Assessing the probability of employment in Greece between the 2004 olympics and the global financial crisis: the cases of the northern Aegean and Ionian islands.
The aim of the paper is to study the impact that various social and demographic characteristics had on the labour market in the Greek regions of the Northern Aegean and Ionian Islands, two well known international tourist destinations, in 2006--the year before the global financial crisis of 2007-09. The year 2006 is well after the Athens 2004 Olympics and its fiscal stimulus, and before the eruption of the financial crisis that developed into an economic and sovereign debt crisis. The changing prospects of employment in the Greek labour market can be seen even during the period of rapid economic growth and in any case before the recent debt and economic crisis of Greece.
The main questions to be answered are:
i. what are the social and demographic characteristics that increase the chances of someone in the examined population finding a job?
ii. whether University graduates face greater difficulties in finding a job than non-University degree holders; this issue is of great importance, since earlier studies (see Meghir, Ioannides & Pissarides, 1989; OECD, 1990; Iliades, 1995; IN.E./GSEE-ADEDY, 1999; Katsikas, 2005) have shown this peculiarity in the Greek labour market.
iii. the prospects of young people in the labour market.
Human capital theory, which underpins many of the important developments in modern economics and provides one of the main explanations for wage and salary differentials by age and occupation is tested, and the uneven incidence of unemployment by skill (education and training). Whether the more educated a person is, the higher the probability of his or her finding a job is examined; the impact of educational level on earnings could not be examined, because this kind of information does not exist in the questionnaire of the Greek Labour Force Survey (LFS).
Previous labour market research for Greece (apart from other studies by the authors of this paper) was based on qualitative research and LFS aggregated data. The analysis of investigating the unemployment risk in the Greek labour market--at Nomenclature of Territorial Units for Statistics (NUTS) 2 level--is based on the micro-data of the Greek LFS. The access to the individual anonymous records of the Greek LFS was not allowed to researchers until the summer of 2005, due to the Data Protection Act.
The article starts by discussing human capital theory and the problem of youth unemployment. Then the article assesses unemployment levels in Greece in the two NUTS-2 regions under examination and in the EU as a whole. The relationship between unemployment and educational level in Greece and the rest of the EU is then discussed, together with some macroeconomic data for Greece and the two regions. A logit model for the year 2006 follows--based on micro-data from the Greek LFS--for the two regions. The article concludes with the impact of the social and demographic characteristics on employment probability in the two regions, ending with some general comments on the merit and value of this study.
HUMAN CAPITAL THEORY
During the late 1950s and early 1960s the current neoclassical theory of the labour market emerged with the development of human capital theory. Gary Becker (1964, 1975) published Human Capital, in which he developed a theory of human capital formation and analysed the rate of return to investment in education and training. However, investment in human capital remains a controversial issue (Woodhall, 1987).
Whilst the human capital literature has highlighted a number of productivity-related characteristics, human capital theorists give most emphasis to the importance of education and training as the main components of productivity (Blaug, 1975). Education, it is suggested, provides the basic skills of reading and writing, cognitive skills, and the "ability to learn" which will increase an individual's productivity in all jobs (general human capital), whilst vocational education, on the other hand, will increase an individual's productivity in a narrower range of jobs by providing more specific skills (specific human capital).
Becker (1962) distinguishes general from specific human capital of workers, and within specific human capital between employer-and employee-financed on-the-job training. Most broadly the theory of specific human capital predicts that where the fixed costs of employment, due to on-the-job training, are greatest, unemployment is lowest (Rees, 1973, pp.118-20).
Following Becker's (1964) analysis on the economic role of human capital, particularly education, there is now a considerable amount of empirical research on the closely related topics of education and skills (see Prais, 1995; Murray & Steedman, 1998) and, more specifically, the increasing role of skilled labour in the economy (Berman, Bound & Griliches, 1994; Machin, 1996; Machin & van Reenen, 1998).
