The Puzzling Pattern of Multiple Job Holding across U.S. Labor Markets

Transcription

1 Southern Economic Journal 2017, 00(00), DOI: /soej The Puzzling Pattern of Multiple Job Holding across U.S. Labor Markets Barry T. Hirsch,* Muhammad M. Husain, and John V. Winters Multiple job holding (MJH) rates differ substantially across U.S. regions, states, and metropolitan areas. Rates decrease markedly with respect to labor market size. These patterns have been largely overlooked, despite being relatively fixed over (at least) the past 20 years. This article explores explanations for these persistent differences. We account for roughly two-thirds of the mean absolute deviation in MJH across local labor markets (MSAs). The results suggest that variation in MJH across labor markets is driven by labor market differences in job opportunities and worker preferences. Most important in explaining variation in MJH are MSA industry and occupation structure, ancestry shares, commute times, and, to a lesser extent, labor market churn. JEL Classification: J21, R2 1. Introduction Working at a secondary as well as a primary job provides an important source of income, human capital accumulation, and job satisfaction for many workers. Understanding the determinants and geographic patterns of multiple job holding (MJH) is thus important for researchers and policymakers. At a given point in time, roughly 5% of U.S. workers hold multiple jobs; a much larger share have held multiple jobs at some point in the past. Not widely recognized is that rates of MJH differ substantially across regions and labor markets in the United States, differences that have persisted over time. These geographic differences in MJH rates have received minimal attention in the academic literature. 1 MJH is far more prevalent in Western North Central, Mountain, Northwest, and New England states than in states elsewhere. Rates are lowest in the South. As we will show, a similar pattern exists across metropolitan areas. Moreover, MJH is found to be substantially higher in nonurban areas than in metropolitan area labor markets. Geographic differences in MJH presumably reflect labor market differences in labor supply and demand. Economists have focused on workers labor supply (work hour) preferences coupled with demand-side constraints on hours worked. Previous studies have shown that total weekly work hours are on average higher for multiple job holders than for single job holders (e.g., Hipple 2010; Hirsch, Husain, and Winters 2016). That said, we do not find labor market (metropolitan * Department of Economics, Andrew Young School of Policy Studies, Georgia State University, Atlanta, GA , USA; IZA, Bonn, Germany; bhirsch@gsu.edu; corresponding author. Department of Economics, Johns Hopkins University, Baltimore, MD 21218, USA; mhusain8@jhu.edu. Department of Economics, Oklahoma State University, Stillwater, OK , USA; IZA, Bonn, Germany; jvwinte@okstate.edu. Received February 2017; accepted May The fixity of the regional pattern over time is documented here, but it can also be seen by comparing BLS annual news releases on multiple job holding by state across years (e.g., U.S. Bureau of Labor Statistics, 2015). Ó 2017 by the Southern Economic Association 1

2 2 Hirsch, Husain, and Winters area) MJH rates to be positively correlated with market-level total (primary plus secondary job) average work hours. 2 Absence of a positive correlation between market-level MJH and total work hours strongly suggests that supply side preferences for work hours are not sufficient to explain the geographic patterns. Demand-related forces are likely to play an important role in determining the opportunity set facing workers and the resulting market-level rates of MJH. For example, high (low) MJH in a labor market may reflect a mix of primary jobs that produce substantive constraints (or opportunities) for lengthy work hours. Market-level factors that reduce the probability of good first-job matches (e.g., low rates of job churn) are likely to lead to high MJH rates. Factors that lower the attractiveness of MJH (e.g., high commuting costs) should lead to lower rates. Unfortunately, we know little from previous literature about the systematic differences in MJH across U.S. labor markets, the reasons for these differences, or whether these differences reflect worker preferences or labor market constraints. The goals of this article are twofold. First, we identify largely unrecognized regional, labor market, and market size patterns in MJH and show that these have been relatively stable over time. Second, we attempt to explain the systematic long-run differences in MJH across metropolitan area (MSA) labor markets by accounting for standard worker and job measures, and for MSA-level measures of job structure, commuting times, worker ancestry, and labor market churn. Based on the relationships found in our analysis, we assess (informally) the role played by MJH in helping improve labor market outcomes and well-being. To preview results of our analysis, we account for about two-thirds of the cross-market (metropolitan area) variation in MJH and offer several notable findings. Differences in industry and occupation structure, commute times, job churn rates, and ancestry patterns explain a significant share of the MJH variation across MSAs. The findings for job structure and commute times suggest that some workers prefer to work longer hours, but are unable to do so via a second job because good second job matches in their area may be limited or time constraints from commuting make MJH too costly. Our finding that MJH is higher in markets with low job churn suggests that high turnover facilitates good primary job matches and lessens the need for multiple jobs. The correlation between ancestry shares and MJH suggests that cultural norms and attitudes toward work affect employment outcomes, albeit in ways we cannot fully understand. Our descriptive evidence reveals substantive and relatively fixed differences in MJH across U.S. regions and labor markets. Although we cannot fully account for or explain such differences, it seems fair to conclude that variation in MJH across labor markets reflects both differences in the opportunity set of primary and secondary jobs, the work preferences of the labor force, and the ease with which workers and jobs are matched. 2. Background and Prior Literature MJH is typically treated by economists as an individual labor supply decision. Reasons for MJH fall into two broad categories, one focusing on hour constraints and a second on job portfolios. In a widely cited article, Shishko and Rostker (1976) provide a now-standard indifference curve diagram showing why workers may increase utility by taking a second job at a wage 2 Based on 259 MSAs during the years (the sample used in our subsequent analysis), we find substantial negative correlations between MSA multiple job holding rates with respect to mean hours worked in first jobs ( 0.22) and in second jobs ( 0.61), but effectively zero correlation ( 0.04) with mean total hours worked.

