Other aspects of the 2002-2003 Time Use Survey By: Víctor Casero Carlos Angulo
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Table of Contents Table of Contents 3 Introduction 5 1 Non-regulated education and training 7 2 The place where the activities are carried out 19 3 The problem with the selfclassification of the relationship with the activity 25 4 Care received by children 29 5 Economic assessment of domestic service for 2003 41 Schedule of tables of chapter 2 45 3
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Introduction In this publication a series of specific analyses of the results of the 2002-2003 Time Use Survey (TUS) are presented. The main purpose of this survey is obtaining primary information in order to gain an insight into the scale of nonremunerated work carried out in households, the distribution of household familiar responsibilities, the population s participation in cultural and entertainment activities, time use of special social groups (youth, unemployed, old age,...) so that family policies and gender equality policies may be set forth or the household sector satellite accounts may be estimated. The time use survey has a harmonized methodology within the EU Statistical Office (Eurostat). It is a non-periodic survey addressed to a sample of 20,603 households which collects information on people daily activities through the filling out of personal diaries and household and individual questionnaires. The activity diary is the most distinctive instrument of the survey. All household members above 10 years old must fill it out in a selected day. The diary time grid spreads over 24 consecutive hours (from 6:00 am to 6:00 am of the following day) and is divided in 10 minute spans. In each of them, the informant must indicate the main activity, the secondary activity he or she is doing at the same time (if applicable) and if at that moment any other acquaintances are present. These activities are codified following a Eurostat harmonized activity list which takes into account 10 main groups: personal care, employment, study, household and family care, volunteer work and meetings, social life and entertainment, sports and outdoor activities, hobbies and games, mass media, and travel and unspecified time use. This statistical research allows to obtain information on the percentage of people who carry out an activity during the day, the average daily duration (in hours and minutes) dedicated to an activity by the people who carry it out, the activity distribution in an average day per type of day (weekday or weekend day) and the percentage of people who carry out that same activity at the same moment of the day (daily activity rhythms). These indicators may be disaggregated based on the week day type or on the year quarter. As for variables relating to the individual, data are classified by gender, age, study level, marital status, relationship with the activity and professional situation, occupation, household income level, household type, etc. Furthermore, on account of the sample design, the study also allows to obtain the main results mentioned above for each one of the autonomous communities. The sample has been enhanced in four autonomous communities -Andalusia, Catalonia, Galicia and Navarre- with whose Statistics Institutes the INE has entered into collaboration agreements, with the aim of obtaining more detailed results. The analyses contained in this publication are aimed at providing information on particular aspects included in the TUS which, due to their characteristics, have not been included in previous publications related to the survey. 5
Thus, the first chapter focuses on the analysis of non-regulated education and training, particularly that which relates to INEM (National Institute of Employment) courses, Workshop Schools or other courses for employment seekers; training courses promoted by the company; computing courses; language courses in language schools; driving schools and preparation of competitive examinations. As far as possible, the profile of the people who take such training and the time they dedicate to it, both in terms of the course duration and week hours dedicated, are detailed. The second chapter completes the exploitation of results by analysing the place where the activities are carried out. Therefore, at the bottom of this publication a table schedule has been added with a structure analogous to that of the exploitation of the results published in June 2004. Thus, information on the average time spent daily in certain places or means of transport, the percentage of people located at certain places or means of transport throughout the day and the so-called place rhythms shall be found there. The third chapter presents the problem of the self-classification of the relationship with the activity in which the two possible classifications of such relationship with the activity gathered in the survey are contrasted, one, in accordance with the common objective methods and the other one, from a direct question made to the informant through which he or she classifies himself or herself and which, thus, is subjective. The fourth chapter on care received by children depicts regular care received by children under 10, with the analysis being divided into three age groups, from 0 to 2, from 3 to 5 and from 6 to 9 years old, based on the schooling situation of the minor as suggested by European criteria. The percentages of children who receive regular care from persons external to the household, the average weekly duration of the care received and the hourly distribution of the regular care received by children in an average weekday are provided. The fifth and last chapter provides an economic assessment of domestic service for 2003 in which various options are presented. Among them, the option which has been obtained exclusively from information of the survey has been considered the most reasonable one. 6
