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Labor Earnings Inequality in Manufacturing During the Great Depression ∗ Labor Earnings Inequality in Manufacturing During the Great Depression ∗ Felipe Benguria Chris Vickers University of Kentucky Auburn University [email protected] [email protected] Nicolas L. Ziebarth Auburn University and NBER [email protected] September 21, 2018 Abstract We study labor earnings inequality in manufacturing during the Great Depres- sion using establishment-level information from the Census of Manufactures. Between 1929 and 1933, the difference in log earnings between the 75th and 25th percentiles of the establishment wage distribution decreases by 10 log points. By 1935, the 75-25 difference is almost back to its 1929 level. Nearly all of these changes in the overall distribution are from changes in the blue collar earnings distribution. Dispersion in hourly wages declines between 1929 and 1935, with the convergence of the South in hourly wages stronger than that observed in annual earnings. ∗We thank participants at the NBER SI 2015 DAE, Census Bureau, William & Mary, UW-La Crosse, Florida State, Gettysburg College, Northwestern University, EBHS 2017, the Washington Area Economic History Seminar, and the University of Michigan for useful comments. We thank Dave Donaldson, Rick Hornbeck, and Jamie Lee for providing county-level data for the 1929 Census of Manufactures. We thank Miguel Morin for providing the transcription of some of the published totals for the 1935 Census of Manufac- tures. The National Science Foundation (SES #1122509 and #1459263) and the University of Iowa provided funding. 1 1 Introduction The Great Depression is still to this day the largest downturn in American economic his- tory. While changes in aggregates such as output and prices are well known, even if their causal explanations are still hotly debated, much less is known about the distributional consequences of the Depression. This gap in our understanding of these distributional changes is striking given the growing interest in the role played by short-run economic fluctuations in the dynamics of earnings inequality. For example, Guvenen et al. (2014) document larger losses in earnings for lower-income households during recent U.S. reces- sions compared to higher-income ones. To address this gap, we draw on establishment-level data from 25 industries from the Census of Manufactures between 1929 and 1935 to study the effects of the Depression on earnings inequality. Our use of labor demand side information is motivated by data availability, but recent work has emphasized the role of firm or establishment character- istics in determining the earnings distribution (Card et al., 2013; Barth et al., 2016; Song et al., 2015). Our sample of industries, while not randomly chosen, covers a wide swathe of manufacturing, from durable goods to consumer products to “high tech” industries. A key feature of this Census is its frequency: We have transcribed schedules from 1929, 1931, 1933, and 1935, allowing us to study income dynamics from the beginning of the Depression through its nadir and the first part of the recovery. For each establishment, we observe the earnings of wage earners and of salaried workers, which we refer to as blue versus white collar workers. This distinction is very close to that between production and non- production workers commonly used today in working with manufacturing establishment data, e.g., Davis and Haltiwanger (1991).1 Our first result is that those in the bottom of the earnings distribution who remained employed gained relative to the median over the first half of the 1930s. In fact, the difference 1To support the claim that this distinction is related to skill as measured by educational differences, we show that there is a positive relationship between educational attainment and the share in white collar employment using data from the 1940 Population Census. 2 in the 75th and 25th percentiles declines by 10 log points between 1929 and 1933 with a decline of a similar magnitude in the difference between the 90th and 10th percentiles. However, by 1935, the 75-25 difference is nearly back to its 1929 (pre-Depression) level while the 90-10 difference declines further from its 1933 level. These changes are driven by shrinking gaps between the bottom and median of the distribution offsetting widening gaps between the top of the earnings distribution and the median. In particular, the 90-50 difference increases by 22 log points between 1929 and 1933 before falling 25 log points from 1933 to 1935. On the other hand, the 50-10 difference declines 30 log points between 1929 and 1933 before increasing 10 log points from 1933 to 1935. Changes in the earnings distribution for blue collar workers account for the nearly all of the changes in the overall earnings distribution. This is not only due to the fact they make up around 90% of the workers covered by our sample, but also because the while