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

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 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 below one's 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 worker's 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 one's 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 Sicherman's (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 one's 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 1976–1991). 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.

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. Amuédo-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 2000–2002.

A recent article by Hirsch, Husain, and Winters (2016) uses a large CPS data set for 1998–2013 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 1998–2011. 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 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 1994–1998 (n = 48 × 4 = 240). Partridge includes state and year fixed effects in his MJH models. In contrast to our analysis, the goal of Partridge's 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 market-level 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 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 survey's 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 18–65 (vs. all workers ages 16+ 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 16–17 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 2015.9 The CPS reports work hours, detailed occupation, and detailed industry 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 1994–2015 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 = 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 1995.

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 2014. 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 1994–2015 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 2015.11 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 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. Men's MJH rates between 1994 and 2015 declined from 6.4 to 4.6%, whereas women's 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 2014–2015 versus 1994–1995. A similar analysis is shown for metropolitan areas based on MJH rates in 2012–2014 versus 1996–1998 (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 1994–1995, 2004–2005, and 2014–2015. 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 2014–2015 (y-axis) and rates 20 years earlier in 1994–1995 (x-axis). The same pattern is shown in 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 2014–2015 MJH state rates on 1994–1995 rates has an R² of 0.63 and a coefficient of 0.67 on the 1994–1995 rates. A similar regression using MJH rankings had an R² of 0.58 and a coefficient of 0.79 on the 1994–1995 rankings. Coefficients on 1994–1995 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 1 and 2 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 1). MSAs with low MJH rates are concentrated in the south, along with several California cities and the large NYC-NJ MSA (Table 2). Table 1 shows labor markets with high MJH dominated by relatively small MSAs (the mean sample size across MSAs is 34,498). In contrast, Table 2 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 1). Comparison of sample sizes understates differences in population given that small (large) markets are oversampled (undersampled). More directly, there is strong within-state correlation between metro and nonmetro MJH rates. Over the entire 1996–2014 time period, the within-state correlation between MSA and non-MSA MJH rates is 0.81.12 The equivalent correlations for our earliest years (1996–1998) and most recent years (2012–2014) 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 1996–2014 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 2012–2014 while the horizontal axis shows the rates for 1996–1998. 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 2012–2014 rate on the 1996–1998 rate produced an R² of 0.32 and a coefficient of 0.46 on the 1996–1998 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 2012–2014 than in 1996–1998. This is due to the secular decline in MJH rates; the coefficient of variation is slightly higher in 2012–2014.

In addition to there being regional patterns and considerable fixity over time, MJH also varies with respect to labor market size. In Table 3, we show the average MJH rates over the September 1995–2014 (n = 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 1995–2014 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 3), 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 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 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 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 1995–2014 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 3, 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 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 2005–2011 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 one's primary job. Job structure effects (measured by industry and occupation shares), however, extend beyond the impact of one's 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 2005–2011 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 1995–2014 urban sample (n = 8,832,284).16 MAD is calculated as follows. For each MJH regression, where 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.

(1)

For each model we extract the residuals, , and calculate their weighted means, , by MSA j for each of the 259 MSAs. We then compute their absolute deviation from zero, (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,

(2)

where is the total sample weight for MSA j, summed over all individuals and time periods within each MSA.

Our analysis examines the extent to which these weighted MADs are reduced as we introduce increasingly dense controls. Table 4 shows the values of MAD using 11 increasingly dense MJH equation models. We first enter individual level attributes from the CPS, followed by MSA level attributes compiled from several datasets. Of course, the contribution of each variable(s) to the measure of dispersion is not independent of the order in which variables are entered. We have examined multiple orderings; conclusions regarding the relative importance of particular sets of variables are reasonably insensitive to order. In subsequent analysis, we show the effects of each set of the attributes both when they are entered first and entered last into the MJH equations. Rankings of attribute importance are highly similar in these alternative approaches.18

The first specification in Table 4 includes only year and month dummies, which we interpret as an “unconditioned” measure of the dispersion in MJH rates across labor markets. The second model adds controls for worker demographic and human capital characteristics—sex, race/ethnicity, foreign-born, marital status, presence of young children, and detailed education and age dummies. The third adds job-level controls measuring public employment and hours worked on the primary job, while the fourth adds workers' broad occupation and industry. The remaining models add MSA-level measures. The fifth model adds MSA commute times, the sixth city size dummies, and the seventh MSA mean earnings, housing values, rental rates, and union density. The eighth model adds MSA-level industry and occupation shares (individual-level industry and occupation were included previously). The ninth model adds MSA measures of ancestry and percent foreign born, the tenth a measure of MSA turnover (churn), and the eleventh measures of MSA log employment growth and unemployment rates averaged over 1995–2014. Given the large number of covariates and model specifications, we do not provide a full set of regression coefficient estimates. In the data Appendix, we include a partial set of coefficient estimates for model 11, our most dense specification.19