1. Non-regulated education and training The nature of time use surveys allows to obtain plenty of information on a wide array of issues of economic or social interest. One of them, which over time does not lose the slightest bit of topicality and appears in many researches and almost daily in mass media, is education and training. Both information from the INE and information available in other publications usually relate to what is known as regulated education and training, which has been successfully completed and which is being carried out or ongoing, and the so-called nonregulated education and training is pushed into the background. Non-regulated and ongoing education and training Based on the education and training section set forth in the 2002-2003 Time Use Survey 1, this article wishes to describe a small part of non-regulated studies, more specifically, of nonregulated and ongoing studies, thereby meaning those taken in the last four weeks. This small part of non-regulated studies is defined by the different courses to be considered and which result from the various options presented to the interviewees: INEM (National Institute of Employment) courses, Workshop Schools or other courses for employment seekers Training course promoted by the company (only for employed persons) Computing course Language course in language schools (other than the Official Language School Escuela Oficial de Idiomas ) Driving schools (driving license) Preparation of competitive examinations (in preparatory schools or with a personal trainer) Along with these options, there were various regulated studies and the clear instruction to indicate the kind of education or training which the informant considers the main one 2. Hence, in the text the description main is added to the course or training. Once the research framework has been defined, it is not superfluous to highlight that the first purpose of the individual questionnaire education and training section was to contrast the information on time dedicated to studies with the information provided by informants in the time use diary. Carrying out only those checkings would have meant to waste the great value which the ongoing education and training questions have by themselves. This analysis is the result of this observation, which at the same time intends to bring new information or information not very known on the subject. Thus, gaining an insight into the mentioned non-regulated ongoing main training courses has been set as the study goal, detailing as much as possible the profile of the people who take such training and the time they dedicate to it, both in terms of course duration and week hours dedicated. 1 Section i. Education and training of the Individual Questionnaire of the 2002-2003 Time Use Survey. Available in electronic version (in Spanish) at: http://www.ine.es/inebase/cgi/um?m=%2ft25%2fe447&o=i nebase&n=&l= 2 See question 28 of the Individual Questionnaire. 7
First conclusions Table 1 indicates the percentage of people who have taken a non-regulated training course in the last four weeks. The conclusion is that only 4.6% of the population take this non-regulated training as a main activity, with the courses promoted by companies standing out, being taken by 1.5% of people. The remaining courses have similar participation percentages. Nonetheless, it must be taken into account that table 1 percentages refer to the whole of the population and that the persons likely to take each course are substantially different from one course to the other. For example, in order to be eligible to the INEM courses, the person must be unemployed, in order to be eligible to the courses promoted by the company, there must be some kind of work connection with the company and in order to obtain the driving license, the person must be over 18, although some citizens begin their preparation some months before. Graph 1. Percentage of people who have taken a non-regulated training course in the last four weeks, by sex Table 1. Percentage of people who have taken a non-regulated training course in the last four weeks Training courses Both sexes Total 4,6 INEM courses, Workshop Schools 0,5 Courses promoted by the company 1,5 Computing 0,8 Language courses in language schools 0,6 Driving schools (driving license) 0,7 Preparation of competitive examinations 0,7 The differences by sex may be seen in graph 1. Particularly, the major differences, with higher percentages for women than for men, are found in language courses in language schools and in the preparation for competitive examinations, 0.7% and 0.8% of women for 0.4% and 0.5% of men respectively; and with a higher percentage in men, courses promoted by companies, with 1.7% of men for 1.2% of women. However, it is appropriate to highlight in this last case that there are more employed men than employed women, on account of which there are more men than women who are likely to take those courses. Graph 2. Percentage of people who have taken a non-regulated training course in the last four weeks, by age. % 3,0 2,5 2,0 1,5 1,0 0,5 0,0 1 2 3 4 5 6 Men Women % 3,0 2,5 2,0 1,5 1,0 0,5 0,0 1 2 3 4 5 6 Under 25 From 25 to 44 From 45 to 64 65 and over Non-regulated and ongoing training courses 1 INEM courses, Workshop Schools 4 Languages in language schools 2 Courses promoted by the company 5 Driving schools 3 Computing 6 Preparation of competitive examinations 8
By ages, as seen in graph 2, people from 25 to 44 years old are those, in general, who take those courses more often. Specifically, they obtain the maximum percentage, 2.7%, in courses promoted by the company, with a wide difference over the rest of ages. It must be highlighted that in that age group there are more employed people than in the other ones. On the other side, the only matter to be highlighted with regard to those under 25 is that they present a higher percentage than the other age groups when it comes to attending driving schools to obtain the driving license, although these data are affected by the fact that it is the age group in which the main occupation of its members is taking regulated studies, as they have stated. Lastly, people from 45 to 64 years old do not stand out in any of the courses, but we may state that the nonregulated studies in which they participate more often are those promoted by companies and computing courses. Training profiles It is specially interesting to know the profile of the persons who take each one of those courses and, thus, table 2 is presented with the percentage distribution of people who have taken non-regulated training courses in the last four weeks, based on various variables. Data confirm the groups to which any subject matter expert would