Panel B shows that gross occupational mobility through unemployment is also procyclical when using (log) productivity instead of (log) unemployment rate. For the linearly de-trended series, the correlations are again high (around 0.70) and the empirical elasticities are positive and significant at a 1% level. Although the HP-filtered series are more noisy, they still yield statistically significantly positive elasticities at a 5% level for the 2000 SOC and 4 task-based categories series; and at a 1% level when considering the 4 task-based categories series without managerial occupations. In the case of mobility across industries, we also find positive and statistically significant elasticities with respect to labor productivity. In terms of the correlation coefficients we find that these are positive with respect to the linearly de-trended productivity, but is nearly zero in the HP-filtered series.[One reason is that the HP-filtered productivity series peaks around 2005, at which time industry mobility of the unemployed is high compared to the average level, but at the same time somewhat lower than in the earlier 2000s. The HP-filtered series weighs the latter more heavily. Further, while industry mobility subsequently meaningfully dropped in the Great Recession, it recovered at a rate mirroring the recovery of unemployment. In contrast, HP-filtered productivity dropped sharply at the beginning of the Great Recession, but also recovered relatively sharply before the beginning of 2010. This behavior is partly behind the relatively low correlation between productivity and unemployment as well. Interestingly, the correlations between mobility and unemployment, and between mobility and output typically are at the same level, and at times even stronger, than the correlation between output and unemployment for the main occupational mobility series.]

Note that across panels A and B all elasticities are higher for the $\Gamma$-corrected series than for the uncorrected series, illustrating that miscoding reduces the cyclical response of gross occupational mobility.[A further exercise, in which we reduced the impact of noise (and very high frequency movements), by isolating (using TRAMO/SEATS) the trend-cycle component, yields similar results. As a consequence of the reduction in noise, standard errors are much smaller, and more of the elasticities are significant at the 1% level.]

Cyclical responsiveness using non-employment spells

Cyclicality Occupational Mobility Using NUN Spells

4c5Q  MA  $\Gamma$-CORRECTED     4c5Q MA UNCORRECTED

2-9              2clinearly de-trend     2cHP 1600     2clinearly de-trend     2cHP 1600

2-9    1lCategory     1lelasticity     1c$\rho$     1lelasticity      1c$\rho$     1lelasticity     1c$\rho$     1lelasticity      1c$\rho$

9cMobility across NUN-spells, wrt Unemployment Rate

2000 SOC      -0.14***     -0.73     -0.13***     -0.43     -0.09***     -0.73     -0.08***     -0.42

(0.02)               (0.04)               (0.01)               (0.02)

1990 SOC      -0.17***     -0.60     -0.16***     -0.35     -0.11***     -0.67     -0.11***     -0.42

(0.03)               (0.06)               (0.02)               (0.03)

4 task-based categories      -0.11***     -0.38     -0.00     -0.01     -0.08***     -0.43     -0.02     -0.07

(0.04)               (0.07)               (0.02)               (0.04)

4 task-based cat. (Excl. Manag.)     -0.14***     -0.48     -0.08     -0.21     -0.10***     -0.53     -0.06*     -0.23

(0.03)               (0.05)               (0.02)               (0.04)

Industries (1990 SIC)     -0.16***     -0.72     -0.21***     -0.55     -0.13***     -0.72     -0.16***     -0.53

(0.02)               (0.04)               (0.02)               (0.03)
N(quarters)         x58            x58            x58               x58

9c*$\ p<0.1$; **$\ p<0.05$; ***$\ p<0.01$

To complement the above analysis, Table <ref> considers the cyclical behavior of occupational mobility among those workers who mixed periods of unemployment with periods of out-of-labor-force during their non-employment spells (NUN-spells). In this case we also find that occupational and industry mobility are procyclical. Point estimates of the elasticities of occupational mobility with respect to unemployment are somewhat lower when using NUN spells rather than pure unemployment spells, but generally these differences are not statistically significant. Point estimates of the procyclical responsiveness of industrial mobility are instead higher. The linearly de-trended data once again yield statistically significant procyclicality at the 1% level across all mobility measures. The HP-filtered series also yields similar results when considering mobility across major occupational groups (both 1990 and 2000 classification) and industry mobility. However, the pattern is statistically weaker for mobility across the 4 task-based categories, but still marginally significant (at the 10% level) in the uncorrected data when managers are excluded.

Cyclical responsiveness at different moments of the unemployment spell

Cyclicality of Mobility at Different Moments of the Unemployment Spell

2000 SOC        1990 SOC        2000 SOC-NUN     OCC*IND     IND       NR/R-M/C      C/NRM/RM

(1)       (2)       (3)       (4)       (5)       (6)       (7)

8cPanel 1. Regression of Individual Mobility on Linearly De-trended Unemployment Rate

8c1(a) unemployment in quarter of hiring

U hiring     -0.0788***     -0.0890***     -0.0583***     -0.0606***     -0.0952***     -0.0575***     -0.0589***

(s.e.)     (0.0180)     (0.0211)     (0.0153)     (0.0192)     (0.0219)     (0.0192)     (0.0191)

8c1(b) unemployment in quarter of separation

U sep     -0.0628***     -0.0691***     -0.0536**     -0.0420*     -0.0586**     -0.0700***     -0.0659***

(s.e.)     (0.0211)     (0.0239)     (0.0206)     (0.0249)     (0.0256)     (0.0211)     (0.0202)

8c1(c) unemployment at hiring and at separation averaged

U ave     -0.0868***     -0.0969***     -0.0704***     -0.0630***     -0.0946***     -0.0776***     -0.0758***

(s.e.)     (0.0218)     (0.0259)     (0.0204)     (0.0239)     (0.0247)     (0.0231)     (0.0222)

8c1(d) unemployment at moment of hiring & separation

U. hiring     -0.0676***     -0.0782***     -0.0429**     -0.0583**     -0.0996***     -0.0241     -0.0306

(s.e.)     (0.0236)     (0.0253)     (0.0204)     (0.0265)     (0.0316)     (0.0242)     (0.0256)

U sep.     -0.0182     -0.0175     -0.0271     -0.0037     0.0071     -0.0540**     -0.0456*

(s.e.)     (0.0277)     (0.0293)     (0.0268)     (0.0334)     (0.0352)     (0.0261)     (0.0267)

8cPanel 2. Regression of Individual Mobility on HP-filtered Unemployment Rate

8c2(a) unemployment in quarter of hiring

U hiring     -0.1594***     -0.1768***     -0.1182***     -0.1198**     -0.2092***     -0.0840**     -0.0959**

(s.e.)     (0.0420)     (0.0449)     (0.0366)     (0.0458)     (0.0543)     (0.0408)     (0.0419)

8c2(b) unemployment in quarter of separation

U sep     -0.0916*     -0.0943*     -0.1017**     -0.0628     -0.1015     -0.1292***     -0.1238***

(s.e.)     (0.0479)     (0.0505)     (0.0455)     (0.0535)     (0.0633)     (0.0402)     (0.0406)

8c2(c) unemployment at hiring and at separation averaged

U ave     -0.2187***     -0.2358***     -0.2078***     -0.1588**     -0.2697***     -0.1895***     -0.1938***

(s.e.)     (0.0590)     (0.0677)     (0.0601)     (0.0635)     (0.0685)     (0.0563)     (0.0547)

8c2(d) unemployment at moment of hiring & separation

U. hiring     -0.1492***     -0.1666***     -0.1127***     -0.1130**     -0.1985***     -0.0671     -0.0794*

(s.e.)     (0.0437)     (0.0460)     (0.0387)     (0.0483)     (0.0579)     (0.0439)     (0.0451)

U sep.     -0.0723     -0.0727     -0.0960**     -0.0482     -0.0756     -0.1205***     -0.1132***

(s.e.)     (0.0467)     (0.0503)     (0.0458)     (0.0521)     (0.0575)     (0.0400)     (0.0406)

8c*$\ p<0.1$; **$\ p<0.05$; ***$\ p<0.01$

The preceding analysis documents the procyclicality of occupational (industry) mobility, measuring the state of the economy at the end of the unemployment spell; i.e. at the time of job finding. Table <ref> investigates whether this result survives when instead we measure the unemployment rate at the beginning of the worker's unemployment spell (at the time of job separation) or use the average of the unemployment rates at job separation or job finding. The first panel considers the case in which we use the linearly de-trended (log) unemployment rate as our cyclical measure and reports estimates of a regression incorporating a linear time trend and a dummy variable for the change of occupational classification at the 2004 panel. The main message from the first two rows is that measuring the unemployment rate at the moment of job separation or job finding does not affect our conclusions. In both cases we find that the occupational (industry) mobility of unemployed workers is procyclical. Note that the point estimates obtained in the first five columns decrease when measuring the unemployment rate at the moment of job separation. However this decrease is not meaningful and the point estimates remain statistically significant. Taking the average of the unemployment rates at job separation and job finding yields the highest empirical responsiveness in the point estimates, with significance at the 1% across all columns. Further, including both the unemployment rate at job separation and at job finding, the point estimates reveal an additional negative impact on mobility when unemployment rates are high both at the time of job separation and job finding (and, in practice, in between), but this impact is not statistically significant.

In the second panel of Table <ref> we relate occupational (industry) mobility to the HP-filtered (log) unemployment rate, measured again at the time of job separation or of job finding. Here we also observe that occupational (industry) mobility remains procyclical. However, we observe a meaningful drop in the responsiveness of occupational mobility with respect to the unemployment rate when using the 2000 SOC and 1990 SOC and in the case of industry mobility. As in the previous case, the clearest response is obtained when we use the average HP-filtered unemployment at the begin and end of the spell, with statistically significant coefficients at the 1% level. When including both HP-filtered unemployment rates, although measured imprecisely, the impact of higher unemployment rate at the end of the spell, ceteris paribus, is additionally negative (and not economically insignificant) for mobility. For mobility across the 4 task-based categories, this impact is at least marginally significant at the 10% level, while for those NUN spells it is significant at the 5% level.

The above results then suggest that our benchmark analysis is conservative in that we have not selected the timing of unemployment to maximize the cyclical responsiveness of the occupational mobility rate among unemployed workers.

Cyclical responsiveness when controlling for demographic characteristics and occupational identities

Next we investigate whether the cyclical behavior of occupational mobility among the unemployed is affected by demographic characteristics and/or the possible effects of source or destination occupations. Panels A and B of Table <ref> consider regressions of the form
of the form

\begin{align}
\mathbf{1}_{\text{occmob}} &= \beta_0 + \beta_{1}\text{Cyclical Variable} +\text{Controls} + \varepsilon, \tag{R1}\label{e:cycl_app_controls}
\end{align}

where as the cyclical variable we use either the linearly de-trended (log) unemployment rate and on the HP-filtered (log) unemployment rate. Each panel is divided into a number of sub-panels that present different set of controls. In the first sub-panel we consider a regression relating the uncorrected mobility to the relevant cyclical variable, a linear time trend, and a dummy for 2000 SOC (implemented in the 2004 and 2008 panels).[We further have investigated the cyclical patterns during 1985-2003 period in isolation. This analysis gives very similar results, although they produce less precise estimates.] In the second sub-panel we add demographic controls to the previous regression, while in the third sub-panel we further add source occupation dummy variables. In the fourth sub-panel we further control for destination occupation dummy variables instead of controlling for source occupation. Column (i) considers the 2000 SOC, column (ii) the 1990 SOC, while column (iv) considers simultaneous occupation and industry mobility and column (v) considers industries based on the 1990 Census classification. We analyse the 4 task-based categories including managerial occupations in column (vi) and excluding managerial occupations in column (vii). In addition, in column (iii) we consider NUN spells instead of just pure unemployment spells. Note that all of these regression extend the corresponding regressions reported in Section 1 of this appendix by including the relevant cyclical variable. Standard errors are derived by clustering at the quarter level.

