What does it take to be (counted as) unemployed? The case of Spain

Transcription

1 What does it take to be (counted as) unemployed? The case of Spain Luis Garrido (UNED and CESC*) Luis Toharia (Universidad de Alcalá and CESC*) February 2003 Submitted to the 2003 EALE Conference, Sevilla, September FIRST DRAFT (*) Centro de Estructuras Sociales Comparadas, Universidad Nacional de Educación a Distancia

2 ABSTRACT This paper analyzes the effects of the new European Commission regulation 1897/2000 which establishes a new definition of unemployment for the purposes of Labour Force Surveys. The paper first examines the conditions that unemployed people have to meet in order to be excluded by the new notion, i.e. being passive job seekers, to turn then to an application to the case of Spain, a country where the new regulation was expected to have strong implications. Various characteristics of workers are related to the process of exclusion, but the most significant result is that there is a wide regional dispersion of exclusion rates, which also seems to vary significantly over time. The conclusion is that determining who are the true unemployed may be more difficult than the regulation hoped for. The second issue, however, is to what extent the very nature of the regulation is warranted, i.e. whether passive job seekers deserve to be excluded from unemployment because their labour market behaviour is closer to that of people outside the labour force. The conclusion here is mixed: in terms of their probability of finding a job within one quarter, passive job seekers are in between active job seekers (the true unemployed) and non-seekers (the inactive). Why then should they be counted in either group? Given the difficulties to isolate them clearly in labour force surveys, and given that passive job-seeking seems to be quite unstable over time, it might be wiser to leave them within unemployment. Defining them as inactive, as the new EC regulation does, does not appear to be justified in any way.

3 1 Introduction Being unemployed is a situation which, in principle, is easy to understand. An unemployed person is someone who is willing to work but is unable to find a job. Of course, this general statement may be qualified in various ways, for example, by imposing some condition about being willing to work at the going wage, as economists usually do, or about the way in which job search is carried out. Labour market statisticians have long established a standard definition of unemployment (adopted at ILO Conferences) whereby an unemployed person has to meet three conditions to be considered, and hence being counted as, unemployed: - not having worked in the past, reference, week, meaning not even one hour and no matter whether for pay or as an independent worker (including unpaid work in a family business; voluntary unpaid activities are not considered as work); - being available for work, i.e. being in a position to be able to start a possible job offered within a fortnight; - having performed active job search, usually meaning that the person can mention at least one way in which s/he has looked for a job in the previous four weeks, from a list of methods defined as active (precise steps taken; as opposed to passive, e.g. waiting to be called). Many countries around the world, including, to be sure, the United States, Canada and the European Union, have adopted this definition as their guideline to measure unemployment. Most of them, as well, use sample household surveys to determine such figures. However, the precise way in which the concepts above are translated into precise questions aimed at extracting the correct information from individuals varies from country to country. The most difficult question refers to job search. The usual recommendation (for example, by EU regulations) is that interviewers read a list of methods at least until three of those considered active have been read. But the practice seems to be varied and no unique procedure is followed. In September 2000, the European Union passed a new regulation concerning the operational definition of unemployment. In simple terms, this regulation defined active search in a more stringent way, especially as regards contact of the unemployed with the public employment services. Although no official statement of the results of 1

4 implementing this new regulation exists, there is at least one country, Spain, where its effects on the unemployment figures have been devastating 1. As a matter of fact, many experts and commentators tend to believe that the Spanish unemployment figures were directly aimed at by the new regulation 2. In the first quarter of 2001, the Spanish Statistical Office amended the LFS questionnaire so that the new definition of unemployment could be implemented. Starting in the first quarter of 2002, together with two other methodological changes (new population figures and a new weighting scheme, both of which increase employment and unemployment), the new definition has officially been adopted. Against this background, the purpose of this paper is to investigate to what extent the changes in the definition of unemployment suggested in the new regulation make sense from an economic and sociological viewpoint. The Spanish case is used as an illustration. Thus, after presenting the new definition of unemployment in more detail in Section 2, showing that the new notion of unemployment is a subset of the concept used before, Section 3 turns to quantifying the reduction of unemployment implied by it in the Spanish case, for which information is available for the eight quarters of 2001 and The reduction is presented not only in terms of the general evolution but also in terms of the characteristics of the unemployed. Section 4 then turns to the issue of whether the elimination of a significant group of workers from the rosters of the unemployed is meaningful. The analysis here is made on a double front: first, a conceptual discussion is presented on the role played by active job search and the recourse to public employment services as a job search method; secondly, an analysis of the employment behaviour of those excluded from the unemployment definition is presented, in terms of their probability of leaving unemployment and getting a job within one quarter. The main conclusion is that, on both counts, one may argue that these people are different from the unemployed as newly defined; however, they are also different from people counted as inactive or not 1 At a Seminar which took place in October 2000 at the Universitat Pompeu Fabra of Barcelona, one of the authors made an early estimation of the potential impacts of the new definition. See Toharia (2000). For the point of view of an international observatory on the debate surrounding the new figures, see EIRO (2002). 2 This is of course impossible to document and is based on casual observation by various people involved in statistical and policy-related matters, including the authors. For example, at one point, Eurostat tried to change the definition of unemployment used in the ECHP leaving unclassified half a million people in Spain. This change was dropped later on, however. 2