point out in principle and, furthermore, they allow to know the magnitude and to quantify differences. Thus, many clichés are consolidated, but some nuances which dash some preconceptions are also discovered. To help us in this analysis, in the last column of the table we have indicated, for each variable, the general distribution of the population, which will allow us to distinguish the existing prevalences. Obviously, a good health condition is a general profile which is present in all courses. Such persons whose perceived health condition is bad or very bad and those who have a chronic disease do not take these studies as the rest of the population. INEM courses, Workshop Schools or other courses for employment seekers According to table 2, among the theoretical members of one of theses courses there are 6 women for each 10 students, and, curiously, also 6 persons from 25 to 44 years old and 3 persons under 25 for each 10 students. With regard to the marital status, there is one married person for each two who are not, as well as one resident in the provincial capital for each two who reside in the remaining municipalities (which coincides with the distribution of this characteristic in the population). Clichés about the income level are confirmed. In an INEM course there are more students with low income levels than in the population and less students from high income level households than in the population. The most represented persons in those courses by study level are those who have reached the secondary education first cycle. Furthermore, by comparison to the population distribution, the sole underrepresented group is the group of the illiterates, without any studies or with primary education, since there would only be 1 for each 10 students, while in the population there are 3 for each 10 persons.. 9
Table 2. Percentage distribution of people who have taken non-regulated training courses in the last four weeks and of population of or over 10 years old, based on various variables. 1 2 3 4 5 6 Population Sex Men 39,1 58,5 45,5 34,9 51,5 38,4 48,9 Women 60,9 41,5 54,5 65,1 48,5 61,6 51,1 Type of municipality Provincial capitals 33,3 44,7 36,6 47,5 29,6 46,0 34,5 Rest of municipalities 66,7 55,3 63,4 52,5 70,4 54,0 65,5 Average net income level per month of the household in which they live Under 1.000 31,1 5,0 15,1 * 10,4 18,8 18,5 26,4 From 1.000 to 1.499,99 30,7 15,7 21,1 13,7 25,0 21,0 24,3 From 1.500 to 1.999,99 17,7 20,1 22,7 19,2 24,3 21,3 18,5 2.000 and over 20,5 59,2 41,2 56,7 32,0 39,2 30,7 Age Under 25 31,1 6,9 12,5 * 13,1 49,1 25,9 19,9 From 25 to 44 56,9 66,5 55,5 62,2 46,5 67,9 36,6 From 45 to 64 11,2 26,6 27,4 19,2 * 4,4 * 6,1 25,4 65 and over * 0,8 * 0,0 * 4,5 * 5,5.. * 0,1 18,2 Study level Illiterates, without any studies or with primary education 14,4 * 4,6 * 6,6 * 7,5 15,0 * 0,0 34,7 Secondary education. First cycle 28,1 16,1 23,8 15,3 38,5 9,2 29,6 Secondary education. Second cycle 24,3 19,2 27,5 13,7 25,8 23,6 15,9 Higher professional training 14,5 15,9 10,3 * 14,0 10,3 11,3 6,1 University education 18,8 44,2 31,7 49,4 * 10,5 55,9 13,7 Perceived health condition Good or very good 87,4 87,5 79,9 88,2 86,5 89,1 72,2 Acceptable * 9,9 10,8 17,3 * 10,9 * 10,8 10,9 19,2 Bad or very bad * 2,7 * 1,7 * 2,8 * 0,9 * 2,7.. 8,6 Marital status Married 32,6 64,5 56,7 43,0 26,8 24,2 55,6 Not married 67,4 35,5 43,3 57,0 73,2 75,8 44,4 Relationship with the activity Active people 76,7 100,0 81,4 72,7 88,0 74,9 53,6 Employed people 22,1 99,8 67,2 67,7 70,0 52,2 48,1 - Employers and self-employed * 0,4 5,4 11,7 * 6,4 * 3,8 * 1,2 8,6 - Employees 21,1 93,8 54,9 60,6 63,8 50,3 38,5 Unemployed 54,6 * 0,2 14,2 * 5,0 18,0 22,7 5,6 Inactive people 23,3.. 18,6 27,3 12,0 25,1 46,4 Students 18,8.. * 4,8 * 8,8 * 5,8 23,2 13,2 Retired or pensioners * 1,5.. 7,4 * 7,3 * 0,6.. 18,9 Housework * 2,9.. * 6,0 * 10,3 * 5,5 * 1,9 13,8 Training courses 1. INEM Courses, Workshop Schools 2. Courses promoted by the company 3. Computing courses 4. Language courses in language schools 5. Driving schools 6. Preparation of competitive examinations * The number of sample observations is lower than 30, due to which it must be cautiously interpreted.. Void data 10
When it comes to the relationship with the activity, unemployed are the most represented group in those courses, since they amount to half of the attendees, followed by employees and students. It is appropriate to make here a digression on the characteristics of those courses, since they do not consist only of INEM courses for unemployed people. Workshop Schools attendees are also included, in the employees or students group based on their particular circumstances, and also other courses for employment seekers, without it being necessary that the person is classified as unemployed. Event the time reference plays here an important role in order to obtain the relationship with the activity in the last week, with the information on the ongoing training referring to the last four weeks. Therefore, it is not strange to find, so to say, new employees, who they have fulfilled the conditions to be considered as such in the last week and who have also taken INEM courses prior to the agreement effectiveness date and within the four relevant weeks. Training courses promoted by the company Unlike INEM courses, in a theoretical class of 10 students, there would only be 4 women, with 2 married persons for each 3 persons and almost the same percentage who come from the provincial capital and from the remaining municipalities, the latter being slightly higher. Both courses coincide in the fact that there are more people from 25 to 44 years old than from the remaining age groups, in this case 2 for each 3 students. There are more persons who take these courses who come from household with a higher income level, with a substantial difference over the rest, that is, 6 persons with an income level higher than 2,000 EUR for each 10 persons who take the course. We also observe that these courses are addressed mainly to employees, since 9 of each 10 persons who take these courses are employees, and 8 of each 10 in the population. With regard to education level, those who participate more often in these courses are persons with university studies, 4 of each 10 students. Computing courses In order to carry out a more accurate