Cyclicality of Mobility Controlling for Demographics and Occupation Identities

2000 SOC        1990 SOC        2000 SOC-NUN     OCC*IND     IND       NR/R-M/C      C/NRM/RM

(1)       (2)       (3)       (4)       (5)       (6)       (7)

8cPanel A. Regression of Individual Mobility on Linearly De-trended Unemployment Rate

8cA1: uncorrected, time controls, no demog, no occ/ind controls

U.rate     -0.0788***     -0.0890***     -0.0583***     -0.0606***     -0.0952***     -0.0575***     -0.0589***

(s.e.)     (0.0180)     (0.0211)     (0.0153)     (0.0192)     (0.0219)     (0.0192)     (0.0191)

8cA2: uncorrected, time and demog. controls, no occ/ind controls

U.rate     -0.0763***     -0.0910***     -0.0552***     -0.0579***     -0.0904***     -0.0542***     -0.0547***

(s.e.)     (0.0176)     (0.0213)     (0.0151)     (0.0199)     (0.0217)     (0.0198)     (0.0198)

8cA3: uncorrected, time, demog. & source occ. Controls

U.rate     -0.0707***     -0.0810***     -0.0521***     -0.0546***     -0.0783***     -0.0560***     -0.0578***

(s.e.)     (0.0176)     (0.0217)     (0.0148)     (0.0200)     (0.0217)     (0.0193)     (0.0192)

8cA4: uncorrected, time, demog. Controls and dest. Occ controls

U.rate     -0.0766***     -0.0831***     -0.0564***     -0.0568***     -0.0817***     -0.0545***     -0.0568***

(s.e.)     (0.0176)     (0.0214)     (0.0146)     (0.0197)     (0.0218)     (0.0197)     (0.0197)

8cPanel B. Regression of Individual Mobility on HP-filtered Unemployment Rate

8cB1: uncorrected, time controls, no demog, no occ/ind controls

HP U.rate     -0.1594***     -0.1768***     -0.1182***     -0.1198**     -0.2092***     -0.0840**     -0.0959**

(s.e.)     (0.0420)     (0.0449)     (0.0366)     (0.0458)     (0.0543)     (0.0408)     (0.0419)

8cB2: uncorrected, time and demog. controls, no occ/ind controls

HP U.rate     -0.1520***     -0.1782***     -0.1088***     -0.1137**     -0.2012***     -0.0751*     -0.0829*

(s.e.)     (0.0418)     (0.0457)     (0.0367)     (0.0472)     (0.0536)     (0.0421)     (0.0438)

8cB3: uncorrected, time, demog. & source occ. Controls

HP U.rate     -0.1409***     -0.1623***     -0.1045***     -0.1090**     -0.1825***     -0.0782*     -0.0884**

(s.e.)     (0.0396)     (0.0451)     (0.0347)     (0.0472)     (0.0542)     (0.0413)     (0.0427)

8cB4: uncorrected, time, demog. Controls and dest. Occ controls

HP U.rate     -0.1476***     -0.1577***     -0.1172***     -0.1115**     -0.1697***     -0.0770     -0.0906*

(s.e.)     (0.0427)     (0.0495)     (0.0364)     (0.0479)     (0.0542)     (0.0466)     (0.0474)

obs       12639     12591     16574     12260     12309     12639     11506

8c*$\ p<0.1$; **$\ p<0.05$; ***$\ p<0.01$

The main message from this table is that including demographic controls, or occupation (industry) fixed effects do not seem to meaningful change the measured responsiveness of mobility with respect to the business cycle. Our evidence therefore strongly suggests that the procyclicality of  occupational/industry mobility among the unemployed is not driven by compositional shifts, whereby in expansions (recessions) we observe more (less) workers who are linked to occupation/industries or demographic characteristics that are associated with higher mobility.

Cyclical responsiveness when controlling for unemployment duration

We now investigate the role of unemployment (non-employment) duration in the cyclicality of gross occupational mobility. To do so, we estimate regressions of the form

\begin{align}
\mathbf{1}_{\text{occmob}} &= \beta_0 + \beta_{1}\text{Cyclical Variable} +\beta_{2}\ \text{(u. duration)} + \text{Controls} + \varepsilon. \tag{R-dur}\label{e:cycl_app_dur}
\end{align}

Panel A show the estimated coefficients of the HP-filtered (log) unemployment rate together and spell duration when using the uncorrected mobility data (see the main text for the $\Gamma$-corrected estimates. Panel B presents these estimates using the uncorrected data by further adding a time trend and controlling for classification changes. Panels C, D and E progressively add demographic controls (gender, education, race, age), source occupation fixed effects or destination occupation fixed effects, respectively. Panel F considers the estimated duration coefficient with added controls over the same uncorrected data, but without a cyclical regressor. Note that because of we use only those quarters with an uncensored duration distribution in the cyclical analysis (and in panel F), these data is a subset of the one used to estimate the overall duration profile in the main text and the previous sections of this appendix.

Cyclicality of Mobility Controlling for Unemployment Duration

2000 SOC        1990 SOC        2000 SOC-NUN       OCC*IND     IND       NR/R-M/C      C/NRM/RM

1c(1)     1c(2)     1c(3)     1c(4)     1c(5)     1c(6)     1c(7)

8cPanel A: uncorrected, no demog, no time, no occ/ind controls
HP U.rate     -0.1655***     -0.1831***     -0.1126***     -0.1251***     -0.2068***     -0.0834*     -0.0974**

(s.e.)     (0.0475)     (0.0518)     (0.0385)     (0.047)     (0.0554)     (0.0449)     (0.0444)

Duration     0.0139***     0.0148***     0.0139***     0.0110***     0.0115***     0.0093***     0.0093***

(s.e.)     (0.0019)     (0.0017)     (0.0017)     (0.0019)     (0.0018)     (0.0015)     (0.0016)
8cPanel B: uncorrected, with time and classification controls
HP U.rate     -0.2037***     -0.2237***     -0.1494***     -0.1549***     -0.2458***     -0.1137***     -0.1240***

(s.e.)     (0.0421)     (0.0453)     (0.0360)     (0.0459)     (0.0540)     (0.0418)     (0.0422)

Duration     0.0142***     0.0151***     0.0141***     0.0112***     0.0117***     0.0095***     0.0095***

(s.e.)     (0.0019)     (0.0017)     (0.0017)     (0.0019)     (0.0018)     (0.0015)     (0.0016)
8cPanel C: uncorrected, with demog, time controls, no occ/ind controls
HP U.rate     -0.2019***     -0.2297***     -0.1419***     -0.1527***     -0.2418***     -0.1085**     -0.1138**

(s.e.)     (0.0417)     (0.0461)     (0.0361)     (0.0471)     (0.0530)     (0.0432)     (0.0444)

Duration     0.0160***     0.0166***     0.0143***     0.0125***     0.0130***     0.0107***     0.0106***

(s.e.)     (0.0019)     (0.0018)     (0.0016)     (0.0018)     (0.0017)     (0.0015)     (0.0016)
8cPanel D: uncorrected, source occupation controls, time and demographic controls
HP U.rate     -0.1863***     -0.2106***     -0.1357***     -0.1436***     -0.2178***     -0.1123***     -0.1206***

(s.e.)     (0.0397)     (0.0457)     (0.0340)     (0.0471)     (0.0534)     (0.0423)     (0.0432)

Duration       0.0148***     0.0155***     0.0134***     0.0112***     0.0112***     0.0109***     0.0109***

(s.e.)     (0.0019)     (0.0017)     (0.0016)     (0.0019)     (0.0018)     (0.0015)     (0.0016)
8cPanel E: uncorrected, destination occupation controls, time and demographic controls
HP U.rate      -0.2036***     -0.2085***     -0.1462***     -0.1545***     -0.2259***     -0.1081**     -0.1188***

(s.e.)     (0.0397)     (0.0457)     (0.0332)     (0.0466)     (0.0521)     (0.0426)     (0.0433)

Duration       0.0152***     0.0156***     0.0133***     0.0117***     0.0113***     0.0109***     0.0109***

(s.e.)     (0.0019)     (0.0017)     (0.0016)     (0.0019)     (0.0018)     (0.0015)     (0.0016)
8cPanel F: comparison (duration profile, no cycle, with time + demog controls)
Duration      0.0149***     0.0153***     0.0136***     0.0117***     0.0119***     0.0102***     0.0101***

(s.e.)     (0.0019)     (0.0018)     (0.0016)     (0.0018)     (0.0018)     (0.0015)     (0.0016)

8c*$\ p<0.1$; **$\ p<0.05$; ***$\ p<0.01$

Relative to the results in Table <ref> (panel B), we observe that controlling for the duration of the unemployment (non-employment) spell increases the cyclical responsiveness of occupational mobility. This arises as the estimated unemployment coefficient conditional on duration captures the vertical shift of the mobility-duration profile. Without controlling for duration, the unemployment coefficient captures both the vertical shift of the profile and the rightward shift of the unemployment duration distribution that one observes in recessions. The inclusion of further controls does not meaningfully change either the slope of the profile or the cyclical responsiveness of occupational mobility. This suggests that the scope to link compositional shifts across source occupations (or demographics) to the observed cyclicality of mobility conditional on completed unemployment duration is limited. Further, it also runs counter to a role of occupational or demographical shifts in our finding that recessions also exhibit a moderately increasing mobility-duration profile for the unemployed

[2000 SOC - Aggregate mobility-duration profile]
[2000 SOC - Marginal mobility-duration profile]

[4RTMC - Aggregate mobility-duration profile]
[4RTMC - Marginal mobility-duration profile]

[4RTMC, excl. Mgt - Aggregate mobility-duration profile]
[4RTMC, excl. Mgt - Marginal mobility-duration profile]
Cyclical responsiveness of the mobility-duration profile

§.§ Cyclical responsiveness of the mobility-duration profile

We now turn to investigate the cyclicality of the mobility-duration profile. Figures <ref>a, c and e depict the mobility-duration profile in periods of high and low unemployment, using the major occupations of the 2000 SOC and the 4 task-based categories. Periods of high unemployment are defined as those in which the HP-filtered (log) unemployment rate lies within the top third of the distribution of HP-filtered (log) unemployment rates; while periods of low unemployment are defined as those in which the HP-filtered (log) unemployment rate lies within the bottom third of the distribution. The thickness of each profile reflects the number of unemployed workers with an unemployment spell of at least $x$ months of duration. It is readily seen that in periods of high unemployment there are both more unemployed workers and longer unemployment spells. The main conclusion from these graphs is that occupational mobility is higher in periods of low unemployment than in periods of high unemployment at all unemployment durations. Figure <ref>a shows that when considering major occupations the two mobility-duration profiles are statistically different from each other for those unemployment durations of up to 6 months. Beyond this point, the mobility-duration profile associated with periods of low unemployment becomes thinner and its confidence bands become wider.[We do not plot the duration profile when the associated confidence interval becomes wider than 0.2.] Figure <ref>e shows that when considering the 4 task-based occupations without the managerial occupations, the two mobility-duration profiles are also statistically different from each at shorter unemployment durations. Including the managerial occupations decreases the precision of the estimates, generating wider confidence intervals.

As a complementary way to investigate the shift of the mobility-duration profile between periods of high and low unemployment, Figures <ref>b, d and f depict the marginal mobility-duration profile. The latter measures the change in occupational mobility at the same completed unemployment duration between periods of high and low unemployment. This is in contrast to the mobility-duration profiles considered in Figures <ref>a, c and e where, for example, a substantial part of those in unemployment at 4 months will still be in unemployment at 5 months, and thus contributing to the average occupational mobility at 4 and 5 months. Since the construction of the marginal mobility-duration profile relies on a much lower number of observations at each duration, we make a functional form assumption on the shift of this profile over the cycle. Specifically, we estimate the probability that a worker changed occupation (industry) at a given unemployment duration as

\begin{align}
\mathbf{1}_{\text{occmob}} &= \beta_0 + \beta_{1}\mathbf{1}_{\text{Cycl}} +\sum_{n=1}^{12}\beta_{2n} \ \text{(u. duration dummy)} + \nonumber \\
& \qquad \qquad  +\beta_{3}\left(\mathbf{1}_{\text{Cycl}}\times \text{u. duration}\right) + \beta_{4}\left(\mathbf{1}_{\text{Cycl}}\times \text{(u. duration)}^2\right) + Controls + \varepsilon, \tag{R-X}\label{e:cycl_app_r1}
\end{align}

where $\mathbf{1}_{\text{Cycl}}$ is the cyclical indicator (0 for periods of high unemployment and 1 for periods of low unemployment), and unemployment duration refers to completed spell duration. We estimate this equation on the uncorrected (from miscoding) SIPP data, controlling for a linear time trend and classification changes. Note that equation <ref> allows us to shift the marginal mobility-duration profile differently at different durations following a quadratic relationship.[We have also experimented with a cubic and quartic relationship. These alternatives at times make the response stronger for durations between 6-10 months, while declining beyond 10 months. However, beyond 10 months, the confidence intervals in all cases become rather wide. Using an alternative specification, where instead of duration dummies for the profile, we use a linear baseline relation between mobility and duration does not change the presented relationship meaningfully. Using a cubic or quartic formulation does not meaningfully change the direction of the shifts or its statistical significance, throughout this section.] Figures <ref>b, d and f show that in times of high unemployment, workers who end their unemployment spells at any duration have a lower probability of changing occupation. The differences in probability of an occupational change is statistically significant for durations between 2 and 10 months when using the 2000 SOC and between 2 and 7 months when using the 4 task-based categories, excluding managerial occupations.

[Major Occupation Groups (1990 SOC) ]
[NRMC ]
Cyclical Occupational Mobility Shift of the Unemployed

Figure <ref> shows the marginal mobility-duration profile by estimating regression <ref> using instead the linearly de-trended unemployment rate as the cyclical indicator. In this case we observe an even stronger difference in the occupational mobility rates of unemployed workers at any completed duration during periods of high and low unemployment rates. In particular, for the 4 task-based categories, including managers, we now see that in periods of high unemployment the entire profile shifts down, and statistically significantly so between 2 and 8 months.

