Predicting the probabilities of participation in formal adult education in Hungary

Transcription

1 Péter Róbert Predicting the probabilities of participation in formal adult education in Hungary SP2 National Report Status: Version

2 1. Introduction and motivation Participation rate in formal adult education is very low in Hungary according to the AES data (and other statistical sources (e.g. KSH 2004). In this national report the learners and nonlearners are investigated chiefly from the viewpoint of their demographic and sociological characteristics as well as of respondents attitudes toward learning. According to a pervious typology of the AES countries, the results from a cluster analysis revealed a possible link between the high level of inequalities in access to adult learning in terms of demographic and sociological obstacles as well as the low frequency of returning to formal studies in adult education (Róbert and Balogh 2010). This seems to be a similar relationship between degree of inequalities and educational outcomes as discovered for students performance in PISA analysis. Jenkins at al. (2006) found that students PISA test scores lower and affected by their social origin stronger in those countries where the school system is more unequal and contains stronger tracking. Thus, lower level of participation in formal lifelong learning may also be affected by stronger educational inequalities. In this national report, focusing only Hungary, a stronger test is performed on the relationship between participation in formal adult education and the possible obstacles of learning in terms of inequalities of access to formal adult schooling programs. For this aim, partly bivariate, partly multivariate statistical methods are applied. Respondents attitudes toward adult education add additional information on respondents decision on lifelong learning in Hungary. The analysis is based on descriptive statistics on the one hand and regression models on the other hand. In the first case, the statistically significant differences are emphasised e.g. on the ground of the adjusted residuals in the cells of the tabulated data. In the latter case, logit models are fitted to the data, separately for the demographic, the sociological, and the attitudinal indicators. The full model investigates the impact of all predictor variables on the odds of returning to formal adult learning. The Hungarian AES data (N=7484) are used in the report. The data are weighted but scaled back to the original sample size. 2. Bivariate statistical relationships 2.1. Learning activities and demographic features This section deals with the inequalities of access to formal adult education in terms of some selected demographic features of the respondents. These features are - gender: women (in contrast to men); - respondents belonging to three age-groups: between 25-35, 36-45, over 45 (the lower age limit is 25, the upper age limit is 64 in the dataset); - living in settlements of high, medium or low level population density (as a measure for level of urbanisation); - respondents having children of various age in the household: below 3 years, between 4-5 years; between 6-13 years, between years (in contrast to the case that no child of the given age is present in the household. Table 1 displays the proportion of these demographically defined groups among the participants and non-participants in formal adult learning. The table reports column percentages, i.e. categories for the place of residence or for the age group add up to 100 2

3 percent, while categories for gender and the children show the actual proportions for the members of the group. Table 1. Participation in formal adult education by demographic characteristics (column %) The respondent participated in formal did not participate in Total adult education* formal adult education is woman 61.7 (+) is aged between (+) is aged between 36 and is aged over (-) lives in high populated area lives in medium populated area lives in low populated area 36.7 (-) has child aged below has child aged between 4 and has child aged between 6 and has child aged between 14 and *Note: Bold: significantly deviates form the total percentage; (+) = overrepresentation, (-) = under-representation In comparison to men, women are significantly overrepresented among learners in formal adult education. Age differences are also marked. The young cohort between years study more frequently, while attendance in formal schooling programs is scarce in Hungary if someone is over 45. Regarding place of residence, inhabitants in rural areas have significantly worse access to formal adult learning in Hungary. Finally, presence of child(ren) with various ages does not seem to make any difference for returning to adult education in a bivariate context Learning activities and sociological features In principle, three sociological features are worth to take into account for investigating the inequality in access to formal adult education: highest level of education when leaving the school system; occupation and labour market participation; income. For level of schooling, three categories will be distinguished: primary = ISCED1-2, secondary = ISCED3-4, and tertiary ISCED5-6. Apparently, those with lower level of education are the early school dropouts and this fact is usually an obstacle for returning to school. Another barrier can be the length of time, one spent out of school, i.e. the length of time elapsed between leaving school and returning to adult education. Unfortunately, only the year of completion of the highest level of schooling is available in the data but not the year when the respondent started the formal adult study program he or she reported in the survey. A period variable can be computed which express the time elapsed since the respondent has left school. It has four categories: below 5 years, 6-10 years, years, longer time. For labour market participation, being in the labour force or being out of the labour force makes the crucial difference. For the first category, the degree of labour market integration is assumed as an important indicator of participation in adult education. Two features are used to define labour market integration: type of work contract which can be either permanent 3