analysis, it is appropriate to highlight the courses main characteristic. Due to that, it is reasonable to consider that a part of these computing and language courses have been included in courses promoted by the company, since for the informant the fact that he or she has been proposed, required, financed, or suggested by the company takes precedence. In commenting the data, from table 2 we infer that in a computing course there will be almost the same number of women and men, those from 25 to 44 years old will be slightly more than half of the students, followed by those from 45 to 64 years old, 3 of each 10. Only one student will be under 25 years old. It will not seem strange either that there are more married people than not married; indeed, in the table we observe that this characteristic has the same percentage distribution in the course than in the population. The most represented study level is university level, well above its presence in the population. Finally, the household purchasing power has an 11
impact when it comes to taking computing courses, since it is observed that the higher the household income level is, the more persons take such courses, taking into account the population distribution. Employees are those who take computing courses more often, but in comparison with the population there are more employers 1 and even more unemployed persons who study computing. Among the inactive persons, the retired or pensioners stand out. Languages in language schools In language courses, the rate of 2 women for each man appears again and the most common age is, of course, from 25 to 44 years old. The type of municipality is more or less similar, unlike the population structure, maybe on account of the lack of education institutions of this type in municipalities which are not provincial capitals. People who study languages more often are those with a higher income level and a higher study level, even more than those who take computing courses. We find another difference between both courses in the marital status distribution; in this case, married people are underrepresented. Finally, the group which prevails most in language studies with regard to the work situation is the group of employed persons and, within that group, employees. Driving schools What makes driving schools different from the rest of courses is that they have small differences by sex and that those who attend them are the 1 Employers, self-empoyed, members of cooperatives, family workers younger ones. Marital status, undoubtedly related to age, drastically differentiates the theoretical members of a driving school group; for each married person who attends the driving school there are 3 who are not married. On the other hand, the type of municipality has almost the same distribution as the population. Taking into account the income level, no clearly defined rule may be observed. Furthermore, the most represented study level is the secondary education first cycle. The work situation reflects a wider disparity in the distribution in driving schools and in the population, mainly with respect to unemployed persons and secondly with respect to employees. It must also be taken into account that in the youngest population group, as indicated above, regulated education students prevail, due to which this group is underrepresented in this course. Preparation of competitive examinations A preparatory school which prepares candidates for competitive examinations shall have more women than men among their students and, if the school is not big enough, none of its students shall be above 44 years old. Furthermore, the higher the study level, taking into account the population distribution, the higher the percentage of people who wish to be civil servants. The type of municipality has the same distribution than in language courses or in courses promoted by the company, that is, half of the people are from provincial capitals and the other half of the rest of municipalities. As in driving schools, for each married person there are 3 who are not married. 12
Finally, when it comes to the relationship with the activity, unemployed are the most represented ones among the candidates to competitive examinations, in comparison with the population distribution, followed by students and employees. Analysis of occupation variables For the group of employed persons, the information collected in the survey allows to make a more exhaustive analysis, highlighting the last considerations. For that purpose, table 3 is presented and, similarly to table 2, it includes in the last column the percentage distribution of each group within the population. INEM courses, Workshop Schools or other courses for employment seekers In table 2 a considerable percentage of employed people who took these kind of courses was obtained, but the lack of samples in most of the analysed variable categories prevents a detailed analysis. Courses promoted by the company Data reflect a higher representation in these courses of people who work in the private sector, 6 for each 10, theoretically speaking, since in general in a course like this there are not persons from both sectors. However, in comparison with the population distribution, there are more public sector employees who participate in courses promoted by their companies or by public bodies. With regard to the working day and working hours, population distributions are maintained. The first work establishment activity of the employed people sets forth there are 7 service sector workers for each 10 attendees of a generic businesscourse, while in the population of employed people it would amount to 6 for each 10. Industry workers follow them in Table 3. Percentage distribution of people who have taken non-regulated training courses in the last four weeks and of the population of employees and employed persons 1, based on various variables 1 2 3 4 5 6 Population 1 Employees employment sector Public sector * 42,6 37,3 32,2 25,6 * 10,0 57,4 22,4 Private sector * 57,4 62,7 67,8 74,4 90,0 42,6 77,6 Employees working day type Full-time 77,6 91,2 88,8 91,6 79,7 72,9 87,3 Part-time * 22,4 8,8 * 11,2 * 8,4 * 20,3 27,1 12,7 Employees working hours type Continuous working day 77,0 59,0 55,6 45,3 48,9 68,4 55,1 Split shift * 23,0 41,0 44,4 54,7 51,1 31,6 44,9 First work establishment activity Agriculture, stockbreeding or fishery * 6,8 * 0,2 * 2,7 * 0,4 * 6,6 * 1,2 5,7 Industry * 3,9 19,2 16,8 26,1 24,9 * 3,9 19,5 Construction * 6,3 6,2 * 5,5 * 1,3 * 14,0 * 1,7 10,8 Services 83,0 74,4 75,0 72,3 54,5 93,2 64,0 1. INEM Courses, Workshop Schools 2. Courses promoted by the company 3. Computing courses 4. Language courses in language schools 5. Driving schools 6. Preparation of competitive examinations * The number of sample observations is lower than 30, due to which it must be cautiously interpreted 1 population is limited to employees for the first three variables, and to occupied people for the last one 13