[2000 SOC - Corrected Mobility in Surviving Spells]
[2000 SOC - Cyclical Shift of Mobility across Percentiles of Completed Spell Distribution]

[NRMC excl. Mgt, Code-Error Corr in Surviving Spells ]
[NRMC excl. Mgt, Cyclical Shift of Mobility across Percentiles of Completed Spell Distribution]
Cyclical change in Mobility, and Rank in Unemployment Duration Distribution

Occupational Mobility and Rank in the Duration Distribution over the Business Cycle

To investigate to what extend the rightward shift in the mobility-duration profile observed in Figures <ref>a, c and e is due to a rightward shift in the unemployment duration distribution, we derive workers' occupational mobility as a function of the rank of their unemployment spell in the duration distribution. We do this both in times of high and low unemployment, as defined above. Figures <ref>a and c, depict the relation between the average occupational mobility rate of the 100-x% of longest unemployment spells with the rank of these unemployment spells in the unemployment duration distribution. For both the major occupational groups of the 2000 SOC and the 4 task-based categories we observe that at any given rank, occupational mobility is lower in periods of high unemployment and this difference appears statistically significant for a wide interval of ranks around the median. This implies that the downward shift of the mobility-duration profile in times of high unemployment goes beyond the rightward shift of the duration distribution associated with recessions. In particular, we do not observe that the business cycle shifts mobility at lower quantiles of the duration distribution in a different direction than it does at the higher quantiles. Therefore, it is not the case that relatively shorter unemployment spells display more occupational attachment in a recession, while simultaneously the relatively longer spells display less occupational attachment.

Figures <ref>b and d, investigate this issue further and depict the estimated change in occupational mobility between period of high and low unemployment on individual panel (uncorrected) data, controlling for a time-trend and classification effects. We analyse occupational mobility at a given percentile of the (completed) duration distribution, rather than the average occupational mobility of the subset of all spells including and above that percentile, as in Figures <ref>a and c. Once again since this exercise involves a lower number of observations we make a functional form assumption on the relationship between unemployment duration and occupational mobility. In particular, we estimate a variant of <ref> as

\begin{align}
\mathbf{1}_{\text{occmob}} &= \beta_0 + \beta_{1}\mathbf{1}_{\text{Cycl}} +\beta_{2,1}\ \text{(u. duration)} + \beta_{2,2}\ \text{(u. duration)}^2 \nonumber \\
& \qquad \qquad +\beta_{3}\left(\mathbf{1}_{\text{Cycl}}\times \text{u. duration}\right) + \beta_{4}\left(\mathbf{1}_{\text{Cycl}}\times \text{(u. duration)}^2\right) + \text{Controls} + \varepsilon, \tag{R-XX}\label{e:cycl_app_r2}
\end{align}

where $\mathbf{1}_{\text{Cycl}}$ is the cyclical indicator (0 for periods of high unemployment and 1 for periods of low unemployment) and instead of unemployment duration dummies we have a smooth quadratic duration profile in recessions, and a (potentially) different quadratic duration profile in booms.[We also have experimented with linear, cubic and quartic specifications of the duration profile, which do not lead to different conclusions for the first-order patterns discussed here, unless stated.]

For mobility across major occupational groups we observe that at any given percentile of the completed unemployment duration distribution, mobility is higher in periods of low unemployment relative to periods of high unemployment. This difference is statistically significant for the vast majority of spells between the 35th and 90th percentile. This pattern is very similar for mobility across the 4 task-based categories, albeit statistically weaker. Thus recessions appear to reduce occupational mobility across a wide range of the percentiles of the unemployment spell distribution. Figure <ref> shows the estimates for the same exercises as before, but using the linearly de-trended unemployment rates as a cyclical indicator. Here we also observe a cyclical shift in mobility across the whole distribution of unemployment spells. In this case, the difference between periods of high and low unemployment is statistically significant at almost all quantiles of the distribution up to the 80-90th percentile (2000 SOC) and between the 35th and 95th percentile (4 task-based caterories).

[21 MOG (2000) Mobility in Outflow from U ]
[4 NRMC excl. Mgt Mobility in Outflow from U ]
Good and Bad Times according to Linearly-Detrended Unemployment Series

[Major Occ. Groups (1990 SOC) ]
[Major Occ. Groups (1990 SOC)]

[Major Industry Groups (1990 Census Ind. Cl.) ]
[Major Industry Groups (1990 Census Ind. Cl.)]
Occupation and Industry Mobility of the Unemployed (1990 Census Classifications)

Conclusion  In recessions, unemployment spells are longer and unemployed workers are less mobile. This pattern is shared across many subsets of the population, when dividing by gender, education, age, occupations and industries. Within a recession as within an expansion, longer-unemployed workers are relatively more mobile. We show that the mobility-duration profile appears to shift down in recessions. However, the cyclical shift down of the duration profile in recession goes beyond a slowdown in the job finding rate. Throughout the quantiles of the unemployment spell distribution, occupational mobility goes down in recessions (statistically so for a substantial part of the distribution). This pattern appears a general feature of occupational and industrial mobility of the unemployed, and is likewise present  for the 1990 SOC and for 1990 Industry Census Classification, in Figure <ref>.

§.§ The cyclical behaviour of net occupational mobility

To study the cyclical behavior of net mobility we split the sample of unemployment spells into two groups. The first group is composed by those spells that ended in quarters with the highest $(100-\underline{x}_b)\%$ of HP filtered (log) unemployment rates. We label these quarters as “downturns”. The second group is composed by those unemployment spells that ended in quarters with the lowest $ \overline{x}_g\%$ of HP filtered (log) unemployment rates. We label these quarters as “expansions”. To proceed we need to choose values for $\overline{x}_g$ and $\underline{x}_b$. The choice of these percentile cutoffs, however, presents the following trade-off. On the one hand, a low $\overline{x}_g$ (and/or a high $\underline{x}_b$) implies a relatively small sample of unemployment spells. This could be problematic, as the law of large numbers is not necessarily strong enough in small samples to mitigate randomness in the direction of observed occupational flows.[Consider for example a set of gross flows obtained from an underlying distribution of flows were net mobility is zero. If this set contains only one observation of occupational mobility, this observation will be categories as a net flow and one would need 100% of gross flows to cover the net flow.] On the other hand, it could be the case that for structural reasons the response of net mobility is larger when we restrict to more extreme cyclical periods. The latter will imply that a low $\overline{x}_g$ (and/or a high $\underline{x}_b$) by itself might go hand-in-hand with more net mobility, because it concentrates on the most responsive periods.

[Net Mobility (and no. observations)]
[High-U times (above 67th pctile), Low-U (below 50th pctile)]
Net Mobility and Definition of High-U and Low-U times

[Times of High U]
[Times of Low U]
Net Mobility per (NRMC) Occupation and Definition of High-U and Low-U times

To investigate whether this trade-off is important for the cyclicality of net mobility, Figure <ref> shows the net flows across the 4 task-based categories as a function of $\underline{x}_b , \overline{x}_g \in[0.2,0.8]$. As described in the main paper these flows are normalised by the number of employment-unemployment-employment spells observed during either expansion and recessions as defined above. The blue curve depicts net flows in expansions and the black curve depicts net flows in downturns. To compare net mobility between expansions and recessions, defined for example as periods with the lowest and highest 33% of HP filtered (log) unemployment rates, one needs to compare the value of the blue curve at the x-coordinate 0.33 with the value of the black curve at the x-coordinate 0.67. The green dashed line (with circles) denotes the average net mobility obtained from calculating net mobility in expansions and in downturns and then averaging over these, weighting them by the number of underlying unemployment spells. In this case, the percentile on the x-axis represents the upper bound of unemployment rates to define expansions, and simultaneously the lower bound to define downturns. In all these cases the net flows are calculated from the implied occupational transition matrix of all unemployment spells that ended in an expansion or a downturn. We then applied the $\Gamma$-correction to this matrix.

Net Mobility Outflows (Inflows) per Occupations, and definition of High-U and Low-U times

The main message that comes out of Figure <ref> is that overall net mobility across task-based categories is countercyclical for any $\underline{x}_b , \overline{x}_g \in[0.2,0.8]$. Thus, the variations in the size of our samples do not seem to affect our finding that net mobility is countercyclical. Figure <ref> presents the same exercise but focusing on individual task-based categories. Figure <ref>a shows net mobility in expansions as a function of $\overline{x}_g$; while Figure <ref>b shows net mobility in downturns as a function of $\underline{x}_b$. Here we observe that the net flows of the routine manual and non-routine manual categories are larger in downturns than in expansions, implying that the net flows of these categories are countercyclical for any $\underline{x}_b , \overline{x}_g \in[0.2,0.8]$. Further, both in expansions and downturns the non-routine manual category exhibits net inflows while the routine manual category exhibits net outflows, consistent with the job polarization literature. The patterns for the cognitive categories, however, are not as clear. For example, we find that the net flows of the non-routine cognitive category are only higher in downturns when considering $\underline{x}_b , \overline{x}_g \in[0.45,0.8]$. Figure <ref> shows all these net mobility flows in more detail, depicting the pairwise expansion and downturn comparison of net flows for each category separately.

In light of the above, when analysing net mobility flows we will take expansions to represent quarters with below median HP filtered (log) unemployment rates and downturns to represent quarters with the 33% highest HP filtered (log) unemployment rates. The benefit of defining the business cycle in this way is that the aforementioned small sample issue seems to be less important in this definition of an expansion, where unemployment spells are less frequently observed. Indeed, the blue curve in Figure <ref>a appears well behaved around the median.[Also note the there is relatively little variation in the average net mobility between the 40th and 65th percentile. As we restrict sample sizes when moving across these different cutoffs, noisier observations of net flows in the smaller set can drive up the average net mobility rate. This does not seem to be the case for our measures.] Figure <ref> also shows that the standard NBER recessions and their immediate aftermath closely correspond to this definition of downturns, while the second half of the 1990s, late 1980s, and mid-2000s till the beginning of the Great Recession closely correspond to this definition of expansions.

[Cyclical Net Mobility (NRMC excl. Management)]

[Cyclical Net Mobility (NRMC incl. Management)]
Net Mobility by Task-based categories

Analysing the business cycles in such a way implies that overall net mobility accounts for 1.1% of workers unemployment spells in expansions, while it accounts for 3.2% of workers unemployment spells in downturns. Figure <ref> depicts these values as the intersection between the left-most vertical line with the blue curve and the intersection between the right-most vertical line with the black curve, respectively.[As an alternative to the above benchmark we also split the sample into two parts with an equal number of observations. In this case expansions correspond to the lowest 61% of HP filtered (log) unemployment rates, and recessions correspond to the highest 39% of HP filtered (log) unemployment rates. This alternative generates a similar cyclical change in net mobility, whereby net mobility accounts for 1.0% of unemployment spells in expansions and accounts for 3.3% of unemployment spells in recessions.] Figure <ref> displays the net mobility patterns for each of the 4 task-based categories also using this business cycle definition. As suggested by Figures <ref> and <ref>, the non-routine manual category exhibits a countercyclical increase in net inflows: in downturns 3.2% of workers' unemployment spells cover the net mobility of workers into non-routine manual occupations, while only 1.1% of spells in expansions. In contrast, the routine manual category exhibits a countercyclical increase in net outflows: in downturns 1.7% of workers' unemployment spells are needed to cover the net mobility of workers out of routine manual occupations, while only 0.7% of spells in expansions.

Figure <ref> display the net mobility across the four task-based categories, but now including managerial occupations in the non-routine cognitive category. In this case net flows of over 2% of all unemployment spells now originate from the non-routine cognitive category, while the routine cognitive category now experiences a net inflow as a result of former managers taking up administrative or sales jobs. Further, the inflow from management mutes somewhat the outflow from the routine manual category. Nevertheless, the same cyclical patterns regarding non-routine and routine manual categories emerge. Net inflows into the former and net outflows from the latter are larger in absolute value during downturns.

[Baseline episodes]

[Alternative (NBER) episodes]
Expansion and downturn episodes: 1986-2013

We now investigate whether the above net mobility patterns appear common across the various expansion and downturn episodes we observe during our sample period. Using the above definition of the business cycle, Figure <ref> distinguishes three expansion and three downturn episodes, where the quarters of our sample are divided into largely connected (continuous) episodes.[We take those quarters with below-median unemployment rates in the second part of the 90s as one period, and exclude the 2008 quarters in the Great Recession that still have below-median unemployment rates from the expansion.] To check the robustness of our results, Figure <ref> considers an alternative definition of the business cycle that is closer to the NBER one. In this case we label as downturns the set of quarters that starting with an NBER recession go until one year after peak unemployment is reached. Expansions are the set of quarters preceding these downturns, going backwards until a previous downturn is reached (with a two quarter gap) or until the sample sizes are about balanced. This alternative definition differs from the baseline one by including early NBER recession quarters in which HP-filtered (log) unemployment rates were low but rising fast, and by limiting the 1990s expansion to the 1997-2000 period. Given that the overall number of net mobility flows is small (see Section 2 of this appendix), note that the main caveat of these exercises is that each episode ends up containing an even smaller number of net flows. As discussed earlier this can generate nosier and potentially (upward) biased estimates.

