1 Theoretical background
The unemployment rate (UR) is commonly known and a core indicator in the field of economics and social science. It is often used by researchers and policy-makers when assessing the level of underemployment in an economy. However, its accuracy has often been subject to controversial discussions. Its critics argue that the unemployment rate does neither reflect the whole picture of movements in the labor market nor indicates the true level of underemployment. On the one hand, there is the dispute about how to define unemployment for example whether people in temporal or permanent labor market measures should be counted as employed or unemployed. On the other hand, the labor force is subject to cyclical variations that additionally distort the information content of the UR. The latter will be addressed in this study.
The cyclical variation in the labor force participation rate (LFPR) has been subject to economic research for a long time and can be traced back to Long (1953). The positive relationship between the LFPR and the business cycle has also been empirically examined for a while (Tella 1964) (Strand and Dernburg 1964). The question where the additional workers come from during upturns and go to during downturns can be explained with the discouraged worker effect (DWE). This concept implies that unless the economy is at full capacity, measuring the unemployed workforce underestimates the true level of underemployment. Hereby, the discouraged workers (DW) represent the fraction of the population that, depending on the economic situation, is part of the workforce i.e. employed or actively looking for a job or part of the inactive population.
In the literature, a lot of different terms like ‘discouraged workers’, ‘discouraged worker effect’, ‘labor reserve’ and ‘hidden unemployment’ are used to refer to similar phenomena. These terms are sometimes used as synonyms, even though they partly represent different concepts and differ in terms of measurement (Fuchs and Weber 2010: 8). In this study the DWE is defined as the variation in the LFPR that is the result of a systematic retreat of workers from the labor market in economic downturns and an entering in economic upturns. At this point, it has to be made clear that the term might be misleading, since there are various reasons for retreat or entering that go beyond what is commonly understood as ‘discouragement’ and may additionally differ between individuals. Rather, it is meant in the sense that “the costs of of active job search increase and the benefits go down” (Lee and Parasnis 2014: 90).
In the case of Germany, there are three different estimates of the DW. Eurostat regularly estimates the DW for all OECD countries based on the Labor Force Survey (Department of International Economic and Social Affairs 1984: 126). The German Institute for Economic Research
Both approaches have in common that they ask currently inactive people about their willingness to work under different scenarios, which allows them to directly identify members of the DW. Further, since the subjects can be directly asked, this approach allows the reseracher to collect more information on the socio-economic background and the circumstances of economic inactivity. However, the major drawback of the survey approach is that people are interviewed about a hypothetical situation for example whether they would be willing to work if the economic situation significantly improved. The responses might be strongly biased in two ways. First, it is socially desirable to work and therefore people will tend to answer that they would work, even if they would not. Second, due to phenomena such as ‘cognitive dissonance’ they might answer that they would not work even if they would (Fuchs and Weber 2010: 14). While both effects have different signs, it is not clear which effect outweighs the other.
In addition to the negative relationship between the economic condition and the labor force participation, there is a second phenomenon that works in the reverse direction and is commonly known as the added worker effect (AWE). It follows the logic that economically inactive people enter the labor market under deteriorating economic conditions to compensate for the reduced household income that is induced by the job loss of other household members. As a result, this phenomenon causes increased labor force participation in deteriorating economic conditions. While the AWE has been known for a long time as well, nowadays, it is often assumed to be negligible and therefore omitted in research on the German labor market (Fuchs and Weber 2005b: 9). However, since strong evidence for the AWE is found this effect is also taken into account when estimating the true level of underemployment. The net of the added workers (AW) and the DW will be referred to as the hidden labor force (HLF).
The purpose of this paper is to contribute to the literature of the DWE and the AWE by introducing some variations to the IAB-approach and offering an alternative estimation of the HLF in Germany. Doing so, my main focus will be the DWE, but the AWE will also be investigated. Further, an emphasis is put on the sociodemographic composition of the HLF. Last but not least, it is investigated whether or not the dynamics of the HLF are subject to asymmetry in economic upturns and downturns, as well as before and after the labor market reforms that came into force in 2005.
The remainder of this paper is organized into five sections. In the remaining part of this section, the related literature and its findings are analyzed. Secondly, the basic model is introduced, the HLF is operationalized and the data is delineated. Thirdly, the empirical results are presented and put into context. In the fourth section, potential limitations will be discussed. The fifth section summarizes the results and concludes the paper.
