Economic Dynamics Newsletter

The changing nature of business cycles

Nir Jaimovich is Professor of Finance and Business Economics at the Marshall Business School at USC. He is an applied macroeconomics whose research interests are at the intersection of macroeconomics and labor economics. Jaimovich’s RePEc/IDEAS profile.

1. Introduction

The U.S. labor market has fared poorly since the Great Recession ended six years ago. The goal of my current research is to understand the reasons behind this lackluster performance. In this research overview, I summarize my recent work with my coauthors. I first describe my work with Henry Siu where we show how jobless recoveries in the aggregate economy relate to the disappearance of “routine occupations.” Next, I discuss my work with Arlene Wong and Sergio Rebelo in which we study how changes in consumption patterns account for a substantial fraction of the fall in U.S. employment in the recent recession.

2. Job Polarization and Jobless Recoveries

In the last three to four decades, many of the occupations that were once commonplace have begun to disappear as they have become obsolete. These occupations, which tend to be middle-skill occupations, involve tasks that are “routine” in the sense that they involve a limited set of tasks which are “rule based,” and can be performed by new technologies. This fact is documented in the “job polarization” literature (see a summary in Acemoglu and Autor (2011)) which shows how employment growth has been in the upper- and the lower-tails of the wage distribution.

During the same time period, in the three recessions (of 1991, 2001, and 2009) that coincided with the job polarization era, aggregate employment continued to decline for years following the turning point in aggregate income and output. These types of behaviors have been coined “jobless recoveries.” In contrast, prior to job polarization and advances in automation and computing, jobless recoveries did not occur.

In “Job Polarization and Jobless Recoveries” (joint with Henry Siu), we first show that the routine job loss is almost completely “bunched” during recessions. More importantly, we show that the root of jobless recoveries can be traced to the disappearance of routine jobs. This is a result of three facts. First, employment in routine occupations account for a significant fraction (about half) of aggregate employment. Second, essentially all of the recessionary contraction in per capita aggregate employment can be attributed to recessionary contractions in the middle-skill, routine occupations. Third, jobless recoveries are observed only in these disappearing, middle-skill jobs. The high- and low-skill occupations to which employment is polarizing either do not experience contractions, or if they do, rebound soon after the turning point in aggregate output. Finally we note that, jobless recoveries were not observed in routine occupations — nor in aggregate employment — prior to the era of job polarization. Hence, jobless recoveries can be traced to the disappearance of routine occupations in recessions.

2.1. The facts

In order to establish the link between job polarization and jobless recoveries, we first consider a simple counterfactual where we ask the following question: How would the aggregate labor market react if routine employment had recovered in the last three recession as it did before the job polarization era? This is an informative exercise since recessions in aggregate employment are due almost entirely to recessions in routine occupations. Our findings are clear: Aggregate employment would have experienced a fast turning with a significant recovery in the employment per capita. Importantly, we note that our emphasis on routine occupations is not simply a relabeling of dynamics in the cyclically sensitive goods-producing industries (manufacturing and construction) nor a relabeling of the dynamics of low educated workers.

2.1.1. U.S. cross-states analysis

To formally test for the relation between job polarization and jobless recoveries we analyze the labor dynamics across the 50 United States and the District of Columbia during the1982 (prior to job polarization era) and 2009 (during the job polarization era) recessions. Specifically, we show that since the onset of job polarization, regions in the US that were most susceptible to the disappearance of routine employment also experience the most jobless recoveries. In contrast, prior to the job polarization era this relation did not exist.

Specifically, to measure a state’s susceptibility to the disappearance of routine employment, we calculate the share of a state’s total employment held in routine occupations, prior to recession. That is, the greater this share, the greater the scope for a permanent drop in routine employment brought on by a recession, all else equal. Then, to measure the states’ strength of the recovery, we measure the per capita employment growth in the first four quarters following the recession’s trough. Given the timing of the variables relative to the recession, the estimated effect cannot be interpreted as due to reverse causality. Furthermore, to address potential omitted variable bias, we include a series of state-specific controls (all averaged at the periods prior to the recession) : (i) the share of goods-producing industries in each state’s output, and (ii) the state’s population share of individuals with low education. Our two key findings are as follows.