[Cyclical Net Mobility (Baseline episodes)]

[Cyclical Net Mobility (Alternative (NBER) episodes)] Net mobility at different expansions and downturns

The resulting net mobility patterns are displaying in Figures <ref> and <ref>. We observe that the overall patterns described in Figure <ref> seem to be common across all expansions and downturns. In particular, we observe that across all episodes the non-routine manual category exhibit net inflows, while the routine manual category exhibits net outflows. The non-routine cognitive category also exhibits net outflows in most episodes across the two business cycle definitions. The routine cognitive net flows, however, are close to zero but change direction across episodes. This means that the overall low net mobility rate over the entire sample period does not mask meaningful reversals of direction or more substantial net mobility over time and this seems to be consistent across different definitions of the business cycle.

We also observe that each expansion episode is associated with less net mobility than in the downturn episodes. Moreover, across both business cycle definitions the net inflows into the non-routine manual category are larger in downturns than in expansions. A difference, however, is that the net inflows during the Great Recession are more pronounced using the business cycle definition that is close to the NBER one. This appears to reflect that in our data net mobility into the non-routine manual category is noticeably higher during the NBER recession quarters of the Great Recession, and less so in the aftermath. Since Figure <ref> includes more of the aforementioned NBER recession quarters, it naturally observe a stronger responds. Note also the cyclical pattern in the net outflows from the routine manual category. These net outflows appear typically larger in downturns than in expansions. This pattern is strongest when using our baseline definition of the business cycle, but when using the definition closer to the NBER one we observe an increase in the net outflows during the late 1990s expansion episode.

§ JOB FINDING HAZARDS AND SPELL DURATION WITH OCCUPATIONAL MOBILITY

In this section we first investigate the re-employment hazard functions of those workers who changed employers through spells of non-employment, differentiating between these workers' degrees of labor market attachments. This connects to our calibration targets. We then analyse the differences in unemployment durations between those workers who changed occupations and those who did not, and how these durations respond over the cycle. This further connects to the model and calibration, including the calibration outcomes. In particular, we document that in cyclical downturns, it is the unemployment duration of movers that lengthens more. We show that this result is robust even when controlling for destination and/or source destinations.

§.§ Job Finding Hazard

The right-hand panel of Figure <ref> shows the probability that a worker is still without a job as a function of the number of months that have passed since losing his previous job. The solid lines capture the survival functions for the set of workers who have experienced unemployed for all months since losing their jobs, while the dashed lines refer to male workers who have been unemployed for at least one month since losing their job, but not necessarily all months, i.e. have a non-employment spell that mixes out of the labour force with unemployment (labelled a “NUN-spell”). We restrict our attention to males here because females have interestingly different patterns (with higher survival in non-employment at longer duration), which nevertheless might be driven by different motives than the conditions in their labour market alone. Males which have been unemployed for every month since losing their job are not displayed separately, because the associated graph stays very close to the depicted “U-spell” category for both males and females.

Within these categories, we can separately investigate the subset of young workers (between 20-30 years old) and prime-aged (between 35-55 years old), drawn in green (with diamond-shaped markers) and dark-blue (with circle markers) respectively. We observe that survival in non-employment is indeed higher for those workers who are not searching for a job in every month, but the general shape and age differences seem preserved across both groups.

[Survival in Nonemployment with Duration]
[Job Finding Rate with Duration]
Job finding out of non-employment

The left-hand panel of Figure <ref> depicts the implied monthly job finding probabilities, with Epanechnikov-kernel weighted local polynomial smoothing (bandwidth 2.5). First note the extent of duration dependence in job finding in the first 10 months, with a job finding rate that falls about 1 percentage point per month for all unemployed workers. This “moderate” duration dependence is in part because we focus on those workers who have become more strongly detached from employment. By construction, the workers we consider are without work for at least a whole month (hence, the job finding rate in the first month refers to the probability of finding a job within the next month, after having been unemployed for a month). We also exclude workers in temporary layoff, who have a higher job finding rate.

As a result of these restrictions, our hazard functions do not exhibit the steep drop typically observed during the first month in unemployment (which can be seen e.g. in Farber and Valletta, 2015, Figure 3, based on the CPS). As argued in the main text, our restrictions are motivated by the finding in the literature that entrants into unemployment can be separated roughly into two different groups: a set of workers who has high job finding rates and behaves differently over the cycle, with most of the cyclical movement in the unemployment rate due to those who are in the second, slower job-finding group. We want to focus on the latter group. Fujita and Moscarini (2017) highlight the roll of recalls for the first group and, importantly also for our paper, highlight the different job finding expectations of the first group. This motivates our exclusion from unemployment/non-employment of those who are classified in the SIPP as “with a firm, on layoff”. Ahn and Hamilton (2019) similarly argue that two different sets of workers enter unemployment, with cyclical movements largely driven by the group which exhibits in comparison less propensity to return to employment.

Aggregate Job Hazard - unconstrained unemployment definition

Figure <ref> depicts the hazard function of those workers who reported conventional unemployment the month before the interview (to `mimic' the CPS), after dropping the aforementioned restrictions on entering unemployment and non-employment. In this case we indeed observe a much stronger duration dependence, where there is a large drop in the hazard function during the first month (see Fujita and Moscarini, 2017, for a similar result also using the SIPP). Thus, the negative duration dependence among the unemployed (non-employed) we consider in this paper is indeed relatively weaker than when considering the full set of conventionally-unemployed workers in the same data. Aside from this, the seam effect on the job finding rate is an additional issue that is clearly visible in Figure <ref>, at four month intervals. Although with our restrictions the seam effect is weaker in Figure <ref>, we will attempt to minimize its impact in the calibration by using survival rates at four month intervals (in addition to the first-month job finding rate).

Note that the extent of negative duration dependence exhibited by each group-specific hazard function depicted in Figure <ref> is not too dissimilar from each other. Workers in the `U' group exhibit the higher absolute change in the job finding rate with duration and workers in the `NUN' group exhibit the lower absolute change, but with similar relative changes of the job finding rate with duration. The main difference between these hazard functions seem to be more in levels, where workers in the `U' group exhibit the highest hazard rates and workers in the `NUN' groups exhibit the lowest hazard rates across all durations. The relative reduction in job finding of prime-aged workers in “pure” unemployment spells (when compared to younger workers) seems to be similarly present in the “mixed” spells of non-employment with some unemployment. Interesting, although not further emphasized in our paper is the non-monotonicity in the job finding rate with duration of the young around 12 months, which is suggestive of a sensitivity to benefit exhaustion that is particularly strong for this group. We abstract from this feature in the paper, but it is worth noting that our calibration targets, which consider survival probabilities in four months intervals, smooth this non-monotonicity out.

§.§ Job finding differences among occupational movers and stayers

§.§.§ Long-run patterns

The positive slope of the mobility-duration profile implies that occupational movers take longer to find jobs than occupational stayers. We now investigate whether this difference is still present after controlling for demographic characteristics and occupational identities. To do this we report several estimates of the difference between the unemployment durations of occupational stayers and movers based on the uncorrected data. These estimates are obtained from regressions of the form

\begin{align}
\text{Duration of U} = \beta_0 + \beta_{\text{occmob}} \mathbf{1}_{\text{occmob}} + \beta_{\text{dm}} \ \text{demog.ctrls} + \beta_{\text{occ}}\text{occ.dum} + \varepsilon, \tag{R4} \label{eq:udur_reg}
\end{align}

where “Duration of U spell” is the individual's completed unemployment spell, $\mathbf{1}_{\text{occmob}}$ is a binary indicator that takes the value of one (zero) if a worker changed (did not change) occupation at the end of his/her unemployment (non-employment) spell, the demographic controls include dummies for gender, education, marital status, race and age, “occ.dum” denotes occupation identity dummies and $\varepsilon$ is the error term. Table <ref> present the results of these regressions by progressively adding demographic and occupation identity controls. Columns (2) and (3) use the sample of completed durations among all unemployed workers, while Columns (4)-(8) restricts the sample to the completed durations of young and prime-aged unemployed workers.

Column (2) shows the results from estimating (<ref>) without any controls other than the occupational mobility dummy. It shows that occupational movers take on average an additional 0.5 months to find a job. This difference arises as the average unemployment duration of occupational stayers is 3.6 months, while the average unemployment duration of occupational movers is 4.1 months. This difference is also economically significant: it represents nearly half of the differences between the average unemployment spell duration in times of low unemployment (expansions) and times of high unemployment (recessions).[We consider times of high (low) unemployment as those quarters in which the HP de-trended (log) unemployment rate lies within the 33% highest (lowest) HP de-trended unemployment rates. The average unemployment length in those quarters with high unemployment is 4.4 months, whereas the average unemployment spell lasts 3.3 months in the quarters with the low unemployment.]

The rest of the columns show that the estimated difference between the average unemployment durations of occupational movers and stayers does not seem to be driven by composition effects. Column (3) reports the results from estimating (<ref>) when adding worker demographic characteristics. It shows that the estimated coefficient of the occupational mobility dummy hardly changes. Column (4) shows that the estimated coefficient of the occupational mobility dummy also hardly changes when restricting the sample to young and prime-aged workers and adding demographic characteristics. In columns (5)-(7) we further add source and destination occupational identity dummies. Here we observe a drop of up to 5 percentage points in the difference between average unemployment durations of occupational movers and stayer. However, note that a difference of 0.45 months still remains economically significant. This suggests that the difference in the average duration of the unemployment spell between occupation movers and stayers is not a result of workers moving out of (or into) occupations in which typically all workers take longer to find jobs. The increased unemployment duration of occupational movers thus seems to be associated with the act of moving itself. Therefore this evidence does not seem to support theories that are based on workers moving into a particular subset of occupations in which newcomers need to spend relatively more time to find jobs because of, for example, re-training.

Unemployment Duration and Occupational Mobility

(1)       (2)       (3)       (4)       (5)       (6)       (7)       (8)

all U     all U     all U     U yng+prm      U yng+prm      U yng+prm      U yng+prm      U yng+prm

Average Unemp. Duration

All     3.91

Occ. Stayers               3.646     3.646     3.627     3.627     3.627     3.627     3.627

(.048)     (.047)     (.052)     (.051)     (.052)     (.051)     (.056)

Occ. Movers               4.146     4.146     4.063     4.063     4.063     4.063     4.063

(.045)     (.045)     (.048)     (.048)     (.048)     (.048)     (.055)

Regression Coefficients

Coeff. Occ. Mob Dummy               0.500     .507      .499      .462      .472      .451      .262

(.065)     (.065)     (.071)     (.072)     (.072)     (.073)     (.114)

Occ. Mob x Prime-Age Dum.                                                                           .336

Worker's Characteristics                         X         X         X         X         X         X

Age (prime-age dummy)                                   X         X         X         X         X

Source Occupation Dummies                                             X                   X         X

Dest. Occupation Dummies                                                       X         X         X

9cF-test Interactions (p-value)

Age x Occ Mob                                    0.008     0.012                         0.024

Worker Char. x Occ Mob.                         0.029     0.118     0.194     0.616     0.630     0.607

Occupation x Occ Mob.                                              0.806     0.463     0.931     0.927

Num. of Observations     10886     10886     10886     8887      8887      8887      8887      8887

Table <ref>, however, does show important differences across age groups. Column (8) reports that prime-aged workers take 0.33 months longer to find a job when they changed occupations than young occupational movers. In this case the role of other demographic characteristics factors appears more limited, once we control for age and occupational identities. The bottom panel of Table <ref> present several F-tests evaluating the equality of the occupational mobility dummies specific to demographic characteristics and occupational identities. These F-test highlight two important results. There is a statistically significant interaction between age and the additional unemployment duration of occupational movers. There is not a statistically significant interaction between the rest of the demographic or occupational identity dummies and the additional unemployment duration of occupational movers.

§.§.§ Business cycle patterns

Table <ref> extends the previous analysis and considers the cyclicality of the difference between the completed unemployment durations of occupational movers and stayers. In Section 3.2 we documented that the mobility-duration profile is procyclical: at any duration the occupational mobility rate is higher in expansions than in recessions. We now show that ties in with a countercyclical distance between the unemployment durations of movers versus stayers. In particular, we estimate

\begin{align}
\text{Duration of U} = \beta_0 + \beta_{\text{occmob}} \mathbf{1}_{\text{occmob}} + \beta_{\text{occ.un}} \mathbf{1}_{\text{occmob}} \times urate+ \beta_{\text{dm}} \ \text{demog.ctrls} + \beta_{\text{occ}}\text{occ.dum} + \varepsilon, \tag{R5} \label{eq:udur_reg_bc}
\end{align}

where the new explanatory variable is relative to equation (<ref>) is the interaction between occupational mobility and the unemployment rate. The latter appears as “diff resp. (responsiveness between) movers vs stayers” in Table <ref>.