3 Multiple Job Holding Across Labor Markets 3 below ones wage at their hours-constrained primary job. Similar diagrams were provided earlier in two largely overlooked articles (Moses 1962; Perlman 1966). 3 Hour constraints on either a primary or secondary job can explain MJH. If a workers primary job (i.e., the job at which an individual works the most hours) has the higher wage but constrained work hours, workers may increase utility by taking a lower paying second job. Second jobs may be short-term. For example, workers with constrained hours may take a second job because of temporary financial or family circumstances, expecting that their preferred long-run match is a single primary job. Workers not facing hour constraints on the primary job might take a higher paying second job that has constrained hours; say, a temporary job or one with limited hours per week. Unlike jobs with hourly pay, salaried jobs do not have explicit hour constraints, but do have an earnings constraint that can work in a similar way, leading some salaried workers to take a second job in order to increase their earnings. 4 We adopt the phrase job portfolio from Renna and Oaxaca (2006), who develop a model of MJH based on personal preferences for job differentiation. 5 We include several explanations for MJH under this category. First, workers may prefer diversity in job tasks, being happier dividing time in two different jobs or occupations. Second, workers may work in a second job as a form of insurance, say diversifying ones human capital or because of employment or income uncertainty in a first job. Third, workers wanting to switch occupations or employers due to a poor match can use a second job to obtain job training that might facilitate a utility-enhancing move. Along these lines, Panos, Pouliakas, and Zangelidis (2014) have examined skill diversification and mobility among British multiple job holders. Using SIPP data, Conway and Kimmel (1998) emphasize the importance of heterogeneous jobs and conclude that it is an important reason for dual jobs, in addition to work hour constraints. Their article also addresses the importance of accounting for MJH when estimating labor supply elasticities. They conclude that multiple job holders have higher wage elasticities than do single job holders, but failure to account for multiple job holders has little bias given that they are a small portion of the workforce sample. An important contribution to the literature on MJH was Paxson and Sichermans (1996) focus on dynamic job holding. Secondary jobs are often short-term jobs. Although relatively few workers hold multiple jobs during any given week, a substantial number of persons have been multiple job holders at some point in the past. The authors use the Panel Study of Income Dynamics (PSID), which asks about jobs that provide earnings in addition to ones primary job over the previous calendar year. Using this definition, they find that 21% of men and 12% of women held dual jobs at some point during the previous calendar year (these figures were averaged over the PSID survey years ). These average annual rates were roughly double the rates that Paxson and Sicherman estimate to be comparable weekly rates based on the PSID definition of multiple jobs. 3 In contrast to Moses (1962) and Perlman (1966), Shishko and Rostker (1976) provide empirical analysis estimating the labor supply responsiveness to multiple jobs. Perlman (1966, p. 242) cites BLS reports of economy-wide multiple job holding rates of 5.3% in 1957 and 5.2% in 1964, the same order of magnitude as current rates, but for what was then a mostly male labor force. 4 Hirsch, Husain, and Winters (2016) make this point. They find a multiple job holding rate for salaried workers that is about half a percentage point lower than for hourly workers. 5 The job portfolio terminology had been used but not highlighted in prior literature (e.g., Paxson and Sicherman 1996).

