A note on the gender wage gap among managerial positions using a counterfactual decomposition approach: sticky floor or glass ceiling?

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  • This article was downloaded by: [University of California Santa Cruz]On: 14 November 2014, At: 21:25Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK

    Applied Economics LettersPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/rael20

    A note on the gender wage gap among managerialpositions using a counterfactual decompositionapproach: sticky floor or glass ceiling?Marco Biagetti a & Sergio Scicchitano a ba Department of Development and Cohesion Policies , Ministry of Economic Development ,Via Sicilia 162c, Rome, 00168, Italyb Department of Public Economics , University of Rome , Via del Castro Laurenziano n. 9,Rome, 00161, ItalyPublished online: 24 Jan 2011.

    To cite this article: Marco Biagetti & Sergio Scicchitano (2011) A note on the gender wage gap among managerial positionsusing a counterfactual decomposition approach: sticky floor or glass ceiling?, Applied Economics Letters, 18:10, 939-943, DOI:10.1080/13504851.2010.518944

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  • A note on the gender wage gap

    among managerial positions using

    a counterfactual decomposition

    approach: sticky floor or glass

    ceiling?

    Marco Biagettia and Sergio Scicchitanoa,b,*

    aDepartment of Development and Cohesion Policies, Ministry of EconomicDevelopment, Via Sicilia 162c, Rome, 00168 ItalybDepartment of Public Economics, University of Rome, Via del CastroLaurenziano n. 9, Rome, 00161 Italy

    In this article, we apply a counterfactual decomposition approach usingQuantile Regression (QR) to the wage distribution of managerial workforcein Italy. We find evidence of both significant sticky floor and glass ceilingeffects for the Gender Wage Gap (GWG). Furthermore, the U-shapedfigure of the pay gap is mostly due to the difference in rewards that thetwo genders receive for their characteristics, whose relative incidence is alsocontinuously increasing as we move to upper quantiles.

    I. Introduction

    In this article, we apply a counterfactual decomposition

    approach using Quantile Regression (QR) to the wage

    distribution of the managerial workforce in Italy. The

    aim is twofold: firstly, to understand whether in the

    Gender Wage Gap (GWG) there exists a sticky floor

    or a glass ceiling effect; secondly, to investigate whether

    that GWG may be attributed more to difference in

    labour market characteristics between the genders or

    to difference in rewards that the two sexes receive for

    their characteristics in the managerial labour market.Despite the fact that generalGWGhas become awide-

    spread research topic in the empirical economic literature,

    only a few papers analyse the wage differentials across

    managerial workers.1 Furthermore, much lesser regard is

    devoted to investigate the existence of sticky floor (higher

    GWG at the bottom of the wage distribution) or glass

    ceiling (higher GWG at the top of the wage distribution)

    effects using QR. Yurtoglu and Zulehner (2009) find that

    the estimated pay gaps for top executive officers of pub-

    licly listed US firms are larger at the bottom than at the

    top of the distribution, that is, they find evidence of a

    sticky floor.2

    However, to the best of our knowledge, despite the

    very large use of counterfactual decomposition using

    QR done by the applied research inGWGwhen study-

    ing the sticky floor and glass ceiling effects along the

    *Corresponding author. E-mail: sergio.scicchitano@uniroma1.it1 See Bertrand and Hallock (2001) and Jurajda and Paligorova (2009).2 Smith et al. (2010), bymeans of a different procedure, demonstrate that inDenmark there is still a considerable glass ceiling butalso evidence of sticky floors.

    Applied Economics Letters ISSN 13504851 print/ISSN 14664291 online # 2011 Taylor & Francishttp://www.informaworld.com

    DOI: 10.1080/13504851.2010.518944

    939

    Applied Economics Letters, 2011, 18, 939943

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  • wage distribution,3 there is no empirical survey apply-

    ing this approach to supervisory works. In particular,

    such method could be particularly useful in this con-

    text, because it decomposes the difference between the

    supervisor male and female log wage distributions into

    one component that is based on the difference in coef-

    ficients and another based on the difference in char-

    acteristics. Hence, different values of the GWG at

    each of the estimated points along the log wage dis-

    tribution could be better explained.In this article, we fill this gap of economic literature,

    using the last European dataset, the Community

    Statistics on Income and Living Conditions survey

    (EU-SILC).We find that along the whole wage distribu-

    tion of the Italian managerial labour market, there are

    both significant sticky floor and glass ceiling effects for

    the GWG. Furthermore, the U-shaped figure of the pay

    gap is mostly due to the difference in rewards that the

    two genders receive for their characteristics, whereas the

    characteristic effect is monotonically decreasing along

    the whole wage distribution.This article is organized as follows. In Section II

    we quickly describe the econometric methodology

    adopted. Section III deals with the data used. Section

    IV presents the results obtained from splitting the

    differences in distribution using QR. In the last section

    we conclude with a possible explanation.

