The gender wage gap across the wage distribution: Evidence in tourism at the sectoral level

Abstract

We conducted a comparative study of the main determinants of the gender wage gap across the wage distribution in the tourism sector from a sectoral perspective. Using matched employer-employee data from Spain, we propose different wage decompositions across the wage distribution based on unconditional quantile regressions. In feminised sectors, such as hospitality and travel agencies, the gender wage gap follows an increasing trend across the wage distribution, whereas in masculinised sectors, such as transportation, the gap follows a decreasing trend, becoming non-significant at the highest wage levels. Except in the case of transportation, gender wage discrimination increases as wages increase and is the component that explains the major part of this gap. The results show that there are differential barriers to the promotion of women at a sectoral level that perpetuate gender roles, particularly in positions of high responsibility.

Keywords

gender wage gap tourism sectoral analysis unconditional quantile regression wage discrimination vertical segregation

Introduction

The service sector, and the tourism industry in particular, are generally associated with employment growth and the creation of jobs for people of different ages and skill levels. Within the group of European countries, the tourism industry is of special relevance in Spain (ILOSTAT, 2018). It comprises a diverse set of economic activities, such as accommodation services, catering, transportation, and cultural services. Riley and Szivas (2003) have observed that, to some extent, tourism is a low-paid industry, although there is evidence to suggest that certain occupations and sub-sectors in this industry are well-paid. In this respect, Choy (1995) and Shu et al. (2022) have observed a significant wage gap between sectors in Hawaii and China, respectively, showing that the highest average wages are in the air transport sector and the lowest in the catering service sector. Similarly, Webster (2014) conducted an analysis of wage levels in the tourism and tourism-associated sectors in the United States, demonstrating that wage levels were below those in the overall economy.

As stated by Riley and Szivas (2003), the intersectoral wage gap observed between tourism sub-sectors are due to their structural characteristics. According to Robinson et al. (2019), the characteristics of tourism industry sub-sectors are more heterogeneous than the activities included in other industries. Tourism activities differ greatly in terms of demand, working conditions, and occupational structure and also have a heterogeneous market structure (Gray, 2004; Shu et al., 2022). Industry concentration can permit firms in certain sectors to pay wages lower than their true marginal productivity (Benmelech et al. 2022). Apart from industry concentration, labour market frictions heighten the market power of firms to set wages. In the monopsony literature, it is a challenge to identify market power firms, which may influence the wage negotiation process at sectoral levels (Lanzona, 2022). In addition, market size and the intensity of competition between firms also play key roles. For instance, greater international competition may reduce employment and wages in domestic tourism firms, but it has been found that the magnitude of their effects varies across sectors (Shu et al., 2022). Similarly, these authors also have shown that the industrial linkages, investment, export, patterns of final demand, and the impact of labour returns to sectoral wages may be key determinants in explaining heterogeneity in wage growth across tourism sectors.

The hospitality industry stands out from other sectors due to its relatively low remuneration and unstable employment, which is due to how final demand affects tourism wages (Shu et al., 2022), and the high rate of labour turnover (Marchante et al., 2007; Casado and Simón, 2016). Authors such as Casado and Simón (2016) or Oliver and Sard (2021) have studied the origin of these differences to determine if they are caused by the productive characteristics of workers or by sectoral discrimination. However, the transport sector commands higher average wages than those of the service sector. According to the Observatory of Transport and Logistics in Spain (OTSL, 2021), the average wage per employee is slightly higher in this sector than that of the entire economy and has followed an upward trend since 2014. This report suggested that these aspects could be due to the evolution of labour productivity, which has increased considerably in recent years. Moreover, its business structure is polarised by large companies coexisting with many small companies of less than ten workers (García et al., 2018; OTLS, 2021). In general, workers in the travel agency sector are highly qualified (Díaz et al., 2015). However, this sector has characteristics that are similar to those of the hospitality sector regarding wages, working conditions, and seasonality of demand (García et al., 2011). The tourism sector also comprises complementary activities, such as renting and cultural and artistic creation, which involve many workers with heterogeneous characteristics (Casani, 2010). Few studies have analysed wages and working conditions in this sector at the international level (Lazzeretti et al., 2015), which is probably due to the diversity of its activities and the scarcity of datasets. This sector is characterised by intensive short-term employment and high seasonality of demand. It is also marked by the high precariousness of wages even among workers with high qualifications, who tend to be moonlighting (EPRS, 2019).

From a gender perspective, primary, manufacturing, and construction industries tend to be male-dominated. However, service industries – particularly those with direct interaction with customers-offer more job opportunities for women, although some service industries, such as transportation, are male-dominated (Pirra et al., 2020). Previous studies have shown the persistence of female over-representation within the service sector, particularly in the hospitality and travel agency industries (Garcia et al., 2011; Oliver and Sard, 2021).

Workers within these sectors are also differentiated by high and low wages, which is an indication of heterogeneous labour segmentation at the sectoral level (Casado and Simón, 2016). This type of segmentation gives rise to a primary market characterised by jobs with good working conditions, high wages, stability, and better promotion options, and a secondary market characterised by jobs with more precarious working conditions, low wages, instability, and few promotion options (Piore, 1969). The gender wage gap may be influenced in a particular way by the unequal participation of men and women in the primary and secondary markets at the sectoral level (Fernández and Rodríguez, 2011; Oliver and Sard, 2021). Previous studies have addressed this issue by analysing the labour and wage disparities between hospitality and other private services (Campos et al., 2011; García et al., 2012). However, this literature does not address how sectoral heterogeneity in the tourism industry may affect the gender wage gap and its discriminatory component.

On the other hand, previous literature has determined that there are factors, such as vertical segregation or the ‘glass ceiling’, that differentially affect specific occupational groups in each sector (Costa et al., 2017; Carvalho et al., 2019; Marfil and Campos, 2021). Thus, it is of interest to address the main determinants of the wage gap and, in particular, its discriminatory component across the wage distribution. This paper contributes to previous literature by addressing the determinants of gender wage inequality across the wage distribution in the tourism industry from a sectoral perspective. Using matched employer-employee data from Spain, we propose different wage decompositions across the wage distribution based on unconditional quantile regressions.

This paper is organised as follows: the next section reviews the literature on the gender wage gap and its determinants in tourism, focussing on its main sectors; the third and fourth sections describe the methodology and the database, respectively; the fifth section presents the discussion and main results; and the final section provides the main conclusions.

Literature review

Economic theory has attempted to explain gender wage differences using a set of theories within the frameworks of labour demand and labour supply. On the supply side, Becker’s Human Capital theory (1964) is the leading theory within the neoclassical literature. It suggests that the discrepancy between men’s and women’s wages is at least partly due to gender productivity gaps. Thus, gender differences in human capital endowments can explain the major part of gender wage inequality. On the demand side, discrimination theories have attempted to analyse situations in which women have the same characteristics as men, but are paid less than their male co-workers. According to Cain (1991), these theories can be classified into three groups: Neoclassical, Marxist, and Institutional theories. Although there is no consensus on which of these is the most effective in explaining discrimination from an empirical point of view (Blau and Kahn, 2006), all three theories agree that gender wage discrimination exists when women are paid less than men for the same job involving equivalent tasks, skills, responsibilities, and working conditions (De Cabo and Garzón, 2007).

The empirical literature has developed a large battery of methodological approaches to decompose the gender wage gap from an empirical point of view. It has analysed its causes and identified a ‘non-discriminatory’ component, which is due to differences in the productive characteristics of men and women, and a ‘discriminatory’ component, which is due to differences in how these characteristics are remunerated by gender (e.g. see Oaxaca, 1973; Blinder, 1973; Juhn et al., 1993; Oaxaca and Ransom, 1994; Machado and Mata, 2005; Korkeamäki and Kyyrä, 2006). Among the determinants of the gender wage gap addressed in the tourism sector, it is worth highlighting those such as education (Lillo and Casado, 2012; Pritchard and Morgan, 2017; Kortt et al., 2018), educational mismatch (García et al., 2012; Campos et al., 2015), and occupational gender segregation (Campos et al., 2009; Segovia et al., 2018; Marfil and Campos, 2021).

Education has traditionally been identified as a relevant factor in explaining wages (Lillo and Ramón, 2005), particularly in highly feminised sectors such as hospitality or travel agencies (Muñoz, 2009; Casado and Simón, 2016; García et al., 2014). However, in recent decades, there has been a decreasing trend in the gender education gap due to the increase in women’s educational levels (Salinas et al., 2014), which are no longer a barrier to women’s access to positions of higher responsibility (Segovia et al., 2018). On the other hand, numerous studies have shown that educational returns remain a key factor in explaining the discriminatory component of the gender wage gap. For example, Lillo and Casado (2015) suggested that returns to education are lower in the Spanish tourism sector than in other sectors; and they are even lower in the hospitality sector – in which women are particularly penalised – than in other private services (García et al. 2011, 2014).

Educational mismatch among workers is another relevant human capital variable used to explain the gender wage gap. This variable is of great international interest due to its implications for labour productivity and market efficiency (Iriondo and Pérez, 2016; Sellami et al., 2020). Educational mismatch is defined as a situation in which workers have skills levels that differ from those required to perform their jobs, distinguishing between over-educated and under-educated workers (Salinas et al., 2014). In line with results obtained in other sectors, in the tourism industry, over-educated individuals obtain lower wages than those who, with similar levels of education and experience, occupy positions for which they are appropriately educated (Lillo and Casado, 2010). Lillo and Casado (2015) have suggested that more than half of the individuals with high educational levels are employed in positions for which they are overeducated. This problem is particularly relevant for young postgraduates in the first stages of labour market insertion. From a gender perspective, the literature suggests that there is a relationship between educational mismatch and wages as evidenced by significant differences in returns by gender (García et al., 2014). According to the results of this study, in the hospitality and travel agency sectors, the wage penalty among women experiencing educational mismatch is greater than it is among men, thus indicating the relevance of this variable on wage discrimination.

The literature has recently shown that occupational gender segregation is another relevant determinant in explaining the gender wage gap (Korkeamäki and Kyyrä, 2006). Previous results obtained in the tourism sector have also shown that gender segregation explains a high percentage of the gender wage difference. Occupational gender segregation hinders promotion among female workers, as they are overrepresented in low-wage occupations and in those with low levels of responsibility in which promotion opportunities are limited (Campos et al., 2009; Carvalho et al., 2019). Muñoz (2009) and García et al. (2014) have shown that wages are lower in feminised sectors, such as hospitality and travel agencies, than in other services. Moreover, they also suggest that women’s wages in these settings are even lower, citing as an explanatory factor the scarcity of women in positions of high responsibility. In contrast, the transport sector, which is characterised by male over-representation, is considered to be a high-wage sector within the services industry (Pirra et al., 2020; OTLS, 2021). However, according to the European Parliamentary Research Service (EPRS, 2019), female employment is high in the cultural and creative arts industries, despite men occupying the most responsible positions within them.