4 4 Hirsch, Husain, and Winters Recent literature has returned to the theme that MJH may be similar to short-term jobs. As noted by Abraham et al. (2013), there are differences in measuring MJH based on household Current Population Survey (CPS) data versus establishment data. Using a data set matching individual worker information with administrative employer-reported data indicated that establishment measures of multiple jobs within the same quarter sometimes fail to coincide with CPS worker reports of MJH. Such discrepancies need not indicate reporting error. A worker with two (or more) jobs within a quarter may not have held multiple jobs during the CPS reference week. Likewise, reports of MJH in the CPS sometime fail to show up as two jobs in administrative payroll records. Although we cannot rule out reporting error in the CPS, such discrepancies will exist if earnings from either the primary or secondary job are not reported to tax authorities (i.e., off the books). There also exists a literature examining MJH and the business cycle. Amuedo-Dorantes and Kimmel (2009) provide a thorough summary of this literature, identifying studies that report a diverse set of results, some finding MJH to be cyclical, some countercyclical, and some acyclic. Their own analysis using data from the NLSY79 finds mixed results. Using state-level employment growth as their business cycle measure, the authors conclude that MJH among men is largely acyclic, whereas MJH among women switched from countercyclical during the 1980s and early 1990s to procylical by A recent article by Hirsch, Husain, and Winters (2016) uses a large CPS data set for to examine how MJH in U.S. labor markets (MSAs) varies with respect to local unemployment rates and employment growth. Theory is ambiguous. Labor supply can be cyclical or countercyclical depending on the strength of income and substitution effects. Even if income effects were dominant, leading to a desire for multiple jobs when unemployment is high, it does not follow that such jobs are available given that markets need not clear in recessions. Absent MSA fixed effects, the authors obtain small but precisely estimated negative coefficients on the unemployment rate (or tiny positive coefficients using employment growth), reflecting higher MJH in low unemployment labor markets. Once labor market fixed effects are added, however, coefficients are close to zero. Similarly, using two-year CPS panels of workers within MSAs, transitions into and out of multiple jobs over a year are uncorrelated with changes in unemployment. The authors conclude that MJH in the United States is effectively acyclic. As compared to the United States, relatively few studies focus on MJH in Europe. Zangelidis (2014) examines European evidence using a large microlevel data set, the European Union Labour Force Survey (EU-LFS) for The MJH rate across 28 EU countries is lower than in the United States, 3.2% in the EU versus about 5% in the United States. Just as the United States displays substantial differences in MJH across states and regions, Zangelidis finds large variation in MJH rates across countries in the EU, ranging from less than 1 to 9%. 6 Livanos and Zangelidis (2012) document large differences in MJH rates across regions of Greece with rural areas with large primary sectors having the highest rates, likely due to low labor demand and weak primary 6 Zangelidis also finds that mean weekly hours on the second job average 12.9 hours across the continent, with little variation across countries. Zangelidis introduces the concept of second job intensity, measured by the percentage of total work hours due to the second job. Among multiple job holders, the intensity measure averages 26.7% across all 28 countries and displays limited variation (values range from 22 to 34%). Hirsch, Husain, and Winters (2016, p. 25) calculate a second job intensity measure for the United States that is just slightly higher than the EU country mean.

5 Multiple Job Holding Across Labor Markets 5 job opportunities in those areas. Livanos and Zangelidis (2012) also find that MJH in Greece is procyclical. 7 An article by Partridge (2002), who uses U.S. state level data to examine the determinants of MJH, recognizes the substantial differences across states in MJH rates. He provides a map of the United States showing the strong regional patterns in BLS MJH rates (similar to our Figure 2, presented subsequently). His estimation sample includes the lower 48 states for the years (n ). Partridge includes state and year fixed effects in his MJH models. In contrast to our analysis, the goal of Partridges article is to estimate the determinants of MJH behavior and not the fixed differences in MJH across states. Partridge nets out time-invariant differences in state MJH by including state fixed effects. He notes the importance of these fixed effects, but does not attempt to account for their variation. A principal goal of our article is to identify and explain the fixed differences in market-level MJH, measured primarily at the MSA rather than state level. Because Partridge used state data for MJH and its covariates, he could not observe the substantial urban/rural and city size differences in MJH uncovered in our work. Most studies focusing on the determinants of MJH have used individual-level data and primarily (or exclusively) individual-level covariates. Geographic and market-level factors affecting U.S. MJH have been largely ignored. We provide analysis that includes a rich set of individual level covariates, but also emphasize market-level determinants. It is reasonable to expect that marketlevel forces can influence rates of MJH. On the demand side, for example, some types of industries or occupations may prefer workers who work limited hours, are temporary, or work nonstandard hours. The presence of such employers is likely to increase the availability and pay for second jobs, thus increasing MJH. These same industries and occupations may also hire primary job workers who are offered limited hours, thus increasing the labor supply of hours-constrained workers desiring second jobs. On the supply side, labor market workforces may differ (conditional on individual covariates) in their preferences regarding total work hours and the types of jobs that they hold. Such labor supply differences will produce differences in MJH. Differences in work preferences may reflect long-standing cultural and historical norms passed through generations within a labor market, or reflect norms acquired through ancestry rather than location per se. If market-level differences in desire for work hours were the only force at work, we would expect to see a positive correlation across labor markets in MJH rates and total work hours (the sum of usual weekly hours on primary and second jobs). As stated previously (footnote 2), there is a negative (but near zero) correlation between market-level MJH and total work hours. To the extent that many workers in rural or small urban markets have strong local preferences, we might see high rates of MJH due to the difficulty in finding a good primary job match. That said, it seems unlikely that differences in preferences and other supply-side factors can by themselves explain the 7 Our summary of the literature is not exhaustive. Other articles using the CPS include Hipple (2010), who provides extensive descriptive evidence (means) on multiple job holding rates for various groups of workers, and Lale (2016), who provides detailed evidence on worker flows, transitions into and out of multiple job holding, and the relationship of multiple job holding and part-time primary jobs. Analyses of U.S. multiple job holding also have used the Survey of Income and Program Participation (SIPP), the Panel Study of Income Dynamics (PSID), and the 1979 National Longitudinal Survey of Youth (NLSY). For example, Conway and Kimmel (1998), Kimmel and Conway (2001), and Krishnan (1990) use SIPP; Paxson and Sicherman (1996) use the PSID (and CPS); and Amuedo-Dorantes and Kimmel (2009) use the NLSY79. These longitudinal data sets provide a rich set of covariates and enable researchers to examine worker transitions over lengthy time periods. Because of its large size and geographic coverage, the CPS is far better suited to examine multiple job holding patterns across labor markets than are these alternative data sets.

