WP/19/191 Manufacturing jobs and inequality Why is the U.S. experience different by Natalija Novta and Evgenia Pugacheva IMF Working Papers describe research in progress by the authors and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the authors and do not necessarily represent the views of the IMF, its cutive Board, or IMF management. IMF Working Paper Research Department Manufacturing jobs and inequality Why is the U.S. experience different 1Prepared by Natalija Novta and Evgenia Pugacheva Authorized for distribution by Oya Celasun September 2019 Abstract We examine the extent to which declining manufacturing employment may have contributed to increasing inequality in advanced economies. This contribution is typically small, except in the United States. We explore two possible explanations the high initial manufacturing wage premium and the high level of income inequality. The manufacturing wage premium declined between the 1980s and the 2000s in the United States, but it does not explain the contemporaneous rise in inequality. Instead, high income inequality played a large role. This is because manufacturing job loss typically implies a move to the service sector, for which the worker is not skilled at first and accepts a low-skill wage. On average, the associated wage cut increases with the overall level of income inequality in the country, conditional on moving down in the wage distribution. Based on a stylized scenario, we calculate that the movement of workers to low-skill service sector jobs can account for about a quarter of the increase in inequality between the 1980s and the 2000s in the United States. Had the U.S. income distribution been more equal, only about one tenth of the actual increase in inequality could have been attributed to the loss of manufacturing jobs, according to our simulations. JEL Classification Numbers D31; D63; E25; J31; L60; O33 Keywords inequality, manufacturing employment, manufacturing wage premium, structural transation Author’s E-Mail Address NNovtaimf.org; EPugachevaimf.org 1We are grateful to Bertrand Gruss, Oya Celasun and Francesco Grigoli for their comments and suggestions. All remaining errors are our own. An earlier version of the analysis presented in this paper was published in Chapter 3 of the April 2018 World Economic Outlook. IMF Working Papers describe research in progress by the authors and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the authors and do not necessarily represent the views of the IMF, its cutive Board, or IMF management. 2019 International Monetary Fund WP/19/191 I. INTRODUCTION There is a concern in advanced economies that the loss of manufacturing employment means the loss of “good” jobs, especially for low- and middle-skilled workers. Historically, manufacturing attracted low-skilled workers from agriculture, offering higher and faster- growing wages. According to Helper, Krueger and Wial 2012, manufacturing used to provide high-wage jobs for workers who would otherwise earn lower wages. Lawrence 2017 states that manufacturing helped the United States achieve more inclusive income growth because it provided opportunities for workers without a college degree to earn relatively high wages and enter the middle class. Over the past decades, however, there was a steady decline in manufacturing employment across advanced economies IMF 2018. Middle-skilled workers in routinizable jobs have suffered the most severe cuts Autor and Dorn, 2013; Goos and Manning, 2007, many of whom have had to switch to low-skill service sector jobs instead. If workers are losing “good” jobs in manufacturing, is this driving an increase in income inequality across countries Several recent studies focusing on the United States demonstrate a link between manufacturing employment decline and a variety of societal problems. Gould 2018 finds that manufacturing decline and low-skilled immigration have contributed to rising wage inequality in the United States. Barany and Siegel 2018 show a link between manufacturing decline and polarization of the job market in the United States. Autor, Dorn and Hanson 2018 find that manufacturing decline has reduced the marriage market value of young men in the United States. Case and Deaton 2017 find that lower job security in manufacturing, and in low- and middle-skilled employment in general, is related to recent increases in mortality and morbidity among white non-Hispanic Americans in midlife. However, in a broad sample of advanced economies IMF 2018 finds that manufacturing employment decline, in general, is not associated with an increase in inequality. In some countriesDenmark, France, Irelandinequality declined despite a strong decline in manufacturing employment for the three different measures of inequality that we consider Figure 1. 2This suggests that factors other than manufacturing can be more important drivers of income inequality. In this paper, we focus on individual advanced economies to explore their different experiences with manufacturing employment decline and income inequality between the 1980s and 2000s. First, we do a decomposition rcise to identify how the decline in the 2The Gini index and generalized entropy are both standard measures of inequality. In this paper we use a sub- class of the generalized entropy index known as mean log deviation, or GE0 for short, which is characterized by a high degree of inequality aversion. The Gini coefficient ranges from 0 complete equality to 1 complete inequality. The GE0 ranges from 0 complete equality to larger positive values of increasing inequality. The hollowing-out index was recently defined by Alichi et al. 2017; its goal is to measure the weight of the middle class in society, with larger values indicating greater inequality. 4 manufacturing sector employment might have affected within- and between-sector inequality. Second, we per simulations to quantify how much of the change in inequality could possibly be attributed to the decline in manufacturing employment between the 1980s and 2000s in each country. Next, we zoom in on the United States and analyze two factors that may have exacerbated the potential for declining manufacturing to contribute to increasing inequality namely, the size of the manufacturing wage premium and the initial level of income inequality. One possibility is that U.S. workers forced to switch from the manufacturing to the service sector experience a large loss in income simply because they lose their manufacturing wage premium. This large loss of income among erly well-paid manufacturing workers could then lead to an overall increase in inequality. We consistently estimate manufacturing wage premia for a set of advanced countries and show that manufacturing wage premia are indeed large in some countries, but insufficient to explain changes in overall income inequality. Another possibility is that the magnitude of the income loss for those forced to switch from the manufacturing to the service sector depends on the existing level of income inequality. Consider the example of a median middle-skill manufacturing worker i.e. a worker receiving a median wage in the wage distribution for middle-skill manufacturing workers. Upon losing their job, and not finding a similar manufacturing job due to shrinking employment in the sector, they will likely seek Figure 1. Manufacturing employment decline and the change in inequality Sources Inequality measures are from the Luxembourg Income Study. Manufacturing employment is from IMF 2018. Note The figure shows the change in inequality and manufacturing employment rate from the 1980s to the 2000s. Red colors indicate the country sample used in this paper’s analysis. Fitted regression lines in black. 