5 in the labour force. On the whole, therefore, excluding them from unemployment is not fully grounded on economic and sociological analysis. As discussed in the concluding section, unemployment is a fuzzy category in which one can find people fully committed to labour market activities and people less so committed. 2 The new definition of unemployment Regulation 1897/2000 of the European Commission defines unemployment in a pretty standard way, following the classical ILO conventions. Thus, an unemployed person is one without work during the reference week, currently available for work and actively seeking work during the four week period ending with the reference week. The new twist of the definition is the specific steps which are considered proofs of active job search. Among them, and most significantly, the precise wording of the relationship between the jobseeker and the public employment offices 3. While theretofore registration with one such office would be considered sufficient proof of active job search, from now on only a contact to find work will be considered a specific step of active job seeking, i.e. a visit to the employment office with the purpose of merely renewing administrative registration will no longer be interpreted as an active job search method. From a purely abstract point of view, there is nothing to question in this stance adopted by the European Commission. It is true that a person who merely visits a public employment office for the purpose of renewing an administrative registration which may provide other benefits in terms of access to various services or courses cannot be taken to be actively seeking work. On the other hand, one may wonder whether this person can reasonably be equated to other who do not want to work, and openly declare it to be so. In this respect, it should be mentioned that all of the labour force survey questionnares used ask whether the person is seeking work or not before asking how this search is being done. So what the new regulation is doing is sending to the pool of the inactive population all those who, despite declaring initially that they are seeking work, cannot prove it because their contact with the public employment offices is passive. We shall come back to this question in Section 4. 3 The new regulation includes other minor changes which we omit here for reasons of space. Their influence in the unemployment figures is negligible. 3

6 What is to be stressed here is that the new unemployed group is a subset of the old group. This is very convenient, as it allows for an analysis in terms of exclusions from the old notion to reach the new one, as shall be presented in Section 3. Starting with the old definition of unemployment, it is worth understanding how the unemployed pool is precisely determined. Following the EC regulations 4, questions on employment must precede any question on job search. So LFS questionnaires first determine whether the interviewee may be classified as employed. If not, then the basic job search question follows. Those providing a positive answer are then asked about search methods and availability (the order here being irrelevant). In general, a large list of possible search methods is read to respondents and no restriction applies as to the number of positive answers given 5. Those able to mention at least one search method and declaring that they are available for work are then classified as unemployed. The rest is inactive population. In general, initial jobseekers excluded from unemployment represent less than 5 percent. As explained above, the new regulation distinguishes between active and passive job search methods. The procedure to determine unemployment is exactly the same as described in the preceding paragraph, with the only caveat that now only those mentioning at least one active method are considered unmeployed. It should be obvious, however, that the probability of being excluded heavily depends on the number of methods mentioned in the first place. Thus, if one person declares two methods, s/he is counted as unemployed under the old definition. If one of them turns out to be considered passive, s/he is still counted as unemployed under the new definition. However, if one person mentions only one method, s/he is counted as undemployed under the old definition but should this method turn out to be passive, s/he should be excluded under the new one. As already mentioned, out of the methods which may turn out to be considered passive under the new regulation, the most significant one is the registration with a public employment office. For simplicity 6, let us consider this method as the only one which might turn out to become passive (should the conditions regarding active contact 4 This point is again mentioned in Regulation 1897/ As a matter of fact, Regulation 1897/2000 states that job search methods are enumerated until at least three active methods have been mentioned. 6 And as a matter of empirical realism, as the other methods considered passive under the new regulation are of negligible significance in practice. 4

7 not be met). This implies that the only people counted as unemployed under the old definition who can be excluded are those who only mention as search method registration in a public employment office. Under these assumptions, an exclusion rate X may be defined as follows: X = α. β + ε [1] Where X is the number of those excluded under the new definition as a proportion of old unemployment, α is the proportion of old unemployed who only declare registration at the PES as search method, β is the proportion of the latter who are excluded because their contact with the employment office is considered passive, and ε is the residual of other exclusions related to other changes included in the regulation (as a proportion of olde unemployment). In the next section we present the results of applying this framework to the Spanish case. 3 The consequences of the new definition: old versus new unemployment In Section 2, we have argued that the implementation of the new unemployment figures to be calculated under EC Regulation 1897/2000 may be understood as a process of exclusion from the unemployment figures defined in the old way. Furthermore, we have argued that the exclusion rate may be decomposed in two main factors plus a residual. In this section, we apply this analytical framework to the Spanish data. We use the eight quarters (from 2001.Q1 to 2002.Q4) for which information can be obtained using both definitions, as the Spanish Statistical Office modified its questionnaire in the first quarter of 2001 so that the new unemployment definition could be used. This section is divided into three subsections. First, we present the general data on exclusions and on the factors behind it; we observe a clear drop in the exclusion rate in 2002, when the new definition was officially used, so that two general periods may de distinguished for further analysis, each one corresponding to one full year. Next, we present disaggregated information on the exclusion rate, trying to see whether there are specific characteristics which make one person more likely to ble excludable, i.e. to behave in a a less active job search manner. Specific attention is made to the regional dimension which turns out to be so important that s full subsection is devoted to it. In the final subsection, we merge all the preceding information in a series of logistic regressions of the probability of being excluded. 5

