DOCUMENT DE TREBALL XREAP2015-02 AN ANALYSIS OF WAGE DIFFERENTIALS BETWEEN FULL- AND PART-TIME WORKERS IN SPAIN Raúl Ramos (AQR-IREA, XREAP) Esteban Sanromá (IEB, XREAP) Hipólito Simón An analysis of wage differentials between full- and part-time workers in Spain* Raul Ramos** AQR-IREA, Universitat de Barcelona & IZA rramos@ub.edu Esteban Sanromá IEB, Universitat de Barcelona esanroma@ub.edu Hipólito Simón Universidad de Alicante-IEI-IEB hsimon@ua.es Abstract This research examines wage differences between part-time and full-time workers using microdata from the Spanish Structure of Earnings Survey. The main contribution of the paper is related to the analysis of differences along the wage distribution using econometric decomposition methods and introducing a regional perspective. The evidence shows that part-time workers in Spain experience a significant wage disadvantage. This disadvantage is worse in the case of female workers and it is not homogenous along the wage distribution, being comparatively more relevant for the most qualified women and becoming positive for the most qualified men. However, the disadvantage is practically explained by the endowments of characteristics, with a leading role of segregation of part-time workers in low-wage firms. From a regional perspective, although in the majority of the regions wage differences tend to be explained by endowments of characteristics, there are several regions where the unexplained part of the differential is significant, particularly in the case of male workers. These regional differences seem to be related to differences in the market power of firms at the regional level. Keywords: Part-time work, wage gap, regional differences. JEL Codes: J31, J22, J41, R23 * This work was supported by the Spanish Ministry of Economy and Competitiveness under grants ECO2013-41022-R, ECO2013-41310-R, 2014 SGR-420 and CSO2014-55780-C3-2-P(National R&D&I Plan). ** Corresponding author: Department of Econometrics, Statistics and Spanish Economy. Av. Diagonal 690, 08034 Barcelona (Spain). 1 1. Introduction The number of part-time jobs has increased slightly but continuously in most advanced economies over the last decades. The ratio of total employment of part-time workers in the OECD increased from 13.9% in 1988 to 15.7% in 2012. Driving this type of employment are demand factors, such as the increasing importance of the service sector and fixed costs per employee, and supply factors, like increasing women’s labour participation (Euwals and Hogerbrugge, 2006 and Montgomery, 1988). Furthermore, many governments have considered part-time jobs as a response to economic crisis, given that they offer greater flexibility to firms regarding the use of their labour force while at the same time facilitating job creation. In particular, after the crisis in the 1970s, some countries had a high share of part-time jobs, like Denmark (21.1%), Canada (16.4%), the Netherlands (19.7%), United Kingdom (20.1%), and United States (14.7%)—a trend that continued in the subsequent years. More currently (2013 data), part-time employment is prevalent in the Netherlands (39.9%), followed by Switzerland (25.4%), Ireland (25.1%), Australia (24.2%), United Kingdom (24.0%), Germany (22.8%), New Zealand (20.6%), Denmark (19.9%), Belgium (19.4%), and Austria (19.3%). Anglo-Saxon countries and some Scandinavian and Central European countries, with different social and labour models, rank highest in part-time employment, and all of them have high rates of female labour force participation. On the contrary, part-time employment has advanced less in Mediterranean countries, particularly in Portugal (7.2%) and Greece (12.1%), with lower rates of female activity. In Central and Eastern European countries, part-time jobs are only starting to develop after the transition to a market economy, with values between 4.4% (Hungary and Czech Republic) and 7.6% (Estonia). In the Spanish case, part-time employment increased slightly, from 4.2% in 1987 to 6.8% in 1995, but it clearly accelerated over the next two decades, up to 10.8%, and even more during the last crisis, up to 15.7% in 2013. This is a moderate figure, similar to the OECD average (15.4%) but lower than the EU (19.6%) and the Eurozone (21.5%) averages. On one hand, the lower presence of part-time jobs is likely because its regulation has not been clear until very recently (Muñoz de Bustillo et al, 2008 and Fernández-Kranz and Rrodríguez-Planas, 2010). On the other hand, firms achieve flexibility mainly through fixed-term contracts (24.0% in 2014, but 31.6% in 2007). A prominent characteristic of part-time employment in Spain, different from the vast majority of countries, is that it is mainly involuntary (62%). Spain’s increasing trend of part-time employment, which will probably continue in the future, is propelled by the legal change approved in late 2013 allowing more flexibility in the number of working hours. The involuntary nature of part-time employment, together with the fact that it represents 26% of total female employment, clearly justifies interest in analysing part-time jobs in 2 Spain. Additionally, although economic theory gives reasons to expect a wage penalisation for parttime employees, research on the Spanish case is scarce and inconclusive—two features that justify interest in advancing in knowledge of the consequences of part-time employment for wages. Finally, international literature on part-time employment does not include studies from the regional perspective on the phenomenon (as far as we know). The Spanish case is especially attractive from this perspective. Regional differences in part-time employment are important (Table 3). For instance, among men, the percentage of part-time workers in the Canary Islands doubles that of Asturias, while among women, Navarra is ahead of Madrid by eleven points. In general, we observe more part-time workers in tourist areas in the South and along the Mediterranean coast, and in the islands in the case of men. On the other hand, observed wage differences between part-time and full-time workers are widely dissimilar at the regional level; aggregated values are negative for both genders, and bigger among women, but regional differences are very important, especially for men. Additionally, Spain is a highly decentralised state (second in the EU after Denmark), and it is the country where regional governments manage a greater percentage of public expenditures, having wide competencies in active labour market policies. Hence, a regional analysis is particularly interesting in the Spanish case. In this paper, we analyse wage penalisation for part-time workers in the Spanish labour market, distinguishing among men and women. One of the novelties of our analysis is that it does not focus solely on differences in terms of average wages, but it is extended to wage differences observed along the wage distribution, an aspect that, to our knowledge, has not been examined in previous studies. Second, wage differences by working time duration and for all regions (and both genders) are quantified, and we approximate the regional factors that explain the regional differences observed. Again, note that it is an approximation for which there are few precedents in the literature. The database used in the empirical analysis is the Encuesta de Estructura Salarial (Survey of Earnings Structure; hereafter, SES). It is a survey designed specifically to determine the characteristics of the wage distribution in the Spanish labour market, making it particularly appropriate for the analysis of wage differentials, e.g., differentials associated with the type of working time. One of the most remarkable features of SES is that it offers abundant information about the characteristics of employees and their jobs, allowing for the introduction of firm fixed effects in the econometric estimations when including observations for various employees in every firm (that is, matched employer-employee microdata). Moreover, data provided by firms does not suffer from the limitations, widely outlined in literature, of surveys addressed to households, where the information about wages and working time suffers from measurement error. Although the SES does not give information about the family setting of the worker, and it is impossible to make any 3 corrections of the potential sample self-selection with the usual techniques, this limitation is minor when we consider that in Spain a minority of part-time workers are voluntary. The structure of the article is as follows. In the second section we review the literature about wage differences between part-time and full-time employees. In the third section, we present the database used in the study. In the fourth section we describe the econometric methodologies used in the empirical analysis. In the fifth section, we present the empirical evidence obtained, and, finally, the paper ends with the main conclusions. 2. Literature review Part-time employment is seen in the majority of the literature as another component of the secondary labour market that grants flexibility while providing employment for less qualified persons. It is closely linked to the labour market segment (students or young adults, women limited by familial responsibilities, advanced age adults, those approaching retirement or lacking in productive capacity, among others). This conception is facilitated by the characteristics usually associated to part-time jobs, like lower wages, high temporality (and absence of firing costs), and— in the case of many countries or the modality of partial employment—fewer worker rights, like paid holidays or remuneration for medical leave. Coherent with this view, the literature has analysed the differences between part-time and full-time employment, confirming that part-time employees have reduced access to social security benefits (Houseman and Matchiko, 1998), fewer labour progress opportunities (Tilly, 1990; Russo and Hassik, 2008), smaller pensions (Gimm and Arber, 1998; O'Connell and Gash, 2003), less labour stability (Muñoz Bustillo et. al., 2008; Fernández-Franz et. al., 2014), and a lower unionisation rate (Belous, 1988). Despite all these differences between part-time and full-time employment, the issue that has garnered the attention of researchers is the study of wage differences. Table 1 presents a list of studies on the topic carried out in the last decade. The main conclusions that can be obtained from these studies are the following: a) There is a negative wage difference (in terms of hourly wage) for part-time employees when compared to full-time employees. b) Part of this raw wage gap is explained by differences in the characteristics between both groups of workers, whether observable or unobservable. c) The characteristics of the job and the firm contribute to explaining the wage differential, given that many part-time workers are engaged in sectors, firms, and low-salary occupations (Hirsch, 2005, among others). 4 d) Despite adding multiple controls, an unexplained part of the wage differential persists, which is the wage penalty associated with working part-time. The wage penalty can be null for young people who accede to their first job (Russo and Hassik, 2008), while there is abundant evidence that the differential increases with age and especially with years worked in part-time positions (Wolf, 2014). Wage increase over time is inferior for part-time workers (Fernández-Kranz and Rodríguez-Planas, 2011). e) While analyses for both genders are abundant and agree that wage penalty is usually greater for women than for men, studies that make disaggregated estimations are scarce. Literature has advanced in disaggregating by levels of qualification, concluding that the wage penalty is greater for the most qualified workers, given that they suffer more from the effects of occupational degradation (Connolly and Gregory, 2009), although quantile regressions have not been carried out to estimate the penalty along the wage distribution. There is also empirical evidence by labour market segment finding that the wage penalty happens entirely due to the concentration of part-time employments in the secondary segment (O'Connell and Gash, 2003). f) Studies with regional disaggregation have not been conducted. Only two studies analyse a territorial level lower than the national level. Harris (1993) studies the issue for women in Northern Ireland, finding a penalty of 19%—a number higher than that which other authors have found for the whole of the United Kingdom. Wolf (2014) distinguishes between West and East Germany, obtaining practically identical results, which is not a surprise given the little regional detail considered. Analysing the estimated wage penalty, we observe that the magnitude is unequal among countries and also among studies in the same country. However, in countries like Australia and South Africa, a positive wage bonus for part-time workers is observed1. The variety of results among countries seems to respond to institutional differences: the wage penalty is systematically higher in Anglo-Saxon countries, representatives of a liberal model, than in Central European countries and, especially, in Nordic countries, characterised by labour markets that are more regulated and that show higher union and social agreement practices. This result is explained by the theory of compensatory wages. Posel and Muller (2007) attributes the existence of such wage premiums in South Africa to the fact that part-time workers do not usually have labour stability or access to unemployment benefits or a retirement pension; furthermore, they benefit from minimal sectorial wages that are high for part-time work. Boot and Wood (2008) argue that high marginal tax rates that plague secondary familial incomes in Australia have to be compensated by the companies to be able to have enough part-time job supply. Additionally, contract part-time workers are compensated with wage premiums, although the latter has only been empirically demonstrated for women. Rodgers (2004) does not observe a premium or a penalty in Australia. 