representation with 2 for each 10, which coincides with the distribution population. Construction workers would be underrepresented in such courses and those who work in agriculture, stockbreeding or fishery do not seem to tend to take this training. Computing courses Computing courses, restricted to the employed people, present the same scene as company courses, that is, a higher percentage of workers of the private sector that of the public one; for each 2 workers of the private sector there would be one of the public sector. And, therefore, the same consideration on the course distribution in comparison with the population distribution is valid, concluding that, proportionally, there are more public employees who take computing courses. Based on the variables of the employees working day and working hours, there are no differences with the population distribution, as in courses promoted by the company. Finally, in computing courses we would find 3 students of the service sector for each 4 students of a class, followed by industry workers. Language courses Language courses have almost the same distribution as the population. When classifying by the employees employment sector, 3 of each 4 employees work in private companies. And while the type of working day outlines the same scene than in the population, the working hour type has a distribution which is slightly contrary to the population distribution, with part-time workers being the most represented ones in a language course. As for the employed persons employment sector and language courses, we may say that it would be hard to find a construction employee and even more an agriculture, stockbreeding or fishery employee. Thus, it may be stated that of each 4 employed persons who study languages, 3 work in the service sector and 1 in the industry. Driving school The typology of these studies, by their nature, has major differences with the rest of courses, as seen in the previous comments. Even if we only consider the employed persons, this statement does not change, and, even, no similarity is found with the rest of the courses described in this section, as opposed to what happened when classifying results by sex, for example. Thus, the distribution by the employees employment sector produces a rate of 9 private sector employees for each 10 students, far from the population distribution. The working day type does not reflect the population distribution either, as happened with the other courses, although full-time workers are still the most represented ones, with 8 of each 10 persons. As for working hours, we may speak of a technical draw. Finally, employed persons from the service sector are those who attend driving schools more often, even if it is below their distribution in the population. For the remaining establishment activities it is the contrary, percentages are higher in courses than in the population. The piece of information which relates to agriculture, stockbreeding and fishery workers may also be highlighted, even though cautiously, since it is the training with a higher presence from this group. 14
Table 4. Percentage of people who have taken a non-regulated training course in the last four weeks, by total duration of the course Training course INEM Company Computing Languages Driving schools Competitive examinations Less than 1 month * 9,7 63,8 26,3 * 3,8 * 2,4 * 2,8 From 1 to less than 3 months 30,7 12,9 22,9 * 5,5 18,9 * 3,1 From 3 to less than 6 months 29,1 5,3 16,0 * 7,5 * 9,2 * 4,8 From 6 months to less than 1 year 18,4 6,4 15,6 28,2 * 4,7 21,9 1 or more years * 10,6 * 4,7 * 5,9 30,5 * 3,9 16,0 Undefined * 1,5 7,0 13,3 24,5 60,6 51,5 * The number of sample observations is lower than 30, due to which it must be cautiously interpreted Preparation of competitive examinations The preparation of competitive examinations throws a drastic change over the previous data when classifying them by employees employment sector. The most represented sector is the public one, with 6 for each 10 candidates to become civil servants. The wide difference between the course rate and the population rate may also be highlighted, with 2 public employees for each 10 employees. The working day type also differs from the rest of the courses, since it presents the highest rate of part-time employees in the whole table 3, that is, 3 for each 10. The same happens with continuous working day, with their highest rate in the whole table 3, that is, 7 for each 10. Thus, the split shift seems to deter the preparation of competitive examinations. Finally, almost all the employed persons who prepare competitive examinations come from the service sector, as inferred from table 3. Education and training total duration The decision to take any course is affected by many circumstances, age, working situation, low training or knowledge, etc. Among them the course total duration is undoubtedly included, the time during which the person shall have to attend it. Thus, although in principle the attendees know with more or less precision the duration of some courses, it is interesting to throw more light by venturing a standard total duration of these courses. There comes the surprise, because sometimes a group of the persons taking a course, and sometimes most of them, declare that the course does not have a definite duration, neither prior to the beginning of the course, nor once they have begun it 1. In accordance with data observed in table 4, the most common total duration of the INEM courses is from one to six months, and there is another important group of courses whose duration is from six months to one year. Courses promoted by companies have a much shorter duration than INEM courses; specifically 63.8% of these courses last less than one month. It is also significant that 7% of this type of courses have an undefined duration. 1 Note that in the individual questionnaire there is a question on the education or training total duration once the interviewee has noted that he or she has taken some kind of education or training in the last four weeks. Therefore, in most cases, they are in the middle of the studies. 15