Youth unemployment rates are generally much higher than unemployment rates for all ages. High youth unemployment rates reflect the difficulties faced by young people in finding jobs. For people below the age of 30, employment is often not "permanent" due to (post)graduate studies and working experience acquisition, plus fulfilment of compulsory military service for men in some countries. However, this does not necessarily mean that the group of unemployed persons aged between 15 and 24 is large because many young people are studying full-time and are therefore neither working nor looking for a job (so they are not part of the labour force which is used as the denominator for calculating the unemployment rate). For this reason, youth unemployment ratios are calculated as well, according to a somewhat different concept: the unemployment ratio calculates the share of unemployed for the whole population.
The EU-27 youth unemployment rate was systematically higher than in the euro area between 2000 and early 2008; since this date, these two rates were very close until mid 2010, when the EU-27 youth unemployment rate started to increase more strongly than that of the EA-17. While youth unemployment thus increased in both areas during the crisis of the labour market since 2008, the increase has been more relevant for the EU-27, despite the lower overall unemployment rate in that area (http://www.epp.eurostat.ec.europa.eu/ statistics explained/index.php/Unemployment statistics). Women in all countries tend to have higher flows into inactivity both from employment and unemployment. Data from the first six waves of the European Community Household Panel Survey (ECHPS) that cover the period 1994-1999 show that in the Mediterranean or "high-gap" countries it seems to be that the gender gaps in unemployment rates are largest among the young, the married and those with young children (Azmat, Guell & Manning, 2004).
UNEMPLOYMENT AND EDUCATION IN THE EU
UNEMPLOYMENT IN EUROPE AND THE EXAMINED AREAS
In Greece, in the years 1988-1998 (16) the unemployment rate climbed from 7.7% in 1988 to 11.5% in 1998 (17) (LFS). In 1995 the unemployment rate in Greece passed the 10% mark for the first time in the second half of the century (Ioakimoglou, 1995). Unemployment in Greece is now a structural phenomenon of considerable dimensions and with a particular dynamic that tends to keep it going. According to Eurostat data, the unemployment rate in the EU-15 increased from 8.2% in 1991 to 10.9% in 1996 (Eurostat, Unemployment in the EU, 1997). The unemployment rate in Greece rose above the EU average for the first time in 1998, and the gap was spreading, since the EU average was falling and the unemployment rate in Greece was still rising (IN.E./GSEE-ADEDY, 2000).
Table 1 indicates that Greece has the highest unemployment rate from both the EU and Euro area averages. The picture alters when unemployment is broken down according to gender. Although Greek youth unemployment in general and the unemployment rates of Greek females have been higher than the respective EU and Euro area averages since the introduction of the euro (2002), Greek males have had lower unemployment rates. The issue of gender inequality in the labour market is also present in the geographical areas of both the Ionian Islands and Northern Aegean.
EDUCATIONAL LEVEL AND UNEMPLOYMENT IN THE EU
It has long since been confirmed in almost all EU and other countries that there is an inverse relationship between the level of education on the one hand and unemployment rates on the other. The reasons are the processes of screening and credentialism, but also the assumed higher productivity of better qualified people. Apparently employers not only associate higher skills with specific performance capabilities, but also with the social and flexible competencies increasingly required in the course of technical progress (CEDEFOP, 1998). (http://epp.eurostat.ec.europa.eu/portal/page/portal/statistics/search_database)
Data on unemployment and qualifications showed deviations with regard to different countries and national data sources, which cannot be presented here in detail. A comparison of statistics from different sources should be done very carefully. Thus, for example, Eurostat relates unemployment rates to the 25-59 year old age group, while the OECD relates unemployment to people between 25 and 64 years of age (18).
Table 2 gives unemployment rates by qualification in different EU countries according to Eurostat data. The differences were enormous. There are only a few countries where this inverse relation between unemployment and qualification did not exist: in Greece and Portugal unemployment among people on ISCED (International Standard Classification of Education) 3 level (Lyceum) was higher than among the less qualified, but not among University graduates (ISCED 5-7); in Italy and Luxembourg, unemployment rates among the highly qualified (ISCED 5-7, University) exceeded those of people with intermediate qualifications.
ISCED 0-1: No qualifications.