1.2 The business cycle indicator
In order to estimate the DWE (and the AWE), a business cycle indicator has to be chosen. In early research, the employment to population ratio was used (Tella 1964) (Strand and Dernburg 1964). However, this indicator was criticized as including autonomous factors and being upwardly biased (Mincer 1966). Benati (2001) proposed the UR of prime aged males
Based on the idea of Armstrong (1999), who used benchmarks on the regional level in order to estimate the HLF in Northern Ireland, the IAB has come up with a full employment value of
1.3 Findings on the size, structure and the dynamics
Strand and Dernburg find for the U.S. that the DWE is “strongest among the female population and the very young and very old males” (Strand and Dernburg 1964). The labor market economist Jacob Mincer found for the U.S. that while an increase in the general UR by one percentage point decreased the LFPR of young Caucasians by two percentage points, it decreased the LFPR of young Afroamericans by five percentage points (Mincer 1966). A more recent study even found that for a panel of European countries, there is only a significant DWE for women (Ozerkek 2013). In general, there is a consensus that different subgroups of the population are not equally affected by the DWE. Past research has in particular pointed out that women react much more sensitively to cyclical fluctuations as men. Further, it seems that there is a tendency that generally marginalized groups are more likely to be subject to the DWE than others.
An emerging strand of research related to the DWE is whether or not it is subject to asymmetry. For Australia, Dixon (1996) found evidence that participation is more sensitive during economic upturns as opposed to downturns, while O‘Brien (2011) finds the opposite for the old male population. Lenten (2001), in contrast, does not find asymmetric behavior for Australia at all. Other research for France, Japan, Sweden and the U.S. has found that while Sweden shows symmetric responses, in general, there is a significantly larger reaction among older females to a deteriorating as compared to an improving economic situation (Darby, Hart, and Vecchi 1998). As of now, no clear pattern for asymmetric reactions has been found.
Research from the 1960s in the US found significant evidence that women and younger people are affected by the AWE (Strand and Dernburg 1964). Presently, the AWE is generally neglected in research on OECD countries. This is mainly due to the fact that the DWE outweighs the AWE and it can only be detected for particular subgroups of the population, which makes it necessary to have highly disaggregated data. Moreover, current research “confirm(s) the hypothesis that the added worker effect is dominant in developing countries while the discouraged worker effect is dominant in the OECD countries” (Lee and Parasnis 2014: 96). A more recent study of Fuchs and Weber (2013) using a trend-cycle decomposition finds evidence for both, the DWE and the AWE in Germany which is in contrast to Fuchs and Weber (2005b).
Using the survey approach, the DIW finds that in
2 Conceptual and methodological issues
2.1 The segments of the labor market
Depending upon the economic situation, members of the HLF are either actively participating in the labor market or not. Since the economic situation varies over time, so does the HLF. An economically inactive person is not necessarily part of the DW, and can hardly be directly identified as such. Rather, we have to think of it that we know that a person was part of the DW, if we observe this person systematically entering the labor force under improved economic conditions. The same applies to the AW, who for example have to systematically enter into the labor market under deteriorating economic conditions in order to be identified as such.
Figures 1 and 2 illustrate the DWE and the AWE. The figures are snapshots of the different sections in labor market under good and bad economic conditions. In order to be consistent, the definitions provided by the ILO (International Labor Organization 1982) as implemented in the German Microcensus (Statistisches Bundesamt 2013) will be used. The whole blue ovals represent the part of the population that is above
As the economic situation deteriorates, the DW leave and the AW enter the workforce as illustrated in the transition from Figure 1 to Figure 2. As can be seen, the size of the DW as well as the size of the AW are larger under bad as compared to good economic conditions. Further, the labor force (the orange oval) is larger under the good as compared to the bad economic condition. This is always the case if the DWE outweighs the AWE.
2.2 Estimating the hidden labor force
At first, the LFPR of each subgroup
In the next step, the potential labor force participation rate of subgroup
Now we can calculate the HLF in a given year by multiplying the difference between the PLFPR and LFPR of group
The LFPRs of the various subgroups for the considered period from 1991 to 2014 are calculated with data that comes from the German Microcensus and is provided upon request by the Federal Statistical Office (Destatis). The Microcensus is a population and labor market survey in the framework of the EU Labor Force Survey and is published annually by the Federal Statistical Office. With the help of this survey, disaggregated LFPRs for 80 subgroups of the population of working age (from 15 to 64 years of age) have been computed. The German population is divided into 10 age groups, into German citizens and non-German citizens, males and females and Western and Eastern Germans (including West-Berlin). The age groups are separated in five year steps from 15 to 64. The reason for distinguishing between Western and Eastern Germans is that both groups were shaped differently during the division of Germany in terms of labor market policy and participation. For example, Eastern German women still exhibit a much higher LFPR as their Western German counterparts as they had a different position in society and were strongly included into the workforce of the German Democratic Republic (see Figure A.1).