Panel A of Table <ref> considers as the measure of the business cycle the log of the aggregate unemployment rate, controlling for a linear time trend. The results presented in the first set of four columns use as the dependent variable spells in which the worker was classified unemployed since loosing his/her job until finding a new one (U-spells). The second set of four columns use as the dependent variable spells in which the worker was classified at least one month as unemployed and the rest as out of the labour force since loosing his/her job until finding a new one (NUN-spells). In each case, the first three columns consider all workers, while the last column restricts the sample to young and prime-aged workers.

Unemployment Duration and Occupational Mobility over the Business Cycle

11cPanel A: Semi-Elasticity Un-/Nonemployment Duration with Log linearly detrended Unemployment rate
4cUnemployment Duration               4cNUN-spell duration
(lr)3-6 (lr)8-11
Coefficient                     (1)       (2)       (3)       (4)                 (1)       (2)       (3)       (4)
Occupation Stayers x U. rate               1.65***      1.64***      1.63***      1.64***                1.07***      1.15***      1.13***      1.14***

(s.e)               (.18)     (.18)     (.18)     (.18)               (.25)     (.25)     (.25)     (.25)

Occupation Movers x U. rate               2.04***      2.03***      2.04***      2.03***                1.72***      1.81***      1.81***      1.81***

(s.e)               (.17)     (.17)     (.17)     (.17)               (.23)     (.23)     (.23)     (.23)

difference resp. mover-stayer               0.40**      0.40**      0.41**      0.40**                0.65**      0.66**      0.68**      0.67**

(s.e)               (.18)     (.18)     (.18)     (.18)               (.31)     (.29)     (.30)     (.29)
Worker's Characteristics                         X         X         X                             X         X         X

Source Occupation                         X                   X                             X                   X

Destination Occupation                                   X         X                                       X         X
11cPanel B: Semi-Elasticity Un-/Nonemployment Duration with HP-Filtered Log Unemployment rate
4cUnemployment Duration               4cNUN-spell duration
(lr)3-6 (lr)8-11
Coefficient                    (1)       (2)       (3)       (4)                 (1)       (2)       (3)       (4)
Occupation Stayers x U. rate                         2.47***                2.47***                2.49***                2.48***                1.345***     1.591***     1.608***     1.616***

(s.e)                (.46)       (.43)       (.43)       (.43)                (.62)     (.60)     (.60)     (.60)

Occupation Movers x U. rate                         3.20***                3.14***                3.21***                3.16***                3.516***     3.67***      3.705***     3.691***

(s.e)                (0.56)       (.54)       (.54)       (.54)                (.60)     (.60)     (.60)     (.59)

difference resp. mover-stayer                         0.73*                0.67*                0.72*                0.68*                2.17***      2.08***      2.098***     2.074***

(s.e)                (.42)       (.40)       (.39)       (.40)                (.76)     (.71)     (.72)     (.72)
Worker's Characteristics                         X         X         X                             X         X         X

Source Occupation                         X                   X                             X                   X

Destination Occupation                                   X         X                                       X         X

Number of observations                9840      9840      9840      9840                15506     15506     15506     15506

Notes: Standard errors in parenthesis. $^{***}$ significant at a 1% level; $^{**}$ significant at a 5% level; $^{*}$ significant at a 10% level. Controls: gender, age, age squared, number of years of education, family status.

For both types of non-employment spells, we observe that when the aggregate unemployment rate increases the differences between the completed spell duration of occupational movers and stayers increases. That is, during recession (times when unemployment is high) occupational movers experience even stronger increases in unemployment duration than occupational stayers do. As shown across the columns, this result also holds (with rather stable coefficients for U-spells) when controlling for demographic characteristics (including a quadratic in age) and dummies for the occupation of origin and destination.[Kroft et al. (2016) find that compositional shifts matter little for the increase in long-term unemployment in recessions; our results are related in the sense that we find that compositional shifts matter little for the increase in the duration difference between occupational movers and stayers in recessions. This increase is proportionally stronger than the drop in occupational mobility of the unemployed that is broadly shared across occupations. Hence the lengthening of spells of occupational movers contributes to the lengthening of unemployment and, in particular, long-term unemployment across occupations in recessions.] Our results also show that the responsiveness of the difference in unemployment durations to the unemployment rate is stronger when considering NUN-spells.

Panel B of Table <ref> instead considers as the measure of the business cycle the cyclical component of the log of aggregate unemployment rate, where the cyclical component is obtained through an HP filter. It is immediate from the table that the conclusions obtained from using this measure are the same as the ones obtained when using the linearly de-trended logged unemployment rate.

§.§.§ Summary
In Section 1 we have documented that at any unemployment duration, occupational movers largely move across all occupations. Here we have shown that conditional on a given occupation, an occupational mover takes longer to find a job than an occupational stayer and that this difference increases in recessions. It is important to highlight that this does not mean that different occupations exhibit the same average unemployment duration. Indeed, we do find differences in the estimated coefficients of the source and destination occupation dummies (see Section 1.5), suggesting that some occupations do lead all workers (occupational movers and stayers) to find jobs fasters than workers in other occupations (in line with Wiczer, 2015). What our results suggest is that even though a worker might have experienced job loss in an occupation that exhibits short or long overall unemployment durations, this worker will take on average longer to find a job if he/she is to change occupations at re-employment and even longer if he/she changes in a recession.

§ OCCUPATIONAL MOBILITY IN THE PSID AND THE CPS

We now turn to investigate some of our main findings using alternative data sources: the Current Population Survey (CPS) and the Panel Study of Income Dynamics (PSID). Analysing occupational mobility through the CPS is helpful even though these data is not corrected for measurement error. In particular, the CPS has the advantage of providing the longest, uninterrupted series of occupational mobility, even spanning into 2021. This allows us to evaluate whether the breaks in the SIPP time series have a meaningful effect on the extent and cyclicality of gross occupational mobility. For this purpose and because the individual-worker panel dimension of the CPS is much shorter relative to the SIPP, we only use these data to investigate the average gross mobility rate. In Section 2 of the main paper we conclude that the CPS and the (uncorrected) SIPP gross mobility series have very similar degrees of procyclicality. Another advantage of using the CPS is that it is easily accessible. We use the CPS data available via IPUMS.[Our STATA do-file is available upon request, while the underlying data can be download from IPUMS, at ]

The PSID is also useful for several reasons: (i) It provides a longer panel dimension than the SIPP. (ii) We can compare our main results with the analysis of Kambourov and Manovskii (2008), who use this data set to provide a highly influential analysis of the occupational mobility patterns found in the US labor market. Therefore, in constructing our sample we closely follow Kambourov and Manovskii (2008, 2009). The details of this sample are described in the “Data Construction” section of this appendix. (iii) It allows us to evaluate the extend of coding error using retrospective coding as an alternative method. In particular, we use the PSID retrospective occupation-industry supplementary data files, which contain the re-coding the PSID staff performed on the occupational mobility records obtained during the 1968-1980 period. Since the 1981-1997 records were not re-coded and collected under independent interviewing, the earlier period can be used to construct “clean” occupational mobility rates and to analyse the effect of measurement error at the coding stage.

§.§ Occupational mobility in the CPS

§.§.§ Extent of occupational mobility

To derive the average gross occupational mobility rate among the unemployed in the CPS we compare the occupation coded immediately before the worker became unemployed with the occupational code at re-employment. From this set of workers, the gross mobility rate is then computed as the proportion of occupational movers among all those who went through unemployment and subsequently found a job. We focus on the 22 major occupational groups of the 2010 SOC, which are provided homogenized for the entire 1976-2021 period by IPUMS. Using the full extent of our sample, we obtain an average occupational mobility rate of 47.5%. One concern could be that since in 1994 the CPS underwent a significant re-design, the occupational mobility rate should be affected. However, computing this rate for the period 1994-2021 yields 46.6%, hardly changing the extent of mobility.

A perhaps more important concern is that in this sample we are including workers in temporary layoffs, who are very likely to return to their previous occupations and employers (see Fujita and Moscarini, 2017). This would bias downward the occupational mobility rate. To leave out temporary layoffs, we drop those unemployed classified as “job loser/on layoff” in the reported reasons for unemployment. This category corresponds to individuals who are on temporary layoff, with the expectation to be recalled with a specific date or within 6 months. Note that in this case we only use data from 1994 onwards as after the 1994 redesign we have available a homogenous series for “reasons for unemployment”, without discrete jumps associated with shifting definitions/survey questions. Once we drop these temporary layoff workers, the average occupational mobility rate indeed increases to 56.9%. This value is very similar to the 55.0% we obtained from the uncorrected SIPP when using the 21 major occupational groups of the 2000 SOC and dropping the temporary layoffs (See Table <ref> in Section 1 of this Appendix).

[Occup. mobility - smoothed series]
[Occup. mobility - deviations from HP trend]

[Occup. mobility - no temp layoffs]
Occupational mobility of the unemployed - CPS

§.§.§ Cyclicality of occupational mobility

Figure <ref> plots the time series of the average gross occupational mobility rate of workers hired from unemployment. To smooth out some of the noise in the quarterly observations, we present a lowess smoothed version in such a way that business cycle patterns remain visible. We also depict this series HP trend (filtered with parameter 1600). The redesign of the CPS in the mid-1990s means that the pre-1994 series might not be fully comparable to the post-1994 series. Following a conservative approach, we have HP filtered the entire 1976-2021 series but then removed observations for the period 1992-1995 to stay clear of the 1994 design break. To capture business cycle conditions, Figure <ref> depicts the unemployment rate (and its HP trend with parameter 1600).

A comparison of these series reveals a procyclical pattern in occupational mobility. When the occupational mobility rate is above its trend, unemployment is typically below its trend. This is clearly visible around the last three recessions (including the Covid recession), but also in the double-dip recession in early 1980s. Figure <ref> shows a similar conclusion using the HP-filtered unemployment and occupational mobility rates for the full sample.  A reasonable concern is that the observed occupational mobility in the previous graphs is affected by an increased importance of temporary layoffs in recessions. Figure <ref> considers the series without temporary layoffs and once again shows a procyclical occupational mobility rate.

[HP-filter: Occup. mobility - up to 2019q4]
[HP-filter:  Occup. mobility - up to 2019q4, no temp layoffs]

[Bandpass filter: Occup. mobility - up to 2019q4]
[Bandpass filter: Occup. mobility - up to 2019q4, no temp layoffs]

Occupational Mobility under Alternative Filtering

To further investigates the robustness of our findings, in Figures <ref> and <ref> we only consider the time series from 1994 up to the fourth quarter of 2019. In this way we focus on the period after the 1994 re-design and drop the large decrease in occupational mobility observed during the Covid recession. While this somewhat changes the de-trended behavior in the last five years of the series, it does not seem to meaningfully affect its procyclicality. In Figures <ref> and <ref> we re-do this exercise but now using bandpass filtering instead of the HP-filter to better deal with the noise in the raw data. If anything, the bandpass filtered series depicts even stronger procyclicality in the occupational mobility series, with clearly visible drops in occupational mobility during and in the aftermath of the 2001 and 2008 recessions.

Finally, Table <ref> investigates the cyclicality of HP and bandpass filtered observations without additional smoothing. In particular, we regress the quarterly observations of the de-trended log gross occupational mobility rate on a constant and the de-trended log unemployment rate. We report the coefficient and standard error associated with the latter. We observe that, after taking out the HP-trend (with parameter 1600), occupational mobility decreases in times of higher unemployment. This relationship is statistically significant when using all the sample period or after the 1994 CPS re-design (with and without temporary layoffs). Using the bandpass filtered series confirms these results.

OLS regression - Cycalility of occupational mobility

6cLevel and Responsiveness of Mobility to the Unemployment Rate

Level     4cCyclical responsiveness
(lr)2-2 (lr)3-6
Ave.     2cHP Filtered (1600)     2cBand-Pass Filtered

(lr)3-4 (lr)5-6
Mob.           1lup to '21     1lup to `19     1lup to '21     1lup to '19

6cQuarterly Occupational Mobility Rate, Hires from U (2010 MOG)

1lAll unemployed, 1976-     0.475     -0.223$^{\star\star\star}$     -0.105$^{\star\star}$     -0.105   $^{\star\star\star}$     -0.104$^{\star\star\star}$

(0.030)     (0.039)     (0.016)     (0.017)

1lAll unemployed, 1994-     0.464     -0.254$^{\star\star\star}$     -0.103$^{\star\star}$     -0.096$^{\star\star\star}$     -0.104$^{\star\star\star}$

(0.036)     (0.050)     (0.018)     (0.019)

1lUnemployed, not on temporary layoffs, 1994-     0.569     -0.044$^{\diamond}$     -0.091$^{\star}$     -0.063$^{\star\star}$     -0.085$^{\star\star\star}$

(0.024)     (0.037)     (0.019)     (0.017)

5l$^{\star\star\star}$ p-value<0.001; $^{\star\star}$ p-value<0.01; $^{\star}$ p-value<0.05; $^{\diamond}$ p-value<0.1.