6 6 Hirsch, Husain, and Winters substantive differences we find in market-level MJH. Demand and other market-level factors are also likely to be important. In what follows, we first document systematic but largely unrecognized geographic patterns of MJH across regions and with respect to labor market size. We then examine labor market differences in industry and occupation structure, along with several other market-level determinants of MJH that can affect the attractiveness and desire for multiple jobs. For example, traffic congestion and long commute times may decrease the willingness of workers to take second jobs. Labor markets with low levels of churn (turnover) may produce imperfect worker-job matches that lead to a desire for second jobs, while at the same time reducing the ease of finding such jobs. 3. Measurement of MJH Using the Current Population Survey We use the U.S. CPS to measure MJH. The CPS began continuous collection of information on MJH in 1994 as part of the surveys major redesign. Prior to 1994, occasional CPS supplements included information on MJH. Since 1994, all employed individuals are asked the question: Last week, did you have more than one job (or business), including part-time, evening, or weekend work? If they answer yes, they are then asked how many jobs (or businesses) they had altogether and how many hours they worked each week at all their jobs. The primary job is defined as the one at which the greatest number of hours were worked. Using monthly CPS data, the U.S. Bureau of Labor Statistics (BLS) defines a multiple job holder as an individual who: (i) holds wage and salary jobs with two or more employers; (ii) combines a wage and salary job with self-employment; or (iii) combines a wage and salary job with one as an unpaid family worker. In our analysis, MJH is defined similarly, with the exception that our sample includes only those workers whose primary job is a wage and salary job and we restrict the sample to nonstudents ages (vs. all workers ages 161 by BLS). These sample criteria produce an estimation sample similar to those commonly seen in wage and employment analyses. MJH rates are affected little by these differences. The exclusion of year olds and workers over 65 raises MJH rates by roughly two-tenths of a percent. Exclusion of full-time students raises MJH rates by less than one-tenth of a percent. Exclusion from the sample of those with a primary self-employment job typically reduces MJH rate estimates by one-tenth of a percent. Overall, our sample has MJH rates in most years that are one- or two-tenths higher than published BLS rates, with the gap being a bit larger in the earliest years and at most one-tenth of a percent in recent years. 8 In this article, we use all rotation groups of the monthly CPS data files from January 1994 through December The CPS reports work hours, detailed occupation, and detailed industry 8 Fewer than 1 in 20 workers counted as multiple job holders by BLS in 2009 held a primary self-employment job (Hipple, 2010, Table 4). It is common for multiple job holders to have secondary self-employment jobs; these workers are included in our sample. Data restrictions influenced our sample criteria. We use the IPUMS CPS monthly files for the analysis, but these include neither an edited BLS field on multiple job holding nor the class of worker for the second job that would show whether workers with a primary self-employment job hold a wage and salary second job. 9 Households are in the CPS survey for eight months. They are interviewed four consecutive months (rotation groups 1 4), then out the next eight months, and then reenter the following 4 months (rotation groups 5 8). An initial working paper version of our article used the quarter sample outgoing rotation groups (rotation groups 4 and 8) in order to have information on individuals earnings on the primary job and union status. We switched to all rotation groups once we determined that rotation group bias existed, with the highest rates of multiple job holding reported in rotation

7 Multiple Job Holding Across Labor Markets 7 for both the primary and second jobs. Earnings are reported only for the primary job. We first focus on differences in MJH with respect to urban versus nonurban markets and then turn to differences across states and metropolitan areas. Our initial analysis focuses on urban/nonurban differences in MJH using a CPS sample of 13,448,612 workers, 9,914,287 or approximately three-fourths (0.737) of whom live in MSAs identified in the CPS, with the remaining 3,534,325 residing in nonurban areas. As we later discuss, households in nonurban areas are oversampled and have lower sample weights, while those in large urban areas have higher weights. This sample excludes CPS files during June-August 1995 (n 5 163,499), in which there were no metropolitan area identifiers, thus precluding identification of urban versus nonurban areas. Analysis of combined urban/nonurban samples at the national and state level include all months of Key analyses in our article focus on the relatively fixed differences in MJH across MSAs and with respect to market size. Analyses focusing on urban-nonurban and metro size differences in MJH use a sample from September 1995 through December Our beginning date corresponds to the introduction of new MSA definitions in the CPS. Our ending date is prior to MSA definition changes in 2015 (we account for changes introduced during 2014). This CPS sample includes 11,962,560 workers, with about three-fourths (0.739) of the sample (8,838,400) living in MSAs identified in the CPS, and the remaining portion of the sample (3,124,160) residing outside these MSAs. 10 Subsequent analysis focuses exclusively on the 259 MSAs present in the CPS for the period September 1995 through December 2014 (202 present over the entire period and 57 small MSAs present in some but not all years). Unless otherwise stated, all analyses in the article use survey weights. To illustrate the (substantial) difference weighting has on descriptive statistics, it is useful to compare weighted and unweighted mean MJH rates. Over the entire period the national, urban, and nonurban weighted mean MJH rates are 5.6, 5.3, and 6.7%. The comparable nonweighted sample means are 6.1, 5.6, and 7.5%. In order to enhance reliability, the CPS oversamples households in less populated markets and undersamples in large markets. Because MJH rates systematically decline with size, it is essential that we use Census survey weights to provide unbiased descriptive statistics for representative populations. Because MJH behavior may be heterogeneous, weighted regressions provide coefficient estimates representing roughly average effects across heterogeneous groups (see Solon, Haider, and Wooldridge 2015). Figure 1 provides national evidence on the trends over time in MJH. National annual rates (triangles) have trended down over time, from 6.3% in 1994 (and a high of 6.7% in 1996) to an eventual 5.0% in Of particular interest for our analysis is the largely unknown difference in MJH rates between those in nonurban (squares) versus metropolitan areas (circles), rates being substantially higher in nonurban areas. The downward trends in MJH rates are similar in urban groups 1 and 5 and lower rates reported the longer one is in the survey (see the note by Hirsch and Winters 2016). This pattern is identical to that reported by Krueger et al. (2017) for unemployment rates, but the bias for multiple job holding is larger than for unemployment. That said, the basic conclusions of our analysis have changed little. In the prior analysis, individual earnings and union status were not important in explaining labor market differences in multiple job holding. Our current analysis measures earnings and union concentration at the MSA level. 10 The CPS does not identify all MSAs, excluding those that are small, roughly 100,000 or below in size (MSAs must have a central city of at least 50,000). Every 10 years Census adds and removes smaller MSAs based on population changes. What we refer to as our nonurban group includes both workers living outside of an MSA, plus those in small MSAs not identified in the CPS. 11 As noted previously, our MJH rates differ slightly from official BLS rates because we require that workers have a primary wage and salary job, limit our sample to ages 18 65, and exclude full-time students.