Having LFPRs on the disaggregated level for different subgroups instead of just one LFPR for the whole population has several advantages. First, it allows us to analyze if a particular subgroup is even subject to one of the two HLF-phenomena. This might be the case since different groups with different sociodemographic backgrounds have a different labor market behavior and might react differently to shocks like a deteriorating economic condition. Second, a much more detailed picture of the HLF can be drawn. Third, it can be controlled for group-specific time trends.
Unfortunately, there are several missing values and high relative standard-errors for the very small subgroup of Eastern German, non-German females between
The official UR from the Federal Ministry of Labor and Social Affairs is used as the business cycle indicator. The official UR is subject to changes in procedure based on labor market policy measures in 2005. The major change regarding the UR was to combine members of unemployment benefits and social welfare. The potential implications for the analysis and how it has been taken into account are set out in more detail in Subsection 3.2 and Subsection 4.2.
2.4 Endogeneity and causality issues
Using the UR as the explanatory variable is due to the fact that it is a good proxy for the economic condition and the business cycle in a country due to its coincident countercyclical nature. In this context, the UR is superior to other indicators like the capacity utilization as it has a special emphasis on the employment dimension of the business cycle which is of central interest. In addition, since the UR is omnipresent in the media for example in newspapers, on the internet and on television it is a good proxy for how people perceive the economic condition of their country. Furthermore, using the UR as the explanatory variable in order to estimate the HLF is state of the art in the literature (O‘Brien 2011) (Lee and Parasnis 2014).
In order to avoid spurious regressions, the LFPRs are detrended. In addition, this captures and controls for other, non-cyclical impacts on the LFPRs that are induced by social and socio-economic changes such as an increasing LFPRs of females due to the altering role of women in society. Other examples are decreasing LFPRs of young people which are associated with longer education and training periods. Further, increasing LFPRs of older people can be explained by improved health and stepwise increased retirement ages. Linear time trends are assumed for each group due to the fact that the underlying socio-economic processes as described above affect the LFPRs gradually and persistently. Moreover, plotting the LFPRs over time additionally suggests linear time trends (see Appendix A.1). As can be seen in Figures 11, 12, and A.1 the LFPRs of most groups (especially the 55-64 year old and female groups) show consistent increasing linear time trends. On the contrary, the LFPRs of the young (15–29 year old) exhibit slightly negative linear time trends. Only the LFPR of the group of medium-aged (30–54) males seems relatively constant over time. Interestingly, the LFPRs of the subgroups exhibit signs of convergence as those with lower initial levels increase faster and the range between the lowest and highest value decreases sharply from 1991 to 2014. Consequently, the LFPRs do not represent rates in the proper meaning of the word, but the variation of the LFPRs around the time trend. Performing unit root tests ensures that the detrended LFPRs are stationary.
Since eq. (3) is based on eqs. (1) and (2), which mutually share (related) variables, the model might suffer from endogeneity and reverse causality bias. When conducting Granger causality estimations, patterns of reverse causality are found. The hypothesis that the LFPRs do not cause UR could be rejected for the AW-groups, however, it could not be rejected for the DW-groups. In order to curb endogeneity, the lagged UR is used. Consequently, a Granger-causal impact of the UR on the LFPRs is captured. This means that it is unlikely that the UR is affected by the LFPRs since the UR precedes the LFPRs (Granger 1969). Especially, given that the results on reverse causality are ambiguous, we consider this to be a minor issue.
