Abstract
Tourism represents a major economic activity in Portugal, with an enormous wealth and employment growth potential. A significant proportion of jobs in the industry tourism are occupied by women, given that this industry is characterized by a relatively higher percentage of female employees. Despite the evidence of female progress with regard to their role in the Portuguese labor market, women continue to earn less than their male counterparts. This is clearly the case of the tourism industry, where statistics reveal a persistent gender wage gap. The objective of this paper is to provide empirical evidence on the determinants of gender wage inequality in the tourism industry in northern Portugal. Relying on firm-level wage equations and production functions, gender wage and productivity differentials are estimated and then compared. The comparison of these differentials allows inferring whether observed wage disparities are attributable to relatively lower female productivity, or instead disparities are due to gender wage discrimination. This approach is applied to tourism industry data gathered in the matched employer-employee data set Quadros de Pessoal (Employee Records). The main findings indicate that female employees in the tourism industry in northern Portugal are less productive than their male colleagues and that gender differences in wages are fully explained by gender differences in productivity.
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Notes
- 1.
The tourism industry is represented by observations regarding the economic sector “hotels and restaurants”, defined by the Portuguese classification of economic activities (Instituto Nacional de Estatística 2003).
- 2.
In terms of tourism, the North region is known as the Oporto and North region.
- 3.
Hellerstein and Neumark (1995) use the same data and empirical framework to compare wage and productivity differentials among workers of different age groups.
- 4.
Ilmakunnas and Maliranta (2005) conclude that this result is not robust with regard to fixed effects estimation.
- 5.
By focusing on firm-level wage equations, it is possible to jointly estimate wage equations and production functions. This joint estimation allows for formal tests on the equality of the coefficients of the wage equations and production functions. Hellerstein et al. (1999) consider that by jointly estimating firm-level wage equations and production functions, potential biases introduced by unobservable effects regarding wages and production will affect the estimations in a similar manner.
- 6.
As referred earlier on, the current study applies an empirical framework similar to that of Hellerstein and Neumark (1999) and Hellerstein et al. (1999). However, rather than estimating firm-level wage equations and production functions based on non-linear regression methods, this study performs the estimations using linear methods as in McDevitt et al. (2009).
- 7.
The firm’s total wage bill represents the aggregation of the individual-level wage equation over all workers employed at the firm (Hellerstein et al. 1999). The individual-level wage equation is expressed as W i = w m M i + w f F i , where W i represents the average wage of employee i, M i and F i are dummy variables for male and female employees, respectively, and w m and w f are the average paid male and female wages. Summing this equation over all workers employed at the firm yields the firm’s total wage bill (14.1).
- 8.
As in Hellerstein et al. (1999), it is assumed that wage differentials between two types of employees within one demographic group are equal to the wage differentials between those same two types of employees within another demographic group. For example, the wage differentials between young aged women and young aged men are assumed to be equal to the wage differentials between old aged women and old aged men. Similarly, the wage differentials between young aged women and old aged women are assumed to be equal to the wage differentials between young aged men and old aged men. It is also assumed that the share of employees in a firm defined by one demographic group is constant across all other demographic groups.
- 9.
In this case, labor input is measured in male equivalent units.
- 10.
- 11.
The firm’s employees are divided into different groups based on five demographic characteristics: gender, education, age, tenure, and occupation. Hence, the employees are classified into two gender groups, five education groups, three age groups, three tenure groups, and eight occupation groups.
- 12.
Given the joint estimation of the firm-level wage equation and production function, a formal test on the equality of the female coefficients (that is, a formal test on the equality of gender wage and productivity differentials) is performed. The null hypothesis of equal coefficients is rejected with a p-value of 0.000.
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Acknowledgement
The authors thank the Portuguese Ministry of Labor and Social Solidarity for the access to the data set used in this paper. The access to the data was provided by the protocol signed between the Ministry and the University of Minho.
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14.7 Appendix
14.7 Appendix
Variable | Description |
|---|---|
Shmale | Share of male employees |
Shfemale | Share of female employees |
Shed4 | Share of employees with highest completed degree of education ≤ 4 years |
Shed6 | Share of employees with highest completed degree of education = 6 years |
Shed9 | Share of employees with highest completed degree of education = 9 years |
Shed12 | Share of employees with highest completed degree of education = 12 years |
Shed15 | Share of employees with highest completed degree of education ≥ 15 years |
Shyoung | Share of young aged employees (age ≤ 29 years) |
Shprime | Share of prime aged employees (30 years ≤ age ≤ 54 years) |
Shold | Share of old aged employees (age ≥ 55 years) |
Shorten | Share of employees with short tenure (tenure ≤ 4 years) |
Shmedten | Share of employees with medium tenure (5 years ≤ tenure ≤ 9 years) |
Shlongten | Share of employees with long tenure (tenure ≥ 10 years) |
Shtopman | Share of executive civil servants, industrial directors, and executives |
Shprofscien | Share of professionals and scientists |
Shmidmantec | Share of middle managers and technicians |
Shadminist | Share of administrative and related workers |
Shservsales | Share of service and sales workers |
Shskllcrfts | Share of skilled workers, craftsmen, and similar |
Shmachassem | Share of machine operators and assembly workers |
Shunskllwrk | Share of unskilled workers |
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Mendes, R., Vareiro, L.C. (2013). Gender, Wages, and Productivity: An Analysis of the Tourism Industry in Northern Portugal. In: Matias, Á., Nijkamp, P., Sarmento, M. (eds) Quantitative Methods in Tourism Economics. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-2879-5_14
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