Looking at the long-term unemployment (LTU) of different skill levels, we again find that intermediate and higher educated people were less affected. This is true for the whole Union except Spain and Greece, where LTU was higher on ISCED levels 3 and 5-7 compared to levels 0-2; for Italy where LTU was the highest on ISCED 3 level; and for Luxembourg and Portugal where the ratios of ISCED levels 0-2 and 3 were equal (Eurostat, Education and Employment Prospects, 1995).
GREECE IN THE EUROPEAN AND WORLD ECONOMY--MACROECONOMIC DATA OF THE EXAMINED AREAS
GREECE AS A WHOLE
According to the 2001 census the entire population of Greece was 11.26 million (ESYE). In 1988, Greece's GDP was equal to 58% of the EU-12 average, whereas in 1996 the country improved its position, since its GDP was 68% of the EU-15 mean and 82.2% of the EU-25 mean in 2004 [source: ESYE (www.statistics.gr)]. In 2008 the Greek GDP (PPP) per capita was 80% of the EU-15 (World Economic Outlook Database, April 2009, IMF). Greece (from 2000 onwards) and Ireland (from the early 1990s onwards) had the highest GDP growth rates in the EU-15 until the global financial crisis of 2007 (Eurostat). Also, according to the UN classification of human development index--which was released on December 18, 2008 and covers the period up to 2006--Greece was ranked 18th in the world and 11th in the EU. Central Greece, Southern Aegean and Attica are the richest regions since 1991, whereas three out of four regions in the west of the country, Epirus, Western Macedonia and Western Greece during the time period 1991-2004 were among the poorest Greek regions in per capita GDP (Eurostat; www. statistics.gr).
Greece has been a member of the EU since 1981 and a member of the Economic and Monetary Union (EMU) of the EU since 1 January 2001. The Greek labour force totalled approximately 4.4 million in 2006--also in 2011--and Greece ranks first in the working hours per year ranking of EU countries from 2008 onwards (see Eurostat statistics database at http://www.epp.eurostat.ec.europa.eu/portal/page/portal/statistics/search d atabase). In 2008, roughly 66% of the workforce in Greece was involved in the service sector, 23% in industry, and 11% in agriculture (ESYE). The Greek economy--based on its annual GDP--expanded at an average annual rate of almost 4% in the time period 2004-2007 (one of the highest rates in the Euro area). Table 3 indicates that after the introduction of the Euro in 2002, Greece displayed the highest GDP growth rate in terms of purchasing power standards (PPS) in 2006. This peak (in terms of rate of growth) in 2006 was explained at the time by "financial market liberalisation coupled with membership in monetary union, which led to a substantial reduction in borrowing costs; buoyant activity in export markets in south-eastern Europe; and the fiscal stimulus and focal point given by the Olympic Games in 2004" (OECD, 2007, p. 11).
Recent evidence, however, indicates that this is not the case. Greece suffers from political and economic corruption and low global competitiveness compared to other EU and especially Eurozone countries. By the end of 2009, and especially at the beginning of 2010, as a result of the global crisis and uncontrolled government spending, huge shadow economy rates and complex bureaucratic procedures, the Greek economy faced its most severe crisis since 1974. However, the need for sustained fiscal consolidation and the issues of productivity and competitiveness were already known several years earlier (see OECD, 2005).
The problem of the second economy in the Greek labour market is related to the structure of the economic system and to a large extent to the swelling of the public sector at the expense of private sector activities. This leads to the increase of tax burdens in terms of both direct and indirect taxation. This fact, combined with an inadequate taxation system, increases the phenomenon of the shadow economy in Greece, with its severe consequences for the labour market and investment (see Negreponti-Delivani, 1990, p. 12; Vavouras, Karavitis & Tsouchlou, 1992, p. 96). Also, according to a study by the University of Linz (2001), the size of the black economy in Greece as a percentage of GDP rose from 24.9% during the period 1990-1993 to 28.7% in 1999-2000. According to the econometric research of Tatsos (2001, p. 92), from 1960 to 1997 the average value of the underground economy in the Greek labour market as a percentage of GDP was 30.1%; the shadow economy in Greece from 1960 to 1997 increased by 40.2% (1960: 26.1%, to 1997: 36.7%). Paleologos and Kassar (2003) estimated the mean value of the black economy in Greece at 25.01% as a percentage of GDP, during the period 1960-2000, with its maximum value recorded in 2000 (36.67%); in total, during the period 1960-2000 the size of the second economy in Greece rose by 82.3%. According to Friedrich Schneider, the mean percentage of the underground economy in 21 selected OECD countries equalled 13.4% of the GDP in 2010; the same year in Greece this share was 29.9% or 69.05 billion euro (source: www.express.gr, on 20/05/2011.