    II. Econometric Specification

    The use of Ordinary Least Squares (OLS) provides

    estimates for the conditional mean only, whereas we

    are interested in analysing wage gender differentials

    across the whole wage distribution for Italian man-

    agers. A useful tool to achieve this purpose is the QR

    method (Koenker and Bassett, 1978), where the thquantile of a variable conditional on some covariates

    can be estimated and the effect of those covariates at

    the bottom, at the median or at the top of the distribu-

    tion can be accounted for.Let yi be the dependent variable and xi the vector of

    the chosen covariates. The relation is given by

    yi xib ei with F 1e jX 0 1

    where F 1e jX represents the th quantile of e con-ditional on x. The estimated th quantile is obtainedby solving the following equation:

    b argminb2

  • q; xf; bf q; xm; bm q; xf; bf q; xf; bm q; xf; bm ; xm; bm

    4

    The right-hand term in the first set of bracketsconstitutes the difference in coefficients (i.e. thecounterfactual distribution), whereas that in the sec-ond set of brackets is the effect of differencescharacteristics.

    III. Data

    We used the latest (2007) wave of the EU-SILC data-base, available since March 2009. Our analysis wasperformed on the log hourly gross wage of Italianindividuals between 25 and 65 years of age whodeclared to be employed in a managerial position.EU-SILC identifies that position with a supervisoryrole where supervisory responsibility includes formalresponsibility for coordinating a group of otheremployees (other than apprentices). Students, peopleserving in the Armed Forces and self-employed werealso excluded. After doing so 3064 individualsremained, 1025 females and 2039 males. The covari-ates considered were (i) citizenship with three cate-gories: citizenship of the same country as that ofresidence (the base category), of any other EU countryand of any other country; (ii) marital status (threecategories: single individuals the base category union without legal basis, union with legal basis);(iii) limitation of activity because of health problemswhose categories are strongly limited (base category),limited, not limited; (iv) level of education with threecategories: the base is for pre-primary, primary andlower secondary school; the second for upper second-ary and post-secondary nontertiary education; the lastonly includes people with at least tertiary education;(v) working experience and its squared; (vi) whether ornot the individual works part-time (of course twocategories); (vii) type of contract (temporary or per-manent); (viii) the size of the unit where a worker isemployed, split into three categories (1119, 2049and more than 50 employees) other than the base (upto 10 employees) and (ix) sector (13 categories). Thesummary statistics for the sample of men and womenused in this article are reported in Table 1.

    IV. Results

    The results from the counterfactual decomposition aredisplayed in Table 2.We also report the OLS estimatesand the observed wage gap for comparison. All the

    coefficients are significant at 1%. Figure 1 plots thedecomposition results at each of the 99%. We findthat women earn less than men, also after control formany personal, human capital and labour marketcharacteristics, along the whole wage distribution.The observed GWG, as reported in the first row ofthe Table 2, is U-shaped and shows the lowest value atthe median ( = .50) quantile.By taking into account the effects of all covariates,

    the counterfactual analysis confirms a more linearU-shaped pattern, thus showing both sticky floor defined as the difference between 10th and 50th quan-tiles and glass ceiling computed with the differencebetween 90th and 50th quantiles effects. Moreover,the pay gap at the two extreme 10th and 90th quantilesis now higher. By decomposing the effect of its determi-nants, the GWG is mainly due to the difference inrewards along the entire distribution, whose relativeincidence is also monotonically increasing as we moveto upper quantiles. Such component, then, evidences aJ-shaped pattern, that is, a glass ceiling effect muchmore pronounced than the sticky floor. Conversely,the characteristic effect is continuously decreasingover the whole distribution.

    V. Conclusions

    Our counterfactual distribution of wages among Italianworkers in a supervisory position demonstrates that theGWG exhibits a U-shaped pattern, that is, it showsboth significant sticky floor and glass ceiling effects.The decomposition results for a selected grid of quan-tiles indicate that the contribution of differences inrewards has been quantitatively much more importantthan that of different covariates at each of the estimatedpoints along the log wage distribution. Furthermore,

    Table 1. Summary statistics for sample

    Women Men

    Variable Mean SD Mean SD

    Log gross hourly wage 2.39 0.45 2.66 0.44Citizenship 2.00 0.12 2.01 0.11Marital status 1.24 0.93 1.44 0.86Limitation because of health

    problems2.81 0.43 2.88 0.37

    Education attained 1.16 0.66 1.01 0.68Work experience 18.02 9.79 20.05 10.07Part time 0.15 0.35 0.18 0.13Permanent 0.91 0.28 0.95 0.22Firm size 1.68 1.26 1.84 1.23Sectors 7.28 3.49 5.40 3.49

    A note on the gender wage gap among managerial positions 941

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  • although the effect of the difference in rewards is