1 5 For instance, Bardasi and Gornick (2008) observe higher wage penalties for part-time women in the US, UK, Canada, and Italy than in Germany and, especially, Sweden, where they did not find any penalty. Pissarides et al. (2005) obtain similar results using data from the European Community Household Panel Survey (ECHP), as in the 15 countries they analyse, the highest wage penalty is for women is in the UK and Ireland. In the case of men, it is UK and Ireland plus Denmark2. Although the numbers of Pissarides et al. refer to the 1980s, Hu and Tijdens (2003) also find a much greater wage penalty in the UK than in the Netherlands. O'Dorchai et al. (2007), working just with men, also estimate a high penalty in Ireland and the UK, plus Italy, while it is nonexistent in Denmark. It seems, then, that the same forces that limit global wage inequality—whether it is government regulations or the presence of labour institutions—restrain the wage gap and the wage penalty for part-time workers. In addition to differences among countries, there are also different estimations for the same country, an aspect that can be appreciated in the case of the UK, because we have various studies separated by relatively few years3. Disparity of results seems to depend on the database used, the methodology of estimation, and on the controls included in the wage equations. Penalisation for British males varies between 15 points (O’Dorchai et al., 2007 with the WSS) and 20 points (Pissarides et. al., 2005). Hu and Tijdens (2003) estimate a penalty of 29 points combined for both genders with the same data as Pissarides et al. (2005). Additional studies for women have found wage penalties that vary between 32 and 0 points. Bardasi and Gornick (2008), with data from the Luxemburg Income Study (LIS) and controlling for self-selection with a multinomial logit, estimate a pay penalty of 15 points, which disappears when sector and occupation controls are introduced. Manning and Petrongolo (2008) obtain similar results with data from the Labour Force Survey (LFS). On the contrary, Connolly and Gregory (2009) with the New Earnings Survey (NES) panel find a wage decrease of 7% for women who reduce their working time without changing occupation, and of 32% if they suffer occupational degradation. The Spanish economy presents, likewise, very dissimilar results. In a first study about the issue, Cebrián et al. (2000) use the first wave of the ECHP carried out in 1994. The authors estimate a wage equation including individual controls and introducing dummy variables for part-time female workers, obtaining a positive wage premium of 12 logarithmic points for female part-time workers and of 30 points for those working less than 15 hours per week. In a later work (Cebrián et al., The most surprising result from Pissarides et al. (2005) is a noticeable positive wage premium for men and women in the Mediterranean countries and for French women, which they attribute to a larger measurement error in the working hours in these countries. 3 This aspect is important, because gross wage difference and wage penalty evolve over time. In particular, since part-time workers are segmented in jobs and low-wage sectors, their wage gap grows as a result of an increase of global wage inequality in the labour market (Manning and Petrongolo, 2008). 2 6 2001), using the ECHP for 1995, they estimate wage equations with individual and job controls (including sector and occupation), and they do not obtain any penalisation for part-time women workers. Pissarides et al. (2005), also working with the ECHP from 1994 to 1999, obtain again a positive premium for Spain (6 logarithmic points among men and 9 points among women) after controlling for occupation4. Pagán (2007), with the extended sample of the ECHP from 2000, also estimates positive wage premiums for both genders, superior for women (14.3 logarithmic points as opposed to 6.3 for men), after controlling for occupations and correcting the sample selection with an ordered probit with four states. O'Dorchai et al. (2007) use SES microdata from 1995 and, after controlling for occupation, estimate a male wage penalty of -6 logarithmic points. Using data from the Continuous Sample of Working Histories (MCVL in Spanish) from 1996 and 2006, Fernández-Kranz and Rodríguez-Planas (2011) obtain for 25 to 25 year-old women a wage penalty that varies between -18.7 logarithmic points, controlling for non-observable individual heterogeneity, and -11.4 points, controlling for occupations and adding firm fixed effects. The latter variable has a bigger effect on the magnitude of the penalty. In more recent work with the same database, Fernández-Kranz et al. (2014) estimate that part-time women from 23 to 45 years old on permanently contracts have wage penalties of -6.1 logarithmic points and -9.0 points if the contract is temporary. Accordingly, the magnitude of the wage penalty in Spain appears to oscillate, looking for the greatest compatibility possible, from -17.1 logarithmic points in the MCVL and +14.3 points in the ECHP, controlling for occupations and correcting the sample selection. In the case of men, the variation goes from +6.3 logarithmic points with the ECHPS to -6.0 points with the SES. It seems to be absolutely necessary, then, to provide additional evidence about this issue. 3. Data This research is based on the microdata of the most recent wave of the SES (2010). The SES is designed as independent cross-section databases updated every four years. Currently, there are four available waves: 1995, 2002, 2006, and 2010). The Spanish National Statistics Institute conducts this survey, and it is Spain’s sample for the European Structure of Earnings Survey carried out in EU member countries in accordance with a harmonised methodology. It is a nationally representative survey on firms that covers employees registered in the social security system throughout the month of October at establishments of any size belonging to the general scheme of the social security system and whose economic activity is framed in sections B to S of the sectoral The authors argue that taking advantage of the panel structure of their data, controlling for the non-observable factors with individual fixed effects does not alter the obtained results, although they do not include such results. 4 7 classification NACE 2009. Therefore, it encompasses the bulk of the private sector of the Spanish economy, excluding only specific sectors such as agriculture and domestic service. The survey design corresponds to a two-stage sampling of employees working in firms registered in the social security system, and one of its most important features is the inclusion of matched employeremployee microdata (i.e., observations for various employees in each establishment). Another feature is that it is a survey specifically designed to determine the characteristics of the wage distribution in the Spanish labour market. Finally, it provides information about the region in which the establishment is located and, by design, it is also representative at the regional level, allowing for regional analysis, which is part of the aim of our research. The survey provides detailed information on wages and worker characteristics (nationality, gender, age, and education); jobs (tenure, type of contract, and supervisory tasks)5; and firms (sector, size, type of collective agreement, and region). Wage information includes various components and covers different time references. The wage concept used in this research is the gross hourly wage, calculated from the wage corresponding to a representative month (October) divided by the number of hours worked in that month. In this calculation, any payment by companies, including commissions, bonuses for night work and weekends, as well as overtime work, has been incorporated. The firm indicates the employee’s status as full- or part-time is indicated in the SES,6 so that the dependent variable in the analysis is a dummy variable differentiating between part-time and full-time. The analysis is carried out separately for men and women. The explanatory variables considered include characteristics of individuals and characteristics of their jobs and firms. The former are controls relating to the nationality of the individual (differentiating between natives and immigrants), the level of general education (distinguishing three levels: primary, secondary, and tertiary education), and age. The characteristics of the job are years of seniority in the current job (including its quadratic form) and type of contract (indefinite or fixed-term), while attributes of the firms are sector (12 categories corresponding to sections NACE-93), size (four categories), type of collective agreement (distinguishing between firm, national sector, and subnational sector agreements), and region of location of the firm. Observations with missing values on key variables and those for individuals aged less than 16 years or over 65 years, or with hourly wages less than 2.5 Euros or greater than 200 Euros have 5 It also includes information on occupation. However, this variable has not been considered in the analysis, given that it potentially suffers from endogeneity in relation to the distribution of individuals between full- and part-time jobs, to the extent that part-time employment is often limited to low-wage occupations (for details, see Manning and Petrongolo, 2008). 6 Specifically, in the case of each worker, the firm has to choose between full-time or part-time in response to the following question: ‘4.1 Type of job.’ It is considered part-time if hours worked are less than the normal working day of the firm or, in the absence of a normal working day, if they are lower than the maximum legally set (it must be stipulated in the contract). 8 been filtered. Moreover, firms with fewer than two observations were excluded from the sample in order to allow the correct identification of firm fixed effects in the econometric estimates. Finally, following most previous studies on the relative wage treatment of part-time workers that limit the analysis to private sector employees, observations corresponding to the public sector (i.e., Section O, Public administration and defence, compulsory social security) have been removed. The final sample is formed by 152,099 observations, which correspond to 89,344 men and 62,755 women. 4. Methodology In the empirical analysis, two econometric methodologies have been used to decompose the wage differences of full- and part-time workers. This is an extension of the Juhn-Murphy-Pierce methodology (Juhn et al., 1991), adapted for use with matched employer-employee data, which permits a detailed decomposition of the average wage differential. The second is the methodology proposed by Fortin, Lemieux, and Firpo (2011), which provides a detailed decomposition of the wage differences throughout the wage distribution. Both techniques are described below. 4.1. Juhn-Murphy-Pierce decomposition First, we use an extension of the Juhn et al. (1991) decomposition, as suggested by Blau and Kahn (1992), specifically adapted for use with matched employer-employee data. This technique departs from the estimation of the following semi-logarithmic wage equation: wij  X i β  εij  a j (1) wherein wij is the natural log of hourly wage of individual i in workplace j; Xi is a vector of controls including individuals’ characteristics and those of their jobs and the companies employing them;  is a vector of parameters to be estimated (including an intercept); ij is a stochastic error term, and aj is an error component corresponding to workplace j and invariant for all individuals working in the same workplace. Following the recommendation of Oaxaca and Ransom (1994) and Neumark (1998), we use as reference wage structure in the decomposition that corresponding to both groups; equation (1) is estimated for the pool of workers (i.e., full- and part-time workers). Identification of workplace effects is guaranteed, given that there is more than one observation per workplace in the dataset. Since the result of Hausman’s contrast indicates that workplace-specific effects are correlated to the rest of the explanatory variables in equation (1), the model is estimated by fixed effects (which is equivalent to estimating by ordinary least squares with a set of workplace dummies). Relying on the properties of the ordinary least squares estimator, after the estimation of equation (1) with the ˆ pooled data and having obtained the values of β A , σA and ηA, the average wage of the subgroup of workers s (s=full- or part-time workers) can be expressed as: 9 ˆ w s  X s     s   s where  ~ ( 0,1) ,  ~ ( 0,1) (2) s s where w stands for the mean natural log of the hourly wage of a given group s; X is a vector of ˆ the average of the set of explanatory variables for group s;  is the vector of coefficients estimated with equation (1) and the pooled data; σ is the standard deviation of wage residuals of the pool of workers;  is the average standardised residual of group s; η is the standard deviation of workplace s s effects of the pool of full- and part-time workers and  is the average standardised workplace effect of group s. Using the pooled wage structure as the market price reference in the decomposition, the wage gap between part- and full-time workers can be written as follows: ˆ ˆ w p - w f  ( X p - X f )  ( p -  f )  (  p -  f )  X        (3) where the subscript p is for part-time workers and f is for full-time workers, and a  prefix denotes the average difference between both groups in the subsequent variable. In brief, equation (3) provides a decomposition of the part-time/full-time wage gap that quantifies the extent to which average wage differences between part-time and full-time workers are related to (a) differences in observed characteristics, (b) the influence of unobserved elements, and (c) the influence of workplace-related factors. More specifically, the first term on the right-hand side of the equation corresponds to the portion of the wage differential attributable to differences in the p f ˆ observed characteristics between the two groups (X - X ) , valued at market prices (  ), which coincides with the ‘explained’ component of the standard Oaxaca-Blinder decomposition. The second term measures the influence of unobserved factors in the model. This component comprises the effect of unobserved ability, motivation, and discrimination, among others, and corresponds to the impact of differences between part- and full-time workers on the average standardised residual ( p - f ) multiplied by the money value per unit difference in the standardised residual ( σ ), which determines the specific wage penalty suffered by the disadvantaged group. Finally, the third term estimates the influence of workplace-related factors. This term is taken as a product of the p f difference in the average standardised workplace effect of part-time and full-time workers (  -  ) , which measures the intensity of part-time workers’ segregation into comparatively low-wage workplaces, and the dispersion of wage differentials across workplaces (η), which determines the degree of the wage penalty for part-time workers resulting from this segregation. 