These results justify our interest in analysing the determinants of gender wage gap inequality, as well as their discriminatory components, across the wage distribution in the tourism industry from a sectoral perspective. In fact, most of the previous studies on this issue have investigated the tourism sector as a whole in different regions and countries (Santos and Varejão, 2007; Thrane, 2008; Muñoz, 2009; Kortt et al., 2018; Carvalho et al., 2019; Zhang and Zhang, 2021), or have focussed on the hospitality sector in particular (Campos et al., 2009; Casado et al., 2020; Segovia et al., 2018; Oliver and Sard, 2021; Marfil and Campos, 2021). However, few studies have analysed the gender wage gap in the other sub-sectors that compose the tourist sector, such as the transport sector (Pirra et al., 2020). In general, such studies only address the sectoral perspective in a very limited way. For example, Fernández et al. (2009) compared the hospitality industry and other private services. Casado et al. (2020) and Oliver and Sarz (2021) analysed the hospitality industry in relation to the rest of the economy, whereas Kort et al. (2018) compared the hospitality sector and the entire tourism sector.

Thus, it seems appropriate to conduct a detailed examination of the gender wage gap from a sectoral perspective, taking into account both the limitations of the previous results as well as sectoral heterogeneity – which is particularly relevant from a gender perspective – in the occupational and wage structure observed in the tourism industry. The aim of this comparative analysis is to identify the determinants of the gender wage gap and its discriminatory components as well as identify the main differences in each of the sectors analysed such that specific measures can be applied to reduce gender discrimination in each of them. Given that there are factors, such as educational mismatch and segregation, that may differentially affect certain occupational groups across the wage distribution, it is relevant to analyse the contribution of these factors to the gender wage gap and its discriminatory component across this distribution.

Methodology

Theoretical model

The theoretical model proposed by Becker (1971) makes it possible to understand the determinants of the gender wage gap and its discriminatory component, based on how female and male employment is incorporated into the employer’s utility and production functions. This model assumes that the establishments of sector $j$ share similar structural characteristics, with no statistically significant differences between them. An establishment produces with the technology $h (H, L),$ where $H$ and $L$ are different types of workers (e.g. workers who belong to high-wage levels, $H$ , and those who belong to low-wage levels, $L$ ). $H$ and $L$ workers may have different productive levels, but the number of male, $M$ , and female, $F$ , workers hired may differ within each group: $M_{H}$ , $M_{L}$ , $F_{H}$ and $F_{L}$ . Although male and female workers may be homogeneous inputs, four wages may exist, $w_{M H}$ , $w_{M L}$ , $w_{F H}$ and $w_{F L}$ , due to the possibility of discrimination. The price of output is normalised to one. The profit of each establishment belonging to sector $j$ can be expressed as follows

π j = h (M_{H} + F_{H}, M_{L} + F_{L}) - w_{M H} M_{H} - w_{F H} F_{H} - w_{M L} M_{L} - w_{F L} F_{L}

(1)

In addition, it considers that the employers of each sector

j

have discriminatory preferences towards the hiring of workers of each gender, because their utility does not only depend on profits, but also on the gender-composition of their labour demand (Neumark, 1988). Thus, employers are willing to reduce their profits by hiring fewer female workers than their optimal level. Their aim is to maximise a utility function like the following

U_{j} = (π j, M_{H}, F_{H}, M_{L}, F_{L})

(2)

Function $U_{j} (.)$ is strictly concave and $U_{M H}^{'} \geq 0$ , $U_{M L}^{'} \geq 0,$ $U_{F H}^{'} \leq 0$ and $U_{F L}^{'} \leq 0$ . The first two inequalities capture discrimination due to nepotism towards men, whereas the last two collect discrimination against women.

As usual, utility maximisation is subject to a sufficient profit constraint following equation (1). Then the first order conditions are as follows

w_{M H} = h_{M H}^{'} - d_{M H}

(3)

w_{M L} = h_{M L}^{'} - d_{M L}

(4)

w_{F H} = h_{F H}^{'} - d_{F H}

(5)

w_{F L} = h_{F L}^{'} - d_{F L}

(6)

where

h^{'} (.)

is the marginal product of labour and

d_{M H}

d_{M L}

d_{F H}

, and

d_{F L}

are the discrimination coefficients, which are functions of the partial derivatives of the utility function. That is:

d_{M H} = - U_{M H}^{'} / U_{π j}^{'}

;

d_{M L} = - U_{M L}^{'} / U_{π j}^{'}

;

d_{F H} = - U_{F H}^{'} / U_{π j}^{'}

;

d_{F L} = - U_{F L}^{'} / U_{π j}^{'}

. Given the signs of the first derivatives of the utility function, the following can be deduced

w_{M H} \geq h_{H}^{'} \geq w_{F H}

(7)

w_{M L} \geq h_{L}^{'} \geq w_{F L}

(8)

Therefore, once the general model has been developed, different assumptions about the structure of employers’ discriminatory preferences and how these relate to the discriminatory and non-discriminatory wage structures can be explored. Two extreme cases can be assumed with regard to the non-discriminatory wage structure. It can be assumed that $d_{M H} = d_{M L} = 0$ , so there is no nepotism towards men in high- and low-wage levels, and only discrimination against women exists; therefore

w_{M H} = h_{M H}^{'} a n d w_{F H} = h_{F H}^{'} - d_{F H}

(9)

w_{M L} = h_{M L}^{'} a n d w_{F L} = h_{F L}^{'} - d_{F L}

(10)

In the opposite case, it can be assumed that there is no discrimination against women

d_{F H} = d_{F L} = 0

, and only nepotism towards men exists in both groups of workers,

H

and

L

. Then

w_{F H} = h_{F H}^{'} a n d w_{M H} = h_{M H}^{'} - d_{M H}

(11)

w_{F L} = h_{F L}^{'} a n d w_{M L} = h_{M L}^{'} - d_{M L}

(12)

Therefore, the gender wage gap in sector $j$ can be explained by three components: productivity inequality between male and female workers, favouritism towards men, and discrimination against women; the latter two refer to employers' preferences and explain the discriminatory component.

Unconditional quantile regressions approach

Based on the discrete wage discrimination model for high- and low-wage workers, $H$ and $L$ , respectively, we propose an econometric specification that makes it possible to analyse the gender wage gap and its discrimination component continuously across the wage distribution. The gender wage decomposition across the wage distribution was initially analysed using methods based on conditional quantile regressions (CQR) (Koenker and Basset, 1978). Starting from the definition of the conditional quantile operator $δ$ of $w$ , $Q_{δ} [w | X = x] = \inf {w : F_{w | x} \geq δ}$ , where $F_{w | x}$ represents the joint distribution of $w$ and $x$ , it can be observed that the workers occupying the different quantiles are not only classified according to their wages, $w$ , but also according to their productive characteristics, $x$ . This approach could misidentify employees within the wage distribution (Firpo, Fortin and Lemieux, 2007, 2018; Oliver and Sard, 2021) by placing, for example, workers with lower wages but higher productive levels than others in the high-wage quantiles. Therefore, the CQR method can only calculate the partial effect of $x$ on the conditional quantile $δ$ of $w$ (Firpo et al., 2009). Thus, it cannot be used to analyse the wage decomposition across the wage distribution because it cannot obtain information on the unconditional marginal effect of the determinants of the gender wage gap.

In response to the weaknesses of the CQR method, Firpo et al. (2007, 2009) developed a wage decomposition methodology based on the calculation of unconditional quantile regressions (UQR). One of the main advantages of this methodological approach is that it can be used to calculate the unconditional marginal effect of the covariates that comprise the different components of the gender wage gap. The operator of the unconditional quantile $θ$ can be defined as the function $Q_{θ} = Q (F_{w} | θ) = \inf {w | F_{w} \geq θ}$ , where $F_{w}$ is the unconditional wage distribution. This definition unambiguously assigns each worker across the wage distribution by their wage level.

The calculation of marginal effects is based on the influence function (IF), which was introduced by Hampel (1974) and later adapted by Firpo et al. (2007, 2009). According to these authors, IF measures the effect of an additional observation (worker) on the quantile $θ$ of the unconditional wage distribution and would be defined as

I F (w; Q_{θ}, F_{w}) = \frac{θ - l {w \leq Q_{θ}}}{f_{w} (Q_{θ})}

(13)

where

f_{w} (Q_{θ}

) is the wage density function evaluated at

Q_{θ}

. The function

l {w \leq Q_{θ}}

is used to identify whether the wage is within the quantile, thus ensuring that only this part of the sample is considered in the estimation. The recentered influence function (RIF) transforms the wage through the IF in relation to the unconditional quantile

Q_{θ}

R I F (w; Q_{θ}, F_{w}) = Q_{θ} + I F (w; Q_{θ}, F_{w})

(14)

Thanks to the law of iterated expectation, the expectation of the RIF can be estimated for the sample of men and women separately for each sector $j$ using a linear Mincerian wage model, in which the dependent variable is the wage transformation through the RIF function

E ({R I F}_{j M} (w_{j M}; Q_{θ}, F_{w}) │ X_{j M}) = Q_{j M}^{θ} = X_{j M}^{θ} β_{j M}^{θ} + ε_{j M}^{θ}

(15)

E ({R I F}_{j F} (w_{j F}; Q_{θ}, F_{w}) │ X_{j F}) = Q_{j F}^{θ} = X_{j F}^{θ} β_{j F}^{θ} + ε_{j F}^{θ}

(16)

where

X_{j .}

is a vector of explanatory variables for male and female workers belonging to sector

j

(i.e. characteristics of the worker, job, and establishment).