4 (unlimited) contract or temporary contract for a limited duration; and having a full-time or a part-time job. On this ground three categories will be distinguished: a fully integrated employee has a full-time job with permanent contract; an integrated employee has either permanent contract in a part-time job or a full-time job with a temporary contract; and a weakly integrated employee has a temporary contract and works in part-time. A further form of flexible participation in the labour force to be distinguished is self-employment. Due to low number of cases, own-account workers and entrepreneurs with employees cannot be separated though they represent clearly different groups. A further occupational indicator will make a difference among respondents on the ground of the exact job. It is assumed as an obstacle for participation in adult education if the employee is working in manual occupations, i.e. in jobs which belong to the ISCO groups starting with 7 (skilled workers), 8 (semi-skilled workers), 9 (unskilled workers) or 6 (agricultural labourers), in contrast to non-manual job holders and respondents being out of labour force. 1 If being out of the labour force, various situations are recorded in the data representing more temporary or more permanent conditions: unemployment, housekeeping, studying, disability, retirement, or other kind of inactivity. The present analysis will focus on unemployment. In fact, there is no variation in the case of disability or retirement, none of the Hungarian respondents in these categories participated in adult education. Similarly, there is no variation for the students: they all are in education. Regarding income the AES dataset provides a variable which puts the respondents into five categories, i.e. into the five income quintile. For Hungary, it turned out that this measure of income quintiles had a strange distribution: about two-third of the respondents belong to the lowest category. This is possible only if the quintile measure was computed for the whole comparative dataset and not for each country separately. This makes basically questionable to use the variable and, consequently, to investigate the role of the financial inequalities in the access to adult education in Hungary. The variable has been left out from the analysis. Table 2 presents learners and non-learners from the perspective of their sociological characteristics as described above. In Hungary, early school dropouts with a completed education of maximum ISCED 1 or 2 are significantly under-represented among those who returned to formal adult education. Graduates, however, who have tertiary level of schooling (ISCED 5 or 6) attend more frequently formal adult learning programs. The length of time elapsed since one has left school makes a strong difference. Participation in adult education is more frequent if the respondent completed his/her highest level of education not so long time ago. In fact, very few people study again in Hungary if 20 years or more elapsed since leaving the school system. Labour market participation seems to be strongly connected to attending formal adult education in Hungary. There is a significant overrepresentation in participation in lifelong learning for those who are in labour force. Unemployed in Hungary, however, do not tend to return to adult education. The same holds for the self-employed; they also have difficulties in finding access to formal adult education. Also the manual workers seem to have significantly worse access to adult education. 1 The AES questionnaire collected information on the job title (ISCO), self-employment, type of work contract, fulltime vs. par-time job only from those who were in the labour force at the time of survey. This means, that being out of labour force is part of the reference category in these indicators. 4

5 Table 2 Participation in formal adult education by sociological characteristics (column %) The respondent participated in formal adult education* did not participate in formal adult education Total has ISCED12 level of schooling 4.3 (-) has ISCED34 level of schooling has ISCED56 level of schooling 36.7 (+) left school maximum 5 years ago 21.9 (+) left school maximum 6-10 years ago 32.1 (+) left school maximum years ago 34.8 (+) left school more than 20 years ago 11.2 (-) is fully integrated in the labour force 60.6 (+) is integrated in the labour force 7.4 (+) is weakly integrated in the labour force 2.7 (+) is self-employed 2.1 (-) is unemployed 2.7 (-) works in manual job (ISCO 6,7,8,9) 10.1 (-) *Note: Bold: significantly deviates form the total percentage; (+) = overrepresentation, (-) = under-representation 2.3. Learning activities and attitudes toward adult education Attitudes toward learning were measured on a 5 point agreement scale in the AES survey where respondents could fully agree or totally disagree with statements that related to education. This is the list of the items taken into account: 1. If you want to be successful at work you need to keep improving your knowledge and skills 2. Employers should be responsible for the training of their employees 3. The skills you need to do a job can t be learned in the classroom 4. Education and training can help you manage your daily life better 5. Learning new things is fun 6. Learning gives you more self-confidence 7. Individuals should be prepared to pay something for their adult learning Part of these opinions (# 1, 4, 5, 6) approach studying in a positive manner from the viewpoint of its benefits. The third item represents the exception; it is an explicit sceptical statement. Item # 2 brings in a different dimension referring to the employers and making them to be responsible (instead of the individuals). As the only one, the last item refers to the financial costs of participation in adult education and calls the attention that people cannot expect to get educated for totally free. This list of attitude questions looks a bit unbalanced and arbitrary but no better attitude questions are available in the AES data. Table 3 provides some insight into these attitudes for the Hungarian respondents. This table displays the descriptive statistics (means and standard deviations) for each of the 7 items, on the one hand; and summarizes the results of a principal component analysis performed on the attitude questions. 5