8 8 Hirsch, Husain, and Winters Figure 1. Annual MJH Rates for U.S. Metro, and Nonmetro Areas, and nonurban areas, although estimates for the latter are more volatile. Although not shown in Figure 1, downward trends in MJH have been stronger among men than among women. Mens MJH rates between 1994 and 2015 declined from 6.4 to 4.6%, whereas womens rates fell from 6.2 to 5.3%. The sharper decline among men than women occurs in both the metropolitan and nonmetropolitan samples. The secular downward trend cannot be accounted for by macroeconomic conditions. MJH is weakly cyclical, but the relationship is close enough to zero to characterize it as acyclic (Hirsch, Husain, and Winters 2016). 4. Systematic Differences in MJH Across Regions, States, and Metropolitan Areas MJH rates differ substantially across regions, states, and labor markets. These differences have substantial fixity over time. Neither the geographic differences in MJH nor the stability of these differences over time is widely recognized. In this section, we provide descriptive evidence on each of these patterns. We first use our CPS data set to show regional and state differences in MJH over time. We then examine evidence on MJH differences across nonurban versus urban areas and show how MJH decreases with metropolitan area size. MJH differences across metropolitan areas display the same regional pattern seen for states. The stability of state MJH is shown through comparisons of MJH rates and relative rankings in versus A similar analysis is shown for metropolitan areas based on MJH rates in versus (we begin with 1996 and end with 2014 due to changes in MSA definitions in 1995 and 2015). Figure 2 provides shade-coded maps of relative MJH rates among U.S. states in , , and Given the downward trend in MJH rates over time, we grouped the states into quartiles, states with the highest MJH rates coded in black, the next quartile in dark gray, the next in light gray, and the lowest in white. Readily evident is the substantial similarity of the shade codes over time, with blocks of black (high MJH) among states in the north central region and northern New England, and blocks of white (low MJH) in the southeast, southwest, California, Nevada, New York, and New Jersey. In the top half of Figure 3 we show a scatterplot of the 51 state MJH rates (D.C. included) in (y-axis) and rates 20 years earlier in (x-axis). The same pattern is shown in

9 Multiple Job Holding Across Labor Markets 9 Figure 2. Quartile Rankings of State MJH Rates, , , and

10 10 Hirsch, Husain, and Winters Figure 3. Scatterplots of Versus State MJH Rates and Ranks. the bottom half of the figure, where the scatterplot is based on MJH rankings rather than rates. MJH rates are closely related over the time period. A weighted OLS regression of MJH state rates on rates has an R 2 of 0.63 and a coefficient of 0.67 on the rates. A similar regression using MJH rankings had an R 2 of 0.58 and a coefficient of 0.79 on the rankings. Coefficients on MJH rates are expected to be below 1.0 given the secular decline in MJH. Measurement error in MJH rates due to sampling may attenuate coefficients in both the rate and rank equations. The principal analysis in this article focuses on MJH differences across urban labor markets based on metropolitan areas identified in the CPS. Evident here are the same regional differences seen previously for states, plus differences in MJH rates by market size. We offer several pieces of evidence. Tables 1a and 1b provide lists of MSAs with the highest and lowest levels of MJH averaged over September 1995 December 2014 for the 202 MSAs continuously included in the CPS over this period. Here, we see regional patterns similar to those seen in the state maps. Relatively high rates are observed for north central MSAs, a few of which are home to large state universities (Table 1a). MSAs with low MJH rates are concentrated in the south, along with several California cities and the large NYC-NJ MSA (Table 1b). Table 1a shows labor markets with high MJH dominated by relatively small MSAs (the mean sample size across MSAs is 34,498). In contrast, Table 1b showing low MJH markets includes several large MSAs (e.g., New York, Houston, and L.A), and has an average sample size of 73,027, more than double the size for high MJH markets (Table 1a). Comparison of sample sizes understates differences in population given that small (large)

