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The main purpose of this article is studying the factors influencing eco‐innovative intensity in the European SMEs. Building upon the 'innovation triangle model', business competences, environmental orientation and network involvement are considered as the main determinants of 'greenness' of innovation in a sample of 3852 SMEs. Four categories of eco‐innovators (leaders, followers, loungers and laggards) are identified, and their profiles/driving factors are described using a generalized ordinal logistic model. Our results confirm that the increasing demand for green products and the adoption of eco‐organizational innovation affect positively the level of environmental innovation, while technological lock‐ins have the opposite effect across all categories. Neither leaders nor laggards are influenced by environmental policies. Small firms and those who give importance to financial constraints tend not to achieve upper categories, while valuing technological capabilities, market power and networks are crucial determinants of being in upper categories of eco‐innovation intensity. Copyright © 2014 John Wiley & Sons, Ltd and ERP Environment

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may ABSTRACTThis paper examines the extent to which the relationship between leaving home and entry into poverty among young people is causal: that is, how far poverty entry is the result of leaving home, rather than arising from heterogeneity or selection. Using propensity score matching, we estimate the effect of home-leaving on entry into poverty and deprivation, with data from the European Community Household Panel. We find that leaving home does have a causal effect on poverty entry, particularly in Scandinavian countries; cross-national differences are partly, but not fully, explained by differences in destinations on leaving home. NON-TECHNICAL SUMMARYPrevious work in the area of youth poverty has found a strong link between young people's living arrangements and the incidence of poverty. Young people who have left home are more likely to be poor than those who remain living with their parents, and of all the events likely to trigger entry into poverty, the home-leaving event is the most important. Research across the 15 countries of the preenlargement European Union has found that the relationship between home-leaving and poverty is rather modest in Southern European countries (where home-leaving occurs relatively late) but very strong in the Scandinavian countries (where it occurs much earlier).However, it is not clear from previous research whether the observed relationship between homeleaving and poverty is causal (that is, that leaving home causes poverty) or whether it arises as the result of selection. For example, if young people with a certain set of characteristics (such as low educational levels), which pre-disposed them to becoming poor, were more likely to leave home early than other youngsters, we would observe a relationship between home-leaving and poverty, without the home-leaving event actually causing the poverty.This paper uses a statistical estimation technique known as propensity score matching to examine these issues of causality. Under this technique, individuals who are observed to leave home in a particular year are matched with individuals who are identical, or almost identical, on a wide range of characteristics (sex, age, employment status, educational levels, income, family structure, and so on) except that these matched individuals did not leave home in that year. The difference between the two matched samples gives an estimate of the degree to which home-leaving "causes" poverty.O...

This paper has contributed to confirming the link between education and health in developed countries. The analysis is based on 11 European Union countries. We estimate country-specific health functions, where the dependent variable is self-reported health status and the education attainment is one of the main inputs. All eight waves (1994-2001) of the European Community Household Panel are deployed. A random effects ordered probit is estimated in order to control, to a given extent, for unobserved heterogeneity. Explanatory variables are both time invariant (education attainment and gender) and time varying (gross wages, hours of work, age and living alone). Results confirm the positive impact of secondary education on health in most cases and tertiary education in all cases, even after controlling for other inputs in the health function and taking unobserved heterogeneity into account. Secondary education has an impact on health in all countries in the sample except for The Netherlands and UK. The effect does not differ between secondary and tertiary education in France, Ireland and Greece. The correlation between education and health is interpreted in different but complementary ways by diverse approaches and we may not disentangle the precise mechanism that connects health with education from our results. Anyway, it seems clear that better coordination is needed between education and health policies to effectively improve health literacy. Other relevant results from our study are that women register poorer health than men, age contributes to worsening health status and wages contribute positively to health.

This paper examines the factors determining variations in spatial rates of overeducation.A quantile regression model has been implemented on a sample of region-yearly data drawn from the EU Survey on Income and Living Conditions (EU-SILC) and several institutional and macroeconomic features captured from other data-sets. Potential determinants of overeducation rates include factors such as labour market risk, financial aid to university students, excess labour demand and institutional factors. We find significant effects both for labour market structural imbalances and institutional factors.The research supports the findings of micro based studies which have found that overeducation is consistent with an assignment interpretation of the labour market.Keywords: Overeducation, regional variation, mismatch JEL classification: C29; I21; J24 Highlights: Overeducation reacts to educated labour supply excess and university enrolment levels  There is limited evidence that overeducation is driven by high returns to education  Overeducation is explained by migration-and sectoral composition of employment  Structural factors play a significant role in determining overeducation rates