Computing courses present the highest variability in total duration, from one month to less than a year, and there is also a significant number of them with an undefined duration. Language courses offered by language schools seem to have an undefined duration or a duration above 6 months. The maximum number of persons who declare that the duration is not defined is obtained with respect to the driving license; we could say they know when they start the course but they do not know when they will finish it. However, 18.9% of people who are studying to obtain their driving license say they will invest in it from one to three months. One of each two competitive examinations candidates which attend preparatory schools or who have a personal trainer does not know the defined total duration of their studies either. Obviously, competitive examination candidates, even if there is a determined examination date, do not dare to hazard whether they will pass on that examination or they will have to wait to the following one or to another subsequent one, or whether they will give up. However, the most frequent duration is from six months to one year (21.9%), and a duration of one or more years is also frequent (16.0%). Average weekly hours dedicated to education and training Finally, informants were asked about the average weekly hours they dedicate to such studies, and, in addition to the time spent at the place in which the course is given, they also had to note the time they dedicate to personal study or to make exercises apart from the specific time of the course, including also the time spent in tutorials or in enquiries to teachers. In table 5 it is observed that the preparation of competitive examinations is the study to which more time is dedicated, followed by INEM courses, Workshop Schools and other courses for employment seekers. From this fact it may be inferred that the non-regulated studies to which more time is dedicated are those which are taken in order to obtain a job. Courses promoted by the company are the following ones in time spent. Therefore, courses related to the work situation, specifically to active people, are those to which more time is dedicated. Table 5. Average weekly hours dedicated to the main non-regulated training course taken in the last four weeks Training course Both sexes Men Women Under 25 From 25 to 44 From 45 to 64 65 and over INEM Courses, Workshop Schools 23:10 23:15 23:07 27:09 21:51 *19:48.. Courses promoted by the company 10:39 11:03 10:07 13:50 10:56 9:07.. Computing courses 9:04 9:24 8:48 12:53 8:43 8:14 *7:47 Language courses in language schools 8:08 6:47 8:51 *15:46 6:52 7:07 *7:47 Driving schools (driving license) 9:11 9:27 8:53 9:14 9:19 *7:10.. Preparation of competitive examinations 26:29 25:56 26:51 29:23 26:00 *20:02.. * The number of sample observations is lower than 30, due to which it must be cautiously interpreted.. Void data 16
Although there are usually many persons who take an interest in them with a view to improve their work situation or obtain a job, courses of a more entertaining nature, computing and languages, present an average weekly dedication of 9 and 8 hours respectively. Finally, in order to obtain the driving license, about 9 hours are spent per week. In table 5, the average daily hours that men and women, and people from the various age groups which have been defined, dedicate to those courses are also quantified. Only language courses present a mentionable difference by sex, specifically that women dedicate two more hours to studying than men. By ages, those who dedicate more time to any kind of courses are the younger ones, and this number decreases with age. The biggest differences in absolute figures arise in INEM courses, Workshop Schools and other courses for employment seekers, since the younger ones dedicate 5 hours more than the rest. However, considering the hours dedicated to each type of studies, the differences in computing courses are more considerable, in which those under 25 spend about 13 hours in those course, 4 hours more than the rest. 17
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2. The place where the activities are carried out The published result of the 2002-2003 Time Use Survey (TUS) do not have a set of tables which provide information on the place in which the activities have been carried out, hereinafter the place, a variable which has proved very interesting 1. Therefore, we take this opportunity to disclose the result tables of this variable, with a structure analogous to those already published. However, we are going to comment briefly on such tables, which we have attached as a schedule, with a view to helping with their interpretation. The TUS really focuses on the activity carried out by the informant, that which he or she has noted in a diary, over 24 hours of the day which has been previously assigned to him or her, and not so much on the place in which the activity has been carried out. Furthermore, methodologically, along with the place, means of transport have been considered. Thus, following the recommendation of Eurostat 2 a classification 3 has been set forth, which has permitted, from the noted main and secondary activities, to assign a code to exploit this variable. Therefore, prior to putting forward the analysis conclusions, the deductive nature of this variable must be highlighted. 1 This variable has been useful for the INE s data collection unit in order to obtain a timetable of time spent at home, thanks to a more in-depth analysis, allowing them to optimize the interviewers work, by maximizing the visited houses and minimizing time and costs. 2 See Annex VI Activity Coding List. Guidelines on Harmonised European Time Use Surveys. September 2000. Eurostat. 