8 3.1 General exclusions Figure 1 plots the number of unemployed under the old definition in Spain since the first quarter of 2000; starting from 2001.Q1, the new unemployment figure is also provided as is also the exclusion rate (right scale, bars) 7. Old unemployment and new unemployment followed a similar path during 2001, as the proportion of exclusions remained more or less stable at around 20%. Then, in the first quarter of 2002, the rate of exclusion fell under 15%, so that unemployment growth was exaggerated by the new definition. Both series evolved in parallel until the last quarter when the proportion of those excluded rose again to well over 16%, so that new unemployment understated the actual growth in the numbers of those excluded. Figure 1. Unemployment in Spain (thousands) under the old and the new definitions, and exclusion rate (Source: LFS microdata files made available by INE) % excluded Unemployed-Old Unemployed-New (thousands) Q1 2000Q2 2000Q3 2000Q4 2001Q1 2001Q2 2001Q3 2001Q4 2002Q1 2002Q2 2002Q3 2002Q (percentages of old unemployed) Figure 2 presents the breakdown of the exclusion rate by its three components as identified in expression [1]. First, the proportion of the old unemployed who only mention registration at a public employment office as job search method (α in 7 The 2000 data are provided to show that the old unemployment figures fits perfectly with the figures for earlier quarters. Also, it should be mentioned that the unemployment figures provided have been calculated using the new weighting scheme which uses newer, higher population figures and also corrects, at the regional level, for the unbalanced age structure of population stemming from the LFS sample. Both of these changes tend to increase the employment as well as the unemployment numbers. 6

9 expression [1]) remained at a level of around 30% during 2001 and then dropped to a figure around 25% for the following year. This was the main factor behind the drop of the esclusion rate in the first quarter of Simple arithmetics shows that some 80% of the total variation of the exclusion rate is explained by this drop. Figure 2. The three components of the exclusion rate, (Source: LFS microdata files made available by INE) percentages of old unemployed Job seekers only through PES Other exclusions Total exclusion rate Proportion of non-active contacts 2001.Q Q Q Q Q Q Q Q percentage of job seekers only through PES As for the proportion of jobseekers declaring registration at the PES as their only search method who were excluded because of their contact with the offices was not to find work (β in expression [1]), it follows a smoothly declining trend, dropping from 68% in the first quarter of 2001 to 53% in the third quarter of 2002, and the jumps back to 63% in the last quarter of In terms of the decline of the total exclusion rate observed between 2001.Q4 and 2002.Q1, the decrease of β explains more or less the remaining 20% of the decline in the total exclusion rate 8. However, its surge in the last quarter of 2002 is the only factor under the recovery of the exclusion rate, given that α remained stable (even declining somewhat). These evolutions might be taken to suggest that the process of obtaining the new unemployment figures from the old ones does not provide a stable exclusion rate, as 8 There is an interaction term implicit in the decomposition, as well as the effect of the small variation of the third term ( ε in expression [1]). 7

10 could be expected from the conceptual definitions. If the purpose of the new regulation was to calculate more finely the number of unemployed workers by cleaning the figures from the contaminating activities of people who should not be considered active job seekers, onw should expect a relatively constant exclusion rate. If this is not the case, it implies that the behaviour of would-be unemployed people is erratic over time, in the sense that at some point in time they meet the requirements of active search and at some other points they do not. Should this be accepted as an explanation, it would imply that the grounds to exclude them from unemployment are rather weak. However, Figures 1 and 2 tend to suggest that the process of exclusion is not erratic. Actually, there are two clearly different periods in the figures. One corresponds to 2001, when the new definition was used on an experimental basis, and the other one corresponds to 2002, when the new definition was formally implemented. In addition, there is another anomalous observation in the last quarter of 2002, when the proportion of those excluded because of a lack of active contact with the public employment offices increased. In order to determine the reasons behind these peculiar evolutions, in the following two subsections, information shall be presented broken down by various characteristics of the unemployed as well as by their region of residence, a very important variable. In all of these analyses, the comparisons will then be made by taking three points of observation: the average of 2001, the average of the first three quarters of 2002 and the gourth quarter of Exclusions by characteristics of the unemployed The first analysis to be presented refers to the gender and age of the unemployed. Figure 3 presents the corresponding breakdowns. A clear pattern emerges in terms of exclusions: they are lower, and rather stable for males under 55 and higher for older men; in the case of females, they tend to rise with age. These patterns remain relatively stable over the three periods considered, with a changing general level, dropping from 2001 to the first three quarters of 2002 and increasing in the last quarter of On the whole, exclusion rates are higher for females than for males, the difference being around 4 percentage points in all three observation moments. 8