11 Multiple Job Holding Across Labor Markets 11 Table 1a. Highest 25 MSA Mean MJH Rates, September 1995 December 2014 Rank Metropolitan Area Name Mean MJH Obs 1 Madison, WI MSA ,350 2 Fargo, ND-MN ,246 3 Sioux Falls, SD ,414 4 Portland, ME MSA ,568 5 Topeka, KS ,300 6 Burlington-South Burlington, VT ,351 7 Fort Collins-Loveland, CO MSA ,669 8 Minneapolis-St Paul-Bloomington, MN-WI ,090 9 Provo-Orem, UT MSA , Eugene-Springfield, OR MSA , Chico, CA Omaha-Council Bluffs, NE-IA , Santa Fe, NM MSA Des Moines, IA , Utica-Rome, NY Honolulu, HI , Duluth-Superior, MN-WI MSA , Kalamazoo-Battle Creek, MI MSA , Appleton-Oshkosh-Neenah, WI MSA , Green Bay, WI , Ann Arbor, MI , Rochester, NY MSA , Norwich-New London, CT-RI , Springfield, IL Olympia, WA ,153 markets are oversampled (undersampled). More directly, there is strong within-state correlation between metro and nonmetro MJH rates. Over the entire time period, the within-state correlation between MSA and non-msa MJH rates is The equivalent correlations for our earliest years ( ) and most recent years ( ) are 0.60 and 0.77, respectively. Figure 4 provides further evidence showing that the state and regional differences in MJH, seen previously in Figure 3, are not driven entirely by the 75% of workers who reside in MSAs. In Figure 4, we provide a scatterplot of within-state mean MJH rates for residents living in metropolitan areas (shown on the vertical axis) and of within-state mean MJH rates for residents living outside CPS designated metro areas. Both sets of means use sample weights and are calculated over the entire period. Clearly evident in Figure 4 is a strong correlation between within state urban and nonurban MSA rates. The stark regional differences seen in MJH reflect both urban and nonurban differences. In order to examine fixity in MSA MJH over time, in Figure 5 we show a scatterplot similar to that seen previously for states. The vertical axis measures the MSA MJH rates calculated for while the horizontal axis shows the rates for Three-year averages are used to reduce sampling error, a concern for smaller cities. As evident in the figure, there is a relatively high degree of similarity in relative rates between the years. A weighted OLS regression of the rate on the rate produced an R 2 of 0.32 and a coefficient of 0.46 on the 12 Excluded are DC, plus a tiny number of states either with no non-msa households or with no designated MSAs in some or all years of the CPS.

12 12 Hirsch, Husain, and Winters Table 1b. Lowest 25 MSA Mean MJH Rates, September 1995 December 2014 Rank Metropolitan Area Name Mean MJH Obs 178 Palm Bay-Melbourne-Titusville, FL , Hickory-Morganton, NC MSA Ocala, FL Los Angeles-Long Beach-Santa Ana, CA , Atlanta, GA MSA , Stockton, CA , Augusta-Richmond County, GA-SC , Lafayette, LA MSA , Riverside-San Bernardino, CA , Flint, MI PMSA , Beaumont-Port Arthur, TX MSA Las Vegas-Paradise, NM , Houston-Baytown-Sugar Land, TX , Mobile, AL Corpus Christi, TX MSA Visalia-Porterville, CA Lake Charles, LA New York-N. New Jersey-Long Island, NY-NJ-PA , Bakersfield, CA MSA , New Orleans, LA MSA , West Palm Beach-Boca Raton, FL MSA , Port St. Lucie-Fort Pierce, FL El Paso, TX MSA , Lakeland-Winter Haven, FL MSA , McAllen-Edinburg-Mission, TX MSA ,867 MSAs with the highest and lowest MJH rates were determined among the 202 MSAs present in the CPS during the entire September 1995 December 2014 period rate. The coefficient is likely less than 1.0 due to both the secular decline in MJH rates and attenuation bias from measurement error in the MSA rates. As seen in Figure 5, the range of metro area MJH rates is lower in than in This is due to the secular decline in MJH rates; the coefficient of variation is slightly higher in In addition to there being regional patterns and considerable fixity over time, MJH also varies with respect to labor market size. In Table 2, we show the average MJH rates over the September (n 5 11,962,560) period for both nonurban and metropolitan areas of varying sizes. In column 1, we show the mean MJH rates among workers residing in nonurban areas, those areas of the country either outside of an MSA or in a small MSA (typically less than a 100,000 population and not identified in the CPS), followed by six groups of MSAs of increasing population size. The mean (weighted) MJH rates over systematically decline with size, ranging from 6.7% for the nonurban areas down to 4.4% among workers in MSAs 5 million plus. Little of the difference by size is accounted for by standard covariates. Adding a detailed set of worker and job attributes (listed in the note to Table 2), the spread between the unadjusted nonurban and largest urban markets decreases only slightly, from 2.3 to 2.1% (columns 1 and 2). 13 Of particular 13 As indicated in the note to Table 2, the MJH dependent variable is coded 100 rather than 1.0 for multiple job holders and zero otherwise. This allows the coefficients shown in Table 2 to be interpreted as percentage rates. Regressions in Table 2 have standard errors clustered by MSA and by nonurban state areas.