As seen in Table 4, line 1, the weighted MAD of MSA MJH absent controls (apart from year and month dummies) is 0.99, a 1 percentage point average absolute difference between MSA rates and the mean rate of MJH in urban areas.20 This average deviation from the mean is 18% the size of the 5.3% mean level of MJH. The second specification, which adds control for worker demographic and human capital characteristics, reduces MAD from 0.99 to 0.82. The third, which adds job-level controls measuring public/private sector employment and hours worked dummies on the primary job, reduces MAD from 0.82 to 0.77. Addition of occupation and industry dummies of the primary job in model 4 slightly increases MAD to 0.79. In short, controlling for individual worker and job measures available in the CPS accounts for a rather modest portion (20%) of the dispersion in MJH across markets (from 0.99 to 0.79).

Beginning with model 5, we introduce variables measured at the MSA rather than individual level. The model 5 specification adds MSA mean commute times (average minutes for a one-way trip from home to work), calculated from the pooled 2000 Census 5% PUMS and 2005–2011 ACS for those persons who work outside the home. Inclusion of this measure sharply reduces unexplained deviations in MJH, with MAD falling from 0.79 in line 4 to 0.64 in line 5. In model 6, we add city size dummies, which has a minimal effect, reducing MAD from 0.642 to 0.639. Of course, commute times and city size are highly correlated. When we reverse the order in which we introduce these two measures (not shown in Table 4), we find that adding city size dummies to line 4 reduces MAD from 0.79 to 0.70, a much weaker impact than seen for commute times. The addition of commute times following conditioning on city size reduces MAD substantially, from 0.70 to 0.64.

Our conclusion from these results is that commute times play an important role in determining MJH rates. Across a wide range of specifications, the coefficient on mean transportation times for a daily commute (in minutes) is about −0.1 and always statistically significant at the 0.01 level. This implies that a 5-minute increase in a labor market's average commute time from home to work (the standard deviation across MSAs is 4.3 minutes) is associated with a 0.5 lower MJH rate (half a percentage point). In work not shown, we fail to observe substantive differences between women and men in the relationship between MJH and commute times, in contrast to findings in Black, Kolesnikova, and Taylor (2014) that show married women's labor force participation to be particularly sensitive to commuting costs.

In model 7, we introduce measures reflecting metro area income and housing values—mean hourly earnings, housing values, and rental values—plus union density (which can affect earnings). The first three of these measures are compiled from the 2000 Census and the ACS. Annual union density measures by MSA have been calculated from the CPS outgoing rotation group files by Hirsch and Macpherson (2003, with annual updates online), available at Unionstats.com. We use the average union density over the full sample period. This set of variables accounts for a small amount of MSA dispersion in MJH, reducing MAD from 0.64 to 0.62.

Although individual level industry and occupation on the primary job (model 4) did not help account for labor market differences in MJH, accounting for an MSA's overall industry and occupation structure (model 8) substantially reduces MAD, from 0.62 to 0.44. Among industries, the employment share of agriculture, forestry, and fishing has a substantial positive effect on MJH, which is consistent with the regional MJH pattern seen in Figure 2. The seasonality of work hours in these primary jobs is likely to create off-season demand for secondary jobs. Shares in business and repair services, mining, and public administration are associated with substantively lower MJH. Among occupations, high rates of MJH are found in MSAs with large employment shares in executive, administrative, and managerial occupations and in administrative support. Large shares in sales occupations are associated with low MJH rates. Although measures of industry and occupation at the individual and market levels need not coincide, we are somewhat surprised by the large effect of the latter absent substantive effects from the former.