3 The detailed classification of places and means of transport may be found in the publication: 2002-2003 Time Use Survey. Volume I. Methodology and National Results. Instituto Nacional de Estadística. Madrid 2004. Also available in: http://www.ine.es /inebase/cgi/um?m=%2ft25%2fe447&o=inebase&n= &L= The inclusion of the place 4 in time use surveys permits, on the one hand, to simplify the main activity classification, since it is not necessary to have a more extensive classification to identify one activity carried out in different places. Thus, there is only one code for the lunch activity and we do not have to use a code for the activity lunch at home, another one for lunch at the parents, another one for lunch at work, etc. However, here we shall only comment on the casuistry of this variable as a whole without relating it to its original activity. Average time spent daily In the first table of the schedule the average time spent daily in certain places or means of transport of the Spaniards is presented. Home is the place in which we spend more time, 16 hours and 28 minutes. The difference between men and women in this aspect is of 2 hours and 30 minutes. We spend 2 hours and 59 minutes 5 at the place of work or school and the difference by sex, which amounts to 1 hour and 53 minutes, is contrary to the time spent at home. In decreasing order of average time spent daily, we find the street (40 minutes), other people s house (29 minutes), restaurants, cafés or bars (19 minutes) and the countryside, outdoors or sports center (13 minutes) which presents the biggest difference, in absolute figures, between men and women after home and place of work (19 minutes for men and 7 minutes for women). 4 The term place shall be used generically to designate places and means of transport. 5 The average time spent daily is obtained as the sum of hours spent at the place divided by the sum of total hours of all the Spaniards, whether they have been in that place or not. 19
Graph 1. Average time spent daily in certain places or means of transport (in hours and minutes) 1:14 0:44 0:24 0:25 2:05 Men 1:04 0:36 0:15 0:32 2:04 1:48 Women 3:57 15:11 17:41 Home Work or school Other people's house Restaurant, café, bar Street, public highway Means of transport Other places Speaking of means of transport, we may highlight the 28 minutes spent in private means of transport, distributed almost exclusively in travels by car (18 minutes) and travels by foot (9 minutes). In public means of transport 8 minutes are spent, especially in travels by bus (5 minutes). The remaining tables of average time spent daily only present those places considered significant and provide disaggregated information for the main time, space, household and individual classification variables. With respect to that, we may highlight, without getting into details, that, based on the kind of week day, the average time spent is different for week days (from Monday to Thursday) and for those considered as weekend days (from Friday to Sunday). Specifically, on week days more than twice of the time is spent at work or at school than during the weekend and also more travels by foot are done and more public means of transport are used. Households with higher income levels spend more time than the rest of households at work, at the second residence and at the beach or in the swimming pool, obviously due to their economic means. They also move around more by car. By type of municipality, the situation is quite balanced. Those who reside in provincial capitals spend more time at the second residence and use more public means of transport. Age is a variable which almost always presents a great analysis depth and usually is correlated to other classification variables such as study level, marital status or relationship with the activity. But, focusing only on age, those of 65 and above have the lowest presence at work or school, since obviously they do not have such occupations. As for them, the time during which they stay at home, which amounts approximately to 20 hours, only exceeded by those who do housework, implies a great difference over the two younger groups (under 20
45) which spend about 15 hours and a half at home. Among the rest of places, the older seem to prefer the street. The younger ones, in comparison to the rest of ages, spend more time at the beach or in the swimming pool and in restaurants, cafés or bars and move around more in public means of transports and by foot. Those from 25 to 44 years old stand out because they spend more time at work or school, almost as much time as those under 25 spend in restaurants, cafés or bars, and because they are those who spend more time in travels by car. Those with a lower study level and widowers coincide with the older ones and spend less time at work and in restaurants, cafés or bars, and more in the street or at home. However, widowers stand out when it comes to spending time at other people s houses. Singles are those who spend less time at home and more at work or school, in restaurants, cafés or bars, using all kinds of means of transport and at the beach or in the swimming pool, and in the countryside, outdoors or sports center. Married people visit their second residence more often than the rest. As for the relationship with the activity, those who spend more time at work are employers (6 hours and 16 minutes in average), those who spend more time at home are those who dedicate to housework (19 hours and 53 minutes), those who spend more time in the street are pensioners (1 hour and 14 minutes) and those who use public means of transport more frequently are students (14 minutes). Finally, there are differences by autonomous communities with regard to the time spent at the second residence, from 12 minutes in average of people from Madrid, to less than 30 seconds in average of people from Navarre 1, and, to a lesser extent, in the time spent in public means of transport, in travels by foot or by car and the average time spent in the countryside, or in restaurants, cafés and bars. People percentage A group of tables is also included which provide the percentage of people located at certain places or means of transport throughout the day with the same detail than in tables of average time spent daily. Data have been obtained by considering the persons who have been at the place at any moment of the day compared to the total of persons. From the data of these tables and of those of average time spent daily we can deduct the average time spent at the different places only by those persons which have been there at any moment of the day. Place rhythms On the other hand, the percentage of people located at certain places or means of transport throughout the day has been obtained, which we shall name place rhythms, by analogy with the published average activity rhythms. The result tables are accompanied by graphs which allow to view this rhythm. Through them, we confirm that the highest percentages of people who are at home appear at night hours; from 1:00 am to 7:00 am the percentage of people is always higher than 90%, coinciding with sleep hours 2. 1 That is the reason why 0:00 appears in table 1.4 2 This piece of information may be contrasted with the published daily activity rhythms. 21
