Daño inducido en la red por vía nuclear y electrónica.

Referring to the compilation of selected related studies (Appendix XI), the present study classifies the determinants of mismatch into some categories: (1) personal characteristics such as gender, ethnicity and age; (2) household characteristics such as number of young children in the household; (3) work related and firm size, including job status, tenure, sector, dummies of industry and dummies of firm size; and (4) residence, including urban/rural and capital/non-capital province. In addition, other macroeconomics variables can be considered in the model, such as GDP per capita as well as the share of manufacturing and regional unemployment. The analysis then focuses on the first four, since some macroeconomic variables are similar to those categories, for instance, the share of manufacturing which is represented by dummies of industry (including manufacturing) in the model.

Personal Characteristics

In terms of personal traits, variables which have received a large amount of attention in recent literature are sex, marital status, ethnicity, age, age squared and subject of study. Firstly, a clear picture on the direction and significance of the effect of sex has yet to emerge. So far, many studies find that male employees face a slightly higher overeducation risk (European Commission, 2012) than female employees. This is also confirmed by Yin (2016); males were more prone to be overeducated than females in China during 1989-2009 period, as China has the patrilineal system is key to traditional Chinese family and gender values (Hu and Scott, 2014), males is perceived to have more responsibility to support the family life than females, and thus more likely to accept jobs that require lower educational level than their own. In contrast, Clark et al. (2017) analyses overeducation in the US, using the NLSY79 data combined with the CPS from 1982 to 1994 with the probit model and finds that females were about 5 to 13 per cent more likely to be overeducated than males. However, Chua and Chun (2016) find that females are more likely to be overeducated, while males are slightly more likely to be matched or undereducated. Other studies have also found that gender has insignificant effect on matches (Büchel and Pollmann-Schult, 2001; Battu and Sloane, 2002; Groot and Brink, 2003; Frenette, 2004; Green and McIntosh, 2007; Capsada-Munsech, 2015).

In terms of marital status, the theory of Differential Overqualification developed by Frank (1978) states that overeducation frequently occurs among females, particularly the married ones. The theory hypothesizes that married individuals, especially women, face a significantly higher probability of being overeducated, which is a consequence of matching problems. In traditional gender role model settings where the couple’s priority is the job match for the husband, the husband acts as a first-mover, i.e. he performs his job search first. After he has found a match, the wife will conduct her job search. However, due to the co-location restriction, she can do that merely within a much smaller market area. The likelihood of finding a job adequate to her qualification level is therefore much lower for her than for her husband, thus explaining a striking incidence of overeducation among married females (Boll et al., 2016). Having said that, McGoldrick and Robst (1996) reject the hypothesis of differential overqualification; instead, it appears for them that the larger number of vacancies in large labour markets are offset by a larger number of job searchers. Likewise, Battu and Sloane (2002) find that marital status have no significant effect on education mismatch.

With respect to ethnicity, Battu and Sloane (2002) assert that there are no studies that explicitly focus on mismatch amongst ethnic minorities. However, the limited research on ethnic group and overeducation finds that overeducation incidence is greater for non- whites (minorities). An argument specific to non-whites is simply that of discrimination. Theoretically, the same argument can be applied for females and different religion. Yet, empirically gender have no significant effect on the probability of being matched or otherwise (Battu and Sloane, 2002). If non-whites find it more difficult to acquire any jobs they may well be more likely to take a job that is not proportional to their qualifications, so that a higher number of non-whites will end up being over-educated. Battu and Sloane further focus on overeducation among ethnic minorities in Britain, using the Fourth National Survey of Ethnic Minorities (FNSEM) in 1993/1994. The ethnic population is composed of six groupings (Caribbean, Indian, Pakistani, African-Asian, Bangladeshi, and Chinese), while education attainment covers non-educational, O-level, A-level and university degrees. The study uses a multinomial logit model to investigate the determinants of over and undereducation. The dependent variable is earnings, which refer to usual gross pay from the sample’s main job including overtime and bonuses

before any deductions37_{. The study finds that only African-Asians have a significantly}

greater likelihood of being mismatched, relative to the omitted category of Indians. Using interaction terms, the study concludes that African-Asians who are born in the UK and who have foreign qualifications are less likely to be overeducated. By way of contrast, Pakistani and Bangladeshi workers with foreign qualifications have a higher probability of being overeducated.