The regional patterns seen in state MJH rates (Figure 2) prompted us to examine the effects of ancestry, as measured in the 2000 Census and annual ACS. Individuals are asked “What is your ancestry or ethnic origin?” This is followed by examples such as Italian, Jamaican, African American, Ukrainian, and so forth. The Census Bureau codes up to two answers for an individual. They do not provide codes for answers that are rare. We use responses on first ancestry (and ignore second measures) and tabulate and include in our MJH regression the percent of a MSA's workers who identify their ancestry as German, Irish, Italian, Nordic, Other Western Europe, Eastern Europe, Latin America, Northern Africa and the Middle East, Sub-Saharan Africa, Asian & other Pacific, Native American, and other white ancestries; English ancestry is the excluded group. In addition, we include a measure of MSA share foreign born. Recall that we already include individual worker measures of race (including Asian), ethnicity, and foreign born (separately for citizen and noncitizen) from the CPS. As seen in line 9, introduction of the percent ancestry variables accounts for a substantive share of the labor market variation in MJH, reducing MAD from 0.44 to 0.35. Relatively high MSA MJH is seen in labor markets with large shares of workers whose ancestry are (beginning with highest) Nordic, Asian-Pacific, English, German, other Western Europe, Italian, Latin American, and Eastern Europe. Low MJH rates are associated with ancestry shares of (beginning with lowest) Northern Africa and the Middle East, Irish, Native-American, and Sub-Saharan Africa.21

Scholars have found strong relationships between ancestry and economic performance across countries (Putterman and Weil 2010) and U.S. counties (e.g., Fulford, Petkov, and Schiantarelli 2015).22 Putterman and Weil (2010) examine cultural variables (e.g., trust and obedience) and measures of institutions. They emphasize that economic outcomes of countries today are strongly correlated with the institutional development of its current population's ancestors from some 500 years ago, more specifically their agricultural and political development in 1500. Fulford, Petkov, and Schiantarelli (2015) likewise find that the historical cultural values and institutions of the ancestors of today's populations in U.S. counties are associated with higher rates of county GDP growth. Interestingly, they find that on average people whose ancestors lived in high income countries disproportionately reside in poorer U.S. counties. This association is driven by the early settlement of the English who remain disproportionately in the South, coupled with later migrations of Italians to large U.S. cities and the southern out-migration of African Americans to northern cities. The authors discuss pathways through which culture can affect economic outcomes. They also conclude that diversity of ancestries in counties increases growth, as long as cultural attitudes are similar.

It is difficult to interpret relationships between ancestry and MJH since we do not regard particular levels of MJH as inherently good or bad outcomes. Areas of the country displaying high MJH rates, in particular the North Central states, have large Nordic and German populations. These high MJH rates may in part reflect a work ethic and cultural values associated with their ancestors. But high MJH rates in these states also reflect low urbanization, low employment turnover, and in some cases weak economies. Cities such as New York, Los Angeles, and Houston, which have among the lowest MSA MJH rates, have diverse populations and vibrant economies. Our analysis clearly points to strong correlations between ancestry and market-level MJH. Our ability to interpret such correlations, however, is limited.

We next introduce a measure of monthly labor market churn (turnover) based on our calculations using all rotation groups of the CPS from September 1995 through December 2014. We examine transitions of all individuals ages 18–65 from the current survey month and prior survey month for six rotation group pairs: 1–2, 2–3, 3–4, 5–6, 6–7, and 7–8 (rotation groups 4–5 are excluded given the 8-month interval between these interviews). For each individual-month pair we measure whether individuals transitioned from not employed to employed (NE), from employed to not employed (EN), employed both months in the same job (EE-same job), employed both months but switched employers (EE-job switch), and not employed in either month (NN). Our measure of monthly churn (turnover) is calculated for each individual and then summed to an MSA measure. Included in the numerator are the number of hires plus the number of separations (NE counts as 1, EN as 1, EE-job switch as 2, and EE-same job and NN as 0), divided by two times MSA employment. This measure corresponds closely to the standard turnover measure used in the literature with establishment level data, wherein the numerator is the sum of hires plus separation and the denominator two times employment (e.g., Decker et al. 2014). In studies using quarterly establishment data, a worker leaving one establishment and joining another within a quarter is counted twice in the numerator, whereas those transitioning into and out of employment across quarters are each counted once.

As seen in line 10, the introduction of the turnover measure accounts for a modest amount of the dispersion in MJH across labor markets, reducing the MAD measure from 0.35 to 0.33. MJH regressions show that MJH is negatively related to the level of turnover. This result supports our earlier argument that churn helps lubricate search and enables good primary job matches with respect to hours and other job attributes, implying that good job matches mitigate desire for second jobs. The result is not inconsistent with the alternative argument that second jobs are frequently short-term and that high rates of churn are often associated with short-term jobs. But it is clear from our data that either short-term primary jobs differ in some way from second jobs, or the association between short jobs and second jobs is not sufficiently strong to produce a positive relationship between MJH and labor market churn.

