. Workers in the bottom quintile of the wage distribution experienced a 35 percent employment decline while those in the top quintile experienced only a 9 percent decline. Large differences across the wage distribution persist even after conditioning on worker age, business industry, business size, and worker location. As a result, average base wages increased by over 5 percent, though this increase arose entirely through a composition effect. Over- all, we document that the speed and magnitude of labor market deterioration during the early parts of the pandemic were unprecedented in the postwar period, particularly for the bottom of the earnings distribution.

Ryan A. Decker

Papers

The Role of Entrepreneurship in US Job Creation and Economic Dynamism

Where has all the skewness gone? The decline in high-growth (young) firms in the U.S.

The U.S. Labor Market during the Beginning of the Pandemic Recession

Changing Business Dynamism and Productivity: Shocks vs. Responsiveness

The U.S. Labor Market During the Beginning of the Pandemic Recession