β_{j M}^{θ}

and

β_{j F}^{θ}

are the vectors of coefficients from the RIF regression for the male and female samples, respectively. For each sector

j,

the decomposition of the gender wage gap into the unconditional quantile,

△_{j}^{θ}

, is based on the model proposed by Oaxaca-Blinder (Oaxaca, 1973; Blinder, 1973). Adding and subtracting the counterfactual component, the gender wage gap decomposition can be expressed as follows

△_{j}^{θ} = {\bar{X}}_{j M}^{θ} β_{j M}^{θ} - {\bar{X}}_{j F}^{θ} β_{j F}^{θ} = {\bar{X}}_{j M}^{θ} β_{j M}^{θ} - {\bar{X}}_{j F}^{θ} β_{j F}^{θ} + {\bar{X}}_{j C}^{θ} β_{j C}^{θ} - {\bar{X}}_{j C}^{θ} β_{j C}^{θ}

(17)

where

{\bar{X}}_{j C}^{θ}

and

β_{j C}^{θ}

are vectors of explanatory variables and coefficients for the counterfactual sample, respectively, which represents the non-discriminatory wage structure. The counterfactual sample assumes that there is no nepotism towards men and there is only discrimination against women along the wage distribution (based on equations (9) and (10)). Therefore, the non-discriminatory wage structure is based on the calculation of the wage that a male worker with his prevailing productive characteristics (

{\bar{X}}_{j C}^{θ} = {\bar{X}}_{j M}^{θ})

would receive if these characteristics were remunerated as if he was a female co-worker (

β_{j C}^{θ} = β_{j F}^{θ})

. Equation (17) can be expressed as follows

△_{j}^{θ} = {\bar{X}}_{j M}^{θ} β_{j M}^{θ} - {\bar{X}}_{j F}^{θ} β_{j F}^{θ} = {\bar{X}}_{j M}^{θ} β_{j M}^{θ} - {\bar{X}}_{j F}^{θ} β_{j F}^{θ} + {\bar{X}}_{j M}^{θ} β_{j F}^{θ} - {\bar{X}}_{j M}^{θ} β_{j F}^{θ} = ({\bar{X}}_{j M}^{θ} - {\bar{X}}_{j F}^{θ}) β_{j F}^{θ} + {\bar{X}}_{j M}^{θ} (β_{j M}^{θ} - β_{j F}^{θ}) = △_{j x}^{θ} + △_{j s}^{θ}

(18)

Using this wage decomposition method, the gender wage gap in each sector $j$ could be expressed as the sum of a non-discriminatory component, $△_{j x}^{θ}$ , and a discriminatory component, $△_{j s}^{θ}$

△_{j x}^{θ} = ({\bar{X}}_{j M}^{θ} - {\bar{X}}_{j F}^{θ}) β_{j F}^{θ}

(19)

△_{j s}^{θ} = {\bar{X}}_{j M}^{θ} (β_{j M}^{θ} - β_{j F}^{θ})

(20)

Data and variables

This study used the most recent data (for 2018) from the Structure of Earnings Survey, which is compiled by the Spanish National Statistics Institute every 4 years (SES-2018). The survey is conducted using harmonised criteria in methodology and content for European Union countries. It contains matched employee-employer microdata on a total of 234 246 workers from 27 549 establishments. We analysed the following activities: hospitality, corresponding to Section I of the Spanish National Classification of Economic Activities (CNAE, 2009), with a sample size of 6943 workers; transportation, corresponding to codes 49, 50, 51, and 52, with a sample size of 6436 workers; travel agencies, corresponding to code 79, with 1210 workers; and complementary services, corresponding to codes 77, 90, 91, and 93, with 3160 workers.

The Structure of Earnings Survey contains detailed information on wages, the personal characteristics of workers, and the characteristics of the job and establishment (Table 1). The dependent variable in our model is log-gross hourly wage, which is based on its transformation by the RIF function. Table 1 presents the variables that comprise the Mincerian semi-logarithmic wage regression shown in equations (15) and (16).

Table 1.

Explanatory variables.

Explanatory variables	Description
Personal characteristics
Gender	= 1 if the worker is male
Nationality	= 1 if the worker has Spanish nationality
	= 2 if the worker has European nationality
	= 3 if the worker has non-European nationality
Education	Years of education
Educational mismatch	= 1 if the worker is adequately educated
	= 2 if the worker is overeducated
	= 3 if the worker is undereducated
Tenure	Years a worker has held the job
Job characteristics
Temporary contract	= 1 if the worker has a fixed-term contract (temporary)
Part-time job	= 1 if the worker is employed part-time
Vertical segregation	Vertical segregation index, based on the Duncan and Duncan dissimilarity index (1955)
Establishment characteristics
Size	= 1 if the establishment is ‘small’ (less than 50 workers)
	= 2 if the establishment is ‘medium’ (between 50 and 199 workers)
	= 3 if the establishment is ‘large’ (more than 200 workers)
Market	= 1 if the establishment operates at a local level
	= 2 if the establishment operates at a national level
	= 3 if the establishment operates at an international level
Agreement	= 1 if business collective agreement
	= 2 if sectorial collective agreement
	= 3 if state collective agreement

Results

Descriptive analysis

Figure 1 shows the wage distribution for men and women in the different sectors analysed. It shows that there is a concentration of workers at lower wage levels in all sectors, but particularly in the hospitality and travel agency sectors. These sectors are marked by being female-intensive, yet women are highly polarised into low-wage jobs. In contrast, there is a more balanced distribution by gender across the wage distribution in complementary services and in transportation, the latter sector being male-dominated.

Figure 1.

Wage distribution. Hospitality, Transportation, Travel Agencies, and Complementary Services. Notes: The frequency scale is different for each sector. The sample has been truncated at a value of 100 for gross hourly wage.

Figure 2 clearly shows that the gender wage gap follows a sectoral heterogeneous pattern across the wage distribution. The gaps are quite narrow in complementary services and hospitality services at the bottom of the wage distribution. This result is probably due to the effect of the minimum wage or wage rigidities imposed by collective agreements mainly on low-wage levels (Oliver and Sarz, 2021). In general, there is higher sectoral heterogeneity in the gender wage gap at the highest percentiles. It is comparatively higher in female-intensive sectors, such as hospitality and travel agencies, reaching values close to 40% in the 90th decile. The transport sector breaks this trend, decreasing to 3% in the 90th decile.

Figure 2.

Percentage of gender wage gap by sector. Notes: The percentage wage differential is calculated on the gross hourly wage.

Table 2 shows that levels of education and tenure are lower among workers in the hospitality industry – particularly among female workers – than among those in other sectors. Non-European immigrant workers, especially men, are particularly segregated in the hospitality sector compared to their situation in other sectors. In the hospitality sector, around 26% of workers are over-educated – particularly women – and relatively few workers are under-educated compared to workers in the other sectors, which tend to be dominated by men. There are more part-time contracts in this sector than in the others, particularly in the case of women.

Table 2.

Descriptive statistics by sector.

	Hospitality		Transportation		Travel agencies		Complementary services
Explanatory variables	Men	Women	Men	Women	Men	Women	Men	Women
Personal characteristics
Spanish	0.892 (0.310)	0.849^*** (0.358)	0.930 (0.254)	0.921 (0.269)	0.943 (0.232)	0.893^** (0.310)	0.948 (0.222)	0.916^*** (0.277)
European immigrant	0.056 (0.229)	0.059 (0.236)	0.055 (0.229)	0.060 (0.237)	0.066 (0.248)	0.043^* (0.204)	0.044 (0.205)	0.043 (0.204)
Non-European immigrant	0.095 (0.294)	0.049^*** (0.216)	0.023 (0.151)	0.010^*** (0.099)	0.042 (0.200)	0.014^*** (0.116)	0.040 (0.195)	0.009^*** (0.094)
Education	10.211 (2.102)	10.086^* (1.986)	10.426 (2.375)	11.725^*** (2.740)	13.236 (2.936)	12.815^** (2.639)	11.325 (2.808)	12.111^*** (2.950)
Adequately educated	0.593 (0.009)	0.624^*** (0.007)	0.591 (0.007)	0.433^*** (0.012)	0.478 (0.027)	0.587^*** (0.017)	0.517 (0.011)	0.432^*** (0.014)
Overeducation	0.248 (0.432)	0.279^* (0.448)	0.222 (0.416)	0.286^*** (0.452)	0.170 (0.376)	0.112^*** (0.315)	0.199 (0.399)	0.228^* (0.419)
Undereducation	0.158 (0.365)	0.097^*** (0.296)	0.187 (0.390)	0.281^*** (0.450)	0.352 (0.478)	0.301^* (0.459)	0.284 (0.451)	0.340^*** (0.474)
Tenure	8.101 (9.590)	7.514^** (7.543)	12.770 (11.617)	11.394^*** (9.752)	8.677 (9.033)	9.396^* (7.612)	7.065 (8.428)	7.653^* (8.148)
Job characteristics
Vertical segregation	0.285 (0.085)	0.306^*** (0.062)	0.562 (0.190)	0.553^* (0.187)	0.112 (0.122)	0.076^*** (0.107)	0.705 (0.394)	0.703 (0.395)
Indefinite contract	0.808 (0.007)	0.810 (0.006)	0.842 (0.005)	0.855 (0.009)	0.937 (0.013)	0.896^** (0.010)	0.728 (0.010)	0.779^*** (0.012)
Temporary contract	0.191 (0.394)	0.189 (0.392)	0.158 (0.365)	0.145 (0.352)	0.063 (0.243)	0.104^** (0.305)	0.271 (0.445)	0.221^*** (0.415)
Full-time job	0.613 (0.009)	0.408^*** (0.007)	0.889 (0.004)	0.776^*** (0.011)	0.886 (0.017)	0.833^** (0.013)	0.661 (0.011)	0.548^*** (0.014)
Part-time job	0.387 (0.487)	0.592^*** (0.492)	0.111 (0.314)	0.224^*** (0.417)	0.113 (0.318)	0.167^** (0.373)	0.339 (0.474)	0.452^*** (0.498)
Establishment characteristics
Small size	0.258 (0.438)	0.213^*** (0.410)	0.298 (0.457)	0.314 (0.464)	0.379 (0.486)	0.409 (0.492)	0.588 (0.492)	0.581 (0.494)
Medium size	0.263 (0.440)	0.225^*** (0.418)	0.216 (0.412)	0.198^* (0.398)	0.215 (0.411)	0.239 (0.426)	0.289 (0.453)	0.265^* (0.442)
Large size	0.479 (0.500)	0.562^*** (0.496)	0.486 (0.500)	0.489 (0.500)	0.406 (0.492)	0.353^* (0.478)	0.123 (0.329)	0.154^** (0.361)
Local market	0.361 (0.480)	0.420^*** (0.494)	0.325 (0.468)	0.209^*** (0.407)	0.069 (0.253)	0.084 (0.278)	0.616 (0.486)	0.621 (0.485)
National market	0.313 (0.464)	0.371^*** (0.483)	0.476 (0.499)	0.404^*** (0.491)	0.719 (0.450)	0.763^* (0.426)	0.376 (0.484)	0.365 (0.482)
International market	0.326 (0.469)	0.209^*** (0.407)	0.199 (0.399)	0.387^*** (0.487)	0.212 (0.409)	0.153^** (0.360)	0.008 (0.091)	0.014 (0.119)
Business agreement	0.113 (0.316)	0.278^*** (0.448)	0.079 (0.269)	0.093^* (0.290)	0.979 (0.143)	0.960 (0.196)	0.268 (0.443)	0.292 (0.455)
Sectoral agreement	0.807 (0.395)	0.651^*** (0.477)	0.433 (0.496)	0.400^** (0.490)	0.006 (0.077)	0.007 (0.083)	0.407 (0.491)	0.370^** (0.483)
State agreement	0.080 (0.272)	0.071^* (0.257)	0.488 (0.500)	0.508^* (0.500)	0.015 (0.121)	0.033^* (0.179)	0.325 (0.468)	0.337 (0.473)
No observations	2711	4232	4913	1523	335	876	1912	1248

The table shows the means and standard deviations (in parentheses). Assuming independent samples and unequal variances, the p-value of the mean comparison test is shown in the columns displaying the men’s means (***p < 0.01, **p < 0.05, and *p < 0.1).