6 Table 3. Descriptive statistics and the structure of the attitudes Attitude items Descriptive statistics* Principal component analysis** Mean Standard Communalities Factor 1+ Factor 2+ deviation (extraction) If you want to be successful at work you need to keep improving your knowledge and skills Employers should be responsible for the training of their employees The skills you need to do a job can t be learned in the classroom Education and training can help you manage your daily life better Learning new things is fun Learning gives you more self-confidence Individuals should be prepared to pay something for their adult learning * Note: Means and standard deviation refer to the five-point agreement scales (1=fully agree; 5=totally disagree). Thus, lower values mean higher agreement. ** Note: Principal component analysis of the 7 items led to 2 factors. Factor 1 has an Eigenvalue of and explains 35.2% of the total variance after rotation. Factor 2 has an Eigenvalue of and explains 16.0% of the total variance after rotation. Variance explained is 51.2 in total. Varimax rotation was applied. + Note: Factor loadings come from the rotated factor solution and values below 0.4 are not shown. 6

7 Since the response scale used in the AES questionnaire gave the value of 1 to fully agreement with the items and the value of 5 to the totally disagreement with the items, the lower means in the first column of Table 3 basically expresses stronger agreement with the given opinion. The majority of these means are between 1.5 and 2.0 and this tells that Hungarians generally tend to accept most of the statements. (One should be aware of the general rule of public opinion questions that respondents usually prefer to agree than disagree.) The two exceptions where the mean values are above 2.0 indicate relatively stronger critic to these two items in the Hungarian context. In this regard, firstly Hungarians believe somewhat less that learning new things is fun. Secondly, Hungarians seem to accept somewhat less that payment for adult education would be kind of requirement. The second column of Table 3 shows the standard deviations and these values tell how univocal the opinions are. The lower is the standard deviation, the higher is the general agreement with the given item. In this regard, the lowest value appears for the last item and this indicates that Hungarians are rather consistent in not much tolerating that they ought to pay for adult learning. (It is perhaps good to know that tuition has a very slowly increasing role in the Hungarian educational system and the majority of the population favours that schooling should be free of charge.) The highest deviation appears for the item on learning is fun and this shows that respondents answers varied a lot in this respect. The principal component analysis of the 7 opinion questions aimed to search for the structure in the attitudes on learning. Apparently all those statements which describe learning as a positive and beneficial thing (items # 1, 4, 5, 6) appear on Factor 1. Factor 2 contains the item on employers duty and the only negative opinion on education, namely that skills cannot be learned in the classroom. In fact, this question has a quite low communality in comparison to the other variables and its factor loading is also lower. This indicates its weaker role in the whole structure of attitudes toward learning. The item on the payment appears on both factors. It means that those Hungarians who have positive attitudes toward education tend to accept that it cannot be totally free. (In fact, as it is phrased in the wording: to pay something sounds quite weak.) Those respondents (on Factor 2), however, who would put the responsibility of adult education to the employers or are more sceptical about the usefulness of the school programs and classroom activities, tend to disagree with paying for learning. 2 These two factors basically reveal a realistic and plausible structure on attitudes toward learning in Hungary. Returning to the bivariate relationship between attitudes and participation in formal adult education, the learners and the non-learners are compared from the perspective of the attitudes in Table 4. For being more parsimonious in presenting the results in numbers, only the extremes on the response scale, the fully agreement answers and the totally disagreement answers are shown in the table for the 7 questions. 2 Understanding of the positive and negative signs of the factor loadings may be confusing at first sight. Due to the coding of the answer scale, negative values usually mean agreement and positive values mean disagreement. Except here, where the rotated factor scores are presented and the positive value for the item on payment means agreement and the negative value means disagreement. 7

8 Table 4. Participation in formal adult education by attitudes toward adult education (column %) Attitude items The respondent participated in formal adult education* did not participate in formal adult education Total fully agree totally disagree fully agree totally disagree fully agree Totally disagree If you want to be successful at work you need to keep 77.7 (+) improving your knowledge and skills Employers should be responsible for the training of 47.6 (+) their employees The skills you need to do a job can t be learned in the 47.3 (+) 4.8 (+) classroom Education and training can help you manage your 61.2 (+) daily life better Learning new things is fun 41.5 (+) 1.6 (-) Learning gives you more self-confidence 68.3 (+) Individuals should be prepared to pay something for their adult learning 30.2 (+) *Note: Bold: significantly deviates form the total percentage; (+) = overrepresentation, (-) = under-representation 8

