Compositional shifts as explanation for aggregate dynamics

The rate with which employees move into and out of unemployment and the rate with which firms create and destroy jobs depends to a large extent on observable character-istics, such as age and education on the worker side or establishment size and industry on the firm side. Before delving into possible economic explanations for either of the trends shown in table 1.2, it is therefore necessary to investigate how much of the ag-gregate changes can be explained mechanically by shifts in the composition of workers and firms over time. In order to do that, this section documents (1) the importance of a changing labor force composition for aggregate worker flows and (2) the marginal effect of working in the PBS-sector on workers’ transition probabilities.

Role of changing labor force composition

Young workers with low education typically move between employment and unemploy-ment more frequently than their older and more educated peers. One potential expla-nation for the secular decline in labor market dynamics is therefore the demographic shift towards an older and more educated labor force, which mechanically increases the share of workers with low transition probabilities, thus reducing aggregate reallocation.

But how much of the declining worker reallocation rates can be explained by this com-positional shift in the labor force?

Previous studies have already investigated this question: Cairo and Cajner (2013) for example find that shifts in the age and educational distribution explain more than 70%

of the decline in worker reallocation. I replicate their result with the worker reallocation measures (EU- and UE-rates) outlined above with a slightly different reference period and find an even larger effect.

However, this approach neglects that there were sizable changes in the composition of occupations and industries as well. Especially the rise of sectors in which worker turnover is higher on average, works against the compositional shift in worker demo-graphics. In order to account for both trends, I regress workers’ transition probabilities on a set of demographic controls (age, age-squared, education, race, gender) as well as dummies for the time-consistent occupation and industry codes in every year separately.

I then conduct two counterfactual experiments: First, I fix the means of the explana-tory variables at their respective levels in the early 1980s and vary only the regression coefficients (including the constant) over the years. The resulting predictions show how separation and hiring rates would have evolved, had the demographic composition and occupational and industry structure remained unchanged since the 1980s. Second, I fix the regression coefficients at their 1980s level and vary only the means of all right-hand side variables over time. The resulting time series show how reallocation rates would have evolved if only the demographic, occupational and industrial composition had changed.

Table 1.3 displays the results of both experiments together with the benchmark prediction, i.e. the actual flow rate averages as in table 1.2. Taken separately, each counterfactual experiment can explain approximately two thirds of the secular decline in worker flow rates.⁶ Both experiments then allow to compute the contribution of compositional shifts towards the overall decline in the respective flow rate: in the first experiment as a residual, in the second experiment directly (see last column). Compo-sitional shifts account for 35-69% (43-66%) of the overall decline in separation (hiring) rates. Taking the average of the lower and upper bound, the mean contribution of changing demographics and changing occupation and industry compositions is slightly above 50%. That is clearly less than the 70% which arise if only demographic shifts regarding age and education are considered. A substantial fraction of the downward trend in worker mobility therefore remains unexplained by observable characteristics.

6The contributions do not add up to 100 because of interaction effects that do not enter in the regression.

Table 1.3: Composition effects

1980-84 2012-16 ∆ contribution compos. effect

EU benchmark 1.9 1.2 -36.6 100.0

-only vary coefficients 1.9 1.5 -23.7 64.9 35.1

only vary composition 1.9 1.4 -25.3 69.3 69.3

average contribution - - - - 52.2

UE benchmark 2.0 1.4 -30.5 100.0

-only vary coefficients 2.0 1.6 -17.5 57.3 42.7

only vary composition 2.0 1.6 -20.1 65.9 65.9

average contribution - - - - 54.3

Notes: Counterfactual EU- and UE-transition rates with a constant demographic, industrial and occu-pational composition (second row) and if only composition changes (third row). Column 1 and 2 show average rates at the beginning and end of the sample. Column 3 shows relative change in %. Column 4 shows the respective contribution of each counterfactual towards the overall decline in EU- and UE-rates. The last column takes the residual in the second row to obtain the contribution of compositional shifts. “Average contribution” denotes the mean between row 2 and 3 in the last column.

Role of shift towards PBS-sector

This paper investigates the link between falling worker reallocation rates and the rise of domestic outsourcing over time. One potential explanation for this link could be that the PBS-sector generally exhibits lower worker reallocation rates on average than other industries. If workers who would have been employed in non-PBS firms in the 1980s now work in the PBS-sector, then the aggregate worker reallocation rate would have decreased mechanically as a result of this cross-industry shift.

Evidently, employees in the PBS-sector might differ dramatically from workers in non-PBS firms regarding their demographic characteristics as well as the occupations they work in. Merely looking at the average reallocation rate in the PBS-sector and com-paring it to the non-PBS sectors would therefore neglect potentially large selection effects. In order to accommodate that concern, I redo the same year-by-year regres-sion of worker flow rates on observable characteristics as in the previous section, now including a dummy variable for PBS-industries rather than detailed industry controls.

The resulting coefficients on the PBS-dummy variable illustrates the marginal effect of working in a PBS-firm compared to a non-PBS firm, after controlling for age, education, race, gender and occupation.

Figure 1.2 shows the coefficient of the PBS-dummy over time for separation and hir-ing rates. The gray-shaded area marks the 95%-confidence interval. The coefficient is always significantly positive, indicating that everything else equal, employees in PBS-firms actually face higher reallocation rates compared to their peers in non-PBS firms.

A purely mechanical shift of employees towards the PBS-sector therefore works against falling worker flow rates. Doing a simple counterfactual exercise as in the previous sec-tion illustrates that result: If the PBS-sector had not grown as a share of employment

over time, then worker flow rates would have fallen by approximately two percentage points more than observed in the data.

Figure 1.2: Coefficient β_{P BS}: 1980-2016

(a) Separation rate

0.002.004.006.008.01

1980 1985 1990 1995 2000 2005 2010 2015

year

(b) Hiring rate

0.002.004.006.008.01

1980 1985 1990 1995 2000 2005 2010 2015

year

Notes: Marginal effects of working in the PBS-sector on separation and hiring rates from 1980-2016 after controlling for observable characteristics (age, education, race, gender, occupation).