The transport sector is dominated by native Spanish male workers. Women in this sector have high levels of education. Nevertheless, a high proportion of men are adequately educated for their jobs, whereas there is a much higher proportion of over-educated or under-educated women. On the other hand, women occupy positions in which there is less occupational inequality between genders. This sector is polarised between large and small establishments.

Travel agency workers have educational levels that are higher than those in the other sectors, particularly in the case of men. In general, women occupy a higher proportion of jobs for which they are adequately educated, whereas men are more likely to occupy jobs for which they are over-educated and under-educated. On the other hand, the lowest degree of occupational gender inequality between the different levels of responsibility stands out in this sector, particularly in those occupations dominated by women. The travel agency and complementary services sectors are both dominated by native Spanish male workers. Complementary service workers have high educational levels, particularly women. Nevertheless, as in the transport sector, women tend to occupy jobs for which they are over-educated and under-educated. In general, temporary contracts are more frequent in the complementary services than in the other sectors. Vertical segregation is also the highest in this activity, without significant differences between genders.

Estimates

Table 3 presents the unconditional quantile regression estimates at the mean for the sectors analysed.

Table 3.

Estimates at the mean by sector and gender.

	Hospitality		Transportation		Travel agencies		Complementary services
Mean	Men	Women	Men	Women	Men	Women	Men	Women
Personal characteristics
European immigrant	0.045 (0.028)	0.063^*** (0.020)	−0.021 (0.028)	−0.001 (0.052)	0.183^* (0.094)	0.023 (0.064)	0.125^** (0.053)	0.030 (0.055)
Non-European immigrant	−0.035 (0.022)	−0.002 (0.022)	−0.129^*** (0.042)	−0.172 (0.122)	−0.075 (0.117)	0.035 (0.111)	0.071 (0.055)	−0.126 (0.120)
Education	0.048^*** (0.003)	0.038^*** (0.003)	0.075^*** (0.003)	0.052^*** (0.005)	0.045^*** (0.012)	0.033^*** (0.007)	0.055^*** (0.005)	0.046^*** (0.005)
Overeducation	−0.076^*** (0.016)	−0.073^*** (0.012)	0.027 (0.018)	0.058^* (0.031)	0.198^*** (0.071)	0.032 (0.046)	−0.164^*** (0.030)	−0.142^*** (0.032)
Undereducation	0.057^*** (0.020)	0.078^*** (0.018)	0.061^*** (0.017)	0.079^** (0.031)	0.109^* (0.064)	0.048 (0.036)	0.065^** (0.028)	0.035 (0.029)
Tenure	0.010^*** (0.000)	0.006^*** (0.001)	0.010^*** (0.001)	0.015^*** (0.001)	0.010^*** (0.003)	0.005^** (0.002)	0.016^*** (0.001)	0.017^*** (0.002)
Job characteristics
Vertical segregation	−0.049^*** (0.017)	−0.051^*** (0.013)	−0.099^*** (0.019)	−0.144^*** (0.037)	0.012 (0.098)	−0.116^** (0.046)	0.045^* (0.027)	−0.061^** (0.030)
Temporary contract	−0.014 (0.013)	−0.014 (0.011)	−0.012 (0.021)	0.144^*** (0.031)	−0.094 (0.076)	−0.037 (0.037)	−0.142^*** (0.026)	0.051^* (0.027)
Part-time job	−1.281^*** (0.092)	−0.400^*** (0.087)	0.039 (0.036)	−0.227^*** (0.067)	1.037^*** (0.208)	0.386^*** (0.132)	−0.187^*** (0.036)	−0.207^*** (0.031)
Establishment characteristics
Medium size	0.090^*** (0.019)	0.050^*** (0.015)	0.106^*** (0.018)	0.140^*** (0.035)	0.074 (0.065)	−0.003 (0.035)	0.095^*** (0.026)	0.097^*** (0.027)
Large size	0.068^*** (0.018)	0.064^*** (0.013)	0.186^*** (0.017)	0.188^*** (0.031)	0.200^*** (0.056)	0.130^*** (0.032)	0.131^*** (0.034)	0.118^*** (0.034)
National market	0.049^*** (0.017)	0.000 (0.011)	−0.042^*** (0.015)	−0.077^** (0.034)	0.216^* (0.117)	0.011 (0.058)	0.032 (0.023)	−0.007 (0.025)
International market	0.102^*** (0.017)	0.077^*** (0.014)	0.072^*** (0.019)	0.045 (0.035)	0.229^* (0.125)	0.099 (0.068)	−0.228^* (0.120)	−0.233^** (0.097)
Sectorial agreement	−0.027 (0.031)	−0.054^*** (0.020)	−0.314^*** (0.026)	−0.192^*** (0.045)	−0.091 (0.22)	−0.060 (0.082)	−0.071^** (0.030)	−0.097^*** (0.031)
Business agreement	0.155^*** (0.026)	0.037^* (0.020)	−0.240^*** (0.016)	−0.238^*** (0.028)	−0.495 (0.348)	−0.136 (0.171)	0.015 (0.027)	−0.032 (0.028)
Constant	1.884^*** (0.059)	1.808^*** (0.049)	1.707^*** (0.048)	1.852^*** (0.089)	1.422^*** (0.240)	1.782^*** (0.125)	1.772^*** (0.084)	1.771^*** (0.086)

However, the literature has highlighted the limitations of this overall approach, because workers' productive characteristics are differentially remunerated according to the wage level analysed (Casado and Simón, 2016). This difference is more marked when analysed from a gender perspective (Casado et al., 2020; Oliver and Sarz, 2021; Marfil and Campos, 2021). We illustrate the effect of the main determinants of the gender wage gap from a quantile perspective¹ in Figures 3–5, which show the marginal effects of education, educational mismatch, and vertical segregation, respectively.

Figure 3.

Marginal effect-RIF: Education. Notes: Values at a 10% significance level. RIF, recent influence function.

Figure 4.

Marginal effect-RIF: Educational mismatch. Overeducation and undereducation. Notes: Values at a 10% significance level. RIF, recent influence function.

Figure 5.

Marginal effect-RIF: Vertical segregation. Notes: Values at 10% significance level. RIF, recent influence function.

Returns to education are lower for workers in the hospitality and travel agency sectors (Table 3) and are lower for women in all the tourism sectors analysed, which is in line with the results of García et al. (2011). However, in general, educational returns increase across the wage distribution (Figure 3). In hospitality, we found little difference in the returns to education between genders, except in high-paying and responsible positions (also see García et al., 2014), which was also the case in the transport sector. However, in the travel agency sector, the wage penalty for women is substantial when their wages are medium and high, although it is lower when their wages are low.

In terms of educational mismatch, overeducation entailed a strong wage penalty in hospitality (also see Campos et al., 2015) and complementary services. As shown in Table 3, this wage penalty was higher for men than for women. Table 3 also shows that undereducation has a positive effect on wages in hospitality and transportation, and has a stronger effect on women’s wages. From a quantile perspective, in hospitality and complementary services, we found a wage penalty for overeducation and a wage advantage for undereducation, both increasing with wage levels by gender. However, the gender wage gap is substantial only in the case of undereducation when wages are high (Figure 4). In the transportation and travel agency sectors, educational mismatch does not have a differential effect across the wage distribution. In both sectors, overeducation does not have a significant impact on wages, whereas the wage advantage of undereducation does not have a uniform pattern across the distribution.

Regarding vertical segregation, men and women experience a wage penalty in both hospitality and transportation, and it is significantly negative only for women in travel agency and complementary services (Table 3). From a quantile perspective, no differential effect was found in terms of vertical segregation across the wage distribution in the transportation and complementary service sectors (Figure 5). At the quantile level, the main differences were found in the hospitality and travel agency sectors. In hospitality, there is a wage penalty due to vertical segregation, which is greater in higher paid jobs and even higher in the case of men (also see Campos et al., 2015). However, in the travel agency industry, wage increases due to promotion are higher among men than among women across the entire wage distribution (Figure 5).

Gender wage decomposition across the wage distribution

Table 4 presents the decomposition – by explanatory factors – of the gender wage gap in all sectors based on the RIF estimates, distinguishing between its discriminatory and non-discriminatory components across the wage distribution (Equation (18)). The upper block of Table 4 shows the absolute contribution of the discriminatory and non-discriminatory components to the gender wage gap, the central block shows the contribution of the observed characteristics to the non-discriminatory component, and the lower block shows the effect of the explanatory variable returns on the discriminatory component. These aspects are evaluated at the 10th, 30th, 50th, 70th, and 90th percentiles of the wage distribution.

Table 4.

Contribution to the gender wage gap by percentile.