9 Not surprisingly, all of the positive feelings toward learning, the agreement with the items # 1, 4, 5, 6 are connected to significantly bigger frequency of participation in formal adult learning. More surprisingly, even those respondents who fully agree with employers responsibilities in adult education are also over-represented among learners. Even more amusingly, Hungarians who fully agree with the difficulties (or impossibility) of learning skills in the classroom are also overrepresented among the learners. Perhaps participation in adult education is needed to have this negative experience (and attitude) in Hungary. More logically, those people who totally disagree with this critical statement are also tend to study more frequently. Fully agreement with paying for studying is also linked to significantly higher level of real participation in formal adult education. Statistically significant (slight) under-representation in learning appears only for one attitude item, for those who disagree that learning is fun and, consequently, do not return to school. Another way to investigate the relationship between the attitudes toward learning and the participation in adult education is comparing the mean values of the two factor scores between the group of learner and non-learners. This is shown in Table 5. The factor scores have a mean value of in total, this comes from the method of the principal component analysis. Given the coding of the attitude questions (1 = fully agreement, 5 = totally disagreement), the negative values mean stronger agreement. This can be seen in the cell for Factor 1 (positive attitudes toward learning) and for those respondents who attended formal adult education (-0.540). The mean value of the same factor is positive for the other group, the non-learners, and the difference between the two means in the two groups is strongly significant. This is not the case for the Factor 2 where its content is also less straightforward as the factor contains quite different statements (see Table 3, last column). The difference between the mean values of Factor 2 in the groups of learners and non-learners is statistically not significant. Table.5 Factors on attitudes toward learning and participation in formal adult education (Mean factor values) The respondent participated in formal adult education did not participate in formal adult education Total (Mean) has positive attitudes toward *** 0.014*** learning (Factor 1) has negative, controversial attitudes toward learning (Factor 2) Note: For the positive attitudes toward learning (Factor 1) there is a significant difference at p<0.000 level between those who participated and did not participate in formal adult education. The difference between the groups is not significant for the second factor. 9

10 3. Multivariate analysis of the participation in adult education 3.1. Direct effects of the demographic and sociological features and of attitudes After exploring the bivariate relationships between the demographic, the sociological and the attitude measures as well as the observed participation in formal adult education, this section aims to investigate how these variables influence the probability of participation in adult learning altogether. The majority of the Hungarian respondents did not return to formal schooling, participation rate in lifelong learning is low. Learners in formal adult education were coded as 1, the rest of the respondents take the value of 0. For the purpose of a causal modelling and explaining the difference between learners and non-learners, the method of logistic regression analysis is applied. The same three types of explanatory variables are used: 1. demographic ones: gender, age, place of residence, and household composition (in terms of children of various age in the family); 2. sociological ones: initial education (= highest level of schooling completed), time elapsed since one has left school, labour market integration and type of occupation (manual vs. non-manual job); 3. attitudes toward learning. Altogether four models are fitted to the data: Model 1-3 deals with the impact of the demographic, sociological, and attitudinal predictors separately, while Model 4 contains all explanatory variables. 3 The models can be evaluated on the ground of their explanatory power (as indicated by the Nagelkerke R Square value). Further information is the difference between the initial and the final -2 log likelihood values. The initial -2 log likelihood value refers to the baseline model which does not include any explanatory variable but only the constant. The final -2 log likelihood value is always smaller because the model improves when the explanatory variables are taken into account. This holds if the decline is statistically significant with the given number of explanatory variables (= degree of freedom). Table 6 displays all of the estimates from the four regression models. Model 1 investigates the impact of demographics and finds that the indicators chosen can explain the probability of returning to formal schooling in Hungary by about 16% (see the Nagelkerke R square). A significant estimation for gender shows that Hungarian women have bigger odds than men to attend formal adult education. There is no simple explanation for this but the result is in line with what one can see for the other countries. Moreover, it is generally well-known that women study longer and attend more school in comparison to men in all of the modern societies. Age is another important factor. In contrast to elder respondents over 45 years, those between 30 and 45 or particularly those below 30 return to school with higher probability. Old age above 45 is a definite obstacle in Hungary; people may feel that they are too old to sit back into the classroom. There is a moderate effect of place of residence. Living in rural (low populated) area is a barrier. Hungarians in towns but not necessary in Budapest have better access to formal adult education in contrast to village people. Presence of small or primary school aged child in the family is also an obstacle and hinders participation in adult education in the multivariate context when this fact is controlled for the other demographic characteristics. Model 2 shows the effects of the sociological indicators defined. This model is fitted to the data in three parts: the first one includes only the impact of the level of education and the time elapsed since leaving school, while the second one contains only the labour market features. This makes possible to see the impact of education and that of occupation separately. The 3 Model 2 was divided into three parts: Model 2a, 2b and 2c. Text explains the reasons below. 10