11 Figure 3. Exclusion rates from old unemployment by gender and age, Spain, (Source: INE, LFS microdata files) Q1:Q Q4 percentage of old unemployed MALES ALL MALES FEMALES ALL FEMALES The second variable that can be analyzed is the level of education. Figure 4 plots the exclusion rates by educational level and gender in the three periods considered. The patterns emerging from this figure are interesting. Starting with males, the somewhat erratic curve depicted for 2001 becomes much smoother and U-shaped in the first three quarters of 2002, with a minimum at lower secondary education and a maximum at the higher education levels. The last quarter seems to maintain a similarly more or less U- shaped form but with some erratic variations, although not coincident with those observed in In the case of females, the curve is similar for 2001 (more or less U- shaped but with a central peak for upper secondary education), then becoming more clearly U-shaped in 2002, although with less steep than for males. In general, the extreme levels of education (primary and university) seem to be more prone to be excluded from unemployment than central levels, be it academic or vocational (with the exception of upper secondary education at some points). 9

12 Figure 4. Exclusion rates from old unemployment by gender and level of education, Spain, (Source: INE, LFS microdata files) percentage of old unemployed Q1:Q Q4 8 Illit.,no education Primary education Lower secondary education Upper secondary education Lower vocational training Upper vocational training M A LES Lower university education Upper university education Illit.,no education Primary education Lower secondary education Upper secondary education FEM A LES Lower vocational training Upper vocational training Lower university education Upper university education In one sense, the results so far are not very surprising. It is well-known that people differ in terms of their search behaviour depending on their gender, age and level of education, and this is what the exclusion process is all about: eliminating from the unemployment count people who do not look for work actively enough. The direction of these variations, however, makes more sense in the case of the age variable (older people search less intensively than younger ones) than in the case of the education variable (one would expect highly qualified people to search more intensely). An interesting variable here is whether the unemployed person receives unemployment benefits. Economists tend to believe generally that receiving unemployment benefits inhibits job search 9. Is there a relationship between unemployment benefits and exclusion? Figure 5 plots the data, again broken down by gender. Interestingly enough, the rate of exclusion is higher for those who, although registered at the employment offices, do currently not receive unemployment benefits, 9 For a somewhat dissenting view, see García and Toharia (2000). Using both LFS data and information from a specific survey to registered unemployed workers, they argue that benefits does indeed inhibit job search to the point that such people is counted as inactive. However, for those remaining as unemployed, the effect is in most cases not statistically significant. 10

13 this being the case in all periods of observation and for males as well as females. Of course, other effects may be at play, such as the duration of unemployment (because benefits are limited in time). Figure 6 shows that, truly enough, higher search durations are associated with higher exclusion rates. The observed effect of higher exclusion rates for those registered but not receiving unemployment benefits might probably be due to their presumably longer search duration. We shall return to this point in the following sub-section. Figure 5. Exclusion rates from old unemployment by gender and unemployment benefit situation, Spain, (Source: INE, LFS microdata files) Q1:Q Q4 22 percentage of old unemployed Receives UB Registered but no UB Not registered Receives UB Registered but no UB MALES FEMALES Not registered 11

14 Figure 6. Exclusion rates from old unemployment by gender and job search duration, Spain, (Source: INE, LFS microdata files) Q1:Q Q4 22 percentage of old unemployed Less than 3 months 3-5 months 6-11 months months MALES 24 months & over Less than 3 months 3-5 months 6-11 months months FEMALES 24 months & over The final variable which is worth being discussed as a breakdown variable for exclusion rates is the region of residence. Figure 7 presents the exclusion rates for the 17 Spanish regions 10 ; the regions have been ranked from highest to lowest in terms of their exclusion rate in This figure is very striking. It shows big differences among Spanish regions regarding the proportion of unemployed excluded by the new definition. Thus, while in Catalonia the percentages remain under 5%, other regions such as Asturias or Extremadura show figures above 35% and even close to or over 50% in Another interesting feature of this figure is the reduction in this high divergence between 2001 and 2002: standard deviation dropped from 11 to 6 points for males, and from 13 to 8 for females Data for the North African towns of Ceuta and Melilla have been excluded from this analysis, due to their abnormally high exclusion rates, over 80%. 11 This decrease is less clear in relative terms, due the general decrease of exclusions. 12