	Hospitality					Transportation					Travel agencies					Complementary services
Logarithm of the gross hourly wage	P ₁₀	P ₃₀	P ₅₀	P ₇₀	P ₉₀	P ₁₀	P ₃₀	P ₅₀	P ₇₀	P ₉₀	P ₁₀	P ₃₀	P ₅₀	P ₇₀	P ₉₀	P ₁₀	P ₃₀	P ₅₀	P ₇₀	P ₉₀
Men	1.856^*** (0.009)	2.057^*** (0.008)	2.216^*** (0.008)	2.394^*** (0.011)	2.887^*** (0.025)	1.970^*** (0.009)	2.286^*** (0.010)	2.555^*** (0.009)	2.817^*** (0.010)	3.320^*** (0.017)	2.000^*** (0.023)	2.196^*** (0.026)	2.401^*** (0.031)	2.638^*** (0.041)	3.238^*** (0.070)	1.793^*** (0.013)	2.063^*** (0.012)	2.290^*** (0.014)	2.571^*** (0.019)	3.053^*** (0.023)
Women	1.812^*** (0.006)	1.993^*** (0.005)	2.116^*** (0.005)	2.263^*** (0.006)	2.567^*** (0.015)	1.904^*** (0.017)	2.215^*** (0.017)	2.472^*** (0.017)	2.769^*** (0.019)	3.295^*** (0.038)	1.865^*** (0.014)	2.064^*** (0.013)	2.200^*** (0.015)	2.409^*** (0.021)	2.883^*** (0.041)	1.761^*** (0.015)	2.013^*** (0.014)	2.218^*** (0.016)	2.487^*** (0.020)	2.922^*** (0.028)
Difference	0.044^*** (0.011)	0.064^*** (0.009)	0.101^*** (0.009)	0.131^*** (0.012)	0.321^*** (0.029)	0.066^*** (0.019)	0.070^*** (0.019)	0.084^*** (0.020)	0.048^** (0.022)	0.025 (0.042)	0.135^*** (0.028)	0.132^*** (0.029)	0.201^*** (0.035)	0.229^*** (0.045)	0.355^*** (0.081)	0.032 (0.020)	0.050^*** (0.019)	0.073^*** (0.022)	0.083^*** (0.028)	0.131^*** (0.036)
Characteristics	0.040^*** (0.004)	0.037^*** (0.004)	0.045^*** (0.004)	0.044^*** (0.005)	0.075^*** (0.012)	−0.072^*** (0.015)	−0.080^*** (0.017)	−0.085^*** (0.017)	−0.096^*** (0.019)	−0.136^*** (0.036)	0.019^** (0.010)	0.039^*** (0.011)	0.045^*** (0.013)	0.061^*** (0.017)	0.077^*** (0.028)	−0.029^** (0.011)	−0.021^* (0.011)	−0.061^*** (0.014)	−0.088^*** (0.018)	−0.078^*** (0.023)
Discrimination	0.003 (0.011)	0.027^*** (0.009)	0.056^*** (0.009)	0.087^*** (0.012)	0.246^*** (0.029)	0.138^*** (0.022)	0.150^*** (0.021)	0.169^*** (0.021)	0.144^*** (0.024)	0.161^*** (0.050)	0.116^*** (0.028)	0.093^*** (0.028)	0.155^*** (0.032)	0.168^*** (0.043)	0.278^*** (0.081)	0.060^*** (0.022)	0.070^*** (0.019)	0.134^*** (0.022)	0.171^*** (0.027)	0.209^*** (0.038)
Characteristics
European immigrant	0.000 (0.000)	0.000 (0.000)	0.000 (0.000)	0.000 (0.000)	−0.001 (0.001)	0.001 (0.001)	0.000 (0.000)	0.000 (0.000)	0.000 (0.994)	0.000 (0.001)	−0.001 (0.002)	0.001 (0.002)	0.001 (0.002)	0.000 (0.002)	0.001 (0.004)	0.000 (0.001)	0.000 (0.001)	0.000 (0.000)	0.000 (0.001)	0.000 (0.001)
Non-European immigrant	0.002 (0.001)	0.000 (0.001)	0.001 (0.001)	0.000 (0.001)	−0.001 (0.003)	−0.005^** (0.002)	−0.003 (0.002)	−0.002 (0.002)	0.000 (0.873)	0.000 (0.005)	−0.003 (0.004)	0.000 (0.003)	0.001 (0.003)	0.002 (0.005)	0.003 (0.010)	−0.009^* (0.005)	0.000 (0.004)	−0.003 (0.005)	−0.003 (0.006)	0.002 (0.009)
Education	0.001 (0.001)	0.002^** (0.001)	0.003^** (0.001)	0.005^** (0.002)	0.013^** (0.005)	−0.045^*** (0.009)	−0.058^*** (0.009)	−0.069^*** (0.009)	−0.086^*** (0.011)	−0.071^*** (0.020)	0.008^* (0.005)	0.015^** (0.007)	0.014^** (0.007)	0.018^** (0.009)	0.017 (0.012)	−0.016^*** (0.006)	−0.017^*** (0.005)	−0.040^*** (0.008)	−0.062^*** (0.011)	−0.050^*** (0.012)
Overeducation	0.000 (0.000)	0.001^* (0.000)	0.002^** (0.001)	0.002^** (0.001)	0.007^*** (0.003)	0.003 (0.003)	0.004 (0.003)	0.004 (0.003)	−0.001 (0.003)	−0.018^*** (0.007)	−0.004 (0.003)	−0.004 (0.003)	0.000 (0.003)	0.002 (0.004)	0.005 (0.008)	0.000 (0.001)	0.001 (0.001)	0.005^* (0.003)	0.008^* (0.004)	0.008^* (0.005)
Undereducation	−0.001 (0.001)	0.003^** (0.001)	0.003^*** (0.001)	0.007^*** (0.002)	0.014^*** (0.004)	−0.012^*** (0.004)	−0.013^*** (0.004)	−0.006 (0.004)	−0.009^** (0.004)	0.009 (0.009)	−0.002 (0.002)	0.004 (0.003)	0.003 (0.003)	0.003 (0.003)	0.000 (0.006)	0.001 (0.002)	0.001 (0.002)	−0.002 (0.002)	−0.004 (0.003)	−0.006 (0.004)
Tenure	0.003^** (0.001)	0.004^** (0.001)	0.004^*** (0.002)	0.004^*** (0.002)	0.005^** (0.002)	0.012^*** (0.004)	0.017^*** (0.005)	0.022^*** (0.005)	0.022^*** (0.006)	0.035^*** (0.010)	0.001 (0.002)	−0.004 (0.003)	−0.004 (0.004)	−0.005 (0.004)	−0.007 (0.007)	−0.004^* (0.002)	−0.006^* (0.003)	−0.010^* (0.005)	−0.013^* (0.007)	−0.020^* (0.011)
Vertical segregation	0.002 (0.002)	0.005^*** (0.002)	0.006^*** (0.002)	0.007^** (0.002)	0.020^*** (0.006)	0.001 (0.001)	−0.001 (0.001)	−0.001 (0.001)	−0.003 (0.002)	−0.004 (0.003)	0.011^** (0.006)	0.012^** (0.005)	0.017^*** (0.006)	0.021^** (0.008)	0.028^* (0.016)	0.000 (0.000)	0.000 (0.002)	0.000 (0.003)	−0.001 (0.004)	−0.001 (0.006)
Temporary contract	0.000 (0.001)	0.000 (0.000)	0.000 (0.000)	0.000 (0.000)	0.000 (0.001)	−0.003 (0.003)	−0.002 (0.002)	−0.001 (0.001)	−0.001 (0.001)	−0.002 (0.002)	0.002 (0.002)	0.007^** (0.003)	0.005^* (0.003)	0.006 (0.004)	0.009 (0.007)	−0.004^* (0.002)	−0.005^** (0.002)	−0.004^* (0.002)	−0.006^* (0.003)	0.003 (0.004)
Part-time job	0.011^*** (0.003)	−0.001 (0.002)	0.000 (0.002)	0.002 (0.003)	0.001 (0.007)	−0.010^** (0.005)	−0.007^* (0.004)	−0.008^* (0.004)	−0.017^*** (0.005)	−0.053^*** (0.012)	0.003 (0.002)	0.002 (0.002)	0.001 (0.002)	0.001 (0.003)	0.002 (0.006)	−0.002 (0.004)	0.003 (0.004)	−0.007^* (0.004)	−0.005 (0.005)	−0.012^* (0.007)
Medium size	0.002^** (0.001)	0.002^** (0.001)	0.003^*** (0.001)	0.002^** (0.001)	0.002 (0.002)	0.002 (0.002)	0.003 (0.002)	0.003 (0.002)	0.003 (0.002)	0.002 (0.002)	0.002 (0.002)	0.002 (0.002)	0.000 (0.001)	0.000 (0.001)	−0.003 (0.004)	0.001 (0.001)	0.003 (0.002)	0.003 (0.002)	0.003 (0.002)	0.001 (0.002)
Large size	−0.004^*** (0.002)	−0.005^*** (0.001)	−0.007^*** (0.002)	−0.006^*** (0.002)	−0.007^** (0.004)	0.000 (0.003)	−0.001 (0.004)	−0.001 (0.003)	−0.001 (0.003)	−0.001 (0.003)	−0.003 (0.002)	0.002 (0.002)	0.007 (0.004)	0.010 (0.007)	0.017 (0.011)	−0.003 (0.002)	−0.004^* (0.002)	−0.006^** (0.003)	−0.005^* (0.003)	−0.002 (0.003)
National market	0.001 (0.001)	0.000 (0.001)	0.001 (0.001)	0.002^** (0.001)	−0.003 (0.002)	0.000 (0.003)	−0.006^* (0.003)	−0.007^** (0.003)	−0.015^*** (0.005)	0.008 (0.007)	0.001 (0.003)	0.000 (0.002)	−0.001 (0.003)	−0.001 (0.004)	−0.001 (0.008)	0.000 (0.001)	0.000 (0.001)	0.000 (0.001)	−0.001 (0.001)	0.000 (0.001)