First, both in the pre and during the polarization era, states with higher routine employment shares experienced greater per capita employment loss during the recession. Hence, the relation between the routine share and employment loss during recessions has not changed. In contrast, the relation between the routine share and the joblessness of recovery has changed since the job polarization era. Specifically, prior to the long-run decline of per capita routine employment, states with higher routine employment shares exhibited stronger employment recoveries. In contrast, during the job polarization era, states that were more susceptible to polarization forces experienced more jobless recoveries. In other words, on average, in 1982, states with higher routine shares experienced larger employment losses during recession and stronger employment gains during recovery; in 2009, on average, states with higher routine shares also experienced larger recessionary losses but weaker employment recoveries.

2.1.2. Cross country analysis Our final piece of evidence builds on the fact that since the 1990s job polarization has also been observed in Western European countries (e.g. Goos et al. (2009) and Goos et al. (2014)). In our work we show that since the job polarization era, on average, there has been a marked fall in the recovery of employment following recessions. This is in contrast to that fact that prior to the job polarization era, employment would expand (on average) following recessions. Importantly, there has been essentially no change in the strength of output recoveries over time. Thus, the international data clearly indicates the emergence of jobless recoveries since the job polarization era (see also Gaggl and Kaufmann (2015)).

2.2. Theory

Having established these facts, we then study a simple search-and-matching model of the labor market linking the phenomena of job polarization and jobless recoveries. Specifically, our framework is a search-and-matching model of the labor market with occupational choice and a routine biased technological change. The search-and-matching framework of Diamond (1982), Mortensen (1982), and Pissarides (1985) is well-suited for our analysis since it emphasizes the dynamic, multi-period nature of employment and occupational choice.

The key mechanism in our model is that workers differ in their ability in performing occupational tasks, and this ability is reflected in the output in a worker-firm match. In the presence of a routine biased technological change, the surplus of matches for routine occupations eventually becomes negative generating a destruction of routine jobs. In our framework, a recession accelerates this disappearance. Moreover, once output recovers, employment does not. This is because workers who used to be suited to routine occupations are facing lower than usual job finding rates as they can no longer go back to their previous routine occupations.

Overall, the model gives rise to routine job losses being “bunched” in recessions despite a smooth routine biased technological change.. Moreover, the model gives rise to aggregate job losses in recessions being concentrated in routine occupations and that jobless recoveries are caused by the disappearance of routine employment. We furthermore demonstrate how the key mechanisms embodied in the model conform with data on transition rates across labor market states, and how these have changed across pre- and post-job polarization eras.

Moreover, the model’s explicit consideration of frictional unemployment also allows us to address the recent discussion of shifts in the Beveridge Curve as well as “mismatch” in the labor market (see, for instance Sahin et al. (2012)) since the end of the Great Recession. Specifically, we show how jobless recoveries caused by job polarization can cause an outward shift of the Beveridge Curve. Nonetheless, such an episode need not result in any increased mismatch between vacancies and unemployed workers.

2.3. Summary

In the last three to four decades, the US labor market has been characterized by job polarization and jobless recoveries. In our work we demonstrate how these are related. We first show that the loss of middle-skill, routine employment is concentrated in economic downturns. Second, we show that job polarization accounts for jobless recoveries. We then propose a simple search-and-matching model of the labor market with occupational choice to rationalize these facts, and we find that the model captures a number of key facts regarding labor market flows.

3. Consumption and the labor market

Over the Great Recession, many U.S. households have seen their real income fall. For instance, between 2007 and 2012, the real median household income fell by approximately 10%. Such changes in income naturally resulted in the adjustment of consumption expenditures. This adjustment led various researchers to argue that lower household demand was key to explaining the significant fall in employment during the Great Recession. These studies have focused on the decline in total household expenditures due to: (i) a decline in quantity consumed across all expenditure categories, (ii) postponement of purchases in some categories (such as large durables), and (iii) lower prices paid as households search more intensely for the lowest possible price (see for example Aguiar, Hurst, and Karabarbounis (2013), Kaplan and Menzio (2015), and Nevo and Wong (2015)).