15 Figure 7. Exclusion rates from old unemployment by gender and region of residence, Spain, (Source: INE, LFS microdata files) Q1:Q Q4 percentage of old unemployed Asturias Extremadura Aragón La Rioja Cantabria Madrid Castilla-La Mancha Galicia Navarra Castilla y León Canarias MALES Murcia Andalucía Com.Valenciana País Vasco Baleares Cataluña La Rioja Asturias Extremadura Aragón Cantabria Navarra Madrid Castilla-La Mancha Galicia FEMALES Castilla y León Andalucía Murcia Canarias Com.Valenciana Baleares País Vasco Cataluña These results are difficult to interpret. Of course, the main reason behind these differences is the varying proportion of job seekers who only mention registration of employment offices as search method 12. And significant differences, both across regions and over time, as they are observed in Figure 7, are difficult to understand. One can argue that the problem resides with the way in which the survey is carried out. Should this be the case, as we believe it to be to a large extent, the conclusion should not be that the Statistical Office is to be blamed. Rather, the problem is that the notion of active job search is difficult to investigate using the methods recommended by the European Commission. A large list of possible methods (most of which are supposed to be read when deliveting the interview), the inherent looseness of the methods included in the list (e.g. asking friends or relatives is a valid active job search method) and the fact, sometimes overlooked, that the respondent may be proxied by someone else in the 12 Not presented here for reasons of space. 13

16 household, impart a significant difficulty to the notion of active search and to the way in which it is asked in labour force survey questionnaires. 3.3 The probability of being excluded, 2001 and 2002 To complete the preceding graphical analyses, we now present a multivariate logistic regression analysis of the probability of being excluded. The regressions have been run separately for males and females and for the three periods considered in the preceding sub-section: 2001, the first three quarters of 2002 and the fourth quarter of The results, presented in Table 1, suggest that the simple graphical analysis carried out in the preceding pages stands up when all the possible cross effects are taken into account. Age produces a U-shaped effect in the case of males and an increasing effect for females. The influence of education is mixed, without a well-defined pattern. It is surprising that males with upper university education tend to have (in 2002) the highest probability of being excluded, an effect which is not so obvious in the case of females. The negative effect of unemployment benefits on the probability of being excluded whithers away, probably due to the inclusion of search duration regressors, which show the expected positive relationship with the probability of exclusion (although concentrated in the extremes, as the coefficients tend not to be statistically different for the groups 6-11 months and months). The region of residence is clearly the most decisive variable, repeating the results already shown in Figure 7. 14

17 Table 1. Logistic regressions of the probability of being excluded from unemployment under the new definition, conditional on being considered unemployed under the old definition, Spain, (Source: estimated from LFS microdata provided by INE) MALES FEMALES Q1-Q3 2002Q4 ALL Q1-Q3 2002Q4 ALL AGE ,18 ** 0,28 ** 0,27 * 0,22 ** -0,41 ** -0,37 ** -0,41 ** -0,39 ** ,09 0,21 ** 0,19 0,15 ** -0,36 ** -0,29 ** -0,33 ** -0,32 ** ,04-0,06 0,00-0,02-0,20 ** -0,37 ** -0,23 ** -0,25 ** ,01 0,12 0,06 0,06-0,25 ** -0,04-0,01-0,14 ** (&) ,04 0,04 0,22 0,06-0,03 0,01 0,01-0, ,16 * -0,11 0,31 * -0,09 0,04 0,02 0,35 ** 0, ,28 ** 0,49 ** -0,02 0,31 ** 0,15 * 0,20 * 0,42 ** 0,19 ** ,30 ** 0,80 ** 0,78 ** 0,55 ** 0,08 0,07 0,34 0,10 LEVEL OF EDUCATION Illit.,no education 0,03 0,22 * 0,13 0,10 * -0,15 * 0,06 0,10-0,05 Primary education -0,09 0,12 0,01 0,00-0,11 ** -0,02 0,06-0,05 Lower secondary education -0,34 ** -0,21 ** -0,32 ** -0,31 ** -0,24 ** 0,02 0,06-0,12 ** Upper secondary education (&) Lower vocational training -0,30 ** 0,09 0,25-0,11 * -0,38 ** -0,08 0,04-0,23 ** Upper vocational training -0,30 ** 0,05-0,07-0,17 ** -0,22 ** -0,04-0,04-0,15 ** Lower university education -0,19 ** 0,14 0,00-0,05-0,11 * 0,04 0,28 ** -0,01 Upper university education -0,12 0,56 ** 0,61 ** 0,22 ** -0,03 0,06 0,19 0,03 UNEMPLOYMENT BENEFIT SITUATION Receives UB (&) Registered but no UB 0,05 0,03 0,05 0,03 0,08 * 0,01 0,07 0,05 * Not registered -0,76 ** -0,62 ** -0,54 ** -0,69 ** -0,85 ** -0,74 ** -0,84 ** -0,82 ** SEARCH DURATION Less than 3 months -0,67 ** -0,61 ** -0,75 ** -0,65 ** -0,32 ** -0,50 ** -0,21 ** -0,37 ** 3-5 months -0,23 ** -0,14 * -0,39 ** -0,22 ** -0,01-0,16 ** -0,13-0,08 ** 6-11 months 0,04 0,15 * -0,31 ** 0,04 0,09 * -0,08 0,13 0, months (&) 24 months & over 0,14 ** 0,32 ** 0,07 0,18 ** 0,18 ** 0,12 ** 0,22 ** 0,16 ** REGION OF RESIDENCE Andalucía (&) Aragón 1,33 ** 1,36 ** 0,24 1,22 ** 1,32 ** 1,59 ** 0,77 ** 1,34 ** Asturias 1,81 ** 0,90 ** 0,91 ** 1,42 ** 1,36 ** 0,83 ** 0,45 ** 1,10 ** Baleares -0,24 0,41 * -0,27 0,00-0,19 0,32 * 0,19 0,05 Canarias 0,26 ** 0,69 ** 0,37 ** 0,41 ** -0,03-0,01-0,02-0,03 Cantabria 1,08 ** 1,01 ** 0,18 0,95 ** 0,81 ** 0,18 0,24 0,58 ** Castilla y León 0,34 ** 0,71 ** 0,39 ** 0,46 ** 0,22 ** 0,45 ** 0,32 ** 0,31 ** Castilla-La Mancha 0,61 ** 0,99 ** 0,70 ** 0,74 ** 0,57 ** 0,84 ** 0,38 ** 0,63 ** Cataluña -1,30 ** -1,52 ** -2,39 ** -1,46 ** -1,52 ** -1,77 ** -1,62 ** -1,61 ** Com.Valenciana -0,19 ** 0,30 ** -0,68 ** -0,07-0,14 ** 0,42 ** -0,34 ** 0,04 Extremadura 1,27 ** 0,64 ** 0,70 ** 1,01 ** 1,23 ** 0,97 ** 0,77 ** 1,07 ** Galicia 0,50 ** 0,39 ** 0,33 ** 0,45 ** 0,21 ** 0,25 ** -0,04 0,19 ** Madrid 0,80 ** 1,22 ** 1,20 ** 1,00 ** 0,57 ** 0,97 ** 0,87 ** 0,74 ** Murcia 0,28 ** -0,08-0,56 * 0,07 0,20 ** 0,21 * -0,38 * 0,14 ** Navarra 0,64 ** 0,35-0,55 0,46 ** 0,69 ** -0,30 0,02 0,33 ** País Vasco -0,37 ** -0,28 * -0,24-0,32 ** -0,50 ** -0,08-0,26 * -0,34 ** La Rioja 1,14 ** 0,31 0,90 * 0,86 ** 1,49 ** 0,81 ** 0,78 ** 1,13 ** Ceuta y Melilla 3,00 ** 3,32 ** 3,28 ** 3,14 ** 3,04 ** 3,12 ** 3,09 ** 3,06 ** YEAR OF OBSERVATION 2001 (&) 2002 Q1-Q ,46 ** ,42 ** 2002 Q ,24 ** ,26 ** Constant -1,49 ** -2,45 ** -1,77 ** -1,64 ** -0,99 ** -1,58 ** -1,50 ** -1,07 ** Sample size (&) indicates reference category included in the constant (**) significant at the 99% level; (*) significant the the 95% level 15