As a result, there are two phenomena left that have an impact on the LFPRs. Firstly, an increase\decrease in the UR reduces\increases the expected returns of searching employment and thus leads to a retreat\entry from\into the labor market. Secondly, an increase\decrease in the lagged UR implies a reduced\increased household income and increases\decreases the expected benefits of searching employment of other household members. Therefore, other economically inactive\active household members enter\retreat into\from the labor market. While the first refers to the DWE, the second describes the AWE. The coefficient
As opposed to Fuchs and Weber (2005b), who regularly publish figures of the DW for the IAB, this approach recognizes that only a net effect can be estimated. Consequently, estimates of the DW and the AW actually represent lower limit estimations since the pure effects cannot be isolated. Furthermore, this study represents a more systematic approach since a general model and business cycle indicator is used consistently for all subgroups. Fuchs and Weber (2005b), in contrast, model the labor market behavior differently for each subgroup. While different models are reasonable and necessary in case of different dynamics and causal chains, the authors leave open why this would be the case here. Fuchs and Weber (2005b: 51–54) illustrates the wide variety of models and (explanatory) variables (even between subsequent cohorts of for example Western German, German males) the authors use to estimate the DWE. This proceeding might be the reason why the authors do not find evidence for the AWE at all.
To summarize, the correlation between the UR and the LFPR is driven by two phenomena, the DWE and the AWE. Both of which take effect in the opposite direction and
In this study, it is made use of the fact that all negative\positive correlation can be attributed to the DWE\AWE. As a result DW-groups are groups for which the DWE significantly outweighs the AWE, while AW-groups are groups for which the AWE significantly outweighs the DWE.
3 Empirical analysis
3.1 The hidden labor force groups
Table 1 shows the results of four different fixed effects regression models with robust standard errors. In the first model all
|No. of groups||77||77||9||24|
|Obs per group||23||23||23||23|
Robust standard errors in parentheses.
Note: Models are fixed effects regression models. See eq. (4) for information on how the ‘long-run effect lagged UR’ is derived. The line ‘weighted’ means whether or not the regression is weighted by the population shares of the subgroups. ‘LFPR’ - ‘Labor Force Participation Rate’. ‘UR’ - ‘Unemployment Rate’. ‘AW’ - ‘Added Workers’. ‘DW’ - ‘Discouraged Workers’.
In order to find out which groups are subject to the DWE and the AWE, OLS regressions with robust standard errors are conducted for each group separately (see eq. (3)). A group qualifies as being a DW-group\AW-group if it has a significantly
In the third model, the sample is reduced to the
3.2 The size of the hidden labor force
As mentioned in Section 1.2, it is necessary to make an assumption about the UR under full capacity conditions in order to be able to estimate the absolute size of the HLF. While a UR of
As mentioned in Subsection 2.3 the unemployment rate is subject to a major change in calculation procedure due to the labor market reforms that were finalized in 2005. I follow Fuchs and Weber (2010) who argue that these reforms have directly increased the full employment UR from
Following eqs. (3)–(6) the HLF can be estimated. A table with figures for the HLF for all years can be found in Appendix 7. Figure 3 illustrates the UR, the DW, the AW and the net of the latter two, the HLF, in Germany in the considered period from 1991 to 2014. Since the changes in the DW, the AW and the HLF directly depend on the changes in the UR, the lines follow a very similar pattern. The deviations between them can be explained with demographic shifts.
As can be seen in the figure, the HLF strongly increased during the
As compared to Fuchs et al. (2015) who estimate the DW to be
3.3 The structure of the discouraged workers
The structure of the DW depends on two variables: the share that the subgroups have in the DW-Pop and their respective
Note: In the first line, the first letter is either ‘E’ for ‘Eastern German’ or ‘W’ for ‘Western German’. The second letter refers to either ‘N’ for ‘non-German’ or ‘G’ for ‘German’. The third letter refers to ‘F’ for ‘female’ or ‘M’ for ‘male’. Empty cells represent insignificant coefficients. Black cells represent the excluded groups (see Subsection 2.3). P-values are based on robust standard errors.
Figures 4, 5, 6, and 7 display the structure of the DW in
As can be seen in Figure 4, women are overrepresented in the DW-Pop. Taking their sensitivity to changes in the UR into account hardly compensates for this as can be seen by the third bar. The share of
Figure 5 displays that Eastern Germans are overrepresented in the DW-Pop, but react weaker to changes in the economic condition which partly compensates for that. The DW-share of the Eastern Germans in
Figure 6 shows that non-German citizens, which account for
In Figure 7, the shares of three aggregated age groups (
The results on the structure of the DW are in line with the literature where women, young and old age groups play the major role in the DW (see Section 1.3). This is in contrast to the estimates of the IAB who counterintuitively find that the medium age groups (
3.4 The structure of the added workers
The structure of the AW also depends on whether particular subgroups are even subject to the AWE and their sensitivity to changes in the economic condition. Table 6 in Appendix A.2 contains figures over time on the structure of the AW broken down by sociodemocrafic characteristics. As can be seen in the table, Non-Germans, Eastern Germans and old people are basically not affected by the AWE.