§.§ Occupational mobility in the PSID

§.§.§ Extent and cyclicality

Using these data, Supplementary Appendix A.3 documents that when conditioning the sample only on those who changed employers through non-employment ($ENE$), the gross occupational mobility rates are high across different levels of aggregation. In particular, the raw data shows that the average occupational mobility rates among the non-employed are 39.6%, 44.7% and 56.2% at a one-, two- and three-digit levels, respectively, for the period 1970-1980. The average occupational mobility rates for these workers for the period 1981-1997 are 41.1%, 47.4% and 60.1% at a one-, two- and three-digit levels, respectively.

To compare these mobility rates with the occupational mobility rate among all workers (employer movers and stayers pooled together), we divide the numerator of the $ENE$ rate by the total number of employed workers at year $t$ (the denominator of our overall rate). In this case we find that the occupational mobility rates through non-employment are 2.5%, 2.8% and 3.5% for the period 1970-1980 and 3.2%, 3.7% and 4.7% for the period 1981-1997, at a one-, two- and three-digit levels, respectively. These rates are consistent with the results of Kambourov and Manovskii (2008), who show that unemployed workers contribute 2.5% to the year-to-year occupational mobility rate of a pooled sample of employer movers and stayers based on a two-digit level. It is important to note, however, that the information presented in Kambourov and Manovskii (2008) does not allow us to infer that their 2.5% estimate is consistent with mobility rates among the non-employed of over 40%. To arrive to this conclusion one has to re-do Kambourov and Manovskii's (2008) analysis as done here.

Table 6 in Supplementary Appendix A.3 shows the marginal effects obtained from probit regressions to further investigate the cyclicality of gross occupational mobility.[These estimates are obtained using the personal weights provided by each survey, but similar results are obtained when using the unweighted data. We also obtained very similar results when using the linear probability model on weighted and unweighted data and when using robust standard errors and clustering standard errors at a yearly level.] In these regressions the dependent variable takes the value of one if the worker changed occupation and zero otherwise. We control for age, education, full or part-time work, occupation of origin, region of residence, aggregate and regional unemployment rates, a quadratic time trend, number of children and the impact of retrospective coding.[As in Kambourov and Manovskii (2008), the education indicator variable takes the value of one when the worker has more than 12 years of education and zero otherwise. This is to avoid small sample problems if we were to divide educational attainment in more categories. The regional unemployment rates are computed using US states unemployment rates.] Our results show that gross occupational mobility of the non-employed is also procyclical.

These results corroborate the ones obtained using the SIPP. Hence, we find that across data sets the probability of an occupational transition among the unemployed (or non-employed) is high and increases in expansions and decreases in recessions. The procyclicality of gross occupational mobility among the unemployed complements the result found by Kambourov and Manovskii (2008), who show that the year-to-year occupational mobility rate of a pooled sample of employer movers and stayers is procyclical. In principle there is no reason to expect that the procyclical pattern these authors find in the overall occupational mobility rate would translate to the occupational mobility rate for the unemployed. Indeed, as discussed above, the latter contributes a small proportion to the overall rate.

§.§.§ Repeat mobility

In the SIPP analysis we found that a large proportion of workers who experienced an occupational change in their previous non-employment spell also experienced an occupational change at the end of their current non-employment spell. Similarly, we found that a large proportion of workers who did not change occupation in their previous non-employment spell experienced an occupational change at the end of their current non-employment spell.

A potential concern with the SIPP structure is that it does not follow workers for a long enough period. While in the calibrated model, we deal with this by imposing an analog restriction on the time window of measurement, such that the data and model statistics capture exactly the same type of repeat mobility, one would still want to extrapolate repeat mobility patterns to apply more generally (to longer periods of workers' labour market history). This extrapolation might generate a bias in the repeat occupational mobility statistics as the statistics in the SIPP will disproportionally capture: (i) those workers with shorter non-employment spells, even though we leave enough time towards the end of the SIPP panel to try to capture longer non-employment spells; and (ii) those workers with short employment durations between consecutive non-employment spells. To analyse the extent of this bias we re-compute the repeat mobility statistics using the PSID based on the sample used to construct the $ENE$ occupational mobility rate depicted in Figure 3.b in Supplementary Appendix A.3. Since these data sets allow us to follow the same workers for a longer period of time, we expect (i) and (ii) to have a much smaller impact.

Repeat Mobility - PSID 1968-1997

2c|All workers      2cMale workers

1cOcc. Mobility      1c|Occ.+Ind. Mobility      1cOcc. Mobility     1cOcc.+Ind. Mobility

Stayer - Stayer     1c67.3     1c|77.2     1c67.4     1c77.0

Stayer - Mover     1c32.7     1c|22.8     1c32.6     1c23.0

Mover - Stayer     1c46.9     1c|58.8     1c45.8     1c57.8

Mover - Mover     1c53.1     1c|41.2     1c54.2     1c42.2

Stayer - Stayer     1c61.9     1c|69.9     1c62.8     1c71.0

Stayer - Mover     1c38.1     1c|30.1     1c37.2     1c29.0

Mover - Stayer     1c40.6     1c|49.7     1c40.5     1c48.8

Mover - Mover     1c59.4     1c|50.2      1c59.5     1c51.2

Stayer - Stayer     1c54.3     1c|61.2     1c57.8     1c64.1

Stayer - Mover     1c45.7     1c|38.8     1c42.2     1c35.9

Mover - Stayer     1c25.9     1c|33.2      1c27.5     1c34.8

Mover - Mover     1c74.1     1c|66.8      1c72.5     1c65.2

5lNote: Total number of observations among all workers (male) = 3,261 (2,467).

Table <ref> shows the repeat mobility statistics at a one-, two- and three-digit level of aggregation, using the 1970 SOC. The proportions are based on weighted data, but similar proportions are obtained using unweighted data. For each level of aggregation we divide the sample by whether a worker was an occupational stayer or an occupational mover after the first non-employment spell. We then compute the proportion of stayers (movers) who, after a subsequent non-employment spell, did not change occupation and the proportion who changed occupation. For each level of aggregation, the first two rows show the proportions for stayer- stayer and stayer-mover. These proportions add up to one. Similarly, the second two rows show the proportions for mover-stayer and mover-mover. Further, the columns labelled “Occ. Mobility” consider workers who only changed occupations. Since these statistics are based on raw (uncorrected) data, the propensity to change occupations would be biased upward. As a way to deal with the latter, we consider simultaneous occupation and industry mobility and re-compute the repeat mobility statistics. These are presented in the columns labelled “Occ. + Ind. Mobility”. As discussed in Section 1 of this appendix, conditioning on simultaneous occupation and industry mobility provides an alternative way to correct for coding errors. In particular, we consider a worker to be an occupational mover if and only if he/she reported a change in occupation and a simultaneous change in industry at the same level of aggregation, where industry changes are based on the 1970 census industries codes.[These repeat mobility statistics are calculated based on up to five consecutive non-employment cycles, where a cycle is constructed as non-employment, employment, non-employment, employment sequence. The number of observations in our repeat mobility sample is then the product of the number of cycles per individual. The majority of workers, however, experience only one cycle, which make up for 80% of the total number of observations. We find that our results do not change if we were to compute these same statistics based only on workers' first cycle.]

The PSID shows a very similar picture to the one obtained from the SIPP. There is a high proportion of workers who changed occupation after a non-employment spell and once again changed occupations after a subsequent non-employment spell. Out of all those workers who were occupational movers after a non-employment spell in the raw data, between 53% (at a one-digit level) and 74% (at a three-digit level) moved occupations once again after a subsequent non-employment spell. There is also an important proportion of workers who did not change occupation after a non-employment spell, but did change occupations after a subsequent non-employment spell. Out of all those workers who were stayers after a non-employment spell, between 33% (at a one-digit level) to 48% (at a three-digit level) moved occupations after the subsequent non-employment spell.

Conditioning simultaneous occupation and industry changes does not drastically change these results. In this case, out of all those workers who were occupational movers after a non-employment spell, between 41% (at a one-digit level) and 67% (at a three-digit level) moved occupations once again after a subsequent non-employment spell. Out of all those workers who were stayers after a non-employment spell, between 23% (at a one-digit level) to 33% (at a three-digit level) moved occupations after the subsequent non-employment spell. That is, conditioning on simultaneous occupation and industry changes, decreases by about 10 percentage points the occupational mobility rates, but still shows a high propensity for repeat mobility among the non-employed.

§ SELF-REPORTED RETROSPECTIVE OCCUPATIONAL MOBILITY

In this section we propose an alternative way to analyse the occupational mobility patterns of the non-employed that is not subject to coding errors. In particular, we use information on workers' self-reported employer and occupational tenure obtained from the SIPP core panels and topical modules. For the majority of SIPP panels the first topical module asks “for how many [months/years] has [the worker] done the kind of work [he] does in this [current] job”, or a variation of it.[This question is asked in topical module in the 1984, 1987, 1990, 1991, 1992 and 1993 panels. In the 1996, 2001, 2004 and 2008, the question is in the core waves and makes explicit reference to the entire working life, while invoking  `occupation'/`line of work' rather than `kind of work': “Considering  entire working life, how many years has [the worker] been in this occupation or line of work?”.] In addition, it records the start date with the current employer and the start and end dates of the most recently finished employer spell previous to the panel.[The question referring to the current employer is “when did [the worker] start working for [explicit employer name]”. The wording means that employer recalls are not (or, at least, should not be) recorded as starts of employment. In the 1996-2008 panels, the question also refer to “... the month/year when [worker's name] began employment with [employer name]”.]

Using this information we restrict attention to those workers whose completed non-employment spells lasted between one and twelve months. This restriction helps us capture a group of workers who have not lost their attachment to the labour market even though they might be categorised as non-participants at some point during the spell[For most of the panels we obtain the duration of the non-employment spell by subtracting the date in which the job with the previous employer ended from the day the re-employment job started with the current employer. In the 1984 panel the duration of the non-employment spell is asked directly. In the 2004 and 2008 panels we can only observe a lower bound of the non-employment spell as we only have information on the year the job with the previous employer ended. For this reason some of our analysis (see below) will not use the 2004 and 2008 panels. Note that the mobility reported in the 1984 panel lies ] Below, we also focus on subsets of this group that e.g. are observed with periods of unemployment. To determine whether or not one of these workers changed occupations, we compare the employer tenure with the occupational tenure information. In particular, out of all those workers who found their current job out of non-employment, we label a worker as an occupational mover when his/her occupational tenure equals (or is very close to) his/her employer tenure and as an occupational stayer when his/her occupational tenure is (sufficiently) greater than his/her employer tenure.[When occupational tenure does not exactly match the employer tenure due to, potentially, recall errors, we consider several plausible adjustments to identify occupational movers and stayers. We discuss these in the next section.] We then compute the occupational mobility rate by dividing the number of non-employed workers who re-gained employment and changed occupation over the number of non-employed workers who re-gained employment.

§.§ The extent of self-reported occupational mobility

Table <ref> shows the extent of occupational mobility of the non-employed using four different samples. The first column (overall) considers all employed workers who (i) went through a spell of non-employment before starting with the current employer, provided that the non-employment spell occurred during the last ten years; (ii) have spent more than two years in the labor force, (iii) have finished their previous job at least one year after entering the labor market and (iv) have held their previous job for at least twelve months.[We have excluded workers who have imputed start and dates of the previous job in the data, or an imputed start date of the current job. In case ambiguity remains about occupational changing, we have excluded these workers as well. If we take a strict approach to assignment of mobility within this subset of workers, mobility is closer to 50% than in the unambiguous group. Hence, we consider the exclusion of ambiguous observations to yield conservative levels of self-reported occupational mobility.] These restrictions are made to focus on those who have had meaningful employment before any spell of non-employment, so we are considering changes of occupation, rather than a start within an occupation at the beginning of working life. These restrictions also help with inferring occupational mobility from occupational tenure.

The second column (attached workers) restricts the “overall” sample to those spells in which the worker entered non-employment because they were laid off, discharged, a temporary job ended, indicated dissatisfaction with the previous job, or because of a non-family/non-personal (“other”) reason.[Including “Other” (but excl.: “other, family reasons”) does not change our conclusions.] We consider these workers to be attached to the labor market as they did not leave their jobs for reasons that indicate leaving the labor force.[For the “overall” and “attached” samples, we only count an occupational move at the end of the non-employment spell when the current job started within six months of the start of the occupation and the previous job lasted for at least one year. For a worker to be considered an occupational mover in these samples, the current job starting date must be within a six months interval around the implied start of the occupation and the previous job must have lasted at least a year. Further, given that occupational tenures above twelve months are reported in full years it was difficult to assess an occupational change when a relatively long tenure in the current job is preceded by a short tenure in the previous job. We take a conservative approach and categorise the latter cases as a employer move without an occupational change.] Given that the above restrictions could not be applied across all panels, these two samples only rely on the topical modules of the 1984, 1987-88 and 1990-1993 SIPP panels.