International market	0.004 (0.002)	0.008^*** (0.002)	0.012^*** (0.002)	0.010^*** (0.002)	0.015^*** (0.005)	−0.017^* (0.009)	−0.007 (0.008)	−0.012 (0.008)	0.019^** (0.010)	−0.033^* (0.020)	0.003 (0.005)	0.002 (0.004)	0.006 (0.005)	0.008 (0.007)	0.011 (0.013)	0.003 (0.002)	0.001 (0.001)	0.002 (0.002)	0.002 (0.002)	0.000 (0.001)
Sectorial agreement	0.010 (0.004)	0.001 (0.003)	0.005 (0.003)	0.009^** (0.004)	0.028^*** (0.010)	0.001 (0.001)	0.003 (0.002)	0.003 (0.002)	0.002 (0.002)	0.004 (0.003)	0.001 (0.002)	0.001 (0.002)	−0.002 (0.002)	−0.003 (0.003)	−0.004 (0.005)	0.002 (0.002)	0.003 (0.002)	0.004 (0.003)	0.003 (0.002)	0.002 (0.002)
Business agreement	0.011^*** (0.004)	0.018 (0.003)	0.011^*** (0.003)	0.000 (0.004)	−0.019^** (0.010)	0.001 (0.001)	−0.008^** (0.004)	−0.011^** (0.005)	−0.009^** (0.004)	−0.014^** (0.007)	0.000 (0.000)	0.000 (0.001)	0.000 (0.002)	0.000 (0.001)	0.000 (0.002)	0.001 (0.001)	0.000 (0.001)	−0.002 (0.002)	−0.004 (0.002)	−0.004 (0.003)
Discrimination
European immigrant	0.000 (0.002)	0.001 (0.002)	0.000 (0.002)	0.000 (0.003)	−0.009 (0.007)	−0.002 (0.004)	−0.004 (0.004)	−0.002 (0.004)	0.003 (0.005)	−0.001 (0.009)	0.011 (0.008)	−0.002 (0.007)	−0.006 (0.009)	0.000 (0.012)	0.023 (0.023)	0.001 (0.004)	0.002 (0.004)	0.003 (0.004)	0.005 (0.005)	0.006 (0.007)
Non-European immigrant	−0.002 (0.004)	−0.002 (0.003)	−0.009^*** (0.003)	−0.009^** (0.004)	−0.008 (0.010)	0.007^* (0.004)	−0.001 (0.004)	0.001 (0.004)	−0.001 (0.004)	0.001 (0.009)	0.003 (0.007)	−0.010 (0.007)	−0.005 (0.008)	−0.009 (0.011)	−0.007 (0.020)	0.008 (0.007)	−0.004 (0.006)	0.001 (0.007)	0.008 (0.008)	0.009 (0.012)
Education	0.027 (0.059)	0.020 (0.047)	−0.03 (0.047)	0.078 (0.063)	0.236 (0.158)	−0.110 (0.083)	0.017 (0.079)	0.09 (0.077)	0.171^* (0.089)	0.698^*** (0.183)	−0.003 (0.183)	0.019 (0.180)	0.365^* (0.210)	−0.077 (0.286)	0.543 (0.541)	−0.017 (0.103)	0.108 (0.091)	−0.034 (0.101)	−0.045 (0.128)	0.270 (0.177)
Overeducation	0.006 (0.006)	0.004 (0.005)	0.013^** (0.005)	0.000 (0.007)	−0.007 (0.017)	−0.003 (0.010)	0.005 (0.010)	0.008 (0.01)	−0.002 (0.011)	−0.040^* (0.023)	0.015 (0.015)	0.017 (0.014)	0.012 (0.017)	0.043^* (0.023)	0.026 (0.043)	0.000 (0.011)	−0.001 (0.010)	0.008 (0.011)	−0.003 (0.014)	−0.006 (0.019)
Undereducation	0.010^* (0.005)	0.003 (0.004)	0.003 (0.004)	−0.008 (0.006)	−0.033^** (0.015)	−0.016^* (0.009)	−0.009 (0.008)	0.010 (0.008)	−0.001 (0.009)	0.027 (0.019)	0.024 (0.026)	0.003 (0.026)	0.045 (0.030)	0.011 (0.041)	0.067 (0.078)	−0.001 (0.014)	0.003 (0.013)	0.000 (0.014)	0.022 (0.018)	0.023 (0.025)
Tenure	−0.008 (0.011)	0.007 (0.009)	0.002 (0.009)	0.033^*** (0.011)	0.086^*** (0.028)	−0.027 (0.027)	−0.014 (0.026)	−0.030 (0.025)	−0.070^** (0.030)	−0.288^*** (0.061)	0.063^** (0.029)	−0.003 (0.028)	0.016 (0.032)	0.037 (0.044)	0.000 (0.083)	0.026 (0.020)	0.026 (0.018)	0.007 (0.019)	0.024 (0.025)	−0.160^*** (0.035)
Vertical segregation	−0.068 (0.046)	−0.120^*** (0.037)	−0.205^*** (0.037)	−0.410^*** (0.048)	−0.563^*** (0.121)	0.033 (0.056)	0.117^** (0.053)	0.139^*** (0.052)	0.197^*** (0.060)	0.196 (0.124)	0.005 (0.028)	0.069^** (0.028)	0.076^** (0.032)	0.088^** (0.044)	0.084 (0.083)	−0.005 (0.042)	0.017 (0.037)	0.007 (0.041)	0.029 (0.053)	0.097 (0.072)
Temporary contract	−0.001 (0.005)	0.002 (0.004)	−0.004 (0.004)	0.000 (0.006)	−0.003 (0.014)	0.016^* (0.009)	0.000 (0.008)	0.004 (0.008)	0.009 (0.009)	0.012 (0.019)	0.002 (0.007)	0.005 (0.007)	0.007 (0.008)	0.023^* (0.012)	0.005 (0.020)	−0.005 (0.014)	0.014 (0.012)	0.023^* (0.014)	0.054^*** (0.017)	0.030 (0.024)
Part-time job	−0.018^** (0.009)	−0.014^** (0.007)	−0.011 (0.007)	0.015^* (0.009)	0.031 (0.023)	−0.011^** (0.005)	−0.008 (0.005)	−0.014^*** (0.005)	−0.020^*** (0.006)	−0.045^*** (0.012)	−0.028^*** (0.010)	−0.008 (0.010)	−0.005 (0.011)	0.003 (0.015)	0.000 (0.028)	−0.044^*** (0.016)	−0.024^* (0.014)	−0.059^*** (0.016)	−0.055^*** (0.020)	−0.096^*** (0.028)
Medium size	−0.001 (0.008)	0.008 (0.006)	0.004 (0.006)	0.022^*** (0.009)	0.035 (0.022)	−0.010 (0.011)	0.002 (0.011)	0.005 (0.010)	−0.010 (0.012)	−0.004 (0.025)	0.025 (0.016)	0.037^** (0.016)	0.027 (0.019)	0.021 (0.025)	0.012 (0.047)	−0.007 (0.014)	−0.013 (0.012)	−0.017 (0.014)	−0.004 (0.017)	0.028 (0.024)
Large size	−0.015 (0.014)	−0.003 (0.011)	−0.001 (0.011)	0.017 (0.015)	0.017 (0.037)	0.001 (0.022)	0.024 (0.021)	0.024 (0.021)	−0.040 (0.024)	−0.044 (0.05)	0.031 (0.027)	0.053^** (0.026)	0.063^** (0.031)	0.043 (0.042)	−0.024 (0.079)	−0.001 (0.008)	0.001 (0.007)	−0.009 (0.007)	−0.008 (0.009)	−0.001 (0.013)
National market	0.017^** (0.008)	0.021^*** (0.007)	0.016^** (0.007)	0.025^*** (0.009)	0.002 (0.022)	0.003 (0.023)	0.010 (0.022)	0.014 (0.022)	0.057^** (0.025)	−0.091^* (0.052)	0.087 (0.094)	0.183^* (0.094)	0.093 (0.109)	0.149 (0.149)	0.187 (0.279)	−0.032^** (0.016)	0.006 (0.014)	0.029^* (0.016)	0.047^** (0.020)	0.015 (0.028)
International market	0.024^*** (0.009)	0.025^*** (0.007)	−0.001 (0.007)	0.001 (0.010)	0.001 (0.025)	−0.008 (0.010)	−0.009 (0.010)	−0.008 (0.010)	0.016 (0.011)	−0.010 (0.023)	0.007 (0.030)	0.032 (0.030)	0.000 (0.035)	0.054 (0.048)	0.007 (0.09)	0.002 (0.002)	0.000 (0.001)	0.002 (0.002)	0.000 (0.002)	−0.003 (0.003)
Sectorial agreement	0.007 (0.005)	0.000 (0.004)	0.002 (0.004)	0.005 (0.006)	0.003 (0.014)	−0.013^** (0.005)	−0.009^* (0.005)	−0.009^* (0.005)	−0.008 (0.006)	−0.008 (0.012)	−0.260 (0.228)	−0.378^* (0.230)	0.075 (0.270)	0.117 (0.366)	0.362 (0.681)	0.050^*** (0.015)	0.003 (0.013)	0.004 (0.014)	−0.010 (0.018)	−0.011 (0.025)
Business agreement	0.163 (0.033)	0.056^** (0.027)	0.034 (0.027)	0.093^*** (0.036)	0.193^** (0.089)	−0.033^* (0.018)	−0.014 (0.017)	0.008 (0.017)	0.014 (0.020)	0.012 (0.041)	−0.003 (0.003)	−0.006 (0.005)	−0.001 (0.003)	0.000 (0.004)	0.002 (0.007)	0.070^*** (0.020)	0.027 (0.018)	0.007 (0.020)	0.022 (0.025)	0.023 (0.035)
Constant	−0.138 (0.097)	0.018 (0.078)	0.243^*** (0.078)	0.226^** (0.104)	0.265 (0.259)	0.312^** (0.131)	0.043 (0.126)	−0.072 (0.122)	−0.172 (0.142)	−0.257 (0.292)	0.138 (0.273)	0.082 (0.270)	−0.607^* (0.316)	−0.334 (0.430)	−1.008 (0.808)	0.015 (0.152)	−0.095 (0.135)	0.161 (0.149)	0.084 (0.189)	−0.016 (0.262)