Figure 8 shows the structure of the AW by age groups. As can be seen, old people do not play a role in the AW-Pop, thus they do not play a role among the AW either. Medium age groups however, account for
A closer look at the structure of the AW by sex clearly indicates that women are extremely overrepresented in the AW-Pop (
4 Robustness and limitations
4.1 Dynamics of the hidden labor force
An important aspect to examine is whether the dynamics of the HLF are subject to asymmetry in economic upturns and downturns. In order to do that, two dummies are defined. Dummy
In the first model, all DW-groups are included and in the other three models one aggregated age group of the DW-groups each (see Table 3). The Null hypothesis that states that the
Asymmetry regressions for discouraged workers.
|No. of groups||24||11||6||7|
|Obs per group||23||23||23||23|
Robust standard errors in parentheses
Note: Models are fixed effects regression models with regime dependent effects. See eq. (4) for information on how the long-run effects are derived. The ‘Asymmetry test’ is performed using the Delta-method (Phillips and Park 1988). The line ‘weighted’ means whether or not the regression is weighted by the population shares of the subgroups. ‘LFPR’ - ‘Labor Force Participation Rate’. ‘UR’ - ‘Unemployment Rate’. ‘Young’ - ‘15–29’. ‘Medium - ‘30–54’. ‘Old’ - ‘55–64’.
On average, the medium-aged part of the DW increased its LFPR by
4.2 Policy shifts in 2005
Despite the encouraging results from Subsection 4.1 that underline the robustness of the approach with regard to upturns and downturns, the analysis raises further points of concern that need to be addressed. As party discussed in Subsection 2.3 and Subsection 3.2, there are policy shifts between 2003 and 2005, known as “Agenda 2010”, that have directly affected the German labor market. Furthermore, the way the LFPRs and UR are computed has changed since 2005. Even if, for the UR this change has been taken into account (see Subsection 3.2), the changes in indicators might still distort the analysis and therefore need to be examined in more depth. Moreover, the transformed labor market conditions after 2005 might alter or even dissolve the underlying mechanisms of the DWE and the AWE.
One way of coping with the potential trend break in 2005 would be using data starting in 2005. However, due to the short time horizon since then and the necessity of a reasonable amount of observations, at the moment, this can only be realized using quarterly instead of yearly data. However, when conducting the analysis using quarterly data it has to kept in mind that different dynamics are potentially being captured. Even if it seems reasonable to assume that economic actors on the labor market need some time to adapt to altering situations there might be dynamics that happen during the year. These short-run dynamics would not be detected using yearly data. This is why an analysis using quarterly data rather constitutes a complement than a substitute to the analysis with yearly data. Both of which reveal information about the DWE- and AWE-dynamics for different time frames. Moreover, even if the effects might differ significantly with respect to the magnitude, in general, they should exhibit the same signs as they follow the same general phenomena. Therefore, a sign test for quarterly data constitutes a robustness test for the applied approach in this study. Table 4 illustrates the results of such an analysis using quarterly data. This table directly corresponds to Table 1 in Subsection 3.1.
Panel regressions using quarterly data.
|1st lag LFPR||0.0972||0.206||0.242||0.280|
|2nd lag LFPR||0.119||0.139||0.120||0.190|
|3rd lag LFPR||0.116||0.157||0.102||0.165|
|4th lag LFPR||0.324||0.451||0.391||0.336|
|No. of groups||80||80||9||24|
|Obs per group||40||40||40||40|
|F-statistic||16.99 ||782.6 ||114.5 ||721.2 |
Robust standard errors in parentheses.
Note: This table corresponds to Table 1 using quarterly data from 2005-2015 based on the Microcensus and own calculations instead of yearly data starting in 1991. Models are fixed effects regression models. Four lags are included to control for the very high persistence in the quarterly LFPRs. See eq. (4) for information on how the ‘long-run effect lagged UR’ is derived. The line ‘weighted’ means whether or not the regression is weighted by the population shares of the subgroups. ‘LFPR’ - ‘Labor Force Participation Rate’. ‘UR’ - ‘Unemployment Rate’. ‘AW’ - ‘Added Workers’. ‘DW’ - ‘Discouraged Workers’.