11 third sociological model is the full with both the educational and the sociological predictors of participation in formal adult education in Hungary. Table 6. Estimates from logistic regression models on the odds of participating in adult education (Unstandardized coefficients) Explanatory variables Model 1 Demogr. Model 2a Sociology Model 2b Sociology Model 2c Sociology Model 3 Attitude Model 4 Full Woman 0.636*** Aged between 25 and *** 1.845*** between 36 and *** 1.576*** over Lives in high populated area in medium populated area in low populated area - - Has child aged below ** ** aged between aged between ** aged between ISCED56 level of schooling 0934* ISCED34 level of schooling 0912* 0.946** 0.880* ISCED12 level of schooling Left school 5 years ago 3.395*** 3.370*** 2.202*** 6-10 years ago 3.004*** 3.055*** 2.054*** years ago 1.969*** 2.099*** 1.279*** more than 20 years ago Fully integrated in the LF 0.874*** ** Integrated in the LF 1.425*** Weakly integrated in the LF 2.321*** Self-employed *** ** Unemployed *** ** Works in manual job *** *** ** Att: improve skills for success *** * Att: employers responsibility Att: can t be learned in class Att: manage daily life better ** * Att: learning is fun Att: more self-confidence * Att: should pay for learning Constant *** *** *** *** *** ** Nagelkerke R square Initial -2 log likelihood* Final -2 log likelihood Decline in -2 log likelihood *** *** *** *** *** *** Degree of freedom * Note: The model contains only the constant Significance: *** p< 0.001; ** p<0.01; *p<0.05; +p<0.1 11

12 Model2a shows that Hungarians with higher level of schooling (ISCED 3-4 or the diploma holders ISCED 5-6) participate in formal adult education with larger probability in contrast to the early school dropouts (ISCED 1 and 2). Similarly to the age effects in the demographic model, the period effects are also substantial in Model 2a. The odds of participating in adult education are getting significantly smaller as more time elapsed since one has left school. In sum, Model 2a explains 19% of the variance of returning to school, this is a bit higher than in Model 1. The decline in the -2 log likelihood value is also more substantial with only 5 independent variables (degree of freedom) in comparison to 9 in Model 1. Generally Model 2b with the occupational features performs worse than Model 2a with the educational ones. The model explains only 6% with 6 predictors. However, the pattern in the effect of the occupational variables is interesting. Integration into the labour market matters in the sense that those employees who are less integrated seem to participate in adult education with higher probability (the effect sizes are growing). Nevertheless, the direction of the causal relationship is not straightforward. It is possible that these employees invest into education because they want to get better integrated. However, it is also possible that the lower degree of integration allow them to study; this is the way how they can find time to participate in adult education. 4 A further important result from Model2b is the significant negative effect for those who work in manual job, revealing that working in these occupations (ISCO job title begins with 6, 7, 8, 9) is a strong obstacle in Hungary. Model 2c shows that level of schooling and labour market and occupational indicators are strongly connected to each other. The impact of education is more moderate in this model and the pattern of labour market integration changes when it is controlled for education. In fact, unemployment and self-employment turn out to decrease the chances of returning to formal adult education. The negative impact of working in manual job is smaller but remains highly significant. Model 3 looks at the role of attitudes. All 7 items are included in the model because the bivariate investigation of relationship between the factor structure and the participation in adult education did not show significant difference for Factor 2. This model has only a small explanatory power of 5% and the decline in -2 log likelihood is significant but moderate as well. Basically, having favourable attitudes toward learning has a positive impact on the odds of participating in formal adult education. (Note, that negative sign of the estimates means here positive effect as full agreement with the statements that skills are needed for work success, or for managing life better, or learning increases self-confidence, was coded as 1 and totally disagreement was coded as 5.) The complete multivariate analysis is performed in Model 4. Apparently, the demographic, the sociological and the attitudinal characteristics are not independent form each other but they have stronger or weaker correlations. This fact influences how the explanatory variables work as they control for each other and the model shows the most influential effects. For the demographics, this is age; neither gender nor region matter anymore. Only having small child aged below 3 remains a significant obstacle of studying. For the sociological features, the role of the time since one has left school remains a strong predictor of participation in formal adult 4 This second assumption would hold better for part-time work than for temporary contract. In Hungary, parttime employment is scarce. Furthermore, employers may support less those workers who they employ only with a temporary contract. These considerations would lead to a bit bigger chance for the previous interpretation of the casual relationship: lower degree of labour market integration is the cause and participation in adult learning is the outcome and not vice versa. 12