Self-reported occupational mobility rates

(a)      (b)      (c)      (d)

Overall      Attached workers      Recent hires      Hires after U

1984-1993     1984-1993      1984-2001     1984-2001

All workers             0.446         0.434         0.372          0.397

(0.007)       (0.008)       (0.013)       (0.019)

Males                   0.467         0.437         0.393         0.439

(0.010)       (0.012)       (0.018)       (0.026)

Females                 0.429         0.433         0.352         0.343

(0.010)       (0.012)       (0.017)       (0.026)

HS dropouts             0.476         0.476         0.409         0.507

(0.021)       (0.025)       (0.032)       (0.046)

HS grads                0.478         0.486         0.405         0.405

(0.013)       (0.015)       (0.021)       (0.031)

Some college            0.437         0.413         0.342         0.347

(0.014)       (0.017)       (0.024)       (0.037)

College grads           0.412         0.380         0.330         0.350

(0.013)       (0.015)       (0.026)       (0.042)

20-30 years old         0.555         0.548         0.441         0.476

(0.015)       (0.018)       (0.025)       (0.039)

35-55 years old         0.387         0.385         0.323         0.347

(0.010)       (0.012)       (0.018)       (0.027)

5p0.7Standard Errors in parentheses. Sample weights: using person weights of SIPP panels within panel, normalized such that average weight of observations across all panels is one, while cross-sectionally weights make the sample representative).

The third column (recent hires) considers those workers whose re-employment job started within six months prior to the time of interview and had a job tenure of at least 12 months before transiting into non-employment. The last restriction enable us to more confidently distinguish the current employer's tenure from the sum of the current and previous employer's tenure, which reduces ambiguous cases when inferring occupational mobility from self-reported occupational tenure. The cost, obviously, is a smaller sample of workers, as logically we can include only workers with a low job tenure at the moment that the retrospective occupational questions are asked. Note also that this sample is selected differently, columns (c) naturally samples more those in the population who are more likely to experience non-employment spells. The fourth column (“Hires after U”) uses an even more restricted sample of “recent hires” for whom the employment status is observed within the core waves and who were unemployed for at least one month prior to re-gaining employment. Since the retrospective question is asked in the early waves of a panel, this leaves relatively little room to observe the unemployment status of those workers in the core wave dataset (which is a requirement to be included in this group). For these samples we use the 1984 up to 2001 panels.[For the most recent two panels, 2004 and 2008, we can find a lower bound and an upper bound on the non-employment spell, but typically not a precise monthly duration. When restricting the 1996 and 2001 panels to provide the same amount of information as the 2004 and 2008 panels (by ignoring start and end months of the previous jobs), we find that the loss of information appears to be small.]

For a worker to be considered as an occupational mover, the current job starting date must be within a one month interval around the implied start of the occupation, or before.[Here we use a one month `margin' to reflect one month of rounding error. The impact of including an additional margin of one month is small, ranging from 0 to 3 percentage points change in the occupational mobility rate at most. Below we use an alternative measure to check the robustness of our measure.] Across all these sample we once again find that non-employed workers' self-reported occupational mobility is high. Combining retrospective employer and occupational tenure information implies that roughly about 40% of non-employed workers accept jobs in a new line of work. Average mobility of recently hired workers in columns (c) and (d), which uses recall of more recent non-employment spells, and allows us to distinguish tenures more clearly, is broadly in line with columns (a) and (b), where the former have a reported mobility in 35-42% range, versus 42-46% for all workers in columns (a) and (b). Retrospective measures of occupational mobility do subtly differ from the ones derived by comparing occupational codes, first because they reflect the workers' own assessment of 'line of work'/occupations, rather than the Census', but also because the occupational tenure in question can reasonably refer to jobs even before the previous job. Nevertheless, we find that the extent of occupational mobility obtained from individuals reporting a “different kind/line of work” aligns very well with the adjusted rate of occupational mobility for the non-employed obtained by comparing the major occupations of the 2000 SOC or the 1990 SOC described in Section 1.1 of this appendix. This confirms that, after a gap in their employment history, about two out of five workers ends up in a different line of work than they had before.[The retrospective question, is consistent with the interpretation of occupational mobility in the main paper, where an occupational change is a start of a new career. In the calibration, this occurs in 44% of unemployment spells, according to the retrospective question, workers start something they have not done before in about 40% of the cases.]

Table <ref> shows that the retrospective occupational mobility rate is also high across several demographic groups. In particular, both male and female workers have mobility rates around 40%, with an average mobility rate across the four samples that is higher for males. Interestingly, in some measures (for example in column (d)) this gap appears meaningful, but on the other hand is smaller when considering women who were classified as “attached” (in column (b)). Occupational mobility upon re-employment is substantial for workers from all education groups, though again we can discern an education gradient, with college workers more often taking re-employment in a line of work they held before. Finally, we find that the perhaps clearest differences in the occupational mobility rates by demographic characteristics is across age groups, where the mobility rate decreases significantly as workers get older. Nevertheless, the mobility rate of prime-aged workers also remains high, between 32.5 and 39%.[The relative drop across age groups is somewhat stronger in our retrospective measure than in the occupational mobility based on comparing codes, falling between 12 to 16 percentage points from young to prime-aged workers. A potential explanation is the difference between comparing occupational codes after an unemployment spell with the occupational code just before, rather than asking whether, at some point in their entire working life, workers have had a line of work similar to the current one. The relevance of this difference, intuitively, seems larger for older workers.]

It is important to note some differences across panels. For example, how the duration of non-employment was solicited: directly as a duration or implied by the starting and end dates of a job. In the 1984 panel individuals are asked explicitly for the duration of the non-employment spell and the tenure on the job, rather than reporting start and end months of employment, as done in more recent panels. Another difference is that the labor market topical module is asked in the interview of wave 2 between 1987 and 1991 panels, but from the 1992 panel onwards in the interview of wave 1. The SIPP was redesigned in 1996 panel and carried over to 2001 panel. Generally, a large part the new design of 1996 is carried forward till the 2008 panel. However, in the labor market topical module there are changes that lead the 2004 and 2008 panels to differ from the 1996 and 2001 panels and make it much harder to determine the combination of non-employment duration and occupational mobility. For this reason these former panels are omitted from the analysis. The setup of the labor market history questionnaire is perhaps the most distinct in the 1984 panel; however, it also contains the most direct question on the employment gap. Implied occupational mobility in this panel is somewhat higher than in other panels, while its sample size is relatively large relative to the other panels in the 1980s. Excluding it from the analysis means that the average implied occupational mobility across the remaining panels is lower, typically, by 1-4 percentage points. For example, mobility of all workers (pooled) in the first column, drops to 42.9%. Nevertheless, even excluding this panel, mobility is high, and broadly shared across many demographic groups.

§.§ Self-reported occupational mobility and non-employment spell duration

We now turn to analyse the mobility-duration profile that arises from the retrospective measure of occupational mobility. Table <ref> reports the results of linear probability models based on the implied occupational mobility rates obtained from self-reported occupational tenure as a function of the time in non-employment between jobs and a set of controls. These include an indicator variable for gender, a quadratic in age and categorical variables for the completed duration of the non-employment spell (where we take 1-4 months as the baseline category) and for workers' educational levels (where we take high school graduate as the baseline category). We also add controls for previous and current occupational tenure and occupation of origin and destination, include a linear time trend and grouped-panel fixed effects to control for differences between SIPP panel setups.

Self-reported Occupational Mobility and Non-employment Duration

6c Panel A. Retrospective Occ. Mobility of All Workers (1984-1993 panels)
(i)       (ii)      (iii)     (iv)      (v)

Overall     Overall     Overall     Attached     Attached
no obs.     5219      5219      5170      3656      3622
NE duration     0.0109***     0.0112***     0.0096***     0.0095***     0.0094***

(s.e.)     (0.0024)     (0.0024)     (0.0023)     (0.0028)     (0.0028)
linear time trend              XXX      XXX     XXX     XXX      XXX

panel FEs            XXX      XXX         XXX

demogr. controls        XXX    XXX          XXX

source occup.                           XXX                   XXX

6c Panel B. Retrospective Occ. Mobility of Recent Hires (1984-2001 panels)
(vi)      (vii)     (viii)     (ix)     (x)

All Hires     All Hires     Hires after U     Hires After U     Hires After U$^\star$
no. obs     1549      1549      692       692       679
NE duration     0.0062     0.0068*     0.0110*     0.0107*     0.0114*

(s.e.)     (0.0039)     (0.0039)     (0.0062)     (0.0061)     (0.0063)
linear time trend               XXX     XXX      XXX    XXX     XXX

panel FEs            XXX          XXX

demogr. controls        XXX         XXX

6p0.8Levels of significance: $^{*} p<0.1$, $^{**} p<0.05$, $^{***} p<0.01$. Observation weighted by person weights within panel, by number of total observations per panel across panels. “Recent Hires” means hired within 6 months of interview. “Hires After U$^\star$” refers to an alternative measure of occupational mobility, where everyone with occupational tenure weakly less than current job tenure (allowing for one month of ambiguity) is a mover, while everyone with an occupational tenure larger than current job tenure + 4 months is a stayer.

In panel A we consider the retrospective information on the (most recent) spell of non-employment of all workers, if they have gone through non-employment after entering the labor force. We observe that self-reported occupational mobility increases with non-employment duration, roughly at a rate of one percentage point per month. These results are well in line with the results presented in the main text, for occupational mobility according to changes in the occupational code.

In the first column we relate the probability of self-reported change in occupation to the reported time between employment. We only allow for a linear time trend in occupational mobility. In the second column, we add controls for gender, race, a quartic in age and marital status, and grouped panel fixed effects. This hardly affects the slope of the mobility-duration profile. In the third column, we include the occupational code on the job previous to the non-employment spell. Interestingly, the earlier panels of the SIPP, up to 1993, include both a question on occupational tenure and the occupation code of this previous job. Controlling for the `source' occupation reduces slightly the point estimate of the mobility - duration profile, but not in a significant way. In the fourth and fifth columns we repeat these regressions for the smaller subset of workers who do not separate for “personal reasons”, i.e. our “attached” sample discussed above. Again, we see that a broadly similar picture appearing, again without much difference when controlling for demographics, grouped panel fixed effects and source occupations.

In panel B, we concentrate on recent hires, i.e. those who started a job, after non-employment, within 6 months of the interview on their labor market history. We apply the same sample selection criteria as discussed above. The smaller sample size means that the gradients of mobility with duration are more imprecisely estimated. Nevertheless, a similar broad (but less precise) conclusion can be drawn. There is a positive slope of mobility with duration that is not too steep, with point estimates well in line with panel A and the occupational code-driven mobility in the main text. Although care has to be taken, given the size of the standard errors, we observe that the point estimates of the slope of the mobility-duration profile of all workers hired after non-employment is somewhat lower, while for those who have been unemployed the point estimates of the slope is rather close to panel A of Table <ref>, and the main text. Although not shown here, the coefficients on education, age, gender paint a similar relative picture as Table <ref>.

The above results show that occupational mobility is high at any non-employment duration and exhibits a moderate increase with the duration of the non-employment spell, where between 50% and 60% of workers with at least 9 months of non-employment duration return to previous occupations at re-employment. These are very similar characteristics as the ones found in our main analysis based on the comparison of occupational codes. Once again we find a prominent decline in the probability of an occupational change with age. We also find small differences in the probability of an occupational change between males and females and across education levels, with the exception of the group of recently hired from unemployment (as in Table <ref>). Note that including indicator variables for the occupations of origin and destination (as done in 1b) does not alter greatly the outcomes for non-employment spell durations and other coefficients, with the exception of the point estimates for the education coefficients. This suggests that specific occupational identities are not the main drivers of both non-employment duration and occupational mobility of workers. This is once again in line with the conclusions of Section 1.5 in this appendix.

§.§ The cyclicality of self-reported occupational mobility for the non-employed

We now turn to investigate the cyclicality of our occupational mobility measure. When comparing the occupational mobility of workers recently hired from non-employment across different panels, Figure <ref> suggests a procyclical pattern. This figure depicts, for each panel, the occupational mobility implied by the answer to the occupational tenure question of those who were recently hired after a period of unemployment. Note that the retrospective work history questions are only asked at the beginning of a panel, so if we were to focus on recent hires we would not have a complete quarterly time series. Rather, we measure this mobility at various points over the business cycle. In the graph, we show the observations in the quarter in which the “recent hiring" typically took place for each panel. One can observe that, with the switch from overlapping panels to sequential panels in 1996, the length between the points for which we have observations widens significantly over time.

