Description of the original sample and the estimation sample

3.6 Conclusions

3.7.3 Description of the original sample and the estimation sample

Comparing the original 2009 ECS with the selected sample, we drop a major share of observations. Therefore, we provide a full description of data availability for the 30 initial countries in the 2009 ECS. Establishment data are presented in for the original sample in Table 3.6 and for the estimation sample in Table 3.7. We explain in detail why we lost observations. The initial number of establishments is 27,160 out of 30 countries which are: Austria, Belgium, Bulgaria, Croatia, Cyprus, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Italy, Ireland, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Turkey, the United King- dom and the Former Yugoslav Republic of Macedonia.

Out of 27,160 establishments, there are 73 and 64 establishments with missing values in FTC and TAW and 142 (210, 201) establishments with missing values in yearly fluctuation (weekly, daily). This leaves us with 26,649 establishments. EPL TAW and EPL FTC is not available in 2008 for Bulgaria, Cyprus, Lithuania, Malta, Romania, Croatia and the Former Yugoslav Republic of Macedonia. This leaves us with 22,802 observations. Furthermore, the variables on temporary employment are not comparable for Spain and Italy (Eurofound, 2010b), which are 1,509 and 1,502 establishments, respectively. Hence, 20 countries and 19,791 establishments are left. Excluding the public owned establishments makes 19,711 observations. Excluding missing values in the other micro-variables further reduces the sample to 18,407 observations. The majority of missing values (approximately 1,000) relate to the variables on the gender and high-skilled share.

This leaves us with only one-third of the original ECS countries. One might be worried that the resulting variation of EPL remains sufficient to identify the coefficients. Although, we are left with only 20 countries, we fortunately do not suffer in terms of variation in EPL. The maximum and the minimum of the EPL indicators do not change when the ten countries are dropped (see Tables 3.8 and 3.9). Standard deviation even increases for EPL for permanent workers, EPL for temporary workers and EPL for fixed-term workers.

Table 3.6: Summary statistics for establishment-level variables (2009): original sample

Variable Mean SD Min Max N

If any temp 0.65 0.48 0 1 27160 If any TAW 0.27 0.44 0 1 27096 If any FTC 0.58 0.49 0 1 27087 Share of FTC 8.39 18.81 0 100 26169 If any WF daily 0.31 0.46 0 1 26950 If any WF weekly 0.42 0.49 0 1 26959 If any WF annual 0.63 0.48 0 1 27018 If any freelancer 0.23 0.42 0 1 27031 If any works council 0.50 0.50 0 1 27160 No. of workers increased between 2006 and 2009 0.34 0.47 0 1 27160 No. of workers decreased between 2006 and 2009 0.27 0.44 0 1 27160 If high absenteeism and/or sickness rates (absent) 0.16 0.37 0 1 27035 Gender share (centered) 41.41 30.09 0 100 26347 High-skilled share (centered) 24.67 28.81 0 100 26126 If flexible working time schemes 0.55 0.50 0 1 26986 Establishment size (1-10) 3.43 2.78 1 10 27160 NACE C-E 0.31 0.46 0 1 27160 NACE F 0.10 0.30 0 1 27160 NACE G 0.15 0.35 0 1 27160 NACE H 0.04 0.18 0 1 27160 NACE I 0.05 0.21 0 1 27160 NACE J 0.02 0.14 0 1 27160 NACE K 0.09 0.29 0 1 27160 NACE L 0.06 0.24 0 1 27160 NACE N 0.07 0.25 0 1 27160 NACE O 0.04 0.20 0 1 27160

Note: Source is ECS 2009 (Eurofound, 2010b). Descriptive statistics with employer weights. Temporary workers (temp), temporary agency worker (TAW), fixed-term contract worker (FTC), workload fluctuation (WF), number (no.). High absenteeism means that an establishment encounters a human resource problem due to absenteeism and/or sickness. The share of high-skilled means the proportion of employees working in high-skilled jobs which usually require an academic degree. NACE Rev. 1.1: C-E Manufacturing and energy; F Construction; G Wholesale and retail trade, repair of goods; H Hotels and restaurants; I Transport and communication; J Financial intermedi- ation; K Real estate and business activities; L Public administration; M Education; N Health and social work; O Other community, social and personal services.