18 4 Is the change meaningful? Longitudinal evidence The results presented in Section 3 suggest that the separation from unemployment of passive job seekers has encountered practical difficulties, at least in the case of Spain, probably the country where such a group was expected to represent the most significant share of total unemployment. In this section, we ask ourselves a different question: does the proposed change make economic sense? That is, no matter how complicated it may be to grasp the real nature of active job search, it is important to do so because the group concerned cannot be sensibly be equated to the other job seekers. As a matter of fact, the implicit argument in the 1897/2000 regulation is that these people ought to be counted within the inactive population, where they really belong. In order to test this hypothesis, in this section we study the process of exit of the various groups of jobless people and their probability of getting a job. The three main groups considered are the unemployed according to the new definition, those excluded from unemployment and the inactive population. The analysis is restricted to those under 65, to avoid the biases that would be present if older people were included in the analysis. The idea is to see whether the labour market behaviour, in terms of their employment dynamics, of the excluded can be equated to that of the inactive population or to that of the active job seekers. First, a descriptive analysis of the gross probability of entering employment within one quarter is presented. A more complete regression analysis follows. The data used comes from the longitudinal version of the Spanish LFS, made available to researchers on a general basis by the Spanish Statistical Office (INE). 4.1 Exit rates towards employment Figure 8 presents the rates of exit from joblessness towards employment within one quarter. Six categories of jobless workers are considered: unemployed, excluded from unemployment and inactive, distinguishing in all three cases betwen those with past job experience and those wihout such experience. Seven observations are available, starting in the first quarter of 2001 and ending in the fourth of

19 Figure 8. Exclusion rates from old unemployment by gender and region of residence, Spain, (Source: INE, LFS microdata files) Unemployed with past job experience Unemployed with exp./excluded Unemployed without past job experience Unemployed w/o exp./excluded Inactives with past job experience Inactives without past job experience Percentage of people in initial period I 2001-II 2001-III 2001-IV 2002-I 2002-II 2002-III 2001-I 2001-II 2001-III 2001-IV 2002-I 2002-II 2002-III MALES Initial period FEMALES The results are very interesting. Both for males and females, there exist a clear modulation of the gross probability of entering employment within one quarter: the unemployed are the group showing the largest probabilities, followed by those excluded from unemployment, leaving way behind the inactives. This hierarchy may be observed both for those with and withour past job experience. The conclusion that stems from this figures is clear: in terms of their probability of finding a job, people excluded from unemployment by the European Commission regulation do behave differently from those who are left inside; however, despite this disadvantage vis-à-vis the active hob seekers, the passive job seekers show a clearly higher propensity to enter employment than those outside the labour force. They thus represent a middle group between the pure unemployed and the pure inactive. Should they be included in one group or the other? The evidence is obviously inconclusive, but what can be said is that this people are not totally detached from the labour market. They may have higher difficulties to find jobs, probably related to their less active job search attitude, but they cannot in any way be equated to inactive people who do not search work for whatever reason. 17