Standard deviations are in shown parentheses. ^***p < 0.01, ^**p < 0.05, and ^*p < 0.1. The percentiles p10, p30, p50, p70, and p90 have been selected for simplicity. All other percentiles are available to any interested reader upon request.

Figure 6 shows the absolute contributions of the non-discriminatory and discriminatory components to the gender wage gap across the wage distribution in the different sectors analysed. In transportation and other complementary services, the higher productivity of women helps to reduce the gender wage gap. Moreover, the negative contribution of the gender productivity gap increases across the wage distribution. In contrast, in the hospitality and travel agency sectors, women are less productive than men across the wage distribution, which contributes to widening the gender wage gap. In all sectors analysed, except for transportation, the contribution of the discriminatory component increases as wages increase.

Figure 6.

Oaxaca-RIF: wage decomposition. Notes: Difference in the logarithm of the hourly wage. RIF, recent influence function.

In the hospitality sector, the gender wage gap is fully explained by the non-discrimination component in the lower 10th percentile of the wage distribution. This result is due to the lower productive characteristics of women who occupy these types of jobs and is particularly associated with tenure and type of contract (Table 4). Differences in education and the high over-representation of women in positions of lower responsibility stand out across the distribution, particularly in higher paid positions. Figure 6 shows that the contribution of the non-discriminatory component remains stable, whereas the discriminatory component changes across the wage distribution. In fact, the discriminatory component follows an increasing trend and explains 76.63% of the gender wage gap at the 90th percentile. This result confirms that, in the hospitality sector, the greater wage discrimination experienced by women in relation to men with the same characteristics increases as the wage level increases. In line with the results of previous studies, this result could be associated with the existence of a ‘glass ceiling’ in the sector (Casado et al., 2020; Marfil and Campos, 2021). Regarding the determinants of the discriminatory component, returns to tenure in the job has a strong effect, particularly in high-paying jobs. Although returns to education differ by gender (Figure 3), this difference is not significant in explaining the gender wage gap. Vertical segregation has a negative impact on the discriminatory component, which suggests that wage increases for women are higher than for their male counterparts when promoted.

In the transport sector, the gender wage gap is significant up to the 70th percentile, whereas there are no significant differences between wages by gender at the highest paid positions. This gap is largely explained by the discriminatory component across the wage distribution, which is partly compensated for by the higher productivity of women (Figure 6). Women also have higher levels of education and better contractual conditions as they move up the wage distribution, which further reduces the gender wage gap (Table 4). This table also shows that the discriminatory component is explained by the difference in returns to education at higher wage levels (70th percentile) and by vertical segregation, with promotion among women being more penalised than promotion among men across the wage distribution.

In the travel agency sector, both components are relevant in explaining the gender wage gap (Figure 6). Although the impact of the non-discriminatory component remains stable across the wage distribution, its relative contribution decreases as wage increases. However, at the lowest wage levels (10th percentile), return differences between men and women in their productive characteristics account for 86% of the gender wage gap, whereas the relative contribution of this discriminatory component decreases and reaches 78% of the gender wage gap at the highest wage levels (90th percentile). Regarding the non-discriminatory component, differences in education by gender had a large impact on the gender wage gap, although no significant differences were found in relation to senior positions (Table 4). However, the over-representation of men in positions of higher responsibility increases the gender wage gap. Regarding the discriminatory component, there were significant differences in educational returns by gender only in intermediate positions. Moreover, women who are promoted are more heavily penalised than men who are promoted, thus discouraging women from accessing positions of high responsibility.

In complementary services, no significant gender wage gap was found at the lowest wage levels (10th percentile), whereas it was both significant and increasing at the middle and high-wage levels of the distribution (Table 4). The discriminatory component explains most of the observed gender wage gap and its effect increases as wages increase (Figure 6). The impact of the non-discriminatory component helps to reduce the gender wage gap due to the higher productivity of women in all the wage levels analysed. In fact, in this sector, women have a higher level of education and tenure in all the percentiles of the wage distribution. They also obtain more permanent contracts than men, except in high-paying positions. In addition, although there is relative gender occupational equality at the different levels of responsibility in the sector, this factor is not relevant in explaining the gender wage differentials.

Conclusions

This paper brings a new dimension to the study of the gender wage gap in the tourism industry by conducting a specific analysis of its determinants across the wage distribution from a sectoral perspective. To this end, we applied the wage decomposition proposed by Firpo et al. (2009) using matched employee-employer data from the Structure of Earnings Survey for 2018. An analysis of the gender gap for the tourism industry as a whole would not reveal the heterogeneity found at the sectoral level in the determinants of the gender wage gap and its discriminatory component. In fact, in relation to the gender wage gap and discrimination, our results show that there are marked differences between the sectors analysed – hospitality, travel agencies, transportation, and complementary services – and that these differences are shown to persist across the wage distribution.

In sectors in which women predominate, such as hospitality and travel agencies, the gender wage gap increases across the wage distribution, whereas in masculinised sectors, such as transportation, the gender wage gap follows a decreasing trend, becoming non-significant at higher wage levels. Regarding the non-discriminatory component of the gender wage gap, in sectors such as transportation and complementary services, women are more productive than men and the productivity gap widens across the wage distribution, with the gender wage gap decreasing as wages increase. In contrast, in hospitality and travel agencies, men are more productive than women at all wage levels, which increases the gender wage gap. However, the gender wage gap discriminatory component increases as wage levels increase in all sectors except transportation. Therefore, as women occupy higher wage positions, the wage disadvantage they experience can only be explained by reasons other than their productive characteristics. This result suggests the existence of ‘invisible’ barriers that perpetuate gender roles, particularly in senior positions, and indicates the possible existence of a so-called ‘glass ceiling’. In contrast, no significant gender wage gap was found at the highest wage levels in the transport sector. This is a special case, because despite the fact that high-wage positions in the transport sector are masculinised, the wage gap is practically non-existent.

The variables human capital and vertical segregation are highly relevant in explaining both components, with marked differences between the sectors analysed and their effect across the wage distribution. In particular, as wages increase, education contributes to widening the gender wage gap in hospitality and travel agencies, whereas it narrows the gender wage gap in transportation and complementary services. In particular, in hospitality, overeducation and undereducation have a positive effect on the non-discriminatory component, with women experiencing greater educational mismatch as they obtain higher paying jobs, whereas in complementary services this effect for women only occurs in the case of overeducation. Vertical segregation increases the gender wage gap in sectors such as hospitality and travel agencies and its effect increases across the wage distribution. However, in transportation and complementary services, the occupational distribution by gender is more equal, and so its effect on the gender wage gap is not significant. It is also worth noting that there is sectoral heterogeneity in incentives for job promotion between genders, with women being penalised more than men. This result again suggests the existence of a differentiated ‘glass ceiling’ between sectors.

Our results have relevant policy implications in the field of gender equality and non-discrimination, which remain pending issues due to their marked sectoral heterogeneity. This study shows that the origin of gender discrimination differs by sector and demonstrates the need for a differentiated approach to setting policies aimed at equal wages and equal opportunities for both genders. In the particular case of the Spanish labour market, the 2012 labour reform (RDL 3/2012) established specific policies to promote gender equality with specific incentives for hiring women, particularly in the case of sectors in which women are under-represented. In contrast, the current labour reform of 2021 (RDL 32/2021) does not establish a specific and differentiated mechanism aimed at improving women’s participation and working conditions. Nevertheless, the main pillars of this reform promote equal treatment and opportunities for both genders and addresses the problem of high job insecurity, which particularly affects women in the sectors analysed. Therefore, in Spain, gender equality could be improved by reinforcing the main specific gender equality measures at the sectoral level.

This work has certain limitations. Firstly, the binary definition of the concept of gender established by the database used prevents a more in-depth analysis of this variable. Secondly, it should be borne in mind that the position held by a worker may be the result of decisions made by both the employer and the employee. Therefore, our data do not include a relevant factor: the supply decision mechanism. The labour supply of men and women may be constrained by family obligations, work-life balance issues, and personal preferences that lead individuals to opt for lower paid jobs, but which allow them more free time. In addition, the analysis of wage discrimination would benefit from more accurate data on company hiring practices and wage-setting mechanisms, which would allow us to obtain information on the reasons for the employer’s decision-making. Finally, the study is limited by the use of cross-sectional data, as it is not possible to control for business cycles and temporary or individual effects.

Footnotes

Acknowledgement

The authors want to thank the support of University of Malaga (I Plan Propio de Investigación y Transferencia).

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The work was supported by the research grant from the University of Malaga received by Marina Marfil-Cotilla.

ORCID iD

Juan A. Campos-Soria

Note

Author biographies

Marina Marfil-Cotilla, Predoctoral Researcher and PhD student in Business and economics in the Department of Applied Economics (Economic Structure) at the University of Malaga. Her research interests relate to labour economics, particularly the analysis of wages.

Juan A. Campos-Soria, PhD, Full Professor at the University of Malaga (Spain). He has participated in international research projects for the Department for Culture, Media and Sport (UK) and the World Travel and Tourism Council (WTTC), as well as numerous national projects for public and private institutions. He has worked on many aspects of economic analysis of tourism, gender economics and labour market, publishing several papers in leading international journals.

Alejandro García-Pozo, PhD, Full Professor at the University of Malaga (Spain). He is currently the head of the Department of Applied Economics (Economic Structure) at this university and the head of labour economics and tourism research group. He has worked on economic analysis of tourism, the return on human capital in tourism sector and sustainable tourism and its impact on labour productivity in hotel establishments, publishing papers in leading international journals.

References

Bañuls

Rodríguez

ABR

(2005) Returns on education in the Spanish tourism labour market. Tourism Economics 11(1): 119–132. DOI: 10.5367/0000000053297176

Becker

(1964) Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education. Chicago: Chicago University Press.

Becker

(1971) The Economies of Discrimination. Chicago: Chicago University Press. DOI: 10.2307/2230052

Benmelech

Bergman

Kim

(2018) Strong employers and weak employees how does employer concentration affect wages? Journal of Human Resources 57(S): S200–S250. DOI: 10.3386/w24307

Blau

Kahn

(2006) The US Gender pay gap in the 1990S: slowing convergence. ILR Review 60(1): 45–66. DOI: 10.1177/001979390606000103

Blinder

(1973) Wage discrimination: reduced form and structural estimates. The Journal of Human Resources 8(4): 436–455. DOI: 10.2307/144855

Cain

(1991) The uses and limits of statistical analysis in measuring economic discrimination. In: Hoffman

(ed), Essays on the Economics of Discrimination. Kalamazoo, MI: W.E. Upjohn Institute for Employment Research, 115–144. DOI: 10.17848/9780585255859.ch7

Campos-Soria

Ortega-Aguaza

Ropero-García

(2009) Gender segregation and wage difference in the hospitality industry. Tourism Economics 15(4): 847–866. DOI: 10.5367/000000009789955152

Campos-Soria

García-Pozo

Sánchez-Ollero

, et al. (2011) A comparative analysis on human capital and wage structure in the Spanish hospitality sector. Journal of Service Science and Management 4(4): 458–468. DOI: 10.4236/jssm.2011.44052

10.

Campos

García

Sánchez

(2015) Gender wage inequality and labour mobility in the hospitality industry. International Journal of Hospitality Management 49, pp. 73–82. DOI: 10.1016/j.ijhm.2015.05.009

11.

Carvalho

Costa

Lykke

, et al. (2019) Beyond the glass ceiling: gendering tourism management. Annals of Tourism Research 75: 79–91. DOI: 10.1016/j.annals.2018.12.022

12.