4.2. Fortin-Lemieux-Firpo decomposition 10 Fortin, Lemieux, and Firpo (2011) have recently proposed a technique that enhances the development of the empirical decompositions of differences between two distributions of a variable. This technique provides a breakdown of the differences between distributions in the value of any distributional statistic (as the value of a quantile or an inequality index) based on differences in the endowments of characteristics and in their returns. This procedure has considerable advantages compared to techniques previously proposed in the literature, which also permit the decomposition of differences between distributions based on construction of counterfactual distributions (DiNardo, Fortin and Lemieux, 1996; Juhn, Murphy and Pierce, 1993; Machado and Mata, 2005 and Melly, 2005, 2006). Thus, whereas the latter techniques consist of aggregated decompositions, which, aside from partial exceptions, provide exclusively the separate effects of characteristics and returns, Fortin, Lemieux, and Firpo’s methodology provides a detailed decomposition that allows us to ascertain the individual contribution of each explanatory variable for both components. This methodology is based on the estimation of a regression in which the independent variable (the wage) is substituted by a transformation of the same, the recentred influence function (RIF). Subsequently, a standard Oaxaca-Blinder decomposition can be developed for any distributional statistic based on the regression results. The influence function measures the effect on distributional statistics of small changes in the underlying distribution. Thus, for a given distributional statistic of the distribution FW, v(F), this function measures the importance of each observation in shaping its value. Fortin, Lemieux, and Firpo (2011) suggest using a recentred version of the influence function having added the statistic of interest, RIF(W)=v(F)+IF(W), since it has as expected value the actual statistic v(F) (insofar as the expectation of the function of influence with respect to distribution of W is, by definition, zero). In the case of the quantiles Q of the unconditioned marginal distribution F , the function W of influence, IF (W , Q ) , is defined as IF(W / Q )    l {W  Q } fW ( Q ) (4) where l {} is an indicator function and fW is the function of density of the marginal distribution of W evaluated in Q . Given that the function of recentered influence, RIF(W , Q ) , is equal to Q  IF(W , Q ) , then the following is fulfilled: RIF(W / Q )  Q  11   l {W  Q } fW ( Q ) (5) The RIF function may be computed empirically in the case of the quantiles by means of a local inversion following calculation of the dummy variable l {W  Q } (which specifies whether the value W is higher or lower than Q ), the estimation of the quantile of the sample Q , and the estimation by means of kernel density functions of the corresponding density function fW evaluated in Q . Following calculation of the RIF function for the quantile, a value is provided for the transformed variable for each observation of the sample. Insofar as the effect of the change in distribution of an explanatory variable in the quantile may be expressed ceteris paribus, as the average partial effect of that variable in the conditioned expectation on its RIF function, and assuming that the conditioned expectation of the RIF function may be modelled as a linear function of the explanatory variables, these values may be used for estimation by means of ordinary least squares of a regression of the RIF variable in a vector of explanatory variables. The estimated coefficients may be interpreted as the effect of an increase in the average value of an explanatory variable in the distribution quantile. The estimated coefficients of that regression may be used for calculation of a standard Oaxaca-Blinder decomposition of different quantiles of the distribution. In the development of the decomposition, the wage structure of the two groups involved in the comparison has also been used as the reference wage structure. Consequently, the decomposition takes the following form:   ˆ* ˆ f ˆ* ˆ* ˆ p Q (X p - X f ) Q  X p( Q  Q )  X f ( Q  Q ) (6) Wherein Q is the difference in the quantile Q of the wage distributions of part- and full time workers, respectively; Xp and X f are the average observed characteristics for part- and fullˆ* ˆp ˆ f time workers, and  Q ,  Q and  Q are the estimated coefficients following regression of the RIF variable of the quantile Q on the group of explanatory variables for part-time workers, full-time  workers, and the pool of both groups respectively. The first component of the right-hand side of the equation represents the effect on the differential between distributions caused by differences in characteristics (or the ‘explained’ component), whereas the second corresponds to the effect of the coefficients (or ‘unexplained’ component). As previously referenced, the contribution of each explanatory factor can be observed in the decomposition results. 5. Results 5.1. Descriptive evidence 12 Table 2 and figure 1 provide information on the wage differential between part-time and full-time workers, measured as the logarithm of the wage per hour and distinguishing between male and female workers. A negative value (positive) of the differential corresponds to a wage disadvantage (advantage) of part-time workers. Looking at the data, we can conclude that in Spain there is a significant negative average wage differential for part-time workers, although the gap is substantially lower in the case of men (-0.104 logarithmic points) than of women (-0.254 logarithmic points). The wage differential, however, is not homogeneous along the wage distribution. For instance, in the case of male workers the differential decreases notably along the wage distribution (until the point to be favourable for part-time workers in the right tail of the distribution), whereas in the case of females, the differential increases along the distribution. The magnitude of the average wage differential associated with part-time work presents, at the same time, high regional heterogeneity (table 3). In the case of female workers, the magnitude of the differential exceeds the national average in regions like Andalusia, Extremadura, or Madrid, whereas in others like Navarra or The Rioja the differential is notably lower (the maximum and minimum values of the wage differential are -0.315 for Extremadura and -0.071 for The Rioja, with a standard deviation of 0.071). Regional differences are even higher in the case of male workers, where in some regions like Cantabria, Aragon, or Navarra the average wage of part-time workers is higher than that of full-time workers (the extreme values of the differential are -0.299 for Madrid and -0,050 for Cantabria, with a standard deviation of 0.100). Tables A.1 and A.2 of the appendix present descriptive statistics of the sample used in the empirical analysis, distinguishing between male and female workers and by the different quartiles of the wage distribution. According to these figures, there are significant differences in the characteristics of full-time and part-time workers, although these differences vary between male and female workers. In particular, in the case of female workers those working part-time present characteristics usually associated with lower wages: lower average educational levels and seniority; greater incidence of fixed-term contracts; less presence in high-wage sectors like manufacturing and construction; and higher presence in small firms without specific collective agreements. In the case of male part-time workers, although some characteristics are clearly unfavourable (e.g., lower seniority; a greater incidence of fixed-term contracts; work in the service sector and in firms without specific collective agreements), other characteristics have the opposite effect (e.g., older and seniority). This last circumstance is mainly explained by the characteristics of the individuals in the high part of the wage distribution, as better endowments of those variables are, in fact, only observed for part-time workers in the right tail of the distribution. This finding is consistent with the fact that most job contracts among part-time male workers are related to partial retirement (Muñoz de Bustillo et al., 2008). 13 5.2. National results The results of applying the extension of the Juhn, Murphy, and Pierce (1991, 1993) methodology for the decomposition of the differentials in average wages between part-time and full-time workers are shown in Table 4, distinguishing between males (left panel) and females (right panel). In particular, the first row of the table shows the value of the raw differential in log hourly wages between the two groups of workers, while the rest of the rows show the value of the different terms of the decomposition (where a negative value indicates that the factor has a negative effect on the wage of part-time workers when compared to full-time workers). For each case, we consider three specifications of the wage equation. The first specification (model 1) only includes explanatory variables related to the socio-demographic characteristics of the individuals (nationality, education, and age). The second specification (model 2) includes the same individual characteristics, but also characteristics related to job and firm (e.g., seniority, type of contract, region, sector, size, and type of collective agreement). The last specification (model 3) includes firm fixed effects instead of firm characteristics. It is important to highlight that the results of models 1 and 2 are equivalent to those that would be obtained from a standard decomposition in two components (characteristics and returns), such as the Oaxaca-Blinder decomposition, whereas results from model 3 also include the third component of the right side of the equation (3). The results of model 1 reveal that when only individual characteristics are considered, the lower wage levels of part-time workers are not fully explained by their relative endowments of characteristics, but by a different wage treatment. In the case of male workers, the component associated with characteristics takes a practically negligible value (a result explained by the fact that the lower endowments of education and the greater presence of immigrants between part-time workers compensate for the higher average age of this group compared to full-time workers), while most of the wage differential corresponds to the unexplained part. In the case of female workers, observable characteristics explain a relevant part of the differential (around 41%) (mostly related to the lower relative educational endowments of part-time female workers), but as before, most of the differential is related to the unexplained part. This implies that the part-time wage-penalty is -10.6 logarithmic points for male and -15.0 points for female workers. The inclusion of additional explanatory variables related to the characteristics of jobs and firms (model 2) substantially increases the contribution of the explained component for both genders. In the case of male workers, apart from individual characteristics related to lower educational levels and seniority, a higher incidence of fixed-term contracts and a higher presence in low-wage sectors are the main variables responsible of the wage disadvantage of part-time workers. The only factor with a positive effect for this group is their higher relative age. For female workers, 14 the factors explaining lower wages of part-time workers are also related to their lower endowments of education and seniority and their higher presence in low-wage sectors, although the contribution of these variables is relatively lower than in the case of male workers. Hence, the most remarkable result of the evidence obtained from model 2 is that, once not only socio-demographic but also job and workplace characteristics are controlled for, a very important part of the wage gap between part-time and full-time workers is explained—the contribution of the explained part of the differential increases to 66% for men and to 82% for women. The unexplained part (or wage penalty in strict sense) is only -3.6 logarithmic points for men and -4.6 logarithmic points for women, representing 34% and 18% of the total, respectively. These values are very different from the wage premium for part-time workers found by Cebrián et al. (2000), Pissarides et al. (2005), and Pagán (2007) using the PHOGUE, but are very close to the results obtained by O'Dorchai et al. (2007) with the Structure of Earnings Survey 1995 for men (-6 logarithmic points), and slightly lower than those obtained by Fernández-Kranz and Rodríguez-Planas (2011) and Fernández-Kranz et al. (2014) using the MCVL (between -6 and -13 logarithmic points). The relevance of characteristics as the main explanatory factor of the wage differential between part-time and full-time workers is also observed when firm level variables are replaced by firm fixed-effects (model 3). According to the obtained results, for both genders the wage disadvantage of part-time workers is almost fully explained by their characteristics. The first term of the decomposition associated with individual and job characteristics explains 29% of the gap for men and 41% for women, and the second term, associated with their relative segregation in lowwage firms, explains 69% of the gap for men and 56% for women. As a consequence, the third component associated with the wage residuals has a minimum influence to explain the gap: it nearly disappears for male workers and reduces to -0.7 logarithmic points for female workers. This last result shows that, in aggregate terms, part-time workers who possess the same productive characteristics as full-time workers and who work in the same firm receive similar wage treatment (i.e., there is no evidence of wage discrimination). Figures 2 and 3 show the results of the decomposition of wage differences between parttime and full-time workers by quantiles obtained after applying the methodology proposed by Fortin, Lemieux, and Firpo (2011). To facilitate the presentation, figure 2 distinguishes between the aggregated contribution of characteristics and returns (or wage penalty), whereas figure 3 shows the detailed results of the separate effect of each explanatory variable associated with the characteristics component7. This evidence has been obtained from a specification of the wage equation that 7 Additional information on the results of the decomposition can be found in tables A.3 and A.4 of the annex. Estimated coefficients by means of unconditional quantile regressions required for the decomposition are available from the authors on request. 