13 Multiple Job Holding Across Labor Markets 13 Figure 4. State Non-MSA Versus MSA Mean MJH Rates ( ), by State. interest is column 3, however, where we add measures of labor market commute times. 14 Controlling for average commute times, the substantive differences in MJH rates previously seen for large versus small MSAs are no longer evident. We provide further evidence on commuting time costs in the next section of the article. 5. What Might Explain Metropolitan Area Differences in MJH? The discussion and evidence in the prior section established that there is considerable variation across U.S. labor markets in rates of MJH and that these differences are relatively stable over time. An obvious question arising from such evidence is: What explains these labor market differences in MJH? We consider several possible explanations below, some that can be measured directly, some that can be imperfectly captured through proxy measures, and some that cannot be readily measured. Our strategy is to begin with the raw differences in MJH rates for our 259 metro labor markets (202 of which were measured continuously over the September 1995 through December 2014 period), and then see to what extent these differences are reduced as we introduce various covariates. The CPS contains detailed measures of individual worker demographics and job types. We first control for differences in worker demographics and human capital (measured by schooling and potential experience). We then add measures of worker job attributes on the primary job (hours worked in primary job and job sector as measured by public, industry, and occupation dummies). We then add MSA level measures based on data from the CPS, the decennial Census, the American Community Surveys (ACS), the Quarterly Census of Employment and Wages (QCEW), and the Local Area Unemployment Statistics (LAUS). These measures include commute times, labor market size, market measures of mean earnings, housing values, rental rates, union density, industry and occupation shares, percent foreign born, ancestry, market level job 14 Average commute times for MSAs and nonurban areas are calculated from the 2000 Census and the ACS, as described subsequently in the next section of the article. Mean commute times for nonurban areas are based on same-state residents not residing in one of our designated MSAs.

14 14 Hirsch, Husain, and Winters Figure 5. Scatterplot of and MSA MJH Rates. churn (turnover), employment growth, and unemployment. 15 These controls account for a substantial share of the dispersion in MJH across markets, but some variation remains unexplained. As discussed at the outset, an economic-based explanation for MJH is that MJH results from hours constraints on the primary job. Hours constraints may be more likely in labor markets with slow rates of labor demand and employment growth, while being less constrained in high growth labor markets. Of course, we cannot easily distinguish between employment growth driven by labor demand versus labor supply. Hirsch, Husain, and Winters (2016) provide clear-cut evidence that labor market MJH rates are not correlated with either local unemployment rates or employment growth after accounting for MSA fixed effects. Absent fixed effects, MJH is weakly procyclical, consistent with a dominant labor demand effect and/or ambiguous labor supply effects due to competing income and substitution effects. An additional explanation for residual differences in labor market MJH is the degree of labor market dynamism or churn, although theory here is ambiguous. Recent literature has noted that the United States is exhibiting a gradual decline in overall labor market turnover, possibly reflecting a lower degree of dynamism in the U.S. economy (Decker et al., 2014). Similar patterns and concerns have been noted with respect to worker mobility. Internal migration within the United States has shown a gradual but steady decline since the early 1980s, raising further concerns that labor mobility and economic dynamism have fallen (Molloy, Smith, and Wozniak 2011, 2014). A typical argument is that high (but not too high) rates of turnover reflect and make possible desirable matching and sorting in the labor market. If that is the case, we would expect high rates of churn to be associated with good primary job matches in which hours are not constrained, and thus lower rates of MJH. This argument complements evidence found by Bleakley and Linn (2012), who show that MSA-level churn, which they measure by worker-specific changes in industry and occupation, is lower in densely populated areas. They find this result for both voluntary separations and involuntary worker displacements (the latter based on evidence from CPS Displaced Worker Survey supplements). Part of the productivity (wage) advantage of large markets is that workers acquire high-quality matches. Using this same logic, good primary job matches 15 Given the fixity of MJH differences across areas, we measure all of our MSA level variables either as an average over time or for a single point in time. Most of these are computed from the pooled 2000 Census and ACS. The exceptions are labor market size (computed from 2006 Census population estimates), union density (CPS), churn (CPS), employment growth (QCEW), and unemployment rate (LAUS).