In “Trading Down and the Business Cycle” (joint with Arlene Wong and Sergio Rebelo), we contribute to this literature as follows. On the empirical front, we combine several microeconomic datasets and document two facts; First, we show that a key way in which households have adjusted to lower incomes is by trading down, i.e. reducing the quality of the goods and services consumed. Second, we show that the production of low-quality goods is less labor intensive than that of high-quality goods. This suggests that as households trade down in the quality of goods and services they consume, the demand for labor falls. Indeed, through simple accounting exercises we find that the trading-down phenomenon accounts for about a quarter to a third of the fall in U.S. employment in the recent recession.

Motivated by these empirical patterns, we then study two business cycle models that embed quality choice, and we find that the presence of quality choice significantly magnifies the response of the economy to real and monetary shocks generating larger booms and deeper recessions. This amplification results from stronger shifts in both labor demand and labor supply which we discuss in detail below.

3.1. Motivating Example

Consider the case of food expenditures during the Great Recession. In real terms, food expenditures fell by about five percent during this period. Total food expenditures are composed of expenditures on “food at home” and “food away from home.” While the expenditures on food at home fell by four percent during this period, the expenditures on food away from home fell by about eight percent during this period. This naturally reflects the fact that the food away from home category is a “luxury” one.

However, the fall of about eight percent in the food away from home category is an average of a fall of about ten percent in expenditures at “full-services restaurants” (establishments with a relatively broad menu and a wait staff offering meals for consumption primarily on-premise) and a much smaller fall of about four percent at “limited-service restaurants” (establishments where food is purchased and paid before eating and there is generally no wait staff). In other words, as American households cut their expenses on dining out during the recession, the fall in consumption at full-services restaurants has been more than twice the fall at limited-service restaurants.

Consider now a common dining experience at an upscale restaurant vs. a fast-food establishment. While in the former, one tends to see many employees at the restaurant, the latter is characterized by a much smaller number of employees. Indeed, formally we find that in limited-service restaurants the number of employees per million dollar of sales is approximately half of that ratio at high end restaurants.

Thus, the shift in consumption expenses towards low end restaurants combined with the lower labor intensity at these restaurants results in a fall in the demand for workers. In what follows we discuss how this pattern was present in other sectors of the economy during the Great Recession.

3.2. Empirical findings

To understand the interaction between the quality of goods and services and the labor intensity used to produce them, we construct a new firm-level data set using several sources. Specifically, we obtain quality proxies from three sources: data scraped from the Yelp! website, the confidential micro data set used to construct the Producer Price Index (PPI), and the Census of Retail Trade. Then, armed with a quality measure for each firm, we merge this information with Compustat data to measure labor intensity per each firm in our data set.

3.2.1. The quality measure

Yelp!

Our first data set comes from Yelp!, a website where consumers share reviews about different goods and services. Specifically, for each store and location pair, Yelp! asks its users to classify the price of the goods and services they purchased into one of four categories: $ (low), $$ (middle), $$$ (high), and $$$$ (very high) (since there are few observations in the very-high category, we merge the high and very high categories into a single high-price category).

In order to construct a quality measure per firm, we first associate each firm (for example, Cost Plus, Inc.) with its brand names and retail chains (for example, Cost Plus owns the retail chain World Market). We find the Yelp! profile for each retail chain and brand in the 18 largest U.S. cities and collect the first match (for example, the first match for World Market in Chicago is the store on 1623 N. Shefield Avenue). We then compute the average price category across the first match for each of the 18 cities (to compute this average, we assign 1 to category low, 2 to middle and 3 to high and very high). We end up covering five North American Industry Classification System sectors: accommodation, apparel, grocery stores, restaurants, home furnishing.

PPI data

Our second data set uses the confidential micro data collected by the Bureau of Labor Statistics (BLS) to construct the “Producer Price Index” (PPI). The PPI data set measures producers’ prices for manufacturing, services, and all the other sectors of the economy.

In order to construct an indicator of quality for each firm, we proceed as follows. For each six-digit level product that an establishment sells we calculate its price relative to the median price in the industry for the same product. For single-product establishments, we use this relative price as the measure of the quality of the product produced by the establishment. For multi-product establishments, we compute the establishment’s relative price as a weighted average of the relative price of different products, weighted by shipment revenue. We then aggregate the establishment ranking to the firm level by taking a shipment-value weighted average.