As can be seen in the table, the results differ from the results in Table 1. While the short-run and long-run effects of UR in the unweighted regression over all groups (column 1) are now insignificant, they are significant but close to zero for the weighted regression (column 2). More important are column 3 and 4. Generally, the coefficients of the respective variables have the same signs. However, in column 3 (AW-groups only), the magnitude of the coefficients associated with the unemployment rate are a bit greater in the short-run and much greater in long-run while being equally significant. In column 4 (DW-groups only), the short-run effect of the unemployment rate is much smaller while the long-run effect is much greater than in Table 1. Moreover, the long-run effect is now insignificant. Consequently, it can be concluded that, as expected, the direction of the effects corresponds to those observed for yearly data. This finding supports the robustness of the results in this study. Further, the magnitude of the effects differs significantly. Yet, this might be the result of methodological issues related to a very short time period. Further, it might come from the, relative to the yearly data, high variance of the quarterly LFPR point estimates that is associated with the continuous estimation using smaller samples respectively since 2005. Therefore, these results have to be taken with caution. But there are several other reason that might explain the difference in the estimated magnitude. First of all, it might be that the short-run dynamics estimated using quarterly data differ from the more gradual ones estimated with yearly data because they refer to different effects within the same phenomenon. Secondly, it might be that the changes in the computations of the indicators, UR and LFPR, since 2005 contributed to some bias in the estimation of the original results. Last but not least, it might be that the labor market reforms that fully came into force in 2005 have altered the DWE and AWE. While the former two cannot be disentangled and directly tested for, the latter can be examined using regression models with regime dependent effects similar to those used in Subsection 4.1 for before and after 2005.
Asymmetry regressions before and after 2005.
|No. of groups||77||77||9||24|
|Obs per group||23||23||23||23|
Note: Models are fixed effects regression models with regime dependent effects. See eq. (4) for information on how the long-run effects are derived. The ‘Asymmetry test’ is performed using the Delta-method (Phillips and Park 1988). The line ‘weighted’ means whether or not the regression is weighted by the population shares of the subgroups. ‘LFPR’ - ‘Labor Force Participation Rate’. ‘UR’ - ‘Unemployment Rate’.
Table 5 contains the results examining whether or not we have a trend break in the data in 2005. The table reveals that overall the effect became significantly less negative since 2005 for both, the weighted and the unweighted model. More specifically, the DWE on average decreased by 25%, thus leading to a significantly lower reaction to altering economic conditions since 2005 among the DW. Keeping in mind the results from Subsection 4.1, this finding is unlikely to be driven by the fact that the period after 2005 coincides with an overall improving labor market situation (as measured by a diminishing UR). While the change in the indicator’s computation might have contributed (or even counteracted) to this change, it seems likely that the altering labor market framework conditions are the main cause of this effect. In fact, a part of the labor market reforms consisted of reducing the unemployment benefits which, in turn, has reduced the opportunity cost of employment. Consequently, the reforms tend to leave people more attached to the labor market and dependent on revenues from employment, reducing the fraction of those who can afford to get discouraged in bad and encouraged in good economic times.
In contrast to the finding for the DWE, column 3 in Table 5 reveals that the AWE is not significantly different before and after 2005. This result is surprising, as it has been assumed that both phenomena generally follow similar incentives, just in reversed directions. Under this narrative we would expect the AW to react stronger to variations in the business cycle as due to reduced unemployment benefits households depend more on the secondary worker under economic hardship. Therefore, this finding raises doubt on the narrative from Subsection 2.4 that the sole underlying fundamental of the AWE is the compensation for reduced household income in economically difficult times. It seems likely that other mechanism are at force that cause people entering the labor market in economically difficult times and retreating under improving conditions. At least, it can be argued that the AWE is no very sensitive to variations in the opportunity cost of employment - the unemployment benefits. It might be that other aspects of the labor market reforms like the more active employment agencies or the transition into a more flexible framework for subcontracted employment provide answers to the puzzle. Moreover, the structure of employment in Germany has changed since 2005. First of all, real wages have been more or less stagnant for most of the period. Furthermore, a boom of employment subject to social security has been observed that is to large extent due to increased secondary, temporary and part-time employment (Goecke et al. 2013). It might therefore be the case that an increased reaction to business cycle variations due to more flexible employment opportunities as well as lower unemployment benefits has increased the reaction among the AW. Further, it seems plausible that the overall structural shift towards stronger labor market dependency and great employment opportunities have engaged a part of the AW permanently, at large, leaving the average response to business cycle variations unchanged. Furthermore, it might be that the AW are heterogeneous with respect to the type of employment. This could imply heterogeneous incentives that, in turn, lead to unexpected outcomes as observed in this context. However, it has to be noted that the framework of this study does not allow to make a conclusive assessment with regard to these questions. It is therefore necessary that follow-up studies focus on the type and circumstances of employment among the DW and the AW and their implications on altering economic conditions.