Turning to age, the literature (such as Boll et al., 2016) asserts that age and overeducation have a negative relationship, suggesting that there is a risk-reducing effect of age. Similarly, Flisi et al. (2014) affirm that older age category implies lower probability of being severely mismatched rather than matched in all countries. Regarding overeducation, common results are found for older age groups, since in almost all countries age groups 55+ and 45-54 have lower probabilities of being overeducated than age group 35-44. But for younger age groups, there are some relevant differences by country: age group 25-34 is less likely to be overeducated than age group 35-44. The effect of the square of age is negative, implying that there are diminishing returns in age. The quadratic function of age variable could capture the fact that the marginal effect of education mismatch declines over time.

Other variables commonly included in previous empirical studies are subject of study (Silles and Dolton (2002), McGuinness (2006), and Boll et al. (2016)). Boll et al. study the overeducation in 25 EU countries using the European Labour Force Survey (EU-LFS) data; and emphasise that high-skilled workers from the fields of agriculture, veterinary and services are much more frequently overeducated than high-skilled workers from the fields of teaching, education and health, and welfare (Ordine and Rose, 2009).

In addition to determinant variables, education mismatch may be influenced by the other skills dimension, such as lifelong learning, on-the-job training. Vocational lifelong learning is the responsibility of the Ministry of Manpower that conducted in both public and private employment training centres. In 2014, there were 1,555 private employment training centres in Indonesia–almost five times the number of public employment training centres (Lee Kuan Yew School of Public Policy and Microsoft, 2016). In addition, for

37 _{Earning is in bands. The study finds that earnings for non-whites overall are significantly lower, with} Bangladeshis having the lowest earnings and African-Asians and the Chinese displaying parity with Whites. It is worth noting that at one point in history, an Indian master degree was considered in the UK equal to a UK undergraduate degree.

lifelong learning, the present study identifies at least two programmes that the government have launched: improving adult literacy and digital business training; according to the Ministry of Education and Culture (2013) around 3 million people participate in literacy programmes in Indonesia. A particular emphasis is placed on increasing women’s literacy levels, combining more generic life skills with literacy courses. For on-the-job training, the government encourages industry or companies to conduct competency-based job training for workers and prospective workers (TNP2K, 2015). Those variables are available in the IFLS data. There are some questions related to on-the-job training and life-long learning: have you ever received any training from your employer? What kind of training did you receive in the last 12 months? The answers are computer, language, technical training, teamwork, leadership, and others.

Household Characteristics

In terms of household characteristics, most of the relevant literature has focused on the presence of children as a determinant of overeducation. Boll et al., (2016) find that the coefficients of children are insignificant in general; children of any number and age composition do not affect overeducation risk for male workers. Yet, the interaction terms with gender are relevant for female workers. More specifically, having an additional child below the age of six is predicted to reduce the overeducation probability significantly for high-skilled female workers. For medium-skilled workers, the risk- reducing effect of small children is of lower magnitude and is only weakly significant. A reason could be because medium-skilled workers are, on average, expected to be less wealthy than the high-skilled ones, which could force them to accept barely adequate jobs when living with children.

Meanwhile, Sloane et al. (1999) assert that the presence of young children in the household poses different effects across gender, as younger children reduce overeducation for males and raise overeducation for females. Hence, females with children are forced to make more compromises in the labour market. Empirically, raising the number of children between the ages of 0 and 2 from zero to one reduces the probability of being overeducated for males by 7.38 per cent and raises the probability of being overeducated for females by 17.32 per cent. Sloane et al use subjective measure based on a question in the Social Change and Economic Life Initiative (SCELI), which is

funded by the Economic and Social Research Council (ESRC). The survey covered six British local labour markets between 1986 and 1987.