Then, having a rank of firms by their relative price we assign the top 15 percent to the high-quality category, the middle 55 percent to the middle-quality category, and the bottom 35 percent to the low-quality category. This is the distribution of firms by quality tier that characterizes the firms included in the Yelp! data. We end up covering three manufacturing sectors ((i)Food, textiles, etc, (ii) Wood, chemical, etc., (iii) Computers, equipment., etc.) and the Retail trade sector.

U.S. Census of Retail Trade

Our third data set comes from the U.S. Census of Retail Trade, and it covers the General merchandise sector. The U.S. Census of Retail Trade splits firms into three price tiers that correspond to three different levels of quality: non-discount stores (high quality), discount department stores (middle quality), other general merchandise stores, including family dollar stores (low quality).

3.2.2. The labor intensity measure

We merge the quality information for each firm in our Yelp! and PPI data sets with data from Compustat on the number of employees and sales. The primary labor intensity measure we use is the ratio of employees to sales. The choice of this measure is dictated by data availability since less than 1/4 of the firms included in Compustat data report the share of labor in total cost, which is a natural measure of labor intensity. In the sample of firms that report the labor share in cost, the correlation between labor share and employees/sales is 0.94. Similarly for General merchandise, the U.S. Census of Retail Trade provides information about employment and sales for each of the three tiers. We use this information to construct labor intensity measures.

3.2.3. Findings

Based on the sales information from Compustat and the U.S. Census of Retail Trade our first finding is that between 2007 and 2012, firms that produce middle- and high-quality items lost market share relative to firms that produce low-quality items. Overall, based on the Yelp! and U.S. Census of Retail Trade data, we find that the low-quality segment gained a market share of about five percent during this period, while the middle-quality segment lost about four percent. Similar magnitudes are observed in the PPI data.

Our second fact is that our measures of labor intensity are increasing in quality. For example, the number of employees per million dollar of sales is 15.1, 9.2, and 6.5, for high-, middle- and low-quality apparel stores, respectively. So, other things equal, a shift of one million dollar of sales from a middle-quality to a low-quality apparel store eliminates roughly three jobs.

Overall, the low-quality segment employs around five workers per million dollars of sales, while the middle and high-quality segments employ around eight and eleven workers per million dollars of sales, respectively.

Having established the two facts, (i) the increase in the share of the low-quality categories, and (ii) their lower labor intensity measures,we then proceed to quantify the effects of trading down on employment by using a simple accounting method. For each sector we compute the change in employment accounted for by changes in market shares. That is, we ask how much would employment have fallen just from the observed changes in the market shares of the different quality segments. In other words, we do not change the “size of the consumption pie,” but rather change its composition across the different quality segments. We find similar results across the different data sets. Specifically, the mere change in the composition of consumption across the quality segments can account for between a quarter to a third of the fall in employment in the industries we analyze.

We then use the same framework, analyzing the role of movements from luxury categories to necessity categories. Specifically we first use the U.S. Consumer Expenditure Survey (CEX) to assign consumption into “luxuries and necessities” categories. This is done by estimating the elasticities of the category budget shares to total household expenditure (the use of Engel Curve slopes of the goods and services to classify the categories into luxuries and necessities is also used in Bils, Klenow and Malin (2012)). We then construct labor intensity measures for each expenditure category. To do so, we first match the CEX expenditure categories with the National Income and Product Accounts (NIPA) personal consumption expenditures categories (PCE). We then further match the PCE categories with the relevant commodities included in the PCE. This allows us to match the commodities to the Input/Output tables and using the Census we then construct a labor intensity measure for each commodity, and thus also for each of the consumption categories.

We find that there is a positive relation between how “luxury” a category is and its labor intensity measure. That is, we find that, on average, the more luxurious a category is, the higher its labor intensity measure is. This positive correlation suggests that recessionary shifts from luxuries to necessities can potentially affect aggregate labor because of variations in labor intensity across categories of luxuries and necessities. While this effect is present qualitatively, we find that quantitatively, category substitution, in stark contrast to the results of changes within categories, accounts for a negligible amount of the drop in aggregate employment.