We focus on those panels which share their survey design with at least one other panel, so that we can also compare within survey design. In particular, the 1987-1993 panels share the same retrospective questioning, but we separate the 1990-1993 panels because we can use the re-coded firm identities to reduce measurement error in these panels. However, this has only a very minor impact on the recent hires, as firm identifiers do not play a large role in extracting the implied occupational mobility from self-reported occupational tenure. Potentially more important is that the timing of the retrospective question changes from wave 2 to wave 1 between the 1990-91 panels and the 1992-93 panels. This means that we observe less of the non-employment spell inside the core waves (and consequently have less monthly observations in which we could distinguish unemployment), which may have relevance for our “hires from unemployment” measure. As noted, from the 1996 panel onwards, the SIPP was redesigned such that we can only use the occupational tenure question to gauge occupational mobility of those who are recently hired.[The 1984 has a different design that is not shared with any other panel, and is omitted from the picture but is included in the regressions below.]

Retrospective self-reported occupational mobility of recent hires (after u), by panel

The first observation from Figure <ref> is the procyclicality of occupational mobility over the entire time series. In times of low unemployment such as 1996 but also late 1989 to early 1990, self-reported occupational mobility is high, while in times of recession (high, or at least rather rapidly increasing unemployment) as in late 1990 to early 1991 and 2001, mobility is lower than in preceding “good times”. Second, to assuage concerns about changes in survey design, we observe that the aforementioned observation occurs precisely across panels that share the same survey design, i.e. the 1990-1991 panels, and the 1996-2001 panels.

The cyclicality of the probability of an occupational change

9cPanel A: Recent Hires from Nonemployment (1984-2001)
(i)       (ii)      (iii)     (iv)      (v)       (vi)      (vii)     (viii)

occ.move        occ.move           occ.move         occ.move           occ.move          occ.move$^\star$      occ.move$^\star$      occ.move$^\star$
no obs.     1546      1546      1546      1546      1546      1507      1507      1507
HP-filt.log(U)     -0.288***     -0.315***     -0.291**     -0.323***     -0.587***     -0.310***     -0.383***     -0.648**

(s.e.)     (0.103)     (0.085)     (0.112)     (0.098)     (0.209)     (0.108)     (0.101)     (0.215)
panel FE v1                 X                   X                             X

panel FE v2                                               X                             X

demog. ctrls                           X         X         X                   X         X

9cPanel B: Recent Hires after Unemployment (1984-2001)
(ix)      (x)       (xi)      (xii)     (xiii)     (xiv)     (xv)      (xvi)

occ.move        occ.move           occ.move         occ.move           occ.move          occ.move$^\star$      occ.move$^\star$      occ.move$^\star$
no obs.     689       689       689       689       689     676       676       676
HP-filt.log(U)     -0.360*     -0.270     -0.342*     -0.246     -0.765***     -0.418**     -0.325     -0.828***

(s.e.)     (0.184)     (0.164)     (0.189)     (0.183)     (0.176)     (0.184)     (0.196)     (0.188)
panel FE v1                 X                   X                             X

panel FE v2                                               X                             X

demog. ctrls                           X         X         X                   X         X
9p0.9Levels of significance: $^{*} p<0.1$, $^{**} p<0.05$, $^{***} p<0.01$. Dependent variable occ.move$^\star$ is our alternative mobility indicator that excludes cases where occupational between 1-4 months longer than current job tenure, while previous job tenure is larger than 12 months. All regressions include a linear time trend. Demographic controls are a quartic in age, a gender dummy, race and education dummies. panel FE v1 has dummies for the following groups of panels, {1984}, {1987, 1988}, {1990, 1991, 1992, 1993 }, {1996, 2001}; panel FE v2 for {1984}, {1987, 1988}, {1990, 1991}, {1992, 1993 }, {1996, 2001} Observation weighted by person weights within panel, by number of total observations per panel across panels. Standard errors clustered by quarter.

We confirm the procyclicality of occupational mobility using regression analysis. Table <ref> shows the estimates of a linear probability model, were the dependent variable again takes the value of one if the worker reported a new line of work and zero otherwise. Our baseline is again occupational mobility as inferred from the occupational tenure question, where all those who have an occupational tenure higher than the current job tenure (allowing for a month of ambiguity) are considered stayers. In our alternative measure (denoted with $\star$, in columns (vi-viii) and (xiv-xvi)), we drop those observations who were coded as stayers but reported an occupational tenure within four months of the current job tenure.

When only controlling for a linear time trend, in columns (i) and (ix), we observe that times of high unemployment, as captured by positive deviations from HP-filtered log unemployment trend, are times of lower self-reported occupational mobility. This is also observed for the alternative measure of self-reported occupational mobility, in columns (vi) and (xiv). In panel A, we take all recent hires, while in panel B we consider all recent hires for whom we observed a period of unemployment. Naturally, the number of observations in panel B is lower, which makes our inference harder. Nevertheless, we also observe a procyclical response of self-reported occupational mobility, statistically significant at the 10%, having clustered standard errors at the quarter level. Restricting stayers to have an occupational tenure more than four months longer than current job tenure leads to a slightly stronger observed procyclical responsiveness.

As noted above, the SIPP's survey design changed over time, which may affect our results. To address this issue, we consider two variants of dummies for sets of panels that share the same survey design. As noted above, the 1990 to 1993 panel share the same survey design apart from a change of the timing of the retrospective question, from wave 2 to wave 1. This can be relevant for observing unemployment during the preceding non-employment spell. Therefore, in the first version (v1), we add four dummies for grouped panels, one for the 1984 panel, one for both 1987 and 1988 panel, one for the 1990-1993 panels, and one for the 1996-2001 panels. In the second version (v2), we add two separate dummies for the 1990-1991 panels and the 1992-1993 panels.

We observe that the responsiveness to the unemployment is much stronger in (v2). This responsiveness is driven by the variation within pairs of two panels, which puts a lot of weight on the behavior of the 1990 vs 1991 panel (and also on the 1996 vs 2001) panel, and less on the lower-frequency (but still business-cycle) behavior of the time series of observations in the subsequent recovery of the 1991 recession as measured relative to the pre-recession 1990 panel and full-recession 1991 panel. While these responses are statistically significant in both panels, we find it conservative to prefer v1 and consider results in these two panels on a similar base to those in 1992 and 1993 panels, and estimate the responsiveness of mobility also taking into account that response to unemployment in the recovery of the 1991 recession.

Overall, controlling for grouped panel effects (v1) does not change the estimated empirical responses much relative to e.g. columns (i) and (ix), while grouped panel effect (v1) leads to stronger procyclical responses. Comparing the estimates in panel B to panel A we do not observe meaningful differences in the point estimates, even though the underlying samples are smaller, with this naturally affecting the precision of our inference. Controlling for demographic characteristics does not change the empirical responsiveness to cyclical unemployment, fully in line with the results of the occupational code-based analysis. Taken together, it appears clear that occupational mobility of recent hires from non-employment, inferred from retrospective questions on occupational tenure and job history, is procyclical. For those hired after unemployment, the evidence points in the same direction, though tends to be statistically weaker as a result of a low numbers.

The cyclicality of the probability of an occupational change

9cPanel A: Recent Hires from Nonemployment (1984-2001)
(i)       (ii)      (iii)     (iv)      (v)       (vi)      (vii)     (viii)

occ.move        occ.move           occ.move         occ.move           occ.move          occ.move$^\star$      occ.move$^\star$      occ.move$^\star$
no obs.     1546      1546      1546      1546      1546      1507      1507      1507
HP-filt.log(U)     -0.270**     -0.304***     -0.271**     -0.311***     -0.583***     -0.291**     -0.370***     -0.660***

(s.e.)     (0.104)     (0.082)     (0.113)     (0.095)     (0.201)     (0.108)     (0.100)     (0.215)

NE duration     0.006**     0.006**     0.007**     0.007**     0.007**     0.006**     0.007**     0.007**

(s.e.)     (0.003)     (0.003)     (0.003)     (0.003)     (0.003)     (0.003)     (0.003)     (0.003)
panel FE v1                 X                   X                             X

panel FE v2                                               X                             X

demog. ctrls                           X         X         X                   X         X

9cPanel B: Recent Hires after Unemployment (1984-2001)
(ix)      (x)       (xi)      (xii)     (xiii)     (xiv)     (xv)      (xvi)

occ.move        occ.move           occ.move         occ.move           occ.move          occ.move$^\star$      occ.move$^\star$      occ.move$^\star$
no obs.     689       689       689       689       689     676       676       676
HP-filt.log(U)     -0.349*     -0.271     -0.333*     -0.250     -0.877***     -0.405**     -0.327     -0.890***

(s.e.)     (0.187)     (0.175)     (0.189)     (0.192)     (0.175)     (0.188)     (0.206)     (0.188)

NE duration     0.013**     0.013**     0.014**     0.013**     0.015**     0.014**     0.013**     0.014**

(s.e.)     (0.006)     (0.006)     (0.006)     (0.006)     (0.006)     (0.006)     (0.006)     (0.006)
panel FE v1                 X                   X                             X

panel FE v2                                               X                             X

demog. ctrls                           X         X         X                   X         X
9p0.9Levels of significance: $^{*} p<0.1$, $^{**} p<0.05$, $^{***} p<0.01$. Dependent variable occ.move$^\star$ is our alternative mobility indicator that excludes cases where occupational between 1-4 months longer than current job tenure, while previous job tenure is larger than 12 months. All regressions include a linear time trend. Demographic controls are a quartic in age, a gender dummy, race and education dummies. panel FE v1 has dummies for the following groups of panels, {1984}, {1987, 1988}, {1990, 1991, 1992, 1993 }, {1996, 2001}; panel FE v2 for {1984}, {1987, 1988}, {1990, 1991}, {1992, 1993 }, {1996, 2001} Observation weighted by person weights within panel, by number of total observations per panel across panels. Standard errors clustered by quarter.

Cyclical Shift of the Mobility-duration profile Finally, we investigate whether the self-reported occupational mobility profile with non-employment duration shifts down with recessions and whether on average exhibits a similar slope as in the pooled cross-sectional sample. Table <ref> shows that this is indeed the case. We observe that the modest positive slope on the non-employment duration is preserved when including the HP-filtered log of the unemployment rate. Controlling for non-employment duration largely leaves the empirical cyclical responsiveness unaffected. This result again mimics the result for the code-based mobility measures as they change with duration and the cycle.

§.§ Retrospective Self-reported Occupational Mobility – Conclusion
In this section, we have investigate occupational mobility after non-employment spells using very different data than in the main text. None of the occupational information used in the occupational code-based measures of the main text has been used in the measures in this section. Further, in the code-based measures of the main text, census coders are the judge of occupational change, while in the retrospective occupational tenure, this judgement is made by the interview subject. It is then comforting to observe that both measures line up well. First, according to workers, they are starting in a new line of work after nonemployment in about 40% cases. Second, with a longer nonemployment duration comes an increased tendency to change one's line of work. However, this tendency is modest, around 6 p.p. higher after 6 months of unemployment, fully consistent with the occupational-code results in the main text and in Section 1 of this appendix. Third, when cyclical unemployment is high, self-reported occupational mobility tends to be cyclically low. Fourth, controlling for the business cycle, we still observe a modest increase of self-reported mobility with nonemployment duration, again keeping with the pattern of code-based occupational mobility documented in the main text.

Once again our estimates show that the probability of an occupational change for the non-employed is procyclical. This holds true for both samples and with or without controlling for the duration of the non-employment spell. In particular, for the sample of recent hires that were unemployed for at least one month before re-employment, we still observe the moderate increase in the probability of an occupational mobility with the duration of non-employment. Likewise, controlling for destination occupations, we also find strong procyclicality in the probability of an occupational change. This suggests that the procyclicality is not driven by shifts in the occupational destinations over the business cycle. This is once again inline with the results of Section 4 of this appendix.

§ DATA CONSTRUCTION

§.§ Survey of Income and Program Participation

The Survey of Income and Programme Participation (SIPP) is a longitudinal data set based on a representative sample of the US civilian non-institutionalized population. It is divided into multi-year panels. Each panel comprise a new sample of individuals and is subdivided into four rotation groups. Individuals in a given rotation group are interviewed every four months such that information for each rotation group is collected for each month. At each interview individuals are asked, among other things, about their employment status as well as their occupations and industrial sectors during employment in the last four months.[See http://www.census.gov/sipp/ for a detailed description of the data set.]

The SIPP offers a high frequency interview schedule and aims explicitly at collecting information on worker turnover. Further, its panel dimension allows us to follow workers over time and construct uninterrupted spells of unemployment (or non-employment) that started with an employment to unemployment transitions and ended in a transition to employment. Its panel dimension also allows us to analyse these workers' occupational mobility patterns conditional on unemployment (or non-employment) duration and their post occupational mobility outcomes as outlined in Section 2 in the main text.