THE REGION OF NORTHERN AEGEAN
The Region of Northern Aegean contains the counties of Lesvos, Samos and Chios, and consists of nine inhabited islands (Lesvos, Limnos, Agios Efstratios, Chios, Oinousses, Psara, Samos, Ikaria and Fournoi). The per capita GDP was 14,500 euro in 2003 (84% of the EU-25 average, and 104% of the Greek GDP mean which was 80.9% of the EU-25, the fourth most affluent region in the country). The region is noteworthy in that in 1995 the region's GDP was just 83% of the country's average. With Mytilini as its centre, it is inhabited by 1.9% of the country's population (204.108 inhabitants according to the 2001 census), thereby being the smallest region. Between the census of 1991 and the census of 2001 the population rose by 3.4%, as opposed to the national average of 6.9%. The region produces 1.9% of the GNP (the smallest contributor), 3.3% of agricultural produce, 0.2% of manufacturing and 1.7% of services. Sixty-seven percent of the region's produce comes from services (data of 2003). The region accounts for 2.8% of the country's cultivated land. Unemployment in the region fell by half a unit in 2001--after a fall of four units in 2000--to 6.6% of the workforce (10.5% for the whole of Greece), the lowest proportion nationally [source: ESYE (www.statistics.gr)].
THE REGION OF THE IONIAN ISLANDS
The Region of the Ionian Islands contains the counties of Zakynthos, Corfu, Kefallinia and Lefkada. The region consists of 32 islands, but only 13 of them are inhabited (the four aforementioned islands plus Antipaxoi, Ereikousa, Ithaka, Kalamos, Kastos, Mathraki, Meganisi, Othonoi and Paxoi). Between the census of 1991 and the census of 2001 the population rose by 9.9%, the third largest rise nationally after the Southern Aegean and Crete [source: ESYE (www.statistics.gr)]. With Corfu as its centre, the region is inhabited by 2% of the country's population and produces 1.7% of the GDP (2003). In 2001 the per capita GDP was equal to 61% of the EU-15 average (69% for Greece as a whole), whereas in 2003 the regional GDP per head was 87% of the country's mean (85% in 1995) and 71% of the EU-25 mean (80.9% for the country as a whole). The region produces 2% of the agricultural produce of the country, 0.15% of manufacturing production and 2% of services. Eighty percent of the region's produce comes from services, with an important contribution to tourism, considering that 20% of the regional GDP stems from hotels and restaurants, the second highest proportion in the country after that of Southern Aegean (data of 2003). In 2001 it accounted for 2% of cultivated land in the country. Unemployment in the region rose by 1.2 units in 2001 to 10.2% of the workforce, the largest rise in the country (11.3% in 2003, 10.5% for the whole of Greece) [source: ESYE (www.statistics.gr)].
ECONOMETRIC MODEL: LOGISTIC REGRESSION FOR UNEMPLOYMENT
THE LOGISTIC REGRESSION BASED ON THE MICRO-DATA OF THE GREEK LFS
European Community Household Panel Survey (ECHPS) and Survey on Income and Living Conditions (SILC) data have been designed, in the case of Greece, for the country as a whole so we cannot really work at a regional level. Also, individual census records do not exist in Greece, as they do in other countries, so the only way is to base the research on the LFS micro-data.