collar earnings distribution shows very little change over this six year period. Our second result is that changes in annual earnings likely understate the decline in inequality adjusted by hours worked among the employed. The early Depression saw sharp declines in the length of the workweek, both for economic reasons and as a result of the National Industrial Recovery Act (NIRA) of 1933. To understand the impacts of these changes in hours, for 1929 and 1935, we take advantage of a question about the “typical” workweek at a given establishment to estimate an establishment’s average hourly wage.2 We find that there is a pronounced decline in wage inequality, driven again by the bottom of the distribution. While the 90-50 gap declines by only 3 log points between 1929 and 1935, the 50-10 gap declines by 30 log points. Next, we account for the causes underlying the changes in earnings dispersion by de- composing changes in the overall distribution into changes in observables, returns to those observables, and unobservables following the procedure in Juhn et al. (1993). We find 2Unfortunately, for 1933, a year with large swings in the workweek over the year, we observe only a workweek number for one week in December. This number is not representative of the year as a whole. For manufacturing, the average workweek in 1933 was 36.4, but only 33.8 in December (Beney, 1936, Table II). For this reason, we exclude 1933 when we focus on hourly wages. 3 that the initial compression in earnings across establishments is driven almost entirely by changes in returns to observables: size of the establishment, “collar” color, industry and region. Changes in the distribution of establishments across these observables, if anything, act in the opposite direction. Changes in the residuals play no role. Echoing previous re- sults using industry level data from Rosenbloom and Sundstrom (1999), we find regional convergence, with low income parts of the country doing relatively better through 1935. If anything, the changes in regional differences between 1929 and 1935 are even more pronounced for hourly wages. The reduction in wages associated with being in the South Atlantic and East South Central regions falls by 29 log points in each region, considerably more than the changes in annual blue collar earnings. That is, focusing on only changes in average earnings understates the degree of regional convergence, since it misses a conver- gence in work hours. The timing of this convergence is consistent with an explanation, at least partially, driven by the NIRA. While this law was invalidated in May 1935, its provi- sions for job sharing would have at affected hours worked and earnings in our last sample period. Wright (1997) also points to the NIRA as a key driver of regional convergence but focuses on the NIRA-imposed minimum wages. On the other hand, our results before the NIRA is enacted highlight that regional convergence is not simply due to this policy change. In order to reconcile the differences in patterns for annual earnings and hourly wages, we examine establishment characteristics that determine the length of the workweek. The most striking finding is a large decline in workweeks in the South relative to the North. While in 1929 workweeks in southern regions were up to 10 log points above those in New England, by 1935 they are, if anything, shorter. There is some limited evidence of com- pression in the distribution of the workweek, as the 75-25 difference declines. This change is driven by changes in the coefficients associated with establishment characteristics, in particular the relationship between geography and the length of the workweek. While there are clear strengths of our dataset, it is also important to keep in mind its 4 limitations and their effects on the interpretation of our results. First, our data are re- stricted to manufacturing. This is similar to the work of Davis and Haltiwanger (1991), who study earnings dispersion between and across manufacturing plants during 1963- 1986. Second, the changes in inequality we measure are for the employed, as is the norm in the literature on earnings dispersion within and across firms (Card et al., 2013; Barth et al., 2016; Song et al., 2015). The Great Depression is a period of high levels of unemploy- ment and, for that reason, taking these extensive margin changes into account would be crucial for extrapolating our findings to inequality including the unemployed. Third, as we noted earlier, we observe hours worked only imperfectly, though this is not unusual in the earnings inequality literature, e.g., Song et al. (2015). It is interesting to compare these results to the longer-run trends in 19th century wage dispersion documented by Atack et al. (2004). They show that inequality in establishment wages increases between 1850 and 1880, and, using the same decomposition, changes in returns to observables play almost no role.
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