20 4.2 The probability of exiting joblessness In order to underpin the results presented in the preceding subsection, it is necessary to take into account the influence of other variables in the probability of finding a job, which might be influencing the gross probabilities examined in Figure 8. To do so, logistic regressions of the probability of finding a job within one quarter have been estimated, for all those without a job in the initial period of observation (active and passive jobseekers as well as inactive). Regressors include gender (although separate regressions have also been run for males and females), age, education, labour market status, past job experience, situation with respect to unemployment benefits (payable to both unemployed and inactive persons as defined by the LFS), all of them observed at the initial moment. Controls for the region of residence and the period of observation have also been included. Pooled regressions for the seven transitions observed have been run 13. Table 2 presents the results of these regressions. The most interesting results from the point of view of this article relate to the coefficients of the labour market status variable. As can be seen, being an active job seeker as defined by the new European Commission regulation provides a clear advantage in terms of finding a job, this being a significant result for males and females alike. The distance, it should be added, is larger for females. In addition, being a passive job seeker also provides a clear, even larger, advantage over non-seekers in terms of finding a job. In this case, the distance is somewhat smaller for females. Other results of these regressions are quite standard: age is negatively correlated to the probability of finding a job, as is past job experience. The level of education shows a U-shaped influence in the case of males, probably due to the well-known divergent skill structure of Spanish employment 14. In the case of females, however, only tertiary education exerts a clearly positive influence. Finally, the situation with respect to unemployment benefits shows an unexpected result, similar to that found earlier in this article (recall Figure 5): receiving unemployment benefits is associated with a higher probability of finding a job, this being the case for males and females. 13 Regressions estimated for each of the transitions individually gave substantially similar results; pooling the regressions has the advantage of increasing sample size. 14 On this point, see, for example, Fina et. al. (2000) 18

21 Table 2. Logistic regressions of the probability of finding a job within one quarter, jobless persons, Spain, (Source: estimated with the matched files of the LFS provided by the INE) ALL MALES FEMALES LABOUR MARKET STATUS Unemployed (new definition) 0,334 ** 0,261 ** 0,383 ** Excluded from unemployment(&) Inactive -0,783 ** -0,874 ** -0,690 ** PAST JOB EXPERIENCE Yes (&) No -0,198 ** -0,213 ** -0,189 ** GENDER Males (&) Females -0,088 ** AGE ,650 ** 0,496 ** 0,735 ** ,535 ** 0,480 ** 0,554 ** ,186 ** 0,212 ** 0,158 ** ,138 ** 0,100 0,156 ** (&) ,268 ** -0,242 ** -0,294 ** ,524 ** -0,637 ** -0,457 ** ,074 ** -1,183 ** -1,022 ** ,758 ** -2,093 ** -1,458 ** LEVEL OF EDUCATION Primary education or less 0,067 ** 0,144 ** -0,004 Lower secondary education 0,043 0,143 ** -0,027 Upper secondary education (&) Tertiary education 0,290 ** 0,204 ** 0,337 ** UNEMPLOYMENT BENEFIT SITUATION Receiving benefits(&) Registered, no benefits -0,304 ** -0,267 ** -0,329 ** Not registered -0,844 ** -0,659 ** -0,964 ** Constant -0,911 ** -0,813 ** -1,600 ** SAMPLE SIZE Note: Controls also included for region of residence and initial period of observation (&) indicates category included in reference term (constant) (**) significant at the 99% level; (*) significant the the 95% level The latter result deserves further investigation. One problem with the regressions presented in Table 2 is that they include a large number of observations from inactive persons, for whom other informations, such as the duration of search is lacking. So, once we have established the result that passive job seekers are clearly different from non-seekers (inactives), it may be worthwhile to rerun the regressions just with the two groups of jobseekers. The results are shown in Table 3. The main difference with the regressions in Table 2 is that a search duration variable has been included. The results provide very similar differences in terms of the advantage of active job search for the probability of finding a job. There are two main differences with respect to the results presented above. First the influence of the education variables 19

22 vanishes except for the more educated who still derive a positive differential. Secondly, the positive effect of receiving unemployment benefits also disappears for males and loses strength and statistical signification in the case of females. On this latter variable, a result which should be stressed is the much lower probability of finding a job for those who declare being not registered in an employment office. This is interesting particularly in the light of the very low probability of being excluded that this group shows. Because they do not seek work though an official emnployment office, the channels they use are probably more informal, but belonging to the active methods category. If the probability of finding a job test should be applied to them, however, they would deserve being excluded from unemployment on much more solid grounds than so-called passive jobseekers do. 20