15 Table 2. MJH Rates by Labor Market Size, Multiple Job Holding Across Labor Markets 15 MSA Size Mean MJH (1) (2) (3) Obs Nonurban 6.7 3,124, t (0.37) (0.32)* (0.29)** 672, t (0.36)* (0.31)** (0.27)** 879, t 1m (0.39)* (0.33)** (0.30)* 1,310, m (0.35)** (0.30)** (0.26)** 2,176, m (0.46)** (0.39)** (0.40) 1,441,998 5m (0.41)** (0.37)** (0.35) 2,357,384 All Urban 5.3 8,838,400 All U.S ,962,560 * designates significance at the.05, ** at the.01 level. The dependent variable is MJH, coded 100 for multiple job holders (rather than one) and zero otherwise. This allows coefficients to be interpreted as percentage rates. Survey weights are used for both means and regression estimates. Model (1) MJH regression has no controls, thus providing mean differences in MJH relative to the omitted nonurban areas (small MSAs and rural areas not designated in the CPS). Model (2) includes detailed categorical variables for age, education, gender, race, ethnicity, marital status, children, foreign born, citizenship, hours worked in primary but not second job, public sector, industry, occupation, year, and month. Model (3) adds average labor market commute times calculated from the 2000 Census and ACS. CPS data are from September 1995 through December should reduce MJH. This is supported by our evidence of substantially lower rates of MJH in urban labor markets versus nonurban areas, as well as our subsequent finding of lower MJH in metropolitan areas with higher churn rates. That said, there may be forces that work in the other direction. Hyatt and Spletzer (2013, 2017) find that secular employment losses are associated with fewer short-term (one-quarter) jobs. Thus, it is possible that the gradual decline in multiple-job holding might be associated with lower churn and fewer short-term jobs. In the analysis that follows, we examine whether MJH rates are related to the level of churn. Rates of turnover at the MSA (and state) levels are constructed from the full September CPS files (i.e., all rotation groups) based on individual monthly individual transitions between employment and nonemployment and job changes among those employed in consecutive months. Another possible explanation for MSA variation in MJH is that low commuting costs in a labor market will be associated with higher MJH rates, and vice versa. This is a natural extension of the work by Black, Kolesnikova, and Taylor (2014), who find that metropolitan areas such as Minneapolis, with low commute times, have higher rates of female labor force participation than do labor markets such as New York City with long commute times. A quick glance at state rates of MJH show Minnesota (and surrounding states) with among the highest MJH rates, while New York has a relatively low rate MJH rate as compared to other northern states. The New York and several California MSAs are among the few nonsouthern metro areas in the list of MSAs with low MJH. MJH decisions could be particularly sensitive to congestion costs. Commuting is largely a fixed cost of employment and hours worked at second jobs are far lower than in primary jobs, so the relative costs of commuting are high in second jobs, leading to a negative relationship between MJH and commute times. Possibly working in the opposite direction, high commute costs might lead to a poorly-matched primary job and thus increase demand for a second job. Census data for 2000 and, for later years, the ACS, provide data on commute times. Given that city size is inversely related to MJH, coupled with evidence seen previously in Table 2, we expect that commute times will explain some portion of the residual differences in MJH across labor markets. The high rates of MJH in the north central states give rise to a fourth possible explanation for systematic regional differences. Ethnic, religious, and cultural differences may affect labor

16 16 Hirsch, Husain, and Winters market outcomes, including MJH. The north central region of the United States has a large number of households whose members are Lutheran and/or of German and Scandinavian heritage. Data on religion by area is not provided by Census or other governmental statistical agencies. The CPS, which provides data on MJH, includes little information on ethnicity, apart from identifying those who are Hispanic. The CPS provides no information on ancestry, with the exception that it records country of origin among those who are foreign born. Data on ancestry, however, is available in the decennial census long form survey in 2000 and the ACS. We compile metro area measures of ancestry combining the 2000 Census with the pooled ACS. These measures allow us to demonstrate whether ancestry differences across U.S. labor markets are correlated with long-run differences in MJH. Finally, the industrial and occupational structure of a metropolitan area could affect MJH. Some types of primary jobs more naturally lend themselves to second job holding than do others, for example, because of the physical demands, time demands, start times, and flexibility. Such differences can be loosely accounted for with detailed occupation and industry controls for ones primary job. Job structure effects (measured by industry and occupation shares), however, extend beyond the impact of ones own primary job. Industries with strong labor demand for temporary, seasonal, or part-time workers, for example, can provide attractive second job opportunities for amenable workers. Moreover, we cannot condition on individual-level occupation and industry for second jobs since these variables are only observed for workers holding multiple jobs. Instead, we use the 2000 Census and ACS to measure metropolitan area industry and occupation shares to assess the effects of job structure on MJH differences across areas. 6. Evidence on MJH Differences Across Labor Markets In this section, we examine why MJH differs across markets, focusing on the explanations offered in the previous section. Our approach is to examine the extent to which controlling for a variety of detailed worker, job, and city attributes can account for differences across labor markets in MJH. To describe the magnitude of MSA differences (dispersion) in MJH, we calculate the mean absolute deviation (MAD) of MJH across our 259 labor markets based on estimates from increasingly dense individual worker OLS MJH equations using the urban sample (n 5 8,832,284). 16 MAD is calculated as follows. For each MJH regression, where m ijt is MJH for individual i in MSA j during time period t, MJH is coded 0 or 100 (rather than 1, so rates are measured as percentages). We estimate MJH regressions (models 1 through 11) with an increasing number of controls. m ijt 5X ijt b1e ijt (1) For each model we extract the residuals, e ijt, and calculate their weighted means, e j,bymsaj for each of the 259 MSAs. We then compute their absolute deviation from zero, je j j (zero being the weighted full sample national mean of residuals, by construction). Finally, we calculate the weighted MAD of MJH across these 259 MSAs. 17 That is, 16 This count is slightly lower than that reported for the full urban sample used in Table 2 due to exclusion of a small number of small MSAs newly added to the CPS mid-year in For MJH covariates measured at the individual level, our approach is conceptually similar to including MSA fixed effects and measuring their mean absolute deviation. A fixed effects approach has two disadvantages. First, all