Table 3.7: Summary statistics for establishment-level variables (2009): estimation sample

Variable Mean SD Min Max N

If any temp 0.67 0.47 0 1 18407 If any TAW 0.30 0.46 0 1 18407 If any FTC 0.60 0.49 0 1 18407 Share of FTC 8.77 18.97 0 100 17995 If any WF daily 0.30 0.46 0 1 18407 If any WF weekly 0.42 0.49 0 1 18407 If any WF annual 0.63 0.48 0 1 18407 If any freelancer 0.22 0.42 0 1 18407 If any works council 0.49 0.50 0 1 18407 No. of workers increased between 2006 and 2009 0.36 0.48 0 1 18407 No. of workers decreased between 2006 and 2009 0.27 0.44 0 1 18407 If high absenteeism and/or sickness rates (absent) 0.17 0.38 0 1 18407 Gender share (centered) 40.53 30.12 0 100 18407 High-skilled share (centered) 23.73 28.30 0 100 18407 If flexible working time schemes 0.57 0.49 0 1 18407 Establishment size (1-10) 3.42 2.81 1 10 18407 NACE C-E 0.32 0.47 0 1 18407 NACE F 0.10 0.30 0 1 18407 NACE G 0.14 0.35 0 1 18407 NACE H 0.03 0.18 0 1 18407 NACE I 0.05 0.21 0 1 18407 NACE J 0.02 0.14 0 1 18407 NACE K 0.10 0.30 0 1 18407 NACE L 0.06 0.23 0 1 18407 NACE N 0.08 0.26 0 1 18407 NACE O 0.04 0.20 0 1 18407

Table 3.8: Summary statistics for country-level variables (2009): original sample

Variable Mean SD Min Max N EPLP 2.309 0.545 1.170 3.510 24786 EPL temp 2.374 1.153 0.290 4.880 24786 EPL FTC 2.018 1.312 0.250 4.250 23215 EPL TAW 2.805 1.359 0.333 5.500 23215 Bargaining coverage rate 63.831 28.646 10.000 100.000 26291 Unemployment rate 6.837 2.747 2.500 14.600 26640

Note: Temporary workers (temp), temporary agency worker (TAW), fixed-term contract worker (FTC), EPL for permanent workers (EPLP), EPL for temporary workers (EPL temp), EPL for temporary agency workers (EPL TAW), EPL for fixed-term workers (EPL FTC). Data sources: EPL 2008 from OECD (2012), national unemployment rate in the first quarter of 2009 Eurostat (2012), bargaining coverage rate Hayter and Stoevska (2011) and Eurofound (2007a).

Table 3.9: Summary statistics for country-level variables (2009): estimation sample

Variable Mean SD Min Max N EPLP 2.344 0.565 1.170 3.510 18407 EPL temp 2.290 1.165 0.290 4.880 18407 EPL FTC 1.925 1.367 0.250 4.250 18407 EPL TAW 2.653 1.336 0.333 5.500 18407 Bargaining coverage rate 63.754 28.780 11.300 100.000 18407 Unemployment rate 6.299 1.948 2.500 10.100 18407

3.7.4 Description of the governance indicators

Three governance indicators of the World Bank (Kaufmann et al., 2004) were chosen to capture the degree of enforcement of EPL for permanent workers due to differences in governance: government effectiveness, rule of law and control of corruption. They are aggregated indicators on perceptions of governance. Govern- ment effectiveness as an aggregated indicator includes the quality of public service provision, as well as the independence of civil services from political pressure and the trustworthiness of the government’s commitment to rules. Rule of law is an aggregated measure of the confidence in rules; for instance, the enforceability of contracts is included. All three indicators are normally distributed and range from around -2.5 to 2.5.

In document Employment Protection Legislation in Europe: Employment and Well-Being (Page 120-125)