The originality of this research is that individual anonymised records are used (micro-data) of the LFS for both employed and unemployed (about 1.5% of the total population of each area). Since 1998 the LFS has been conducted four times a year--instead of once per year until 1997--with a sample of about 80,000 records for the whole country (0.7% of the total population) in each of the four quarters (ESYE).
Table 4 displays the frequency distribution of the binary variables (see separate file). Apart from the system missing records, following the limitation of age (15-64 years old) and removing the non-active population, the following numbers of records eligible for analysis in each area in 2006 were found as shown in Table 4.
Frequencies on Table 4 represent the tested sample occurring from the LFS, and meet Eurostat's reliability limits (see http://circa.europa.eu/irc/dsis/employment/info/data/eu_lfs/).
The basic aim of this study is to indicate the impact that various social and demographic characteristics had on people's job prospects in the regions of the Northern Aegean and Ionian islands in 2006. The effect of demographic variables such as age, gender, marital status, residence location, immigrant status, as well as educational level on employment status is investigated with a logistic regression model due to the categorical nature of the dependent variable. The dependent variable is employment (employed versus unemployed). The explanatory variables are gender, age group (four categories), marital status, (six) levels of education achieved, area of residence (urban areas, semi-urban areas and rural areas) and immigrant status.
Results for the Ionian islands and the Northern Aegean
Table 5 displays the odds of being unemployed. The Exp(bk) column displays the odds ratio. Odds ratios lower than 1.000 correspond to decreases and odds ratios greater than 1.000 correspond to increases in odds. Odds ratios close to 1.000 indicate that unit changes in that independent variable do not affect the dependent variable.
FOR THE IONIAN ISLANDS
The odds of being unemployed compared to being employed are increased considerably by being female rather than male. The odds of being unemployed compared to being employed are decreased by being not-married rather than married. The odds of being unemployed compared to being employed are decreased by being 25 years old or more. The odds of being unemployed compared to being employed are decreased considerably by not holding a first degree. In addition, the odds of being unemployed compared to being employed are decreased considerably by completing postgraduate education rather than holding a first degree. Location also plays an important role as the odds of being unemployed compared to being employed are decreased considerably by being a resident of urban areas rather than rural areas. In addition, the odds of being unemployed compared to being employed are increased by being a resident of semi-urban areas rather than rural areas. Finally, the odds of being unemployed compared to being employed are increased greatly by not being an immigrant rather than being one. However, as Table 6 indicates, the possibility of employment between immigrants and non-immigrants is independent of their relation.
The robustness checks provide evidence of structural validity. The odds ratios for the robustness check of rural areas remain similar, except for the case of immigrants (decrease at half). The odds of individuals aged under 30 increase in relation to the latter, while the odds of individuals aged 30 and more decrease.
FOR NORTHERN AEGEAN
The results for Northern Aegean are differentiated from the Ionian Islands only in the case of educational level completed or area of residence. The odds of being unemployed compared to being employed are increased by being female rather than male, and decrease by being not-married rather than married, or by being 25 years old or over. Moreover, the odds of being unemployed compared to being employed are decreased (with respect to university graduates) only by completing postgraduate education or the compulsory level rather than holding a first degree, and by being a resident of rural areas. Finally, the odds of being unemployed compared to being employed are increased greatly by not being an immigrant rather than being one. However, as Table 6 indicates, the possibility of employment between immigrants and non-immigrants is independent of their relation.
The robustness check based on the dichotomy of age (above or less than 30 years of age) indicate that the results discussed above represent the average of the sample population. The odds of individuals aged under 30 increase in relation to the latter except for the case of immigrants (decrease at one third), while the odds of individuals aged 30 and over decrease.
For both the Ionian Islands and Northern Aegean, the parameter estimates of gender and the contrasting variables of age are significant. (19) The rest of the parameter estimates on Table 5 are not significant. Nevertheless, the sample's compliance with Eurostat's reliability limits and the robustness of the results are signs of practical significance. Recent research (see, for example, Gelman and Stern, 2006) highlights that statistical significance is not the same as practical significance. It also considers that the dichotomisation into significant and non-significant results encourages the dismissal of observed differences in favour of the usually less interesting null hypothesis of no difference. Moreover, the lack of statistical insignificance of some estimates could also be due to the seasonal characteristic of the dependent variable, as both areas are heavily dependent on tourism. (For further information about the econometric analysis of the Greek labour market at NUTS-2 level, see also, Rodokanakis, 2009 and 2010; Rodokanakis & Vlachos, 2013a and 2013b.)