15 includes socio-demographic individual characteristics and job and firm characteristics (model 2). In figure 3, variables have been grouped according to these three categories to facilitate interpretation. In the case of male workers, and similar to the results obtained for the decomposition of average wages, the wage penalty associated with part-time jobs observed in the left and in the medium part of the distribution is related to the worst endowment of characteristics of individuals with part-time contracts (characteristics), but also to a relative wage penalty (coefficients). However, the wage differential decreases along the wage distribution until the point that in the right tail it is favourable to part-time workers. The component associated with characteristics shows a slowly increasing profile along the distribution, so that for upper quantiles it contributes less negatively to the observed wage gap (figure 2)—a result that is explained by the higher incidence of permanent contracts in the right tail of the wage distribution (figure 3 and table A.4). In any case, the reduction of the wage gap along the wage distribution is mainly explained by the fact that the wage penalty reduces its intensity in the right tail of the distribution until a point where it becomes a wage premium for the last two deciles (figure 2). In the case of women, the results in figure 2 confirm that differences in endowments of characteristics are systematically the main driver of the lower wages of part-time female workers (figure 2). The influence of this factor is not, however, homogenous along the wage distribution, as it is comparatively more reduced in the left tail but growing (in absolute terms) along the distribution. The results of the detailed decomposition reveal that the increasing profile of this component is due to the increasing importance of the individual characteristics and, in particular, of the endowments of education, since the differences in this particular domain are very important and negative for workers in the right tail of the distribution (figure 3 and table A.4). The contribution of the component associated with coefficients also shows an increasing trend along the wage distribution, which means that women in the right part of the distribution, who have more experience, higher levels of education, and higher levels of seniority (table A.4 of the annex) suffer a greater wage penalty. In particular, this reflects lower returns to education and to experience (proxied by age)—a circumstance derived from the three phenomena highlighted in the literature. On one hand, part-time workers receive a lower reward to their experience due to the fact that in working fewer hours they accumulate less human capital. On the other hand, the returns to education are lower as the transition from full-time to part-time work usually involves occupational downgrading (Connolly and Gregory, 2009), particularly in those countries, like Spain, where the law does not contemplate the right of the employee to reduce working time and keep the same job8. 8 In countries like Norway, the Netherlands, Sweden, Germany, or France the worker can change to part-time status with neither loss of category nor responsibility, except in determinate circumstances, which minimises the risk of occupational downgrading. In the UK, where this possibility does not exist, Connolly and Gregory (2009) have found that 25.6% of the women that opt by part-time jobs suffer occupational downgrading, representing an hourly wage decrease of 32%. 16 Finally, those who work part-time have a lower probability of being promoted in the firm (Russo and Hassik, 2008) and show less wage progress when they achieve it (Wolf, 2014 and FernándezKranz and Rodríguez-Planas, 2011). Probably, these three factors affect, in greater measure, those who have more studies (more degradation) and more years of experience (more lost promotions)9. 5.3. Regional results Tables 5 and 6 show the results of the Juhn, Murphy, and Pierce (1991, 1993) decomposition of average wages between part-time and full-time workers for each of the 17 Spanish regions. The obtained results show marked differences between regions, particularly for male workers. In the case of female workers, differences in endowments of characteristics (component (1) of the decomposition) are systematically unfavourable for part-time workers in all considered regions. Differences in observable characteristics are the main reason for the negative wage gap for part-time female workers. A similar picture is obtained when looking at the effect of the relative distribution of workers among firms (component (2)). While it is true that in most regions the greater part of the observed wage gap is explained by different endowments of individual and firm characteristics, there are some regions where the unexplained part of the differential (component (3)) is particularly relevant. In fact, while in most regions this component is negative (wage penalty), there is a significant number of regions where this component is positive (wage premium). As previously explained, regional differences in the relative contribution of observed characteristics are more complicit in the case of the men, as for this group there are regions where the first component can exert a positive or negative effect—a result that reveals that in some regions part-time workers have better endowments of productive characteristics. Map 1 shows regional differences in the relative contribution of wage residuals (component (3)). In the case of men, the wage penalty for part-time workers is higher in the southern part of the country (plus The Rioja, Catalonia, and Balearic islands), whereas positive premiums are observed in the northern part of Spain, especially in the Basque Country and Navarra, which are regions of high per capita income, and Galicia, a region with a specific productive structure. In the case of women, higher penalties are observed in the richer regions (Madrid, Catalonia, Basque Country, and the Balearic islands) plus Cantabria, whereas small positive premiums are observed in medium-income regions (Navarra, Aragon, Canary Islands, and Galicia). These results show that apart from regional differences in the characteristics of individuals (like educational level or the age) or of firms (like size Unlike female workers, these phenomena are not observed for male workers. In fact, the returns to experience do not have the same negative impact for males. The most reasonable explanation is that in a lot of cases these workers change to part-time jobs after several years of full-time dedication, having accumulated specific human capital at the same level as other full-time workers. Men’s returns to education do not have the same penalty as for women, reflecting the absence of occupational downgrading although working part-time—a result that is in line with the fact that there is a significant proportion of advanced age men who access a partial retirement, probably without changing firms or occupations. 9 17 or sectoral structure), there are other regional factors determining returns to these characteristics for different groups of workers. The literature has pointed out different factors that can explain the existence of a wage penalty (or premium) for part-time workers. First, as previously mentioned, the literature tends to consider part-time workers as part of the secondary labour market (outsiders), who are mostly temporary with reduced job stability10. This implies that the wages of part-time workers are more sensitive to the business cycle when compared to full-time workers (insiders). Thus, we will expect wage penalty increases associated with part-time work in high unemployment periods accompanied by positive shifts of labour supply. Figure 4 relates the unexplained component of the wage decomposition for male workers in the different regions with the male unemployment rate, while for female workers the comparison is carried out in terms of activity rates11. As can be seen in this figure, there is a negative correlation between the considered variables; that is to say, in regions with higher values of the unemployment rate, there is a higher wage penalty, whereas in regions with lower unemployment rates (males) and low activity rates (females), there is a wage premium for part-time workers. These results confirm that in regions with a higher availability of workers (higher activity and/or higher unemployment), firms have more power to push down the wages of workers in the secondary market, penalising more those in the segment with lower bargaining power. Second, the literature has also highlights the role of institutions in explaining wage differentials between different types of workers. One of these institutional features is related to the presence of unions with capacity to prevent discriminatory practices among firms, making relevant the degree of unionisation of part-time workers compared to full-time workers (Riley, 1997). Although in the Spanish case the degree of union membership does not determine the bargaining power of firms12, one should expect that a higher union density in a particular region would result in a higher capacity to defend the rights of the workers and combat social inequalities, including wages. Accordingly, regions with higher union density are expected to have a lower wage penalty for parttime workers. This is precisely what we observe in the figure: a positive correlation both for men and women13. Another way of proxying union strength is by means of the relative importance of firm-level collective agreements in the region. Such agreements imply a greater presence of unions in firms and a lower wage penalty and, this is exactly the result observed both for men and women14. 10 Tilly (1996) applies this approach to the labour market of part-time workers, and O'Connell and Gash (2003) provide evidence for Ireland in this regard. 11 Data on activity and unemployment rates by gender and region have been obtained from the Spanish Labour Force Survey and correspond to the average value in 2010. 12 As in the considered period, sectorial collective agreements are of legal application “erga omnes” (i.e., to all workers and firms). This is automatically translated into a high coverage of agreements, independent of union membership. 13 Data on union density by region have been obtained from the 2010 Survey of Quality of Life at Work. 14 The data on the percentage of workers affected by firm collective agreements by region comes from the Statistics of Collective Agreements of Work of the Ministry of Employment and Social Security. 18 A third explanatory factor pointed out in the literature is the monopsonistic power of firms due to the limited geographic mobility of part-time workers—an aspect particularly relevant for female workers, as they are much more restricted than male workers in terms of job search. In particular, as Ermisch and Wright (1991) show, geographic mobility is much more intense for the head of the household, usually males. Due to their lack of mobility and limited job search capacity, firms face a relatively inelastic labour supply that makes it possible for them to reduce wages. The limitations of the SES regarding the territorial level of detail does not make it possible to consider the possible impact in terms of wage penalty of the job search capacity of the unemployed at the local labour market level. For this reason, we have tried to proxy this phenomenon by focusing on inter-regional mobility. In particular, we expect that the higher the number of workers entering a region in the last years, the higher the number of potentially part-time workers who are geographically limited15. As seen in the figure, the correlation is negative16, and the effect is the expected one: a greater availability of part-time workers in the region reduces their bargaining power and increases the wage penalty. The evidence presented reveals that regional differences in the wage penalty associated with part-time workers when compared to full-time workers is related to a higher sensitivity to labour market conditions (unemployment, labour supply, monopsonistic power of firms) and to the uneven capacity of unions to prevent such discrimination. 