Casado-Díaz

Simón

(2016) Wage differences in the hospitality sector. Tourism Management 52: 96–109. DOI: 10.1016/j.tourman.2015.06.015

13.

Casado-Díaz

Driha

Simón

(2020) The gender wage gap in hospitality: new evidence from Spain. Cornell Hospitality Quarterly 63(3): 399–417. DOI: 10.1177/1938965520971273

14.

Casani

(2010) Industrias de la Creatividad 20+20. Madrid: EOI.

15.

Choy

(1995) The quality of tourism employment. Tourism Management 16(2): 129–137. DOI: 10.1016/0261-5177(94)00023-4

16.

Costa

Bakas

Breda

, et al. (2017) ‘Emotional’ female managers: how gendered roles influence tourism management discourse. Journal of Hospitality and Tourism Management 33: 149–156. DOI: 10.1016/j.jhtm.2017.09.011

17.

De Cabo

Garzón

(2007) Diferencia y discriminación salarial por razón de sexo. Madrid: Estudios del Instituto de la Mujer, CEET.

18.

Díaz

Martín-Consuegra

Esteban

(2015) Perceptions of service cannibalisation: the moderating effect of the type of travel agency. Tourism Management 48: 329–342. DOI: 10.1016/j.tourman.2014.12.004

19.

EPRS (2019) European Parliamentary Research Service. Employment in the cultural and creative sectors. Strasbourg, France: European Parliament. https://www.europarl.europa.eu/

20.

Liz

Lindsay

(1998) The gap between male and female pay: what does the case of hotel and catering tell us? The Service Industries Journal 18(4): 126–144. DOI: 10.1080/02642069800000045

21.

Fernández

Pena-Boquete

Pereira

(2009) Labor conditions in the Spanish hotels and restaurants industry. Tourism Analysis 14(3): 293–312. DOI: 10.3727/108354209789705039

22.

Fernández-Kranz

Rodríguez-Planas

(2011) The part-time pay penalty in a segmented labor market. Labour Economics 18(5): 591–606. DOI: 10.1016/j.labeco.2011.01.001

23.

Firpo

Fortin

Lemieux

(2007) Decomposing Wage Distributions Using Recentered Influence Functions Regressions. Vancouver: Mimeo, University of British Columbia.

24.

Firpo

Fortin

Lemieux

(2009) Unconditional quantile regressions. Econometrica 77(3): 953–973. DOI: 10.3982/ECTA6822

25.

Firpo

Fortin

Lemieux

(2018) Decomposing wage distributions using recentered influence function regressions. Econometrics 6(2): 28. DOI: 10.3390/econometrics6020028

26.

García-Pozo

Campos-Soria

Sánchez-Ollero

, et al. (2012) The regional wage gap in the Spanish hospitality sector based on a gender perspective. International Journal of Hospitality Management 31(1): 266–275. DOI: 10.1016/j.ijhm.2011.06.007

27.

García-Pozo

Sánchez-Ollero

Marchante-Mera

(2014) Educational mismatch and returns on human capital in the Spanish hospitality and travel agency sectors. Tourism Economics 20(2): 337–353. DOI: 10.5367/te.2013.0273

28.

García-Pozo

Sánchez-Ollero

Ons-Cappa

(2018) Impact of introducing eco-innovation measures on productivity in transport sector companies. International Journal of Sustainable Transportation 12: 561–571. DOI: 10.1080/15568318.2017.1414340

29.

Gray

(2004) The social construction of the service sector: institutional structures and labour market outcomes. Geoforum 35(1): 23–34. DOI: 10.1016/j.geoforum.2003.07.001

30.

Hampel

(1974) The influence curve and its role in robust estimation. Journal of the American Statistical Association 60: 383–393. DOI: 10.1080/01621459.1974.10482962

31.

ILOSTAT (2018) International Labour Organization Statistic. Genève, Switzerland: International Labour Organization. https://ilostat.ilo.org/data/

32.

Iriondo

Pérez-Amaral

(2016) The effect of educational mismatch on wages in Europe. Journal of Policy Modeling 38(2): 304–323. DOI: 10.1016/j.jpolmod.2015.12.008

33.

Juhn

Murphy

Pierce

(1993) Wage inequality and the rise in returns to skill. Journal of Political Economy 101(3): 410–442. DOI: 10.1086/261881

34.

Koenker

Bassett

(1978) Regression quantiles. Econometrica 46(1): 33–50. DOI: 10.2307/1913643

35.

Korkeamäki

Kyyrä

(2006) A gender wage gap decomposition for matched employer-employee data. Labour Economics 13(5): 611–638. DOI: 10.1016/j.labeco.2005.02.001

36.

Kortt

Sinnewe

Pervan

(2018) The gender wage gap in the tourism industry: evidence from Australia. Tourism Analysis 23: 137–149. DOI: 10.3727/108354217X15143857878697

37.

Lanzona Jr

(2022) Monopsony and gender wage discrimination in the Philippines. Qeios. DOI: 10.32388/VWLDCW.2

38.

Lazzeretti

Capone

Seçilmiş

(2015) In search of a Mediterranean creativity. Cultural and creative industries in Italy, Spain and Turkey. European Planning Studies 24(3): 568–588. DOI: 10.1080/09654313.2015.1082979

39.

Lillo-Bañuls

Casado-Díaz

(2010) Rewards to education in the tourism sec- tor: one step ahead. Tourism Economics 16(1): 11–23. DOI: 10.5367/000000010790872033

40.

Lillo-Bañuls

Casado-Díaz

(2012) Individual returns to education in the Spanish tourism sector during the economic crisis. Tourism Economics 18(6): 1229–1249. DOI: 10.5367/te.2012.0171

41.

Lillo-Bañuls

Casado-Díaz

(2015) Exploring the relationship between educational mismatch, earnings and job satisfaction in the tourism industry. Current Issues in Tourism 18(4): 361–375. DOI: 10.1080/13683500.2014.915796

42.

Machado

Mata

(2005) Counterfactual decomposition of changes in wage distributions using quantile regression. Journal of Applied Econometrics 20: 445–465. DOI: 10.1002/jae.788

43.

Marchante

Ortega

Pagán

(2007) An analysis of educational mismatch and labor mobility in the hospitality industry. Journal of Hospitality and Tourism Research 31: 299–320. DOI: 10.1177/1096348007299920

44.

Marfil

Campos

(2021) Decomposing the gender wage gap in the hospitality industry: a quantile approach. International Journal of Hospitality Management 94(102826): 1–10. DOI: 10.1016/j.ijhm.2020.102826

45.

Muñoz-Bullón

(2009) The gap between male and female pay in the Spanish tourism industry. Tourism Management 30(5): 638–649. DOI: 10.1016/j.tourman.2008.11.007

46.

Neumark

(1988) Employers' discriminatory behavior and the estimation of wage discrimination. The Journal of Human Resources 23: 279–295. DOI: 10.2307/145830

47.

Oaxaca

(1973) Male–female wage differentials in urban labor markets. International Economic Review 14(3): 693–709. DOI: 10.2307/2525981

48.

Oaxaca

Ransom

(1994) On discrimination and the decomposition of wage differentials. Journal of Econometrics 61(1): 5–21. DOI: 10.1016/0304-4076(94)90074-4

49.

Oliver

Sard

(2021) Gender wage gap in hospitality. Journal of Hospitality and Tourism Research 45(2): 345–372. DOI: 10.1177/1096348020909423

50.

OTLS - Observatory of (2021) Transport and logistic in Spain. División de Prospectiva y Tecnología del Transporte. Madrid: Secretaría General de Transporte. Informe anual 2019 .

51.

Piore

(1969) On-the-job training in dual labor markets. In Weber

Cassell y

Woodrow

. Public-Private Manpower Policies. Madison, Wisconsin: Industrial Relations Research Association, 101–132.

52.

Pirra

Carboni

Diana

(2020) Assessing gender gaps in educational provision, research and employment opportunities in the transport sector at the European level. Education Sciences 10(5): 123. DOI: 10.3390/educsci10050123

53.

Pozo

Mera

AJM

Ollero

JLS

(2011) Occupational differences in the return on human capital in the Spanish travel agency and hospitality industries. Tourism Economics 17(6): 1325–1345. DOI: 10.5367/te.2011.0085

54.

Pritchard

Morgan

(2017) Tourism’s lost leaders: analysing gender and performance. Annals of Tourism Research 63: 34–47. DOI: 10.1016/j.annals.2016.12.011

55.

Riley

Szivas

(2003) Pay determination: a socioeconomic framework. Annals of Tourism Research 30(2): 446–464. DOI: 10.1016/S01607383(02)00101-9

56.

Robinson

Martins

Solnet

, et al. (2019) Sustaining precarity: critically examining tourism and employment. Journal of Sustainable Tourism 27(7): 1008–1025. DOI: 10.1080/09669582.2018.1538230

57.

Salinas

Rahona

Murillo

(2014) Gender wage differentials and educational mismatch: an application to the Spanish case. Applied Economics 45(30): 4226–4235. DOI: 10.1080/00036846.2013.781260

58.

Santos

Varejão

(2007) Employment, pay and discrimination in the tourism industry. Tourism Economics 13(2): 225–240. DOI: 10.5367/000000007780823186

59.

Segovia

Figueroa

Fuentes

, et al. (2018) Incorporating a gender approach in the hospitality industry: female executives’ perceptions. International Journal of Hospitality Management 76(A): 184–193. DOI: 10.1016/j.ijhm.2018.05.008

60.

Sellami

Verhaest

Nonneman

, et al. (2020) Education as investment, consumption or adapting to social norm: implications for educational mismatch among graduates. Education Economics 28(1): 26–45. DOI: 10.1080/09645292.2019.1680955

61.

Shu

Liu

, et al. (2022) Understanding wage differences across tourism-characteristic sectors: insights from an extended input-output analysis. Journal of Hospitality and Tourism Management 51: 88–104. DOI: 10.1016/j.jhtm.2022.02.030

62.

Thrane

(2008) Earnings differentiation in the tourism industry: gender, human capital and socio-demographic effects. Tourism Management 29(3): 514–524. DOI: 10.1016/j.tourman.2007.05.017

63.

Webster

(2014) Wage differentials and their determinants in US tourism and tourism-associated industries. Tourism Economics 20(4): 695–725. DOI: 10.5367/te.2013.0304

64.

Zhang

(2021) A qualitative comparative analysis of tourism and gender equality in emerging economies. Journal of Hospitality and Tourism Management 46: 284–292. DOI: 10.1016/j.jhtm.2021.01.009