Binary logistic regression was employed in order to determine the effects of gender, marital status, age, education, area of residence and immigration on unemployment/employment. The results for Northern Aegean are differentiated from those of the Ionian Islands only in the case of education level completed or area of residence. For the Ionian Islands, the individuals most vulnerable to unemployment are those holding a first degree, while residents of urban areas are less vulnerable to unemployment. For Northern Aegean, the individuals most vulnerable to unemployment are TEI graduates, those who have completed secondary education, and primary school graduates
and below, while residents of rural areas are less vulnerable to unemployment. In both areas, the odds of being unemployed increase for non-married and females, and the most vulnerable age group for unemployment is between 15-24 years of age. Finally, immigrants are less vulnerable to unemployment than locals. The robustness checks based on age grouping provide evidence of structural validity and vary according to the distribution of the population.
The results of educational variables are compatible with human capital theory, i.e. the more educated a person is, his/her position in the labour market is improved. Even though in the Ionian Islands individuals holding a first degree are more vulnerable to unemployment than other educational categories, those with higher education (TEI or over) are generally better off in both areas.
Given the great dependence of both regions' economic growth on tourism and agriculture, training policies should be combined with economic policies that aim to foster high value-added services, which would increase the demand for qualified workers and as a result improve real income per capita. For example, in the tourism industry, performance is not only subject to industry and governance structures (for these issues see Buhalis, 2001), but depends also on the quality/ability of Greek entrepreneurship--in terms of education/training--to conform with a demanding and rapidly evolving business environment. The dependence on mainly foreign tourist agents and the monopsonistic power of multinational enterprises (MNEs) force Greek small-medium enterprises (SMEs)--which constitute the majority of the Greek hospitality sector--to operate on very thin profit margins (Papageorgiou, 2008). The research would merit attention of a wider international readership, since the paper offers evidence that could be useful for comparative research among European regions.
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University of Bath, England
Vasileios A Vlachos
University of Macedonia, Greece
(16) The percentage of unemployment is characterized by an augmentative tendency from 1988 to 1998 with the exception of the two year period 1989-1990, during which it shows a temporary decrease.
(17) On the basis of Eurostat figures, unemployment in 1998 was 10.8% (1997=9.6%). However, on the basis of the definitions used up until 1997, the unemployment rate in 1998 was 11.5%.
(18) In addition there are different definitions of educational attainment. Eurostat (in: Education across the EU, 1996) has defined a combined variable "education and training level achieved" based on two questions in the LFS (question for attained general level of education, and for attainment of vocational or university training), whereas OECD refers to the conventional ISCED nomenclature.
(19) The parameter estimate of gender is significant at 10% for the Ionian islands, and 5% for Northern Aegean. The contrasting variables of age are significant at 1% for the Ionian islands, and 1% to 5% for Northern Aegean.