6. Conclusions This research has examined wage differentials between part-time and full-time workers in Spain using individual data from the Structure of Earnings Survey and econometric decomposition methods. The empirical analysis has been carried out distinguishing between male and female workers, and it contributes to the literature by considering differences along the wage distribution as well as introducing a regional perspective into the analysis. The obtained results allow us to conclude that part-time workers in Spain experience a significant wage disadvantage, and that disadvantage is higher for female workers. This disadvantage is not homogenous along the wage distribution, as in the case of women it tends to increase along the distribution, whereas in the case of the men it shows a declining profile until the point at which it ascertains a wage advantage in the right tail of the distribution. The results of the econometric decompositions show that the wage penalty associated with part-time workers, both male and female, is almost fully explained by their relative characteristics The percentage of workers arriving to the region between 3 and 5 years before in 2010 has been obtained from the Statistics of Labour and Geographic Mobility, elaborated by the National Institute of Statistics. 16 Although in theory a negative correlation for female workers is predicted, the effect is better observed for male workers. 15 19 and, in particular, by segregation of part-time workers in low-wage firms. In fact, we do not observe significant wage differences between full-time and part-time workers with similar observable productive characteristics working in the same firm. However, the wage penalty associated with part-time workers differs substantially along the wage distribution, declining for women and increasing for men. This seems to indicate that comparatively, part-time work penalises lowqualified men and high-qualified women the most. From a regional perspective, although in the majority of the Spanish regions the greater part of the observed average wage differential between part-time and full-time workers tends to be explained by endowments of characteristics, there are several regions where the unexplained part of the differential is significant, particularly in the case of male workers. Overall, regional differences seem to be related to differences in the market power of firms in the regional markets. In fact, a lower wage penalty is observed in those regions with lower unemployment rates, lower activity rates, and lower immigrant flows in the last few years, as well as a greater presence of unions that can bargain and prevent firm discriminatory practices. To conclude, our analysis could suffer from a limitation related to the database used in the study. It is impossible to take into account the potential selection bias associated with the selection of working part-time or full-time by individuals. The usual form to correct this problem requires the use of valid exclusion restrictions, which is not possible using the SES. However, as most part-time employment in Spain is involuntary, the mentioned limitation could be less important in the Spanish context. 7. References Aaronson, D. and E. French (2004): “The Effect of Part-Time Work on Wages: Evidence from the Social Security Rules”, Journal of Labor Economics Vol 22, Nº 2, pp. 329-352. Bardasi, E. and J.C. Gornick (2008): “Working for less? Women's part-time wage penalties across countries”, Feminist Economics, vol 14 (1), pp. 37-72. Belous, R. (1989): The Contingent Economy: The Growth of the Temporary, Part-Time and Subcontracted Workforce, National Planning Association, Washington, DC. Booth, A.L. and M. Wood (2008): “Back-To-Front Down Under? Part-Time/Full-Time Wage Differentials In Australia”, Industrial Relations , Vol. 47, No. 1 (January), pp. 114-135. Cebrián, I., V. Gash, G. Moreno, P. O’Connell and L. Toharia (2000): “Pheripheral labour in peripheral markets? Mobility and working time within transitional labour markets among women in Ireland and Spain” in O’Reilly, I. Cebrián and M. Lallement (eds.), Working-Time Changes. Social Integration Trough Transitional Labour Markets, Edward Edgar, pp. 205-250. Cebrián, I., G. Moreno and L. Toharia (2001) “Trabajo a tiempo parcial y duración de la jornada en la Unión Europea: Características, salarios, pobreza”, in INE: Condiciones de vida en España y en Europa, INE, Madrid, pp. 241-280. Connolly, S. and M. Gregory (2009): “The part-time pay penalty: earnings trajectoris of British Women”, Oxford Economic Papers 61 (2009), i76-i97. Di Nardo, J.; Fortin, N.M. and T. Lemieux. (1996): “Labor market institutions and the distribution of wages, 1973-1992: A semi-parametric approach”, Econometrica, 64 (5), pp. 1011-1044. 20 Ermisch, J. and R. Wright (1991): “Wage offers and and full-time and part-time employment by British women”, Journal of Human Ressources, Vol 25, Nº 1, pp. 111-133. Euwals, R. and Hogerbrugger, M. (2006): “Explaining the Growth of Part-time Employment: Factors of Supply and Demand”, Labour, Vol. 20 (3), pp. 533-557. Fernández-Kranz, D. and N. Rodríguez-Planas (2010): “El contrato a tiempo parcial”, Papeles de Economía Española, Vol. 124, pp. 148-163. Fernández-Kranz, D. and N. Rodríguez-Planas (2011): “The part-time penalty in a segmented labor marked”, Labour Economics, 18, pp 591-606. Fernández-Kranz, D.; Paul, M. and Rodríguez-Planas, N. (2014): “Part-Time Work, Fixed-Term Contracts, and the Returns to Experience”, Oxford Bulletin of Economics and Statistics, forthcoming, http://dx.doi.org/10.1111/obes.12073. Fortin, N.; Lemieux, T. and Firpo, S. (2011): “Decomposition Methods in Economics”, Handbook of Labor Economics, Vol. 4, Chapter 1, pp. 1-102. Elsevier. Ginn, J. and S. Arber (1998): ‘‘How Does Part-Time Work Lead to Low Pension Income?’’ en O’Reilly, J.y C. Fagan (eds.), Part-Time Prospects: An International Comparison of Part-Time Work in Europe, North America, and the Pacific Rim,. Routledge, pp. 156-173. Hardoy, I. and P. Schone (2006): “The part-time wage gap. How large is it really?”, British Journal of Industrial Relations, Vol. 44, No. 2, pp. 263-82. Harris, R. (1993): “Part-time female earnings: an analysis using Northen Ireland NES data”, Applied Economics, 25, pp. 1-12. Hirsch, B.T. (2005): “Why do Part-Time Workers Earn Less? The Role of Workers and Job Skills”, Industrial and Labor Relations Review, Vol. 58, Nº. 4, pp. 525-551. Houseman, S. and O. Machiko (1998): ‘‘What is the Nature of Part-Time Work in the United States and Japan?’’ en O’Reilly, J.y C. Fagan (eds.), Part-Time Prospects: An International Comparison of Part-Time Work in Europe, North America, and the Pacific Rim,. Routledge, pp. 232-251. Hu, Y. and K. Tijdens (2003): Choices for part-time jobs and the impacts on the wage differentials. A comparative study for Great Britain and the Netherlands, IRISS at CEPS/INSTEAD, IRISS WP SERIES Nº. 2003-05. Jepsen, M., S. O’Dorchai, R. Plasman and F. Rycx (2005): “The Wage Penalty Induced by Part-Time Work: The Case of Belgium”, Brussels Economic Review, Vol. 48, No. 1-2, pp. 73-94. Juhn, C., K. Murphy and B. Pierce (1991): “Accounting for the Slowdown in Black-White Convergence”, en M. Osters (ed.) Workers and Their Wages, 107-143. Washington DC: American Enterprise Institute Press. Juhn, C.; Murphy, K. and B. Pierce (1993): “Wage inequality and the rise in returns to skill”, Journal of Political Economy, Vol. 101, pp. 410-442. Machado, J. and J. A. F. Mata (2005): “Conterfactual decomposition of changes in wage distributions using quantile regression”, Journal of Applied Econometrics, 20 (4), pp. 445-465. Manning, A. and B. Petrongolo (2008): “The Part-time Pay Penalty for Women in Britain”, Economic Journal, Vol. 118, No. 526, F28-F51. Melly, B. (2005): “Decomposition of differences in distribution using quantile regression”, Labour Economics, Elsevier, 12 (4), pp. 577-590. Melly, B. (2006): “Estimation of counterfactual distributions using quantile regression“, mimeo, Swiss Institute for International Economics and Applied Economic Research, University of St. Gallen. Montgomery, M. (1988): "On the Determinants of Employer Demand for Part-Time Workers", The Review of Economics and Statistics, Vol. 70 (1), pp. 112-117. Mumford, K. and P.N. Smith (2009): “What determines the part-time and gender earnings gaps in Britain: evidence from the workplace”, Oxford Economic Papers 61, pp. i56-i75. Muñoz de Bustillo, R., E. Fernández and J.I. Antón (2008): El trabajo a tiempo parcial en España en el contexto de la Unión Europea. Características, condiciones de trabajo y perspectivas, Ministerio de Trabajo e Inmigración, Colección Informes y Estudios, Serie Empleo nº 36, Madrid. Neumark, D. (1988): “Employer´s discriminatory behaviour and the estimation of wage discrimination”, Journal of Human Resources, Vol. 23, pp. 279-295. Oaxaca, R. and M. Ransom (1994): “On discrimination and the decomposition of wage differentials”, Journal of Econometrics, 61, pp. 5-22. O’Connell, P.J. and V. Gash (2003): “The Effects of Working Time, Segmentation and Labour Market Mobility on Wages and Pensions in Ireland”, British Journal of Industrial Relations 41, pp. 71-95. O'Dorchai, S., R. Plasman and F. Rycx, (2007): "The part-time wage penalty in European countries: how large is it for men?", International Journal of Manpower, Vol. 28, Nº 7 pp. 571-603 21 Pissarides, C.; P. Garibaldi; C. Olivetti, B. Petrongolo and E. Wasmer (2005): “Women in the labor force: How well is Europe doing?” in T. Boeri, D. Del Boca and C. Pissarides (eds.), Women at Work: An Economic Perspective, Oxford University Press, pp. 9-120. Posel, D. and C. Muller (2007): Is there evidence of a wage penalty to female parttime employment in South Africa? University of Kwazulu-Natal, Working Paper Number 61. Preston, A. and S. Yu (2015): “Is there a part-time/full-time pay differential in Australia?”, Journal of Industrial Relations, Vol 57 (1), pp. 24-47. Riley, N. (1997): “Determinants of union membership: a review”, Labour, Vol. 11, Nº 2, pp. 265-301. Rodgers, J.R. (2004): “Hourly Wages of Full-Time and Part-Time Employees in Australia”, Australian Journal of Labour Economics 7(June):231–54. Russo, G. and W. Hassink (2008): “The Part-Time Wage Gap: A Career Perspective”, De Economist, Vol. 156, No. 2, pp. 145-74. Tilly, C. (1990): Short Hours, Short Shrift: Causes and Consequences of Part-Time Work, The Economic Policy Institute, Washington, DC. Tilly, C. (1996): Halh a job: Bad and good part-time jobs in a changing labour market, Temple University Press, Philadelphia. Wolf, E.(2014): The German part-time wage gap: bad news for men?, SOEP papers 663/2014. 22 Figures, maps and tables Figure 1. Wage differentials between part- and full-time workers along the wage distribution. 23 Figure 2. Aggregate decomposition of wage differentials between part- and full-time employees. Fortin-LemieuxFirpo decomposition. Males (upper panel) and females (lower panel). .1 0 -.1 -.2 Difference in the logarithm of hourly wages Males 0 .2 .4 Quantiles Total difference .6 Characteristics .8 1 Coefficients   0 -.1 -.2 -.3 -.4 Difference in the logarithm of hourly wages Females 0 .2 .4 Quantiles Total difference .6 Characteristics .8 1 Coefficients   Notes: Individual characteristics and job and firm attributes have been considered as explanatory variables. 24   Figure 3. Detailed decomposition of wage differentials between part- and full-time employees. Fortin-LemieuxFirpo decomposition. Males (upper panel) and females (lower panel).   -.08 -.04 -.06 -.02 0 .02 Difference in the logarithm of hourly wages Males 0 .2 .4 Characteristics Quantiles .6 Individuals .8 Jobs 1 Firms   0 -.1 -.2 -.3 Difference in the logarithm of hourly wages Females 0 .2 Characteristics .4 Quantiles Individuals .6 .8 Jobs 1 Firms   Notes: Individual characteristics and job and firm attributes have been considered as explanatory variables. 25   Figure 4. Unexplained component of wage differentials between part-time and full-time workers and regional characteristics by gender. MEN- Unexplained component of wage differential GA CB AS -.04 -.02 0 CL AR CT RI CN IB EX MC VC AN CM MD .1 .15 .2 .25 Unemployment rate .3 PV CM RI MD 0 CN IB .1 .2 .3 % workers firm collective agreement .4 CB AS AR CL EX VC CM RI MD .15 .2 .25 Union density .3 NC GA PV AS AR -.04 -.02 0 -.04 -.02 0 CN CL MC CT IB AN EX VC CT AN .02 .04 .06 .02 .04 .06 GA CB MC .1 NC AS GA PV -.04 -.02 0 PV NC .02 .04 .06 .02 .04 .06 NC CB CL AR ANMC EX CN CT IB VC CM RI MD .04 .06 .08 .1 % workers between 3 and 5 years of residence in the region .01 .02 .03 NC AR CN GA RI VC CL CM AN -.02 -.01 EX CB PV MC IB CT MD Activity rate AR .55 .6 NC GA -.02 -.01 IB 0 CT MD AS RI CB RI VC CM CL EX PV AN IB CT MD .15 PV .1 .2 .3 % workers firm collective agreement .4 AS CB .2 .25 Union density .3 NC AR CN GA AS 0 CM CL -.02 -.01 0 VC MC .1 CN MC AN EX GA .01 .02 .03 .5 .01 .02 .03 .45 CN 0 0 AS NC AR -.02 -.01 .01 .02 .03 WOMEN - Unexplained component of wage differential EX PV ANMC CL VC CM IB CT RI CB MD .04 .06 .08 .1 % workers between 3 and 5 years of residence in the region 26 Map 1. Regional differences in the unexplained component of wage differentials between part-time and full-time workers Men Women [-.037,-.004] (5) (-.004,.004] (4) (.004,.025] (5) (.025,.066] (3) [-.021,-.008] (5) (-.008,-.002] (5) (-.002,.001] (3) (.001,.03] (4) 27 Table 1. Recent studies about part-time versus full-time wage differences Authors Country Years Databases Samples/Gender Sample/Age Ireland 1994 ECHP Men and Women 17 and more 1984-88 ECHP 2001 Hirsch (2005) USA 1995-2002 HILDA (households) Census Population Survey Men +Women together Men and Women 15-65 Rodgers (2004) Netherland and Unites Kingdom Australia Men and Women 16 and more Belgium 1995 SES (firms) Women ... Pissarides et al (2005) United Kingdom Finland Denmark Germany Netherland Belgium Austria Ireland France Italy Spain Portugal Greece 1994-99 ECHP Men and Women 16-61 Hardoy and Schone (2006) O’Dorchai et al (2007) Norway 1997-98 LLS (households) 20-60 Belgium Denmark Italy Spain Ireland United Kingdom 1995 SES (firms) Women (only voluntary PT) Men O’Connell and Gash (2003) Hu and Tijdens (2003) Jepsen et al (2005) ... ... 