Graph 2. Percentage of people located at certain places at the same moment of the day at the beginning of the hour. Both sexes. % 35 30 25 20 15 10 5 0 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 24:00 02:00 04:00 Day hour Work or school Other people's house Restaurant, café, bar Street, public highway Other places Means of transport The presence at home at lunch time, from 2:00 pm to 4:00 pm in Spain of more than half of the population also stands out. Those who are not at home during those hours, as seen in graph 2, are mainly at work or school or in some means of transport. Only 3% of Spaniards are at other people s house and 2% at restaurants, cafés or bars at such hours. Between 1 and 3% are in the street and around 8% in other places 1. In addition to home, there are two places which play a leading role throughout the day. From 6:00 am to 5:00 pm, the main place is work or school (the highest presence concentrates from 10:00 am to 12:00 1 In graph 2 other places are considered (see classification for more details): Second residence, beach, countryside, shopping center, bank, hotel, park, doctor, hospital, dentist, hairdresser, library, church, pensioners home... am). From 6:00 pm and until 5:00 am, other places play the leading role, and they obtain their highest point at 7:00 pm (18.8%), and from that moment the presence decreases rapidly. It may also be highlighted that both places rhythm has a cyclical nature although with a different scale. Means of transport (both public and private) have a high participation above 5% from 8:00 am until 10:00 pm. Graphically, it is the only place which has three peaks at 9:00 am (6.2%), at 2:00 pm (11.5%) and at 8:00 pm (12%), which we associate mainly to the activities of going to work, going to take lunch and dinner and coming back from work, respectively. Finally, the activities of being in the street and at other people s house obtain their peak at 7:00 pm with 10.2% and 4.6% of persons respectively. However, the rhythm of these two places is quite different. The 22
activity of being in the street also has a cyclical nature, while the activity of being at other people s house has an increasing trend until it achieves its peak and, subsequently, decreases when night comes. 23
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3. The problem with the self-classification of the relationship with the activity In the 2002-2003 Time Use Survey (TUS) there are two possible classifications for the informants relationship with the activity. The first one, which we agree to name objective, is obtained from a series of questions of the TUS individual questionnaire based on the questions of the Economically Active Population Survey (Encuesta de Población Activa, EAPS). The second classification, which we may call subjective, is the answer to the situation in which the informant deemed himself or herself to be, that is, a self-classification (question 21 of the individual questionnaire). When we put in common the information obtained by these two variables, we come to the conclusion that one of each four unemployed as per the objective criterion classifies himself or herself as a student or as doing housework. The specific data may be seen in table 1a where the distribution of the subjective classification is presented for each objective classification category. To put it in a different way, the presented percentages amount to 100% for each objective criterion category (in columns). If the distribution of the objective category is considered for each subjective category, which may be seen in table 1b where the presented percentages amount to 100% for each subjective criterion category (in rows), the comment is almost identical. In this case, 64.2% of those who classify themselves as unemployed would be objectively considered unemployed. And 34.4% of subjective unemployed would be classified as objectively inactive. In spite of the data differences, the information of this table is the same, only varying the percentages by the denominator considered in each case. Therefore, only the first type tables will be analysed, that is, the selfclassification categories distribution for Table 1a. Percentage of persons in relation to the activity. Classification by objective and subjective criteria, by sex Total persons Men Women Objective criterion Objective criterion Objective criterion Subjective criterion Employed Unemployed Inactive Employed Unemployed Inactive Employed Unemployed Inactive Employed 98,3% 0,5% 0,3% 98,8% 0,7% 0,5% 97,6% 0,3% 0,3% Unemployed 0,2% 74,9% 4,6% 0,2% 85,5% 4,9% 0,3% 67,1% 4,4% Inactive 1,5% 24,6% 95,1% 1,1% 13,8% 94,6% 2,1% 32,7% 95,4% Student 0,7% 10,8% 27,1% 0,7% 11,3% 36,2% 0,8% 10,4% 21,7% Pensioner 0,3% 1,2% 38,8% 0,3% 1,7% 56,2% 0,2% 0,8% 28,3% Housework 0,4% 12,3% 28,3% 0,0% 0,4% 1,1% 0,9% 21,1% 44,5% Table 1b. Percentage of persons in relation to the activity. Classification by subjective and objective criteria, by sex Total persons Men Women Objective criterion Objective criterion Objective criterion Subjective criterion Employed Unemployed Inactive Employed Unemployed Inactive Employed Unemployed Inactive Employed 99,6% 0,1% 0,4% 99,6% 0,1% 0,3% 99,5% 0,0% 0,4% Unemployed 1,4% 64,2% 34,4% 1,5% 68,1% 30,3% 1,3% 60,8% 37,8% Inactive 1,5% 2,8% 95,7% 1,7% 1,8% 96,4% 1,3% 3,4% 95,3% Student 2,5% 4,2% 93,3% 2,8% 3,8% 93,4% 2,1% 4,7% 93,2% Pensioner 0,7% 0,3% 99,0% 0,9% 0,4% 98,8% 0,5% 0,3% 99,2% Housework 1,3% 4,7% 94,1% 4,9% 4,3% 90,8% 1,2% 4,7% 94,1% 25