13 education. The same holds for the unemployment and self-employment or the manual worker position, these features are definite obstacles for participating in adult learning in Hungary. Even fully integrated labour force participation has a significant negative impact if controlled for all other indicators. Regarding the attitudes, the belief that skills and knowledge improve success at work and helps to manage daily life also increases the odds of returning to formal schooling even if all socio-demographic features are taken into account. The complete model explains about 26% of the variation of returning to formal adult education in Hungary Interaction terms between demographic, sociological features and attitudes The previous section investigated the impact of Hungarians demographic and sociological features as well as their attitudes on the decisions about returning to formal schooling. In addition to the direct effects, in principle it is possible that combinations of these effects also pay important role in influencing participation in adult education. This section discusses some of these possibilities and investigates the so called interaction effects between the independent variables analyzed so far. Though a large bunch of interaction terms were tested, only the significant ones are examined in more details. When certain combinations turned out to be insignificant, only this fact is mentioned in order to be more parsimonious but the related estimates are not reported. Table 7 displays the significant results regarding these interaction terms. The main effects for those predictors for which no significant interaction terms appeared in the analysis are not presented in this table. (This occurred only for some attitude items.) The summary statistics for this model with the significant interaction terms show a large decrease in the -2 log likelihood value with a degree of freedom of 76 (the number of all main effects and the interaction terms taken into account). Considering the interactions between the indicators largely improve the statistical model in terms of its explanatory power, as well. The variance of the probability of participation in formal adult education is explained by this model with interaction terms as much as by 37%. Starting with the demographic features, women turned out to be more active in returning to formal adult schooling. The assumption arises that this result may differ according to the place of residence. Interaction terms between gender and region turned out to be insignificant: women s advantage is the same for the different areas with different level of urbanisation in Hungary. Having small children in the family seemed to have a significant obstacle for participation in adult education. However, it is possible that this result does not hold for men and women in the same way. The interaction terms between gender and the presence of (small) child in the family turned out to be insignificant: at first fight small children seem to hinder both women and men in returning to adult education in Hungary in the same way. Apparently, there may be different mechanisms behind this finding: e.g. mothers of small children do not study because they have to take care of the child, while fathers of small children do not study because they have to work more and have to earn more money. The role of the presence of children in the family in lifelong learning does not vary by region either. When testing the combinations between respondents demographic features, only one of the interactions terms was found to influence significantly participation in formal adult learning. This interaction puts the gender differences into a new light and indicates that women have particularly big chances to return to adult education when they are aged between 36 and 45. This finding should be interpreted together with the other significant interaction terms between age of the child in the family and time elapsed since one left school. The interaction 13

14 terms reveal that a child aged below 3 decreases the probability of attendance in formal adult learning within 5 or even within 10 years after leaving school. Moreover, having a primary school aged child of 6-13 years old decreases the odds for returning to study within 10 or even within 20 years after leaving school. The duration of time elapsed since one has left school and the age of the child fits well and shows clearly this strong demographic obstacle for lifelong learning in Hungary. 5 At the same time, there is no significant interaction between gender and level of completed schooling or between gender and labour force participation in affecting participation in formal adult education. The next significant interaction term for the demographic and sociological characteristics reveals an interesting effect for the level of education and the place of residence. The main effects show that respondent have higher odds for participating in adult education if they have higher levels of initial schooling or if they live in bigger and more urbanized settlement. However, the relative probability of participation in adult learning is relatively smaller for the diploma holders (ISCED 5-6) in Budapest and in big cities in contrast to early school dropouts living in low populated areas in Hungary. In other words, Hungarian diploma holders tend to return to formal schooling particularly if they live in smaller settlements, in country towns or villages. This holds even for those with a completed education of ISCED 3-4. Though costs of studying may be larger for them, participation in adult education may give an opportunity to them to travel from their (smaller) place of residence to some (bigger) city where they usually study for a (second) diploma. 6 One more significant interaction term reveals that respondents who are fully integrated in the labour force participate in adult education with lower probability if they live in highly populated area. In other words, again, combining work and study and bearing the higher costs of it, is more typical for these employees if they live in the countryside, in smaller settlements and in medium or low populated area. With respect to the combinations for the sociological characteristics, several possibilities were investigated without much success. E.g. the impact of the forms of labour force participation on lifelong learning does not show any particular variation by level of initial education. Nevertheless, it seems that more integrated participation in the labour force postpones studying. The probability of signing up to adult education is significantly lower for these employees or the self-employed if not much time elapsed since they have left school. This holds for the unemployed as well. However, the interaction term is not present for those are weakly integrated into the labour force. With other words, the interaction effects confirm that they are the employees who start to study again already when not much time passed since they have left the schooling system. No significant interaction terms appear for the manual worker category. Their disadvantage in access to formal adult learning seems to be very general phenomenon which does not vary by gender, cohort, or place of residence either. 5 A three-way interaction term between the indicators for the child, for the time elapsed since one has left school and for gender turned out to be significant and reveals that women, in fact, have more difficulties in access to formal adult education than men if there is a small or primary school aged child in the family despite of the insignificant interaction between gender and child. 6 A three-way interaction term between the indicators for place of residence, level of initial education and gender turned out to be significant for the subgroup of women with tertiary level of school (ISCED 5-6) and living in high populated area. The coefficient is negative; the odds of participating in adult education are lower for these people. Indirectly this means that women with tertiary level of schooling tend to return to adult learning with higher probability if they live in medium or low populated area, in less urbanized settlements. 14