Thus to summarize, our findings suggest that of the two forms of substitution, the adjustment of consumption towards low-quality products within categories is of a first-order importance for aggregate employment. Our simple accounting exercises suggest that this adjustment accounts for about a quarter to a third of the fall in aggregate employment during the Great Recession. In contrast, the movements from luxury categories to necessity categories, did not contribute to the fall in aggregate employment, from the perspective of the variation in labor intensity and observed changes in consumption patterns.

3.3. Theory

We view these empirical findings as indicative of the importance of studying the general equilibrium effects that quality-trade-down can have on the economy. To study the effects of trading down from a theoretical perspective, we embed quality choice into two otherwise standard models: a flexible-price model and a Calvo-style sticky-price model. We find that the presence of quality choice magnifies the response of these economies to real and monetary shocks. We begin by discussing the key ingredients of the model and then discuss our findings.

The Production Function

We are interested in a production function that is consistent with our key empirical facts. That is, that higher quality firms are characterized by higher labor intensity and that higher quality goods charge higher prices. A natural production function that delivers this result is a constant elasticity of substitution (CES) production function augmented with quality.

The Utility Function

Naturally, in order for households to consume a product with a higher quality (and for households to be willing to pay a higher price), there has to be a benefit. As such we need to introduce quality into the utility function. Moreover, a natural requirement is that quality be a normal good, so that higher income consumers choose goods of higher quality. While this condition seems natural, it imposes restrictions on the form of the utility function. Specifically in order for quality to be a normal good, the utility function must be non-homothetic in consumption. With this requirement in mind, we show that a utility function that is separable in consumption and hours worked and where quality multiplies the consumption function satisfies this condition. An advantage of this functional form is that it nests the usual separable utility in consumption and hours worked as a special case.

3.3.1. Findings

We first consider a flexible price model. Specifically, this is a simple extension of the basic two-sector real business cycle model with the modified production and utility function discussed above. This greatly simplifies the comparison of versions of the model with and without quality choice. We find that the presence of quality choice magnifies the response of our model economies to real and monetary shocks, generating larger booms and deeper recessions. Moreover, the model generates a relative variation of hours and output that is very close to the one observed in the U.S. data which is traditionally been difficult to achieve in RBC models (e.g. Rogerson (1988), Hansen (1985) and Benhabib, Rogerson, and Wright (1991)).

This amplification results from stronger shifts in both labor demand and labor supply. Consider the case of an expansion. In standard business-cycle models, the response of workers to an increase in the real wage is muted by the presence of decreasing marginal utility of consumption. As workers who supply more labor use the additional income to raise their consumption, their marginal utility of consumption declines. The possibility of consuming higher quality goods reduces this fall, resulting in a larger increase in the labor supply. At the same time, the production of higher quality goods requires more labor, generating a larger increase in the labor demand than in a model without quality choice.

The quality-augmented model has two other interesting properties. First, it can generate comovement between employment in the consumption and investment sectors, a property that is generally difficult to obtain (see Christiano and Fitzgerald (1998) for a discussion). Second, the model produces an endogenous, countercyclical labor wedge. As Shimer (2009) discusses, this type of wedge is necessary in order to reconcile business-cycle models with the empirical behavior of hours worked.

We are also interested in showing that the same mechanism that amplifies real shocks also amplifies nominal shocks. We do so by embedding quality choice in an model with Calvo style sticky prices. Similar mechanisms to the one discussed above generate an amplification of monetary shocks.

3.4. Summary

In our work we show that during the Great Recession consumers traded down in the quality of the goods and services they consumed. Since lower quality products are generally less labor intensive, this trading down reduced the demand for labor, accounting for between a quarter to a third of the decline in employment during the Great Recession. We then consider models where consumers change the quality of the goods they consume over the course of the business cycle. We find that introducing quality choice improves the performance of business cycle models.

4. References

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Jaimovich, Nir, and Henry E. Siu, 2012. “The Trend is the Cycle: Job Polarization and Jobless Recoveries,” NBER working paper 18334.

Jaimovich, Nir, Sergio Rebelo, and Arlene Wong, 2015. “Trading down and the business cycle,” Federal Reserve Bank of Atlanta CQER working paper 2015-5.

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