The final two measures we address are long-run employment growth and unemployment, each of which are added to Model 11. Log employment growth is measured over the 1995–2014 period, calculated from the QCEW (with county data aggregated to the MSA level). The unemployment rate is constructed from the LAUS data base provided by BLS. As seen in line 11 of Table 4, the MAD measure remains unchanged at 0.33 after accounting for employment growth and unemployment. Employment growth and the unemployment rate are not strongly correlated across labor markets (e.g., Rappaport 2012). Neither business cycle measure has a substantive effect on MJH, consistent with previous findings in Hirsch, Husain, and Winters (2016).

Values of MAD provide simple summary measures of how well the various models perform in accounting for labor market differences in MJH. An alternative way to compare models is to view the entire distribution of the 259 MSA mean residuals from both sparse and dense MJH models previously reported in Table 4. We provide this comparison in Figure 6, based on residuals from MJH model 1 (year and month dummies only), model 4 (model 1 plus the full set of CPS individual-level covariates), and model 11 (model 4 plus all MSA-level covariates). Readily evident is that the distribution of MSA mean residuals from model 11 is far steeper and more concentrated around zero than are the mean MSA residuals from models 1 and 4. As compared to models 1 and 4, model 11 results in a much larger share of MSAs having predicted MJH rates within plus or minus one percentage point of their actual rates.

Because changes in MAD are not invariant to the order in which variables are entered, assessing the relative importance of various worker and MSA attributes in accounting for market differences in MJH is not straightforward. In order to judge the relative impact of our measures we estimate the impact of each set of variables on MAD using two alternative methods of analysis, presented in Table 5. The “last-in” approach measures the impact of each set of variables on MAD when entered last (i.e., the 11th entry, as was the addition of employment growth and unemployment in Table 4). Being a last entry, the observed reduction in MAD represents the effect of unique variation in the added variables, variation uncorrelated with previously entered variables. For example, entering commute times last enables us to observe its effect on MJH differences not already explained by other covariates, some of which (e.g., city size) may be highly correlated with commute time. The “first-in” approach measures the impact of each set of variables when entered first (apart from year and month dummies). Reduction in MAD from a “first-in” set of variables reflects the contribution of both unique variation and correlation with other omitted covariates. Entering MSA city size first, for example, captures a large share of the variation in commute times across MSAs.

Comparing results from a “last-in” and “first-in” analysis is informative and, in this case, reinforces the conclusions reached previously based on the results in Table 4. In the top half of Table 5 we show the seven sets of variables that produce the largest “marginal” reduction in MAD when entered last. In the bottom half of Table 5 we show the seven sets that produce the largest reduction in MAD when entered first. As it turns out, the same four sets of variables produce the largest ΔMAD in both the last-in and first-in analyses. These are MSA industry and occupation shares, percentage foreign and ancestry shares, mean commute times, and labor market churn (turnover). These same sets of variables were previously determined to be important based on our prior analysis seen in Table 4. In results not shown, we also ordered the importance of variables based both on reductions in the mean squared errors and the increases in R² from the various MJH models. The resulting orderings of the MJH covariates were highly similar to those shown in Table 5 based on changes in MAD.

7 Concluding Remarks

For many workers and households, MJH provides highly-valued income, human capital accumulation, or worker satisfaction. MJH may also reflect work hour constraints or poor matches in primary jobs. Although a relatively small share of workers hold multiple jobs at a given point in time, many workers have held multiple jobs at some point in their lives. A largely overlooked labor market pattern is that MJH rates differ substantially across regions, states, and labor markets (MSAs). The relative differences across geographic areas display substantial fixity over the 20-year span of our data. We document these persistent difference in regional and market-specific MJH across the United States and explore alternative explanations for the differences. The mean absolute deviation in MJH absent covariates, apart from year and month dummies, is 1 percentage point, relative to a mean urban rate over the 1995–2014 period of 5.3%.

Although individual characteristics help explain what types of workers choose to take second jobs, the geographic differences in MJH cannot readily be accounted for by individual worker attributes. Rather, systematic differences in labor market characteristics help us understand the forces leading to differences in MJH rates. Controlling for a broad set of covariates measured at the worker and labor market levels reduces our measure of MJH dispersion by two thirds, indicating substantive progress in understanding the correlates of MJH differences across labor markets. Most important in accounting for MJH differences are MSA-level variables measuring industry and occupation shares, differences in population ancestry shares, commute times, labor market churn, and to a lesser extent, individual level measures of primary job work hours and public sector employment. The city size gradient, which shows far lower levels of MJH in highly-populated markets, appears to be the result of better primary job matching in large markets, lessening the need for second jobs, coupled with longer commute times that make such jobs less attractive.