15 Table 7. Selected estimates for the significant interaction terms between the indicators influencing participation in adult education (Unstandardized coefficients) Explanatory variables and interaction terms* Estimates Main effects Woman Aged between 25 and between 36 and over 45 - Lives in high populated area 2.480* in medium populated area in low populated area - Has child aged below aged between ** ISCED56 level of schooling ISCED34 level of schooling ISCED12 level of schooling - Left school 5 years ago 9.158*** 6-10 years ago 9.003*** years ago 6.807** more than 20 years ago - Fully integrated in the LF 4.950* Integrated in the LF 8.932** Self-employed Unemployed Attitude: improvement of skills needed for success at work ** Attitude: learning is fun Attitude: learning gives more self-confidence 0.579* Interaction terms Woman aged between * Has child aged below 3 and left school 1-5 years ago ** Has child aged below 3 and left school 6-10 years ago ** Has child aged between 6 and 13 and left school 6-10 years ago * Has child aged between 6 and 13 and left school years ago *** Having ISCED 5-6 level of schooling and living in high populated area *** Having ISCED 3-4 level of schooling and living in high populated area * Fully integrated in the LF and living in high populated area ** Fully integrated in the LF and left school 1-5 years ago ** Fully integrated in the LF and left school 6-10 years ago ** Fully integrated in the LF and left school years ago * Integrated in the LF and left school 1-5 years ago ** Integrated in the LF and left school 6-10 years ago ** Integrated in the LF and left school years ago * Self-employed and left school 1-5 years ago * Self-employed and left school 6-10 years ago * Self-employed and left school years ago Unemployed and left school 1-5 years ago * Unemployed and left school 6-10 years ago * Unemployed and left school years ago Living in high populated area and agreeing that learning is fun 0.581** Living in medium populated area and agreeing that learning is fun 0.410* Fully integrated in the LF and agrees with improvement of skills is needed for success 1.566** Unemployed and agrees with improvement of skills is needed for success Fully integrated in the LF and agrees with learning gives more self-confidence ** Integrated in the LF and agrees with learning gives more self-confidence * Unemployed and agrees with learning gives more self-confidence Constant *** 15

16 Nagelkerke R square Initial -2 log likelihood* Final -2 log likelihood Decline in -2 log likelihood Degree of freedom 76 *Note: All indicators are included in the estimation but the main effects of those variables (attitudinal ones) for which no significant interaction terms were found are not shown in the table in order to make reading of the results easier. Significance: *** p< 0.001; ** p<0.01; *p<0.05; +p<0.1 Only few significant interaction terms appear for the attitude questions, as well, though several options were tested again. Related models reveal e.g. that the attitudes toward learning do not differ by gender, by level of completed education or by age cohort. Place of residence, however, matters in the sense that there is a significant positive interaction effect between living in high or medium populated areas (in cities or towns in contrast to villages) and agreeing with the statement that learning is fun. One should be cautious when interpreting this result because of the coding of the attitude questions. If the negative sign means the positive attitude toward learning and this increases the probability of participation in adult education, a positive coefficient for an interaction term means, in fact, a decrease in the same probability. If this occurs for the highly and medium populated area as in this case, it means, in fact, that the positive attitude toward learning (i.e. learning is fun) supports participation in adult education particularly for those who live in rural areas. From this perspective the interaction reveals that a positive attitude toward learning will particularly increase the odds for returning to formal schooling if someone lives in low populated area, in smaller settlements where the probability of adult education is lower otherwise. This looks like a quite plausible result. The further significant interaction terms between labour market participation and attitudes should be interpreted in a similar manner. On the one hand, both those who are fully integrated employees and those who are unemployed participate in adult education with lower probability if they think that improvement of skills is needed for success at work (strong negative main effect and positive estimates for the interaction terms). In other words, this means that those employees who are less integrated into the labour force will participate in formal adult education with higher probability if they have a positive attitude toward the usefulness of learning for success at work. This improves the understanding while lower degree of integration in the labour market leads to higher level of participation in formal adult education in Hungary. On the other hand, there is a significant interaction with the attitude question that relates to learning and the increase of self-confidence. Since the main effect of this attitude item turns to positive in the final model (see Table 7), the negative signs of the coefficients for the interaction terms mean that this belief about self-confidence particularly influential for those who are more integrated into the labour force but also for the unemployed when it is about participation in adult education. 4. Few concluding remarks on the Hungarian case The Hungarian national study intended to investigate the reasons and correlations of the particularly low level of attendance in formal adult education in the country. The main assumption was that low participation in adult learning is related to various forms of inequalities regarding access to adult education. After the bivariate and multivariate analysis of the issue, some lessons can be summarized as follows. 16