28 Part-time wage penalty M 0% W -9% * M 0% W 0% Neth. -11%* UK -3%* M -3 p. log. W -9 p. log. M -33 p. log.* W -18 p. log.* M -22 p. log.* W -10 p. log.* M +19 p. log.* W +15 p. log.* -4 p. log.* +1 p. log. UK M -20 p. log.* W -12 p. log.* FINL M -9 p. log.* W -2p. log. DEN M -15 p. log.* W-6 p. log.* GERM M 0p. log. W -10 p. log.* NET M -11 p. log.* W -4 p. log.* BELG M -6 p. log. W +3 p. log. AUS M -12 p. log.* W +6 p. log.* IREL M -12 p. log.* W-8 p. log.* FRA M 0 p. log. W +4 p. log.* ITAL M +15 p. log.* W +16 p. log.* SPA M +6 p. log.* W +19 p. log.* PORT M 0 p. log. W -4 p. log.* GRE M +12 p. log.* W +14 p. log.* - 0,5% -10,9%* BEL -7 p. log.* DEN +1 p. log. ITA -13 p. log.* SPA -6 p. log.* IREL -29 p. log.* U K -15 p. log.* Control occupations No No Yes Yes Yes Control self-selection No No No No Ordered Probit Firm Fixed Effects No No No No No Yes Multinomial Logit No No No Yes Yes Yes Yes No Yes Yes No No No No FE individual FE individual No No No (1) No No No No No No No No No Yes Yes Yes No Probit No No No No Posel and Muller (2007) South Africa 2001-2004 LFS (households) Men ... Spain 2000 ECHP Men and Women 16-64 Russo and Massik (2008) Netherland WCS (firms) United Kingdom LFS (households) Men and Women wich do not change company Women ... Manning and Petrongolo (2008) 1997-98 and 19992000 2001-03 16-64 Mumford and Smith (2008) United Kingdom 2004 BWERS (firms) Men and Women ... Bardasi and Gornick (2008) Canada USA United Kingdom Germany Italy Sweden Australia 1994-1995 Luxembourg Income Study Women 25-59 2001-04 HILDA (households) Men and Women 18-60 United Kingdom 1975-2001 New Earnings Survey Women 16 and more Fernández-Kranz and Rodríguez-Planas (2011) Spain 1996-2006 MCVL Women 25-45 Fernández-Kranz, Paul and Rodríguez-Planas (2014) Spain 1996-2006 MCVL Women 23-45 Germany (East and West) 1984-2010 SOEP (households) Men and Women 20-60 Australia 2010 AWS (households) Men and Women ... Pagán (2007) Boot and Wood (2008) Connelly and Gregory (2009) Wolf (2014) Preston and Yu (2015) + 34% * + 40% * + 50% * M +6 p. log.* W +14 p. log.* M - 3%* W - 1% * -11 p. log.* - 3 p. log.* -11 p. log.* - 2 p. log. * M 0 p. log. W -11 p. log.* M +5 p. log.* W -3 p. log.* M +1 p. log.* W -8 p. log.* CAN -9 p. log.* USA -17 p. log.* UK -1 p. log. GER -8 p. log.* ITA -15 p. log.* SWE -3 p. log.* M Casual +10%* M No Cas +15%* W Casual + 15%* W No Cas + 10%* -10 p. log.* -2 p. log.* -7 p. log.* -32 p. log.* -19 p. log.* -17 p. log.* -13 p. log.* -11 p. log.* Permanent Contract -6 p. log.* Temporary Contract -9 p. log.* West M -12%* M -11%* W 0% W +1% East M -11%* M -10%* W +1% W +2% M -8,9 p.log* W -1,1 p. log* W Casual +7,1%* No Yes Yes Yes No No Individual FE Ordered Probit No No No No Yesi No No No Yes No Yes No No Yes Yes Yes Yes Yes No No Probit Probit No No No No No No Yes No No No No No No No No Yes Yes No Yes Individual FE No No Yes Yes No No Yes No Yes No (2) EF Individual FE EF Individual FE Movers FE Movers FE Individual FE Individual FE Individual FE Individual FE Multiequational Model and Probit No No No No No No Yes Yes Yes No Yes No Yes No Yes No Yes Yes Individual FE No No No * Statistically significant at the usual levels (1% or 5%, depending on each paper). (1) According to the authors, results are robust to controlling for self selection by a probit model or by individual fixed effect when they work with the sample of movers from full-time to part-time. (2) Authors explain that they introduced additional controls (occupations and more) and results did not change. 29 Table 2. Wage differentials between part- and full-time workers in Spain. Males Females Average -0.104 -0.254 Percentiles 5 -0.191 -0.099 10 -0.178 -0.112 20 -0.160 -0.137 30 -0.165 -0.176 40 -0.159 -0.219 50 -0.155 -0.266 60 -0.125 -0.319 70 -0.099 -0.358 80 -0.080 -0.388 90 -0.016 -0.391 95 0.084 -0.375 Notes: The wage gap corresponds to the differential of the logarithm of the hourly wage. Table 3. Incidence of part-time work and wage differences between part-time and full-time workers in Spanish regions. Incidence of Wage differential part-time work part-time/full-time Males Females Males Females Andalusia 0.089 0.296 -0.164 -0.305 Aragon 0.063 0.261 0.028 -0.146 Asturias 0.054 0.213 0.028 -0.165 Balearic Islands 0.071 0.220 -0.010 -0.192 Canary Islands 0.111 0.231 -0.139 -0.268 Cantabria 0.058 0.233 0.050 -0.230 Castilla-Leon 0.061 0.252 -0.016 -0.246 Castilla La Mancha 0.064 0.279 -0.032 -0.269 Catalonia 0.079 0.235 -0.151 -0.247 Valencia 0.099 0.306 -0.138 -0.209 Extremadura 0.069 0.273 -0.104 -0.315 Galicia 0.067 0.226 0.045 -0.192 Madrid 0.073 0.208 -0.299 -0.314 Murcia 0.090 0.286 -0.125 -0.271 Navarre 0.063 0.314 0.049 -0.071 Basque Country 0.058 0.306 0.037 -0.227 The Rioja 0.068 0.290 -0.083 -0.100 Unweighted average 0.073 0.261 -0.060 -0.222 Regional standard deviation 0.016 0.036 0.100 0.071 Maximum 0.111 0.314 0.050 -0.071 Minimum 0.054 0.208 -0.299 -0.315 Notes: The wage gap corresponds to the differential of the logarithm of the hourly wage. 30 Table 4. Decomposition of the differential in average wages between part- and full-time workers. Juhn-Murphy-Pierce decomposition. Wage differential Characteristics (1) Nationality Educational attainment Age Tenure Type of contract Region Activity sector Firm size Collective agreement Firm fixed effects (2) Wage residuals (3) Model 1 -0.104 0.002(-2.0) -0.003 -0.026 0.031 -0.106(102.0) Males Model 2 -0.104 -0.069(66.3) -0.001 -0.022 0.014 -0.016 -0.016 0.007 -0.040 0.003 0.003 -0.036(34.6) Model 3 -0.104 -0.030(28.8) 0.000 -0.017 0.011 -0.014 -0.010 Model 1 -0.254 -0.104(40.9) -0.002 -0.104 0.001 - -0.074(69.2) 0.000(0.0) -0.150(59.1) Females Model 2 -0.254 -0.208(81.9) 0.000 -0.088 0.000 -0.048 0.000 -0.005 -0.052 -0.006 -0.010 -0.046(18.1) Model 3 -0.254 -0.104(40.9) 0.000 -0.069 0.000 -0.034 -0.001 -0.143(56.3) -0.007(2.8) Notes: The table shows the results obtained after applying equation (3) to the 2010 wave of the Encuesta de Estructura Salarial. Model 1 corresponds to a specification of the wage equation that includes individual characteristics (nationality, age and education); model 2 incorporates to the specification attributes of jobs and firms (tenure, type of contract, region, sector, size and type of collective agreement), whereas model 3 includes individual and job attributes and firm fixed effects instead of firm attributes. The percentage of the wage differential explained by each term appears in brackets. 31 Table 5. Decomposition of the differential in average wages between part- and full-time workers. Juhn-Murphy-Pierce decomposition. Regional disaggregated analysis. Males. Wage differential Characteristics (1) Nationality Educational attainment Age Tenure Type of contract Firm fixed effects (2) Wage residuals (3) Andal. -0.164 -0.042 0.000 -0.007 -0.001 -0.020 -0.013 -0.122 0.000 Arag. 0.028 0.075 -0.001 0.002 0.035 0.038 0.000 -0.056 0.009 Astur. 0.028 0.028 0.000 -0.036 0.041 0.011 0.012 -0.024 0.025 Balear. -0.010 -0.015 0.000 -0.024 0.018 -0.009 0.000 0.003 0.003 Canar. -0.139 -0.040 0.000 0.007 -0.007 -0.032 -0.008 -0.111 0.012 Cantab. 0.050 0.059 -0.001 -0.005 0.047 0.042 -0.025 -0.035 0.025 C-L -0.016 0.013 0.000 -0.001 0.018 0.006 -0.010 -0.042 0.012 C-LM -0.032 0.017 0.000 -0.012 0.016 0.014 0.000 -0.026 -0.023 Catal. -0.151 -0.068 -0.001 -0.008 -0.004 -0.039 -0.016 -0.087 0.004 Val. -0.138 -0.019 0.000 0.002 0.010 -0.032 0.002 -0.113 -0.006 Extre. -0.104 -0.021 -0.001 0.004 0.002 -0.019 -0.007 -0.074 -0.004 Galic. 0.045 -0.002 0.000 -0.013 0.015 0.021 -0.025 0.004 0.043 Madrid -0.299 -0.121 -0.001 -0.060 -0.003 -0.035 -0.021 -0.142 -0.037 Murcia -0.125 -0.023 0.001 -0.016 0.006 -0.013 0.000 -0.105 0.003 Navarra 0.049 0.017 0.001 -0.013 0.024 0.036 -0.030 -0.034 0.066 Basq. 0.037 0.015 0.001 -0.031 0.050 -0.002 -0.003 -0.007 0.029 Rioja -0.083 -0.004 -0.001 0.004 0.008 -0.005 -0.010 -0.057 -0.023 Notes: The table shows the results obtained after applying equation (3) to the 2010 wave of the Encuesta de Estructura Salarial with a specification of the wage equation that includes individual and job characteristics and firm fixed effects. Table 6. Decomposition of the differential in average wages between part- and full-time workers. Juhn-Murphy-Pierce decomposition. Regional disaggregated analysis. Females. Wage differential Characteristics (1) Nationality Educational attainment Age Tenure Type of contract Firm fixed effects (2) Wage residuals (3) Andal. -0.305 -0.100 0.000 -0.066 -0.001 -0.034 0.002 -0.200 -0.005 Arag. -0.146 -0.085 0.000 -0.055 0.000 -0.030 0.000 -0.088 0.027 Astur. -0.165 -0.082 0.000 -0.069 0.000 -0.020 0.006 -0.084 0.001 Balear. -0.192 -0.090 -0.001 -0.084 0.004 -0.010 0.000 -0.089 -0.013 Canar. -0.268 -0.089 0.000 -0.063 0.000 -0.025 0.000 -0.198 0.019 Cantab. -0.230 -0.091 0.000 -0.068 -0.002 -0.021 0.000 -0.127 -0.012 C-L -0.246 -0.101 0.000 -0.058 -0.001 -0.038 -0.005 -0.142 -0.002 C-LM -0.269 -0.108 0.001 -0.089 0.001 -0.023 0.003 -0.159 -0.002 Catal. -0.247 -0.106 -0.001 -0.053 -0.003 -0.046 -0.003 -0.125 -0.016 Val. -0.209 -0.092 0.000 -0.060 0.002 -0.029 -0.005 -0.117 0.000 Extre. -0.315 -0.105 0.001 -0.075 -0.001 -0.023 -0.007 -0.203 -0.007 Galic. -0.192 -0.077 0.000 -0.054 0.001 -0.021 -0.002 -0.130 0.014 Madrid -0.314 -0.131 0.002 -0.097 0.003 -0.037 -0.002 -0.162 -0.021 Murcia -0.271 -0.100 0.004 -0.070 -0.001 -0.032 -0.001 -0.166 -0.006 Navarra -0.071 -0.079 -0.001 -0.077 0.008 -0.011 0.001 -0.022 0.030 Basq. -0.227 -0.095 0.001 -0.061 0.003 -0.034 -0.004 -0.124 -0.008 Notes: The table shows the results obtained after applying equation (3) to the 2010 wave of the Encuesta de Estructura Salarial with a specification of the wage equation that includes individual and job characteristics and firm fixed effects. 32 Rioja -0.100 -0.082 0.001 -0.047 0.000 -0.038 0.003 -0.018 0.000 Appendix 0 kdensity LNSALHORA4 .5 1 1.5 Figure A.1. Kernel density functions of the logarithm of hourly wages of part- and full-time males and females. 1 2 3 Males Full-time Females Full-time 33 x 4 Males Part-time Females Part-time 5 Table A.1. Descriptive statistics. Males. Part-time Average Total Average Parts of the wage distribution Parts of the wage distribution Average S.D. Average S.D. p75 p75 Logarithm of hourly wage 2.423 0.494 1.874 2.201 2.519 3.097 2.318 0.591 1.709 2.040 2.404 3.121 Primary education 0.183 0.387 0.278 0.232 0.157 0.067 0.233 0.423 0.259 0.224 0.251 0.198 Secondary education 0.598 0.490 0.670 0.664 0.621 0.436 0.585 0.493 0.637 0.668 0.555 0.482 Tertiary education 0.219 0.414 0.052 0.104 0.222 0.497 0.181 0.385 0.104 0.108 0.194 0.320 Age 40.640 10.150 37.390 39.330 41.010 44.810 42.740 14.830 36.110 37.890 45.030 51.900 Tenure 9.351 9.676 4.738 7.638 10.530 14.490 9.877 13.410 3.292 4.954 11.870 19.400 Fixed-term contract 0.811 0.391 0.665 0.781 0.863 0.935 0.444 0.497 0.501 0.548 0.419 0.307 Andalusia 0.099 0.299 0.103 0.104 0.090 0.101 0.111 0.314 0.134 0.121 0.095 0.095 Aragon 0.042 0.201 0.035 0.051 0.050 0.034 0.038 0.191 0.028 0.037 0.044 0.042 Asturias 0.034 0.181 0.034 0.037 0.038 0.026 0.028 0.164 0.025 0.021 0.026 0.038 Balearics 0.023 0.151 0.030 0.028 0.020 0.016 0.052 0.223 0.044 0.052 0.062 0.051 Canary Islands 0.042 0.201 0.073 0.037 0.030 0.029 0.033 0.179 0.061 0.027 0.026 0.019 Cantabria 0.027 0.161 0.033 0.030 0.025 0.019 0.026 0.158 0.024 0.024 0.023 0.032 Castilla-Leon 0.054 0.227 0.067 0.053 0.052 0.045 0.045 0.208 0.050 0.040 0.041 0.051 Castilla La Mancha 0.042 0.201 0.055 0.047 0.037 0.029 0.032 0.176 0.029 0.036 0.037 0.026 Catalonia 0.163 0.369 0.102 0.152 0.190 0.209 0.187 0.390 0.139 0.195 0.216 0.199 Valencia 0.083 0.275 0.091 0.092 0.081 0.066 0.084 0.278 0.093 0.091 0.100 0.053 Extremadura 0.024 0.151 0.050 0.020 0.013 0.012 0.017 0.127 0.030 0.019 0.011 0.007 Galicia 0.055 0.228 0.081 0.065 0.043 0.031 0.040 0.196 0.042 0.045 0.034 0.040 Madrid 0.171 0.377 0.138 0.138 0.169 0.240 0.172 0.377 0.209 0.181 0.142 0.157 Murcia 0.031 0.174 0.046 0.036 0.024 0.020 0.026 0.159 0.038 0.027 0.025 0.014 Navarre 0.030 0.169 0.015 0.033 0.044 0.027 0.029 0.166 0.009 0.026 0.032 0.047 Basque Country 0.063 0.243 0.027 0.055 0.082 0.089 0.068 0.252 0.034 0.040 0.077 0.124 The Rioja 0.016 0.125 0.022 0.021 0.014 0.007 0.012 0.109 0.012 0.020 0.010 0.007 Industry 0.424 0.494 0.354 0.438 0.488 0.415 0.288 0.453 0.161 0.208 0.345 0.437 Construction 0.122 0.327 0.171 0.154 0.096 0.066 0.037 0.188 0.027 0.047 0.042 0.031 Services 0.454 0.498 0.475 0.408 0.416 0.518 0.675 0.468 0.812 0.745 0.613 0.532 Firm size less than 20 0.105 0.307 0.196 0.122 0.060 0.043 0.138 0.345 0.203 0.195 0.103 0.051 Firm size 10-49 0.238 0.426 0.332 0.287 0.198 0.137 0.195 0.396 0.236 0.228 0.182 0.134 Firm size 50-199 0.277 0.447 0.261 0.298 0.297 0.250 0.226 0.418 0.214 0.224 0.244 0.221 Firm size 500 or more 0.380 0.485 0.211 0.293 0.445 0.571 0.441 0.497 0.348 0.353 0.471 0.594 National sect. collect. agr. 0.286 0.452 0.319 0.282 0.255 0.288 0.282 0.450 0.380 0.312 0.238 0.200 Sub-national sect. coll. agr. 0.416 0.493 0.527 0.507 0.385 0.243 0.388 0.487 0.362 0.466 0.424 0.302 Firm collective agreement 0.299 0.458 0.154 0.211 0.360 0.469 0.329 0.470 0.258 0.222 0.338 0.498 Number of observations 81,578 20,394 20,394 20,394 20,394 7,766 1,941 1,941 1,941 1,941 Notes: The table includes descriptive (mean and standard deviation) for each of the groups (individuals with full- and part-time) and the value of the average of the variables for the four sections of the wage distribution defined by the three quartiles. Total Full-time 34 Table A.2. Descriptive statistics. Females. Full-time Part-time Average Total Average Parts of the wage distribution Parts of the wage distribution Average S.D. Average S.D. p75 p75 Logarithm of hourly wage 2.282 0.481 1.736 2.062 2.384 2.945 2.028 0.400 1.623 1.854 2.059 2.576 Primary education 0.102 0.303 0.191 0.129 0.064 0.024 0.200 0.400 0.261 0.240 0.199 0.101 Secondary education 0.542 0.498 0.703 0.651 0.539 0.274 0.637 0.481 0.678 0.672 0.669 0.529 