Table 1: Unemployment rates by sex and age (annual average %) Gender/ 2002 2006 2009 Geographical Area Aged Total Aged Total Aged Total 15-24 15-24 15-24 MALES European Union 17.9 8.3 16.9 7.6 20.8 9 Euro area 15.3 7.5 15.7 7.5 20.4 9.3 Greece 19.9 6.8 17.7 5.6 19.4 6.9 Ionian islands 23 8.5 20 6.8 22.4 6.6 Northern Aegean 19.7 7.1 15.5 5.4 20 2.9 FEMALES European Union 18.1 9.7 17.5 8.9 18.3 8.8 Euro area 17 9.7 17.4 9.5 18.5 9.6 Greece 35.3 15.7 34.7 13.6 33.9 13.2 Ionian islands 20.4 12.6 45.4 17.9 32.1 14 Northern Aegean 49.3 15.2 59.5 16.2 29.9 12.1 TOTAL European Union 18 8.9 17.2 8.2 19.7 8.9 Euro area 16 8.4 16.5 8.4 19.5 9.5 Greece 26.8 10.3 25.2 8.9 25.8 9.5 Ionian islands 22 10.1 31.3 11.2 26.1 9.7 Northern Aegean 31.2 9.8 33.3 9.4 25 6 Source: Eurostat Table 2: Unemployment rates by level of educational attainment (1); EU 1994 Country ISCED 0-2 (c) ISCED 3(b) ISCED 5-7 (a) Belgium 12.5 7.5 3.7 Denmark 12.6 8.3 4.6 Germany 14.8 8.9 5.3 Greece 6.2 8.3 5.3 Spain 22.4 20 15.1 France 14.8 9.7 6.6 Ireland 21 9.1 5.3 Italy 9.3 7.4 8.1 Luxembourg 3.7 1.9 2.4 Netherlands 12.6 7.7 5.5 Portugal 6.1 6.4 2.4 UK 11.2 7.9 4.1 EU-12 13.2 8.8 6.1 (1) 25-59 years old Source: Eurostat: Education and Employment prospects, 1995. (a) All first and higher degrees. All teaching, nursing qualifications. HNC/HND. (b) 1 or more A-level passes, GNVQ 3 and equivalent, NVQ 3 and equivalent. Trade apprenticeship. GNVQ 2 or equivalent, NVQ2 or equivalent. (c) ISCED 2: 1 or more O-level/GCSE passes, 1 or more CSE passes. All other qualifications. Table 3--Greece: Gross Domestic Product at market prices Indicator/year Millions of purchasing Annual change(%) power standards 2000 174,747.70 9.00% 2001 187,343.10 7.20% 2002 202,738.20 8.20% 2003 211,503.20 4.30% 2004 224,360.40 6.10% 2005 226,322.20 0.90% 2006 243,265.80 7.50% 2007 251,706.00 3.50% 2008 259,770.50 3.20% 2009 249,870.00 -3.80% 2010 247,634.80 -0.90% Table 4--Descriptive statistics Variables Ionian islands (2006) 695 cases Frequencies Share Employed 615 88.5% Unemployed 80 11.5% Males 417 60.0% Females 278 40.0% Married or divorced or widows 493 70.9% Aged 15-24 61 8.8% Aged 25-34 136 19.6% Aged 35-44 184 26.5% Aged 45-64 314 45.2% MSc or PhD holders 5 0.7% University graduates 70 10.1% TEI graduates 75 10.8% 12 years of schooling 201 28.9% 9 years compulsory education 81 11.7% Primary school graduates and below 263 37.8% Urban areas 173 24.9% Semi-urban areas 86 12.4% Rural areas 436 62.7% Non-immigrants 653 94.0% Immigrants 42 6.0% Variables Northern Aegean (2006) 706 cases Frequencies Share Employed 642 90.9% Unemployed 64 8.1% Males 435 61.6% Females 271 38.4% Married or divorced or widows 505 71.5% Aged 15-24 67 9.5% Aged 25-34 178 25.2% Aged 35-44 188 26.6% Aged 45-64 273 38.7% MSc or PhD holders 2 0.3% University graduates 105 14.9% TEI graduates 98 13.9% 12 years of schooling 233 33.0% 9 years compulsory education 83 11.8% Primary school graduates and below 185 26.2% Urban areas 240 34.0% Semi-urban areas 134 19.0% Rural areas 332 47.0% Non-immigrants 684 96.9% Immigrants 22 3.1% Table 5--Results for the Ionian Islands and Northern Aegean (2006) Ionian islands Variables bk