17 1. Old age as obstacle is present is Hungary, respondents being older than 45 years seem to feel to be too old for the role of (adult) learner and to sit back to school again. 2. Small but even preschool aged child is obstacle in Hungary and it hinders participation in adult education. This barrier is stronger for women than men, though women generally have higher participation rates in adult education. 3. Being an early school dropout, leaving education at younger age and spending longer time out of the schooling decrease the odds of returning to school. As known also from general statistics, having a higher level of first completed education, a secondary or even tertiary level of schooling increases the probability of lifelong learning. An interesting result is that this probability is relatively bigger for those respondents living in low populated area. A possible reason may be that a regular travel from a smaller village or town to a bigger and more urbanized settlement (in order to attend classes in formal education) can be an extra incentive for these people. In other words, participation in adult education legitimizes these regular travels. 4. Lower level of labour market integration increases the participation in adult education. These employees may feel that studying will help them to get better integrated. This result is supported by another finding that these learners particularly agree with a related attitude item about the positive impact of learning on the success at work. 5. Though generally less people study in the low populated area, the regional differences not very large, even if statistically significant. As mentioned above, some people in the countryside are ready to bear the costs for studying and attending adult education programs. This seems to be particularly the case of women if they have a tertiary level of schooling. The positive attitude that learning is fun makes people to return to adult education also chiefly from the countryside, the low populated area in Hungary. Apparently, the multivariate analyses reveal the chief obstacles of participation in adult education and give some hints to the motivations as well. Investigating the role of combined indicators (interaction terms) also contributed to find the substantial reasons of the low participation in formal adult learning. On this ground, the increase of participation rate would require the next policy measures. 1. Particularly older aged people should be encouraged to start to study again. Early school dropouts with a low level of initial schooling (ISCED 1-2) form another target group of policy measures. 2. Availability of child care institutions should be improved. The number of these institutions strongly declined after the collapse of socialism, particularly few places are available in state-run nurseries or kindergarten and private institutions are expensive. Learning institutions in Hungary do not contribute to handling or reducing this problem as data from the Hungarian survey of adult learners (in SP3) also reveal: only a couple of respondents (about 3%) mentioned that child care is available in the institute where they study. 3. Incentives to participate in adult education look quite complex in Hungary according to results based on the interactions between the independent variables. As labour market entry is getting flexible and a permanent job contract is getting to be scarce for new labour market entrants, employees should be encouraged to return to study by offering them more secure integration into the labour force. Particularly self-employed seem to have no enough motivations to enter adult education programs; they should be a target group. Unemployment could be a situation when people could think that adult 17

18 studies would help them to return to the labour force. This is not the case in Hungary, so better measures to involve unemployed into adult education are needed. 4. Incentives to participate in adult education may not be linked only to work- or careerrelated reasons. About 60% of adult Hungarian learners (in SP3) claimed to study mainly due to job-related reasons. However, the definitely stronger motivation of people living in low populated area, in less urbanized settlements with probably less cultural and intellectual attractions make these people to start to study in adult education programs due to other causes than work-related ones. Apparently, these adult learners are not early school dropouts. Women are strongly over-represented among these learners. However, any further word would be purely speculative since AES data do not provide information on motives of learners. Though no direct and straightforward policy measures can be connected to this finding for policy makers dealing with lifelong learning in Hungary, it is still worth to be aware of these incentives of certain adult learners. 5. References Jenkins, S. P., Micklewright, J. and Schnepf, S. V. (2006). Social segregation in secondary schools: How does England compare with other countries? University of Southampton, Southampton Statistical Research Institute, Application & Policy Working Paper A06/01 or University of Essex, ISER, Working Paper ( KSH (2004) Az élethosszig tartó tanulás. (Lifelong Learning.) Budapest: Hungarian Central Statistical Office Róbert, P. and Balogh, A. (2010) Chapter X in Formal Adult Education in Context. The View of European Statistics. SP2 Comparative Report 18