Tertiary education 0.356 0.479 0.105 0.220 0.397 0.701 0.163 0.369 0.061 0.088 0.131 0.370 Age 39.080 9.826 37.090 37.770 39.360 42.110 39.200 10.920 39.300 39.200 38.900 39.410 Tenure 8.320 9.059 4.590 6.562 9.498 12.630 5.114 6.623 3.360 4.283 5.315 7.500 Fixed-term contract 0.800 0.400 0.743 0.777 0.817 0.862 0.666 0.472 0.621 0.668 0.701 0.675 Andalusia 0.080 0.272 0.077 0.087 0.080 0.078 0.134 0.341 0.166 0.138 0.119 0.113 Aragon 0.038 0.191 0.038 0.047 0.041 0.025 0.036 0.186 0.027 0.037 0.043 0.037 Asturias 0.025 0.156 0.043 0.028 0.017 0.013 0.025 0.155 0.038 0.030 0.014 0.016 Balearics 0.026 0.158 0.025 0.023 0.027 0.027 0.037 0.189 0.029 0.027 0.043 0.050 Canary Islands 0.044 0.205 0.059 0.045 0.037 0.035 0.040 0.196 0.068 0.035 0.029 0.027 Cantabria 0.018 0.135 0.023 0.018 0.017 0.016 0.023 0.149 0.017 0.032 0.027 0.015 Castilla-Leon 0.048 0.213 0.052 0.047 0.046 0.045 0.055 0.228 0.049 0.070 0.054 0.048 Castilla La Mancha 0.037 0.188 0.044 0.039 0.032 0.032 0.029 0.167 0.034 0.032 0.026 0.024 Catalonia 0.201 0.401 0.142 0.202 0.231 0.228 0.187 0.390 0.146 0.156 0.218 0.228 Valencia 0.076 0.265 0.101 0.083 0.070 0.050 0.080 0.271 0.096 0.095 0.064 0.064 Extremadura 0.016 0.125 0.023 0.011 0.014 0.015 0.020 0.139 0.036 0.015 0.016 0.012 Galicia 0.059 0.236 0.094 0.061 0.048 0.035 0.047 0.211 0.070 0.052 0.036 0.029 Madrid 0.223 0.416 0.182 0.192 0.226 0.291 0.170 0.376 0.138 0.187 0.164 0.189 Murcia 0.026 0.158 0.033 0.024 0.024 0.021 0.027 0.163 0.037 0.027 0.025 0.020 Navarre 0.020 0.141 0.017 0.027 0.026 0.011 0.020 0.139 0.008 0.016 0.029 0.026 Basque Country 0.052 0.222 0.029 0.051 0.056 0.072 0.060 0.237 0.029 0.033 0.082 0.094 The Rioja 0.012 0.108 0.018 0.017 0.008 0.005 0.013 0.111 0.012 0.018 0.011 0.009 Industry 0.220 0.414 0.231 0.245 0.217 0.185 0.093 0.291 0.095 0.083 0.091 0.105 Construction 0.014 0.117 0.007 0.016 0.017 0.015 0.005 0.073 0.003 0.005 0.007 0.006 Services 0.766 0.423 0.762 0.739 0.765 0.800 0.902 0.298 0.903 0.912 0.902 0.889 Firm size less than 20 0.085 0.280 0.160 0.094 0.057 0.031 0.123 0.329 0.163 0.122 0.115 0.094 Firm size 10-49 0.171 0.377 0.249 0.202 0.144 0.091 0.153 0.360 0.153 0.153 0.162 0.142 Firm size 50-199 0.218 0.413 0.243 0.251 0.212 0.168 0.235 0.424 0.250 0.248 0.230 0.212 Firm size 500 or more 0.525 0.499 0.349 0.453 0.587 0.711 0.489 0.500 0.434 0.477 0.493 0.552 National sect. collect. agr. 0.349 0.477 0.421 0.353 0.316 0.307 0.304 0.460 0.331 0.299 0.292 0.294 Sub-national sect. coll. agr. 0.303 0.460 0.388 0.370 0.269 0.186 0.458 0.498 0.433 0.530 0.490 0.380 Firm collective agreement 0.348 0.476 0.192 0.277 0.415 0.507 0.238 0.426 0.236 0.172 0.217 0.327 Number of observations 45,338 11,334 11,334 11,334 11,334 17,417 4,354 4,354 4,354 4,354 Notes: The table includes descriptive (mean and standard deviation) for each of the groups (individuals with full- and part-time) and the value of the average of the variables for the four sections of the wage distribution defined by the three quartiles. Total 35 Table A.3. Decomposition of wage differentials between part- and full-time workers. Fortin-Lemieux-Firpo decomposition. Males. Total Part-time Full-time Difference Characteristics Coefficients Characteristics Nationality Age Education Tenure Contract Region Sector Size Collective agreement N 10th perc. 1.692 (0.006)*** 1.867 (0.001)*** -0.176 (0.006)*** -0.074 (0.002)*** -0.102 (0.005)*** -0.002 (0.000)*** 0.002 (0.000)*** -0.006 (0.001)*** -0.018 (0.001)*** -0.025 (0.002)*** 0.005 (0.001)*** -0.028 (0.001)*** 0.000 (0.001) -0.001 (0.000)*** 89.344 * p<0,1; ** p<0,05; *** p<0,01 1 Quantiles Median 2.176 (0.006)*** 2.345 (0.002)*** -0.169 (0.006)*** -0.089 (0.004)*** -0.080 (0.005)*** -0.003 (0.000)*** 0.008 (0.001)*** -0.019 (0.002)*** -0.022 (0.002)*** -0.033 (0.002)*** 0.009 (0.001)*** -0.032 (0.001)*** 0.001 (0.001) 0.003 (0.001)*** 89.344 90th perc. 3.090 (0.019)*** 3.084 (0.004)*** 0.006 (0.020) -0.053 (0.009)*** 0.059 (0.016)*** 0.002 (0.000)*** 0.029 (0.003)*** -0.042 (0.004)*** -0.012 (0.004)*** 0.011 (0.004)*** 0.008 (0.001)*** -0.060 (0.003)*** 0.005 (0.001)*** 0.005 (0.001)*** 89.344 Table A.4. Decomposition of wage differentials between part- and full-time workers. Fortin-Lemieux-Firpo decomposition. Females. Total Part-time Full-time Difference Characteristics Coefficients Characteristics Nationality Age Education Tenure Contract Region Sector Size Collective agreement N 10th perc. 1.616 (0.003)*** 1.724 (0.002)*** -0.107 (0.003)*** -0.068 (0.002)*** -0.039 (0.003)*** -0.001 (0.000)*** -0.000 (0.000) -0.029 (0.001)*** -0.019 (0.001)*** -0.003 (0.001)*** -0.001 (0.001)** -0.011 (0.001)*** -0.004 (0.000)*** 0.001 (0.001) 62,755 * p<0,1; ** p<0,05; *** p<0,01 2 Quantiles Median 1.944 (0.003)*** 2.216 (0.003)*** -0.272 (0.004)*** -0.218 (0.003)*** -0.054 (0.004)*** -0.001 (0.000)*** 0.000 (0.000) -0.091 (0.002)*** -0.056 (0.002)*** -0.003 (0.001)*** -0.004 (0.001)*** -0.043 (0.001)*** -0.006 (0.001)*** -0.014 (0.001)*** 62,755 90th perc. 2.557 (0.009)*** 2.948 (0.005)*** -0.390 (0.010)*** -0.297 (0.005)*** -0.093 (0.009)*** 0.003 (0.000)*** 0.001 (0.001) -0.142 (0.003)*** -0.068 (0.003)*** 0.005 (0.001)*** -0.013 (0.001)*** -0.063 (0.003)*** -0.005 (0.001)*** -0.014 (0.001)*** 62,755 SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP 2006 CREAP2006-01 Matas, A. (GEAP); Raymond, J.Ll. (GEAP) "Economic development and changes in car ownership patterns" (Juny 2006) CREAP2006-02 Trillas, F. (IEB); Montolio, D. (IEB); Duch, N. (IEB) "Productive efficiency and regulatory reform: The case of Vehicle Inspection Services" (Setembre 2006) CREAP2006-03 Bel, G. (PPRE-IREA); Fageda, X. (PPRE-IREA) "Factors explaining local privatization: A meta-regression analysis" (Octubre 2006) CREAP2006-04 Fernàndez-Villadangos, L. (PPRE-IREA) "Are two-part tariffs efficient when consumers plan ahead?: An empirical study" (Octubre 2006) CREAP2006-05 Artís, M. (AQR-IREA); Ramos, R. (AQR-IREA); Suriñach, J. (AQR-IREA) "Job losses, outsourcing and relocation: Empirical evidence using microdata" (Octubre 2006) CREAP2006-06 Alcañiz, M. (RISC-IREA); Costa, A.; Guillén, M. (RISC-IREA); Luna, C.; Rovira, C. "Calculation of the variance in surveys of the economic climate” (Novembre 2006) CREAP2006-07 Albalate, D. (PPRE-IREA) "Lowering blood alcohol content levels to save lives: The European Experience” (Desembre 2006) CREAP2006-08 Garrido, A. (IEB); Arqué, P. (IEB) “The choice of banking firm: Are the interest rate a significant criteria?” (Desembre 2006) CREAP2006-09 Segarra, A. (GRIT); Teruel-Carrizosa, M. (GRIT) "Productivity growth and competition in spanish manufacturing firms: What has happened in recent years?” (Desembre 2006) CREAP2006-10 Andonova, V.; Díaz-Serrano, Luis. (CREB) "Political institutions and the development of telecommunications” (Desembre 2006) CREAP2006-11 Raymond, J.L.(GEAP); Roig, J.L.. (GEAP) "Capital humano: un análisis comparativo Catalunya-España” (Desembre 2006) CREAP2006-12 Rodríguez, M.(CREB); Stoyanova, A. (CREB) "Changes in the demand for private medical insurance following a shift in tax incentives” (Desembre 2006) CREAP2006-13 Royuela, V. (AQR-IREA); Lambiri, D.; Biagi, B. "Economía urbana y calidad de vida. Una revisión del estado del conocimiento en España” (Desembre 2006) SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP CREAP2006-14 Camarero, M.; Carrion-i-Silvestre, J.LL. (AQR-IREA).;Tamarit, C. "New evidence of the real interest rate parity for OECD countries using panel unit root tests with breaks” (Desembre 2006) CREAP2006-15 Karanassou, M.; Sala, H. (GEAP).;Snower , D. J. "The macroeconomics of the labor market: Three fundamental views” (Desembre 2006) 2007 XREAP2007-01 Castany, L (AQR-IREA); López-Bazo, E. (AQR-IREA).;Moreno , R. (AQR-IREA) "Decomposing differences in total factor productivity across firm size” (Març 2007) XREAP2007-02 Raymond, J. Ll. (GEAP); Roig, J. Ll. (GEAP) “Una propuesta de evaluación de las externalidades de capital humano en la empresa" (Abril 2007) XREAP2007-03 Durán, J. M. (IEB); Esteller, A. (IEB) “An empirical analysis of wealth taxation: Equity vs. Tax compliance” (Juny 2007) XREAP2007-04 Matas, A. (GEAP); Raymond, J.Ll. (GEAP) “Cross-section data, disequilibrium situations and estimated coefficients: evidence from car ownership demand” (Juny 2007) XREAP2007-05 Jofre-Montseny, J. (IEB); Solé-Ollé, A. (IEB) “Tax differentials and agglomeration economies in intraregional firm location” (Juny 2007) XREAP2007-06 Álvarez-Albelo, C. (CREB); Hernández-Martín, R. “Explaining high economic growth in small tourism countries with a dynamic general equilibrium model” (Juliol 2007) XREAP2007-07 Duch, N. (IEB); Montolio, D. (IEB); Mediavilla, M. “Evaluating the impact of public subsidies on a firm’s performance: a quasi-experimental approach” (Juliol 2007) XREAP2007-08 Segarra-Blasco, A. (GRIT) “Innovation sources and productivity: a quantile regression analysis” (Octubre 2007) XREAP2007-09 Albalate, D. (PPRE-IREA) “Shifting death to their Alternatives: The case of Toll Motorways” (Octubre 2007) XREAP2007-10 Segarra-Blasco, A. (GRIT); Garcia-Quevedo, J. (IEB); Teruel-Carrizosa, M. (GRIT) “Barriers to innovation and public policy in catalonia” (Novembre 2007) XREAP2007-11 Bel, G. (PPRE-IREA); Foote, J. “Comparison of recent toll road concession transactions in the United States and France” (Novembre 2007) SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP XREAP2007-12 Segarra-Blasco, A. (GRIT); “Innovation, R&D spillovers and productivity: the role of knowledge-intensive services” (Novembre 2007) XREAP2007-13 Bermúdez Morata, Ll. (RFA-IREA); Guillén Estany, M. (RFA-IREA), Solé Auró, A. (RFA-IREA) “Impacto de la inmigración sobre la esperanza de vida en salud y en discapacidad de la población española” (Novembre 2007) XREAP2007-14 Calaeys, P. (AQR-IREA); Ramos, R. (AQR-IREA), Suriñach, J. (AQR-IREA) “Fiscal sustainability across government tiers” (Desembre 2007) XREAP2007-15 Sánchez Hugalbe, A. (IEB) “Influencia de la inmigración en la elección escolar” (Desembre 2007) 2008 XREAP2008-01 Durán Weitkamp, C. (GRIT); Martín Bofarull, M. (GRIT) ; Pablo Martí, F. “Economic effects of road accessibility in the Pyrenees: User perspective” (Gener 2008) XREAP2008-02 Díaz-Serrano, L.; Stoyanova, A. P. (CREB) “The Causal Relationship between Individual’s Choice Behavior and Self-Reported Satisfaction: the Case of Residential Mobility in the EU” (Març 2008) XREAP2008-03 Matas, A. (GEAP); Raymond, J. L. (GEAP); Roig, J. L. (GEAP) “Car ownership and access to jobs in Spain” (Abril 2008) XREAP2008-04 Bel, G. (PPRE-IREA) ; Fageda, X. (PPRE-IREA) “Privatization and competition in the delivery of local services: An empirical examination of the dual market hypothesis” (Abril 2008) XREAP2008-05 Matas, A. (GEAP); Raymond, J. L. (GEAP); Roig, J. L. (GEAP) “Job accessibility and employment probability” (Maig 2008) XREAP2008-06 Basher, S. A.; Carrión, J. Ll. (AQR-IREA) Deconstructing Shocks and Persistence in OECD Real Exchange Rates (Juny 2008) XREAP2008-07 Sanromá, E. (IEB); Ramos, R. (AQR-IREA); Simón, H. Portabilidad del capital humano y asimilación de los inmigrantes. Evidencia para España (Juliol 2008) XREAP2008-08 Basher, S. A.; Carrión, J. Ll. (AQR-IREA) Price level convergence, purchasing power parity and multiple structural breaks: An application to US cities (Juliol 2008) XREAP2008-09 Bermúdez, Ll. (RFA-IREA) A priori ratemaking using bivariate poisson regression models (Juliol 2008) SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP XREAP2008-10 Solé-Ollé, A. (IEB), Hortas Rico, M. (IEB) Does urban sprawl increase the costs of providing local public services? Evidence from Spanish municipalities (Novembre 2008) XREAP2008-11 Teruel-Carrizosa, M. (GRIT), Segarra-Blasco, A. (GRIT) Immigration and Firm Growth: Evidence from Spanish cities (Novembre 2008) XREAP2008-12 Duch-Brown, N. (IEB), García-Quevedo, J. (IEB), Montolio, D. (IEB) Assessing the assignation of public subsidies: Do the experts choose the most efficient R&D projects? (Novembre 2008) XREAP2008-13 Bilotkach, V., Fageda, X. (PPRE-IREA), Flores-Fillol, R. Scheduled service versus personal transportation: the role of distance (Desembre 2008) XREAP2008-14 Albalate, D. (PPRE-IREA), Gel, G. (PPRE-IREA) Tourism and urban transport: Holding demand pressure under supply constraints (Desembre 2008) 2009 XREAP2009-01 Calonge, S. (CREB); Tejada, O. “A theoretical and practical study on linear reforms of dual taxes” (Febrer 2009) XREAP2009-02 Albalate, D. (PPRE-IREA); Fernández-Villadangos, L. (PPRE-IREA) “Exploring Determinants of Urban Motorcycle Accident Severity: The Case of Barcelona” (Març 2009) XREAP2009-03 Borrell, J. R. (PPRE-IREA); Fernández-Villadangos, L. (PPRE-IREA) “Assessing excess profits from different entry regulations” (Abril 2009) XREAP2009-04 Sanromá, E. (IEB); Ramos, R. (AQR-IREA), Simon, H. “Los salarios de los inmigrantes en el mercado de trabajo español. ¿Importa el origen del capital humano?” (Abril 2009) XREAP2009-05 Jiménez, J. L.; Perdiguero, J. (PPRE-IREA) “(No)competition in the Spanish retailing gasoline market: a variance filter approach” (Maig 2009) XREAP2009-06 Álvarez-Albelo,C. D. (CREB), Manresa, A. (CREB), Pigem-Vigo, M. (CREB) “International trade as the sole engine of growth for an economy” (Juny 2009) XREAP2009-07 Callejón, M. (PPRE-IREA), Ortún V, M. “The Black Box of Business Dynamics” (Setembre 2009) XREAP2009-08 Lucena, A. (CREB) “The antecedents and innovation consequences of organizational search: empirical evidence for Spain” (Octubre 2009) SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP XREAP2009-09 Domènech Campmajó, L. (PPRE-IREA) “Competition between TV Platforms” (Octubre 2009) XREAP2009-10 Solé-Auró, A. (RFA-IREA),Guillén, M. (RFA-IREA), Crimmins, E. M. “Health care utilization among immigrants and native-born populations in 11 European countries. Results from the Survey of Health, Ageing and Retirement in Europe” (Octubre 2009) XREAP2009-11 Segarra, A. (GRIT), Teruel, M. (GRIT) “Small firms, growth and financial constraints” (Octubre 2009) XREAP2009-12 Matas, A. (GEAP), Raymond, J.Ll. (GEAP), Ruiz, A. (GEAP) “Traffic