Other household-related variables that could affect mismatch determinant are number of adults with unemployment status in the household, number of inactive people, and number of older people.

In contrast, Dolton and Silles (2002) conduct a research based on the Newcastle Alumni Survey, collected at the University of Newcastle-upon-Tyne in 1998, and find that there was no measurable effect between children and marital status on overeducation in the UK labour market (in particular university graduates). A possible explanation is that the sample is not large enough to allow a meaningful interaction between family commitments and gender.

Work Related and Firm Size

In work related, another potential variable is working experience. Both the Human Capital Theory and the Job Competition Theory confirm that there is a negative relationship between work experience and overeducation risk. Moreover, the Career Mobility Theory asserts that the longer a worker stays in a firm, the higher is the likelihood of advancement into better positions with higher skill requirements and thus a lower overeducation risk. Meanwhile, Groot (1996) finds a positive relationship between experience and overeducation probability, since low-productive workers receive fewer job offers and therefore tend to remain stuck in bad matches which under-utilize their skills. The effect of tenure is similar to experience, both of them significantly reduce the risk of overeducation (Büchel and Pollmann-Schult, 2004). This is because individuals accumulate human capital by working. Furthermore, human capital can be dichotomised into general and firm-specific human capitals. Moreover, the quadratic function of experience and tenure (squared) variable could capture the fact that on-the-job training investments decline over time in a standard lifecycle human capital model, as explained in Chapter 3.2.2.

In terms of job status, some studies use full-time and part-time (less than 30 hours a week) as job status variable (Lindley and Machin, 2016) which is commonly determined by the number of working hours a week. Other studies prefer to use the alternative proxy i.e. working hours per week or per year (Clark et al., 2012, and Boll et al., 2016). In particular,

Boll et al. (2016) find that working hours have a negative and significant coefficient, which implies that workers with more working hours are less likely to become overeducated, especially in high-skilled jobs. This is because jobs with longer working time can create better opportunities for training participation and advancement, thereby improving the match quality over time. Similarly, Frank (1978) and Ofek and Merrill (1997) add that part-time work leads to a higher probability of being overeducated. Sloane

et al. (1999) also assert that being a part-time worker reduces the probability of being

undereducated and increases the probability of being overeducated. They argue that a part-time work for the overeducated may simply facilitate job search, thereby representing a short-lived mismatch as part of a longer-run career development path. Meanwhile, the European Commission (2012) does not detect any significant associations between education mismatch and working hours or job status. Morano (2014) also finds that part-time and temporary employments both lead to higher overeducation.

Turning to sectors, Dolton and Vignoles (2000) study overeducation in the UK using the 1980 National Survey of Graduates and Diplomats. They put forward that education mismatch, particularly overeducation, is found in broadly equal proportions in both the public and private sectors. However, a higher proportion of those working in government administration were specifically overeducated in 1986. Similarly, Ortiz (2010) studies overeducation in France, Italy, and Spain, using The European Community Household Panel from 1999 to 2001. The method used to calculate overeducation is RM. The Multinomial Logit (MNL) is also applied to estimate the determinants. The study finds that working in the public sector increases the likelihood of being overeducated, relative to working in the private sector. The main reason is working in the public sector is generally considered more secure. In contrast, Yin (2016) finds that workers in the private and collective sectors in China are more likely to be overeducated than individuals in the government sector. This is likely due to the government sector having higher wages, stable working environment and attractive welfare, which makes it easier to hire matched workers. Another possible reason is because individuals may find jobs in the private sector only temporarily to gain experience in order to find better matched and stable jobs in the government sector later.