    J-shaped, with a much more pronounced glass ceiling

    effect than that of sticky floor, its relative incidence is

    also continuously increasing along the distribution.We can now briefly speculate on the possible expla-

    nations of such findings. On the one hand, it is well

    known that in Italy the workfamily composite recon-

    ciliation index is quite low, comparedwith that of many

    other industrial countries (OECD, 2001). Moreover,

    there is also evidence that countries with less generous

    workfamily policies have a wider wage gap at the

    bottom of the wage distribution and a lower gap at

    the top, suggesting that the positive effect of family-

    friendly policies dominates at the bottom of the distri-

    bution (Arulampalam et al., 2007). Thus, following

    Yurtoglu and Zulehner (2009), the lack of an adequate

    workfamily reconciliation package may also induce

    managerial women to prefer family to career than

    compared with men, especially at the bottom of the

    wage distribution. On the other hand, it is also well

    documented that idiosyncratic, sociological and cul-

    tural reasons may have a major role in discriminating

    against women, particularly for the high hierarchical

    levels, because the supervisory positions are tradition-

    ally considered as mens prerogative (OECD, 2002). In

    this framework, as we move up the wage distribution,

    although other reasons may be also called, gender

    stereotypes may be much likely to considerably influ-

    ence the increasing incidence of the coefficient effect,

    especially at the top of the wage distribution.

    Acknowledgements

    We thankMarcoMarini for invaluable suggestions and

    comments. The views expressed in this article are those

    of the authors and, in particular, do not necessarily

    Table 2. Decompositions of changes in gender wage gap using quantile regression

    OLS = .10 = .20 = .30 = .40 = .50 = .60 = .70 = .80 = .90

    Raw 0.269 0.303 0.208 0.216 0.223 0.201 0.254 0.251 0.288 0.288(0.017) (0.048) (0.004) (0.037) (0.0112) (0.032) (0.027) (0.021) (0.022) (0.016)

    Totaldifference

    0.273 0.323 0.239 0.212 0.207 0.214 0.230 0.256 0.294 0.331

    (0.017) (0.006) (0.005) (0.005) (0.006) (0.007) (0.009) (0.009) (0.012) (0.024)Characteristics 0.071 0.158 0.096 0.072 0.062 0.057 0.051 0.049 0.048 0.048

    (0.013) (0.020) (0.016) (0.015) (0.014) (0.014) (0.014) (0.015) (0.017) (0.023)26% 49% 40% 34% 30% 26% 22% 19% 16% 15%

    Coefficients 0.202 0.165 0.143 0.140 0.145 0.158 0.179 0.207 0.246 0.282(0.015) (0.032) (0.022) (0.019) (0.015) (0.013) (0.012) (0.011) (0.011) (0.014)74% 51% 60% 66% 70% 74% 78% 81% 84% 85%

    Note: Bootstrap SEs with 100 replications are given in parentheses. All coefficients are significant at 1%. Percentages are in italics.

    0.1

    6E-16

    0.1

    0.2

    0.3

    0.4

    0.5

    0.6

    Quantile

    Total differential Characteristics Coefficients Raw

    0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95

    Fig. 1. Decomposition of differences in distribution using quantile regression

    Note: Results obtained by applying decomposition at each of the 99%. Bootstrap SE with 100 replications.

    942 M. Biagetti and S. Scicchitano

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  • reflect those of theMinistry of EconomicDevelopment.

    The usual disclaimer applies.

    References

    Arulampalam, W., Booth, A. L. and Bryan, M. L. (2007) Isthere a glass ceiling over Europe? Exploring the genderpay gap across the wage distribution, Industrial andLabor Relations Review, 60, 16386.

    Bertrand, M. and Hallock, K. (2001) The gender gap in topcorporate jobs, Industrial and Labor Relations Review,55, 121.

    Jurajda, S. and Paligorova, T. (2009) Czech female man-agers and their wages, Labour Economics, 16, 34251.

    Koenker, R. and Bassett, G. (1978) Regression quantiles,Econometrica, 46, 3350.

    Melly, B. (2005) Decomposition of differences in distributionusing quantile regression,Labour Economics, 12, 57790.

    OECD (2001) Employment Outook, OECD, Paris.OECD (2002) Employment Outook, OECD, Paris.Smith, N., Smith, V. and Verner, M. (2010) The gender

    pay gap in top corporate jobs in Denmark: glassceilings, sticky floors or both?, IZA DiscussionPapers 4848, Institute for the Study of Labor (IZA),Germany.

    Wahlberg, R. (2010) The gender wage gap across thewage distribution in the private and publicsectors in Sweden, Applied Economic Letters,DOI: 10.1080/13504850903035915.

    Yurtoglu, B. and Zulehner, C. (2009) Sticky floors and glassceilings in top corporate jobs. Available at http://ssrn.com/abstract=1470860 (accessed 9 September2009).

    A note on the gender wage gap among managerial positions 943

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