As set out in this section, the study is not free of limitations. It became clear that the DWE and the AWE exhibit different magnitudes for different time frames. Neither exclusively using yearly nor quarterly data allows to draw a complete picture including all interdependencies at force. Moreover, transforming economic framework conditions continuously alter the response of the DW towards the business cycle. It should be kept in mind that the DW have reacted on average 25% weaker towards variations in the economic condition after the finalization of the labor market reforms in Germany in 2005 as compared to before. On the contrary, the methodology used in this study has proven to be robust with respect to economic upturns and downturns. Further, the response of the AW was left unchanged following the reforms. Moreover, it could be shown that the direction of the effects is robust for different groups of the population irrespective of the time frame of the dynamics under observation. Nevertheless, the finding of non-responding AW to the reforms of the first half of the 2000s constitutes an unresolved puzzle that needs to be addressed in subsequent research.
This paper underlines the importance of going beyond the UR when assessing the true level of underemployment in an economy. Further, this paper shows that the inaccuracy of the UR increases the further away the economy moves from full employment. Depending on the subgroup, there is not only strong evidence for the DWE, but also for the AWE which is neglected in current research on the German labor market. Since the extent to which a particular subgroup is affected by the DWE and the AWE strongly varies across the population, it is crucial to work with disaggregated data when investigating the two.
The analysis shows that given the current structure of the population, the absolute DWE exceeds the AWE which implies that the hidden labor force is always positive. Furthermore, the AWE is an important stabilizer of the workforce, as the AW on average comprise
Except for the DW of medium age who react slightly stronger to economic upturns as compared to downturns, there is no evidence for asymmetric responses. The asymmetry of medium-aged workers might be due to their strong labor market dependency which tends to not allow them to retreat under deteriorating economic conditions. However, the magnitude of this effect is negligible in absolute terms.
Information on the structure and the dynamic of the DW and the AW is crucial for policy-makers. As it turned out, the policy reforms, “Agenda 2010”, have reduced the response of the DW to the business cycle on average by 25%, leaving the response of the AW unchanged. Neglecting these effects when setting up policies might flaw their effectiveness and result in unexpected outcomes. Future research on the DWE and the AWE should focus on further disentangling the two. Moreover, working with data from panel surveys which is generally possible but limited due to legal obstacles would be another way to shed further light into the issue (Böhm 2011). A great advantage was that it would allow to analyze the circumstances of cyclical changes in the labor market participation in more detail. Further, it would give us deeper insights into the the type of employment of the DW and the AW which is crucial for understanding and predicting their response to policies. Last but not least, subsequent research should focus on obtaining a conclusive picture regarding the time dimension of the DWE and the AWE. As a result, this would allow policy-makers to design and implement much more effective labor market measures.
I would like to express my gratitude to Jesus Crespo Cuaresma for invaluable and constructive feedback and support. Further, I wish to thank the ifo Center for Business Cycle Analysis and Surveys for the collaboration during the early stages of the study, and the German Federal Statistical Office for the provision of the data. Last but not least, I want to thank Peter Winker and two anonymous referees for insightful comments.
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Code and Datasets
The author(s) published code and data associated with this article in the ZBW Journal Data Archive, a storage platform for datasets. See: https://doi.org/10.15456/jbnst.2017231.110104.
A.1 The labor force participation rate over time
A.2 The hidden labor force disaggregated
Hidden labor force shares over time.
|Discouraged workers||Added workers|
A.3 Hidden labor force estimates
Estimates over time.
Note: Column 2–5 are in thousands. Column 6–11 are in percent\percentage points. ‘Pop’ - ‘Total Population between 15 and 64’. ‘DW’ - ‘Discouraged Workers’. ‘AW’ - ‘Added Workers’. ‘HLF’ - ‘Hidden Labor Force’. ‘UR’ - ‘Unemployment Rate’. ‘LoU’ - ‘Level of Underemployment’ (