forecasts under uncertainty and capacity constraints” (Novembre 2009) XREAP2009-13 Sole-Ollé, A. (IEB) “Inter-regional redistribution through infrastructure investment: tactical or programmatic?” (Novembre 2009) XREAP2009-14 Del Barrio-Castro, T., García-Quevedo, J. (IEB) “The determinants of university patenting: Do incentives matter?” (Novembre 2009) XREAP2009-15 Ramos, R. (AQR-IREA), Suriñach, J. (AQR-IREA), Artís, M. (AQR-IREA) “Human capital spillovers, productivity and regional convergence in Spain” (Novembre 2009) XREAP2009-16 Álvarez-Albelo, C. D. (CREB), Hernández-Martín, R. “The commons and anti-commons problems in the tourism economy” (Desembre 2009) 2010 XREAP2010-01 García-López, M. A. (GEAP) “The Accessibility City. When Transport Infrastructure Matters in Urban Spatial Structure” (Febrer 2010) XREAP2010-02 García-Quevedo, J. (IEB), Mas-Verdú, F. (IEB), Polo-Otero, J. (IEB) “Which firms want PhDs? The effect of the university-industry relationship on the PhD labour market” (Març 2010) XREAP2010-03 Pitt, D., Guillén, M. (RFA-IREA) “An introduction to parametric and non-parametric models for bivariate positive insurance claim severity distributions” (Març 2010) XREAP2010-04 Bermúdez, Ll. (RFA-IREA), Karlis, D. “Modelling dependence in a ratemaking procedure with multivariate Poisson regression models” (Abril 2010) XREAP2010-05 Di Paolo, A. (IEB) “Parental education and family characteristics: educational opportunities across cohorts in Italy and Spain” (Maig 2010) SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP XREAP2010-06 Simón, H. (IEB), Ramos, R. (AQR-IREA), Sanromá, E. (IEB) “Movilidad ocupacional de los inmigrantes en una economía de bajas cualificaciones. El caso de España” (Juny 2010) XREAP2010-07 Di Paolo, A. (GEAP & IEB), Raymond, J. Ll. (GEAP & IEB) “Language knowledge and earnings in Catalonia” (Juliol 2010) XREAP2010-08 Bolancé, C. (RFA-IREA), Alemany, R. (RFA-IREA), Guillén, M. (RFA-IREA) “Prediction of the economic cost of individual long-term care in the Spanish population” (Setembre 2010) XREAP2010-09 Di Paolo, A. (GEAP & IEB) “Knowledge of catalan, public/private sector choice and earnings: Evidence from a double sample selection model” (Setembre 2010) XREAP2010-10 Coad, A., Segarra, A. (GRIT), Teruel, M. (GRIT) “Like milk or wine: Does firm performance improve with age?” (Setembre 2010) XREAP2010-11 Di Paolo, A. (GEAP & IEB), Raymond, J. Ll. (GEAP & IEB), Calero, J. (IEB) “Exploring educational mobility in Europe” (Octubre 2010) XREAP2010-12 Borrell, A. (GiM-IREA), Fernández-Villadangos, L. (GiM-IREA) “Clustering or scattering: the underlying reason for regulating distance among retail outlets” (Desembre 2010) XREAP2010-13 Di Paolo, A. (GEAP & IEB) “School composition effects in Spain” (Desembre 2010) XREAP2010-14 Fageda, X. (GiM-IREA), Flores-Fillol, R. “Technology, Business Models and Network Structure in the Airline Industry” (Desembre 2010) XREAP2010-15 Albalate, D. (GiM-IREA), Bel, G. (GiM-IREA), Fageda, X. (GiM-IREA) “Is it Redistribution or Centralization? On the Determinants of Government Investment in Infrastructure” (Desembre 2010) XREAP2010-16 Oppedisano, V., Turati, G. “What are the causes of educational inequalities and of their evolution over time in Europe? Evidence from PISA” (Desembre 2010) XREAP2010-17 Canova, L., Vaglio, A. “Why do educated mothers matter? A model of parental help” (Desembre 2010) 2011 XREAP2011-01 Fageda, X. (GiM-IREA), Perdiguero, J. (GiM-IREA) “An empirical analysis of a merger between a network and low-cost airlines” (Maig 2011) SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP XREAP2011-02 Moreno-Torres, I. (ACCO, CRES & GiM-IREA) “What if there was a stronger pharmaceutical price competition in Spain? When regulation has a similar effect to collusion” (Maig 2011) XREAP2011-03 Miguélez, E. (AQR-IREA); Gómez-Miguélez, I. “Singling out individual inventors from patent data” (Maig 2011) XREAP2011-04 Moreno-Torres, I. (ACCO, CRES & GiM-IREA) “Generic drugs in Spain: price competition vs. moral hazard” (Maig 2011) XREAP2011-05 Nieto, S. (AQR-IREA), Ramos, R. (AQR-IREA) “¿Afecta la sobreeducación de los padres al rendimiento académico de sus hijos?” (Maig 2011) XREAP2011-06 Pitt, D., Guillén, M. (RFA-IREA), Bolancé, C. (RFA-IREA) “Estimation of Parametric and Nonparametric Models for Univariate Claim Severity Distributions - an approach using R” (Juny 2011) XREAP2011-07 Guillén, M. (RFA-IREA), Comas-Herrera, A. “How much risk is mitigated by LTC Insurance? A case study of the public system in Spain” (Juny 2011) XREAP2011-08 Ayuso, M. (RFA-IREA), Guillén, M. (RFA-IREA), Bolancé, C. (RFA-IREA) “Loss risk through fraud in car insurance” (Juny 2011) XREAP2011-09 Duch-Brown, N. (IEB), García-Quevedo, J. (IEB), Montolio, D. (IEB) “The link between public support and private R&D effort: What is the optimal subsidy?” (Juny 2011) XREAP2011-10 Bermúdez, Ll. (RFA-IREA), Karlis, D. “Mixture of bivariate Poisson regression models with an application to insurance” (Juliol 2011) XREAP2011-11 Varela-Irimia, X-L. (GRIT) “Age effects, unobserved characteristics and hedonic price indexes: The Spanish car market in the 1990s” (Agost 2011) XREAP2011-12 Bermúdez, Ll. (RFA-IREA), Ferri, A. (RFA-IREA), Guillén, M. (RFA-IREA) “A correlation sensitivity analysis of non-life underwriting risk in solvency capital requirement estimation” (Setembre 2011) XREAP2011-13 Guillén, M. (RFA-IREA), Pérez-Marín, A. (RFA-IREA), Alcañiz, M. (RFA-IREA) “A logistic regression approach to estimating customer profit loss due to lapses in insurance” (Octubre 2011) XREAP2011-14 Jiménez, J. L., Perdiguero, J. (GiM-IREA), García, C. “Evaluation of subsidies programs to sell green cars: Impact on prices, quantities and efficiency” (Octubre 2011) SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP XREAP2011-15 Arespa, M. (CREB) “A New Open Economy Macroeconomic Model with Endogenous Portfolio Diversification and Firms Entry” (Octubre 2011) XREAP2011-16 Matas, A. (GEAP), Raymond, J. L. (GEAP), Roig, J.L. (GEAP) “The impact of agglomeration effects and accessibility on wages” (Novembre 2011) XREAP2011-17 Segarra, A. (GRIT) “R&D cooperation between Spanish firms and scientific partners: what is the role of tertiary education?” (Novembre 2011) XREAP2011-18 García-Pérez, J. I.; Hidalgo-Hidalgo, M.; Robles-Zurita, J. A. “Does grade retention affect achievement? Some evidence from PISA” (Novembre 2011) XREAP2011-19 Arespa, M. (CREB) “Macroeconomics of extensive margins: a simple model” (Novembre 2011) XREAP2011-20 García-Quevedo, J. (IEB), Pellegrino, G. (IEB), Vivarelli, M. “The determinants of YICs’ R&D activity” (Desembre 2011) XREAP2011-21 González-Val, R. (IEB), Olmo, J. “Growth in a Cross-Section of Cities: Location, Increasing Returns or Random Growth?” (Desembre 2011) XREAP2011-22 Gombau, V. (GRIT), Segarra, A. (GRIT) “The Innovation and Imitation Dichotomy in Spanish firms: do absorptive capacity and the technological frontier matter?” (Desembre 2011) 2012 XREAP2012-01 Borrell, J. R. (GiM-IREA), Jiménez, J. L., García, C. “Evaluating Antitrust Leniency Programs” (Gener 2012) XREAP2012-02 Ferri, A. (RFA-IREA), Guillén, M. (RFA-IREA), Bermúdez, Ll. (RFA-IREA) “Solvency capital estimation and risk measures” (Gener 2012) XREAP2012-03 Ferri, A. (RFA-IREA), Bermúdez, Ll. (RFA-IREA), Guillén, M. (RFA-IREA) “How to use the standard model with own data” (Febrer 2012) XREAP2012-04 Perdiguero, J. (GiM-IREA), Borrell, J.R. (GiM-IREA) “Driving competition in local gasoline markets” (Març 2012) XREAP2012-05 D’Amico, G., Guillen, M. (RFA-IREA), Manca, R. “Discrete time Non-homogeneous Semi-Markov Processes applied to Models for Disability Insurance” (Març 2012) SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP XREAP2012-06 Bové-Sans, M. A. (GRIT), Laguado-Ramírez, R. “Quantitative analysis of image factors in a cultural heritage tourist destination” (Abril 2012) XREAP2012-07 Tello, C. (AQR-IREA), Ramos, R. (AQR-IREA), Artís, M. (AQR-IREA) “Changes in wage structure in Mexico going beyond the mean: An analysis of differences in distribution, 1987-2008” (Maig 2012) XREAP2012-08 Jofre-Monseny, J. (IEB), Marín-López, R. (IEB), Viladecans-Marsal, E. (IEB) “What underlies localization and urbanization economies? Evidence from the location of new firms” (Maig 2012) XREAP2012-09 Muñiz, I. (GEAP), Calatayud, D., Dobaño, R. “Los límites de la compacidad urbana como instrumento a favor de la sostenibilidad. La hipótesis de la compensación en Barcelona medida a través de la huella ecológica de la movilidad y la vivienda” (Maig 2012) XREAP2012-10 Arqué-Castells, P. (GEAP), Mohnen, P. “Sunk costs, extensive R&D subsidies and permanent inducement effects” (Maig 2012) XREAP2012-11 Boj, E. (CREB), Delicado, P., Fortiana, J., Esteve, A., Caballé, A. “Local Distance-Based Generalized Linear Models using the dbstats package for R” (Maig 2012) XREAP2012-12 Royuela, V. (AQR-IREA) “What about people in European Regional Science?” (Maig 2012) XREAP2012-13 Osorio A. M. (RFA-IREA), Bolancé, C. (RFA-IREA), Madise, N. “Intermediary and structural determinants of early childhood health in Colombia: exploring the role of communities” (Juny 2012) XREAP2012-14 Miguelez. E. (AQR-IREA), Moreno, R. (AQR-IREA) “Do labour mobility and networks foster geographical knowledge diffusion? The case of European regions” (Juliol 2012) XREAP2012-15 Teixidó-Figueras, J. (GRIT), Duró, J. A. (GRIT) “Ecological Footprint Inequality: A methodological review and some results” (Setembre 2012) XREAP2012-16 Varela-Irimia, X-L. (GRIT) “Profitability, uncertainty and multi-product firm product proliferation: The Spanish car industry” (Setembre 2012) XREAP2012-17 Duró, J. A. (GRIT), Teixidó-Figueras, J. (GRIT) “Ecological Footprint Inequality across countries: the role of environment intensity, income and interaction effects” (Octubre 2012) XREAP2012-18 Manresa, A. (CREB), Sancho, F. “Leontief versus Ghosh: two faces of the same coin” (Octubre 2012) SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP XREAP2012-19 Alemany, R. (RFA-IREA), Bolancé, C. (RFA-IREA), Guillén, M. (RFA-IREA) “Nonparametric estimation of Value-at-Risk” (Octubre 2012) XREAP2012-20 Herrera-Idárraga, P. (AQR-IREA), López-Bazo, E. (AQR-IREA), Motellón, E. (AQR-IREA) “Informality and overeducation in the labor market of a developing country” (Novembre 2012) XREAP2012-21 Di Paolo, A. (AQR-IREA) “(Endogenous) occupational choices and job satisfaction among recent PhD recipients: evidence from Catalonia” (Desembre 2012) 2013 XREAP2013-01 Segarra, A. (GRIT), García-Quevedo, J. (IEB), Teruel, M. (GRIT) “Financial constraints and the failure of innovation projects” (Març 2013) XREAP2013-02 Osorio, A. M. (RFA-IREA), Bolancé, C. (RFA-IREA), Madise, N., Rathmann, K. “Social Determinants of Child Health in Colombia: Can Community Education Moderate the Effect of Family Characteristics?” (Març 2013) XREAP2013-03 Teixidó-Figueras, J. (GRIT), Duró, J. A. (GRIT) “The building blocks of international ecological footprint inequality: a regression-based decomposition” (Abril 2013) XREAP2013-04 Salcedo-Sanz, S., Carro-Calvo, L., Claramunt, M. (CREB), Castañer, A. (CREB), Marmol, M. (CREB) “An Analysis of Black-box Optimization Problems in Reinsurance: Evolutionary-based Approaches” (Maig 2013) XREAP2013-05 Alcañiz, M. (RFA), Guillén, M. (RFA), Sánchez-Moscona, D. (RFA), Santolino, M. (RFA), Llatje, O., Ramon, Ll. “Prevalence of alcohol-impaired drivers based on random breath tests in a roadside survey” (Juliol 2013) XREAP2013-06 Matas, A. (GEAP & IEB), Raymond, J. Ll. (GEAP & IEB), Roig, J. L. (GEAP) “How market access shapes human capital investment in a peripheral country” (Octubre 2013) XREAP2013-07 Di Paolo, A. (AQR-IREA), Tansel, A. “Returns to Foreign Language Skills in a Developing Country: The Case of Turkey” (Novembre 2013) XREAP2013-08 Fernández Gual, V. (GRIT), Segarra, A. (GRIT) “The Impact of Cooperation on R&D, Innovation andProductivity: an Analysis of Spanish Manufacturing and Services Firms” (Novembre 2013) XREAP2013-09 Bahraoui, Z. (RFA); Bolancé, C. (RFA); Pérez-Marín. A. M. (RFA) “Testing extreme value copulas to estimate the quantile” (Novembre 2013) 2014 XREAP2014-01 Solé-Auró, A. (RFA), Alcañiz, M. (RFA) “Are we living longer but less healthy? Trends in mortality and morbidity in Catalonia (Spain), 1994-2011” (Gener 2014) SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP XREAP2014-02 Teixidó-Figueres, J. (GRIT), Duro, J. A. (GRIT) “Spatial Polarization of the Ecological Footprint distribution” (Febrer 2014) XREAP2014-03 Cristobal-Cebolla, A.; Gil Lafuente, A. M. (RFA), Merigó Lindhal, J. M. (RFA) “La importancia del control de los costes de la no-calidad en la empresa” (Febrer 2014) XREAP2014-04 Castañer, A. (CREB); Claramunt, M.M. (CREB) “Optimal stop-loss reinsurance: a dependence analysis” (Abril 2014) XREAP2014-05 Di Paolo, A. (AQR-IREA); Matas, A. (GEAP); Raymond, J. Ll. (GEAP) “Job accessibility, employment and job-education mismatch in the metropolitan area of Barcelona” (Maig 2014) XREAP2014-06 Di Paolo, A. (AQR-IREA); Mañé, F. “Are we wasting our talent? Overqualification and overskilling among PhD graduates” (Juny 2014) XREAP2014-07 Segarra, A. (GRIT); Teruel, M. (GRIT); Bové, M. A. (GRIT) “A territorial approach to R&D subsidies: Empirical evidence for Catalonian firms” (Setembre 2014) XREAP2014-08 Ramos, R. (AQR-IREA); Sanromá, E. (IEB); Simón, H. “Public-private sector wage differentials by type of contract: evidence from Spain” (Octubre 2014) XREAP2014-09 Bel, G. (GiM-IREA); Bolancé, C. (Riskcenter-IREA); Guillén, M. (Riskcenter-IREA); Rosell, J. (GiM-IREA) “The environmental effects of changing speed limits: a quantile regression approach” (Desembre 2014) 2015 XREAP2015-01 Bolance, C. (Riskcenter-IREA); Bahraoui, Z. (Riskcenter-IREA), Alemany, R. (Risckcenter-IREA) “Estimating extreme value cumulative distribution functions using bias-corrected kernel approaches” (Gener 2015) XREAP2015-02 Ramos, R. (AQR-IREA); Sanromá, E. (IEB), Simón, H. “An analysis of wage differentials between full- and part-time workers in Spain” (Agost 2015) xarxa.xreap@gmail.com