s.e. Sig. Exp(bk) Gender 1,057 0,579 0,068 2,879 Marital status -1,126 0,865 0,193 0,324 Aged 15-24 -- -- -- -- Aged 25-34 -1,903 0,743 0,010 0,149 Aged 35-44 -2,544 0,957 0,008 0,079 Aged 45-64 -4,124 1,472 0,005 0,016 MSc or PhD holders -2,558 6,450 0,692 0,077 University graduates -- -- -- -- TEI graduates -0,170 1,013 0,867 0,844 12 years of schooling -1,188 1,011 0,240 0,305 9 years compulsory education -1,079 1,197 0,367 0,340 Primary school graduates and below -0,891 1,134 0,432 0,410 Urban areas -0,755 0,890 0,396 0,470 Semi-urban areas 0,282 0,787 0,720 1,326 Rural areas -- -- -- -- Non-immigrants 2,650 2,217 0,232 14,155 Constant -3,660 2,594 0,158 0,026 Gender 1,607 0,664 0,016 4,988 Marital status -0,185 0,828 0,824 0,832 Aged 15-24 -- -- -- -- Aged 25-34 -1,582 0,782 0,043 0,206 Aged 35-44 -2,392 1,035 0,021 0,091 Aged 45-64 -4,092 1,380 0,003 0,017 MSc or PhD holders -3,607 52,065 0,945 0,027 University graduates -- -- -- -- TEI graduates 2,219 1,457 0,128 9,194 12 years of schooling 1,165 1,463 0,426 3,204 9 years compulsory education -0,453 1,757 0,797 0,636 Primary school graduates and below 1,877 1,612 0,244 6,536 Urban areas 0,139 0,718 0,847 1,149 Semi-urban areas 0,180 0,828 0,827 1,198 Rural areas -- -- -- -- Non-immigrants 3,777 3,206 0,239 43,680 Constant -8,120 3,679 0,027 0,000 Robustness check Aged less than 30 Aged 30 and more Variables Sig. Exp(bk) Sig. Exp(bk) Gender 0,055 3,868 0,452 2,269 Marital status 0,942 0,914 0,135 0,194 Aged 15-24 n.a. n.a. n.a. n.a. Aged 25-34 n.a. n.a. n.a. n.a. Aged 35-44 n.a. n.a. n.a. n.a. Aged 45-64 n.a. n.a. n.a. n.a. MSc or PhD holders 0,933 0,724 0,842 0,087 University graduates -- -- -- -- TEI graduates 0,662 1,815 0,631 1,988 12 years of schooling 0,722 1,610 0,111 0,043 9 years compulsory education 0,929 1,159 0,660 0,447 Primary school graduates and below 0,532 2,577 0,157 0,082 Urban areas 0,402 0,391 0,444 0,334 Semi-urban areas 0,509 1,884 0,701 0,441 Rural areas -- -- -- -- Non-immigrants 0,673 20,069 0,528 6,284 Constant 0,407 0,002 0,144 0,007 Gender 0,086 3,922 0,149 5,612 Marital status 0,765 1,320 0,555 0,435 Aged 15-24 n.a. n.a. n.a. n.a. Aged 25-34 n.a. n.a. n.a. n.a. Aged 35-44 n.a. n.a. n.a. n.a. Aged 45-64 n.a. n.a. n.a. n.a. MSc or PhD holders 0,907 0,181 0,926 0,126 University graduates -- -- -- -- TEI graduates 0,314 32,383 0,593 2,682 12 years of schooling 0,470 12,147 0,875 1,305 9 years compulsory education 0,651 5,142 0,436 0,106 Primary school graduates and below 0,763 4,022 0,533 2,973 Urban areas 0,693 0,710 0,743 1,477 Semi-urban areas 0,704 0,663 0,703 1,682 Rural areas -- -- -- -- Non-immigrants 0,814 8,616 0,390 22,926 Constant 0,407 0,000 0,033 0,000 Table 6--Cross-tabulations Immigrants Nonimmigrants Ionian islands Employed 36 579 Unemployed 6 74 Pearson Chi-Square 0.338 Assympt. Sig. (2-sided) 0.561 Northern Aegean Employed 20 622 Unemployed 2 62 Pearson Chi-Square 0 Assympt. Sig. (2-sided) 0.997
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|Author:||Rodokanakis, Stavros; Vlachos, Vasileios A.|
|Publication:||International Journal of Employment Studies|
|Date:||Oct 1, 2012|
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