5 employment in the service sector. Without specific skills needed in the service sector, this worker will likely have to start at a low-skill service sector job and accept a wage cut relative to their old job in manufacturing.3Of course, there is some probability that they obtain a high wage job in the service sector, but this probability is likely low when they are new to the sector. Conditional on accepting a wage cut, middle-skill manufacturing workers will, on average, suffer a larger pay cut in countries with high wage dispersion, such as the U.S., than in countries with low wage dispersion. 4To explore these questions, we use micro-level survey data from the Luxembourg Income Study LIS database, which offers household and personal ination on income, sector of employment, type of occupation, and other demographic characteristics. Based on this data, we estimate manufacturing wage premia, country-wide inequality, which we further decompose into inequality within and between different sectors of employment, and we per a shift-share analysis. We focus on seven advanced economiesAustria, Denmark, Finland, France, Germany, the United Kingdom, and the United Statesthat have detailed employment data going back to the late 1980s. We then per a thought-experiment using simulations with LIS data to quantify how much of the actual change in inequality could be attributed to the decline in manufacturing employment, and how these calculations are affected if we eliminate the manufacturing wage premium or compress the initial income distribution. We present two main findings. First, the decline in manufacturing employment does not explain a large share of the change in inequality in any of the seven countries we study. Our decomposition rcise suggests that factors other than manufacturing are likely more important in explaining changes in overall inequality. However, the United States stands out as the country with the highest contribution of manufacturing decline toward rising inequality. For the United States, our simulations suggest that about a quarter of the rise in inequality could be attributed to the loss of manufacturing jobs. 3In the United States, based on data from the Current Population Survey between 1994-2008, the majority of manufacturing workers who lost their job and found a new job, moved to the service sector around 55. Of those who are employed after manufacturing job loss, and switched to the service sector, over 60 experienced a decline in wages relative to their manufacturing wage. Among those who worked in manufacturing for more than 10 years before losing their job, around 80 experience a wage decline. The median wage decline was around 35, and it is around 45 for those with more than 10 years of working in manufacturing. 4Based on data from the Luxembourg Income Study, detailed in Appendix Table 3, the income of a median middle-skill manufacturing worker in the United States in the 1980s was 107 of an overall median worker in the U.S., while the income of a 25 thpercentile low-skill service sector worker was 50 of an overall median worker. In contrast, in Finland, which has a more equal income distribution, these numbers are 103 80 for a median middle-skill manufacturing 25 thpercentile low-skill service sector worker. Appendix Figures 1 and 2 present the clear positive relationship between income inequality and the difference between median middle- skill manufacturing wage and 25 thpercentile low-skill service sector wages. Of course, over time the movement of workers from middle-skill manufacturing jobs to low-skill service sector jobs may have further endogenously lowered the low-skill service sector wages Autor 2015. 6 Second, to the best of our knowledge, we are the first paper to explore factors that might exacerbate the relationship between manufacturing employment decline and increasing inequality. We explore manufacturing wage premia and initial inequality as two such factors. We find that high initial inequality in the United States may have made manufacturing job loss particularly costly, more so than an initial manufacturing wage premium. The rest of the paper is organized as follows. Section II introduces the data used in the analysis. Section III presents stylized facts on inequality and the shift-share analysis. Section IV presents our estimates of the manufacturing wage premia across advanced economies. In section V we present our simulation rcises. Section VI concludes. II. DATA We use micro-level survey data from the Luxembourg Income Study LIS database, which compiles independent datasets from many advanced countries and emerging market economies, and currently covers 48 countries with the earliest survey dating back to 1967. LIS provides income, employment, and demographic data in two “flavors” at the household level and at the personal level, i.e. separately for each household member. The major, and unique, strength of LIS data is that surveys are fully harmonized, so that reliable cross- country analysis of inequality is possible. However, weaknesses are that countries are not surveyed in every year, households cannot be linked over time, and surveys before the 1990s are rare and not as detailed. We look at advanced economies and compare two decades the 1980s, when manufacturing employment was relatively high, and the 2000s, when manufacturing employment declined significantly in many advanced economies. Our sample includes seven countries Austria 1987, 2007, Denmark 1987, 2007, Finland 1987, 2007, France 1989, 2005, Germany 51989, 2007, the United Kingdom 1986, 2007, and the United States 1986, 2007. 6We do not consider years after 2008 to exclude changes in manufacturing employment or inequality that might be attributed to the Global Financial Crisis. Throughout the text when we refer to the 1980s or the 2000s, we are referring to the specific years listed above. 5The data for Germany in 1989 is available only for the Federal Republic of Germany. We made a choice to use the data from 1989 rather than from the 1990s after reunification. We feel this choice is appropriate given our focus on manufacturing, which was largely driven by industries in West Germany. 6LIS provides cross-country harmonized dataset based on underlying data from Statistics Austria Microcensus Austria 1987, Survey on Income and Living Conditions Austria 2007, Finland 2007, Law Model Denmark, Income Distribution Survey Finland, Household Budget Survey France, German Socio-Economic Panel Germany, Family Expenditure Survey UK 1986, Family Resources Survey UK 2007, Current Population Survey USA. continued 7 To calculate inequality, we use disposable household income, which comprises of labor income, capital income, social security transfer income, less income taxes and social security contributions. Following the LIS ology for calculating inequality, household income is equivalized by the square ro