23 Table 3. Logistic regressions of the probability of finding a job within one quarter, jobseekers only, Spain, (Source: estimated with the matched files of the LFS provided by the INE) ALL MALES FEMALES LABOUR MARKET STATUS Excluded from unemployment (&) Unemployed (new definition) 0,311 ** 0,221 ** 0,371 ** PAST JOB EXPERIENCE Yes (&) No -0,115 ** -0,115 ** -0,114 ** GENDER Males (&) Females -0,076 ** AGE ,352 ** 0,198 ** 0,488 ** ,325 ** 0,200 ** 0,431 ** ,107 * 0,076 0,136 * ,107 * 0,070 0,136 * (&) ,033-0,002-0, ,199 ** -0,256 ** -0, ,506 ** -0,567 ** -0,462 ** ,859 ** -1,095 ** -0,445 * LEVEL OF EDUCATION Primary education or less 0,025 0,067-0,042 Lower secondary education 0,056 0,087 0,030 Upper secondary education(&) Tertiary education 0,196 ** 0,108 * 0,238 ** UNEMPLOYMENT BENEFIT SITUATION Receiving benefits (&) Registered, no benefits -0,076 ** -0,049-0,087 * Not registered -0,138 ** -0,026-0,207 ** SEARCH DURATION Less than 3 months 0,751 ** 0,772 ** 0,710 ** 3-5 months 0,599 ** 0,572 ** 0,622 ** 6-11 months 0,381 ** 0,368 ** 0,389 ** months (&) 24 months & over -0,283 ** -0,350 ** -0,230 ** CONSTANT -1,646 ** -1,445 ** -2,361 ** SAMPLE SIZE Note: Controls also included for region of residence and initial period of observation (&) indicates category included in reference term (constant) (**) significant at the 99% level; (*) significant the the 95% level 21

24 5 Final remarks This paper has explored the consequences of the new definition of unemployment introduced by the European Commission in its 1897/200 regulation. Our first point has been to make it clear that the new definition implied a process of exclusion of some workers considered to behave in a passive job search way, enough to let them bc considered inactive people, similar to those who do not even declare that they are seeking work. We have next analyzed this process of exclusion for the case of Spain, a country particularly interesting in this context, as it is one of the paradigmatic cases of extensive passive job search through employment offices. The first result we have come up with has been that the main element behind exclusions, i.e. behind passive jobseeking is the number of search methods mentioned, in particular, the fact that a substantial proportion of job seekers only declare registration in a public employment office as search method. We have further analyzed the specific characteristics of people which increase their propensity to act as passive job seekers and hence to be excluded. Alongside more standard variables, such as age, gender, education and job search duration, influencing job search in the expected way, we have found that unemployment benefits do not seem to clearly influence active job search. More importantly, we have found that there have been significant differences across regions, which furthermore have tended to change over time. This has suggested a first conclusion: even accepting that the difference between active and passive job seekers may be meaningful, there are clear difficulties to clearly define in practice these two groups through labour force surveys. The European Commission regulation does not appear to have established clear instruments to do so. Next we have questioned the initial distinction which provides the starting point for the EC regulation: are there differences between active and passive jobseekers in terms of their economic behaviour? Our analysis has centered on the probability of finding a job within one quarter. Passive jobseekers have been found to be an intermediate group in between active jobseekers and nonseekers, although the distance with the latter appears to be larger. The final question is whether passive jobseekers should be excluded from the unemployment count. On a purely conceptual basis, the answer may be positive. On a practical, LFS-oriented, account, the answer is less clear, given the difficulties to clearly 22

25 identify, on a stable basis, the group of passive jobseekers. On a labour market perspective, finally, the answer is mixed, as passive jobseekers are an intermediate group. On the whole, then, the mere exclusion of passive jobseekers from unemployment may be justified, but their inclusion with the inactive is not. The advantages (conceptual, and political?) of reducing unemployment through a statistical artifact may be outweighed by the practical and analytical disadvantages stemming from the higher volatility of the new unemployment concept and the unwarranted inflation of the inactive population group. References EIRO (European Industrial Relations Observatory (2002), Controversy over new definition for measuring unemployment, available on the internet at the address European Commission (2000), Regulation no. 1897/2000, Official Journal of the European Communities, L.228, 8 th September, pp Fina, Ll., Toharia, L., García Serrano C. and Mañé, F. (2000), Cambio ocupacional y necesidades educativas de la economía española, in F. Sáez (ed.), Formación y empleo, Madrid, Fundación Argentaria, 2000, pp García, I. and Toharia, L. (2000), Prestaciones por desempleo y búsqueda de empleo, Revista de economía aplicada, 23, vol. VIII, autumn 2000, pp Toharia, L. (2000), La nueva definición de desempleo, in Round table on Regulation and Labour Market Institutions, Workshop to present the new Labour Sciences Degree Curriculum, Universitat Pompeu Fabra, Barcelona, october, mimeo. 23