Regarding occupation, Dolton and Silles (2002) study the determinants of graduate overeducation in the UK using data from the Newcastle Alumni Survey. They employ cohort effect to analyse the effect of initial overeducation on the probability of being

overeducated in the future. Most importantly, the study includes occupation (manager, professional and associate professor, with the base group being other occupations). The study finds that graduates in professional, associate professional and managerial occupations have a greater propensity to be in graduate level jobs than those in the base group. In similar vein, Morano (2014) analyses the determinants of overeducation in Italy using the Continuous Labour Force Survey (Rilevazione Continua delle Forze di Lavoro). The study uses some occupation categories in the model: director, manager, blue-collar worker and trainer, with the base group being clerical jobs. The study finds that these occupations have negative and significant coefficients; with the exception of blue-collar workers who have a positive and significant coefficient. Negative and significant coefficients indicate that the probability of being overeducated is lower for these categories than for the base group (clerical jobs).

Another possible variable to influence education mismatch is industry, as Allen (2016) asserts that occupation mismatch in Indonesia tends to be associated with the low education levels of production workers and agricultural laborers. On the other hand, a large number of clerks are over-qualified for their jobs. Undereducation is also a challenge in higher-level occupations. The high levels of under-qualification and lower levels of over-qualification point towards an issue of skill shortages. Meanwhile, Morano (2014) finds that workers in the service sector are less likely to be overeducated than those in the agricultural or industry sectors. These results can be interpreted by the different nature of employment in the three economic sectors and the relatively less skilled nature of the jobs in the agriculture or industry sectors with respect to service work.

Furthermore, firm sizes could also determine education mismatch, as indicated in previous empirical studies. For instance, Dolton and Vignoles (2000) find that overeducation was the highest amongst those who work for small firms (of less than 20 people), although generally the incidence of overeducation does not decrease linearly with firm size. Interestingly, more than 70 per cent of the graduates who are overeducated and work in small firms claim to require no qualifications for their job. It may be that the lack of benchmark jobs and formal qualifications causes a higher incidence of overeducation to be recorded amongst graduates who work in small firms. Morano (2014) also finds that overeducation decreases as firm size increases, which is consistent with the idea that bigger firms have more accurate recruitment techniques which reduce the risk of hiring a worker who does not match the educational requirements associated to the

vacancy (Dolton and Silles, 2001). Moreover, there is a wider range of positions that enables the management to internally relocate workers in case of mismatch in big firms. Yin (2016) also adds that workers in large firms with between 20 and 100 employees and in firms with fewer than 20 employees are less likely to be overeducated than workers in firms with more than 100 employees. This could be due to employees in large firms having more opportunities to be promoted, having better career prospects and receiving more fringe benefits compared to those who work in a small company. Thus, overqualified workers may voluntarily choose to stay in large firms due to the consideration of the above benefits. This finding is in contrast to Morano (2014), implying that firm size definition, country and time period may lead to different conclusions. Another alternative variable is job contract length. Boll et al. (2016) consider the incidence of overeducation is strongly related both to job type (includes contract length) and firm characteristics in 25 European Countries. The study argues that people with fixed-term contracts are more likely to work in positions for which they are overeducated than people with permanent contracts. This is due to the transitory nature of fixed-term jobs; workers are less concerned about qualification levels, as they tend to view these matches as mere temporary solutions on their way to more favourable permanent positions. Similarly, Green and McIntosh (2007) and Ortiz (2010) also identify evidence for a significantly lower overeducation risk among workers in permanent positions. In addition to determinant variables, Chua and Chun (2016) emphasise that labour markets in developing countries are unique for their large shares of informal sector employment. This sector is comprised largely by either microenterprises or menial wage work, where high level skill or training is not required. The study also adds that the formal sector has better matches than the informal sector, even the informal salaried and self- employed sectors. In fact, overeducation is particularly severe among self-employed workers, which plausibly explained by the preponderance of small businesses with low skill requirements.

Area

In terms of residence or area category, urban/rural is used in some studies, such as Clark