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Are women less likely to be managers in the UK labour market?
Journal Pre-proof Are Women Less Likely to be Managers in the UK Labour Market? 1 1We would like to thank the Editor and two anonymous referees for their suggested revisions. We would also like to thank the audience of the “Twelfth Biennial Asian Consumer and Family Economics Association (ACFEA) Conference” in China for their valuable comments.
Congmin peng, Po-wen she PII:
S0264-9993(18)31729-2
DOI:
https://doi.org/10.1016/j.econmod.2019.10.021
Reference:
ECMODE 5044
To appear in:
Economic Modelling
Received Date:
28 November 2018
Accepted Date:
22 October 2019
Please cite this article as: Congmin peng, Po-wen she, Are Women Less Likely to be Managers in the UK Labour Market? 1 1We would like to thank the Editor and two anonymous referees for their suggested revisions. We would also like to thank the audience of the “Twelfth Biennial Asian Consumer and Family Economics Association (ACFEA) Conference” in China for their valuable comments., Economic Modelling (2019), https://doi.org/10.1016/j.econmod.2019.10.021
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Journal Pre-proof
Are Women Less Likely to be Managers in the UK Labour Market?
CONGMIN PENG Anglia Ruskin University Correspondence: [email protected] PO-WEN SHE University of Cambridge
Journal Pre-proof
Are Women Less Likely to be Managers in the UK Labour Market? *
Abstract This paper investigates the gender gap within management roles in the UK. We find that female workers are less likely to become managers than male workers because women are traditionally expected to provide the majority of care for their family. This implies that women are pressured to do more informal work than men, which limits their formal working experience and impedes their career development. We also find that the probability of becoming a manager adopts an inverted U-shape relationship with age. This likelihood of becoming a manager also increases with employment duration and educational attainment due to the accumulation of relevant experience and human capital.
Keywords: gender discrimination, occupational transition, UK labour market
We would like to thank the Editor and two anonymous referees for their suggested revisions. We would also like to thank the audience of the “Twelfth Biennial Asian Consumer and Family Economics Association (ACFEA) Conference” in China for their valuable comments. *
1
Journal Pre-proof 1. Introduction In recent decades, the gender gaps in employment rates, pay and working hours have been widely discussed by the general public.1 In part because of societal pressure, in 1997 the UK government introduced several labour market policies that were designed to help low-income families and groups which were isolated from the labour market, such as women with young children. However, these gender gaps are still common in the labour market, especially at higher levels. In 2013, the Office for National Statistics (ONS) found that only 35% of senior management roles were occupied by women.2 Furthermore, women only held a sixth of all senior roles in top UK companies.3 The intention of this paper is to identify the mechanisms that underly these findings. Managers
require
professional
expertise,
which
places
women
in
disadvantaged positions. In many cultures, women are traditionally expected to be responsible for performing duties such as cooking, cleaning, and raising children. This restricts women’s capacity to enter the workforce and impairs their competitiveness. Due to the general requirement to perform time-consuming housework, women have less time to devote to their careers, which can impede their accumulation of work experience and technical expertise. Women, therefore, often experience more difficulties at work than men, especially in management roles. Figure 1 shows the employment rates of men and women with and without dependent children. The solid line with square data points is the employment rate for men with children. The solid line with triangular data points is the employment rate https://timewise.co.uk/article/article-real-reasons-behind-gender-pay-gap/ This is slightly higher than the EU average of 33%. Source: http://webarchive.nationalarchives.gov.uk/20160108153731/http://www.ons.gov.uk/ons/rel/lmac/wome n-in-the-labour-market/2013/sty-women-in-work.html 3 Source: https://www.ft.com/content/0713fe70-18f8-11e6-bb7d-ee563a5a1cc1 1 2
2
Journal Pre-proof for men without children. The dashed line with square data points is the employment rate for women without children. The dashed line with square data points is the employment rate for women with children. Figure 1 provides some important insights. Firstly, the employment rate of mothers has increased by 11.8% to 73.7% over the past two decades. Secondly, women’s employment rate is considerably lower than that for men. Thirdly, the employment rate of fathers is 20% higher than that of mothers.
%
Figure 1. Employment rates of men and women with and without dependent children 95 90 85 80 75 70 65 60 1995
2000
2005
Men - with dependent children Women - with dependent children
2010
Year
2015
Men - without dependent children Women - without dependent children
Source: Labour Force Survey Figure 2 presents the employment rates of men and women with and without dependent children across age groups. It shows that mothers aged under 50 are less likely to be employed than women under 50 without dependent children, but fathers are more likely to be employed than men without dependent children.
3
Journal Pre-proof Figure 2. Employment rates of men and women with and without dependent children 100 90 80 70 60 50 40 30 20 10 0 16 to 24 women with children
25 to 34
35 to 49
50 to 64
women without children
men with children
men without children
Source: Labour Force Survey 2017
Figure 3 displays the differences in employment rates and the number of dependent children in couple-based families and single parent families. It shows that the more children couple-based families have, the less likely it is that both parents work full-time. Three categories form the majority: both parents working full-time, both parents employed (full-time job for the man and part-time job for the woman), and only the father working full-time. Figure 3 also shows that when a family has more dependent children, the mother is more likely to be unemployed. This is consistent with the societal expectation that women should raise children and be responsible for housework, and are thereby more likely to sacrifice potential careers.
4
Journal Pre-proof Figure 3: Percentage of coupled-families by employment composition and number of dependent children 3 or more children
Two children
One child 0
20
40
60
80
100
Both employed full time Both employed (female full-time; male part-time) Both employed (male full-time; female part-time) Both employed part time Both not employed Father only employed - full time
Source: Labour Force Survey 2017 Figure 4 presents the proportion of managers for each gender in the UK. The solid line with diamond data points is the proportion of male managers, and the dashed line with triangle data points is the proportion of female managers. Male managers accounted for 67.1% of the total and female managers accounted for 32.9% in 2001. In 2017, the proportion of female managers was 37.6%, while the proportion
Figure 4: Proportion of managers by gender (%) 70.00 65.00 60.00 55.00 50.00 45.00 40.00 35.00 30.00 25.00
Male
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
20.00
Female
5
Journal Pre-proof of male managers was 62.4%. Therefore, the proportion of female managers increased by 4.7% over these 15 years, a relatively small rate of change. This study investigates why women still have a lower probability of becoming mangers than men. Recent studies have examined the cyclicality of occupational and sectoral mobility in the UK labour market, which motivates this paper to examine whether the business cycle affects the likelihood of being promoted (Carrillo-Tudela et al., 2016). Carrillo-Tudela et al. (2016) note that promotions to management positions is a specific
type
of
occupational
mobility.
Therefore,
this
paper
integrates
macroeconomic factors with individuals’ demographic information and employment status in order to contribute to the existing literature. This study is the first to document workers’ occupational movements from non-management to management positions by disaggregating movements into employment-to-employment (EE) transitions and unemployment-to-employment (UE) transitions in the UK labour market. We investigate gender differences within occupational movements and the effects of children on the probability of becoming a manger. This study is structured as follows. Section 2 provides a literature review of gender imbalances within management roles. Section 3 introduces an econometric model to investigate the determinants of occupational transitions into management. Section 4 provides the results of the empirical analysis. Section 5 concludes.
2. Literature Review A successfully functioning labour market pairs workers’ expertise with vacant positions. However, this process of pairing is slowed by frictions, which include factors such as moving costs and re-training. These frictions cause inefficiency in 6
Journal Pre-proof reallocation. The occupational transition rate is used to measure the extent of reallocation and pairing. Longhi and Taylor (2011) find that occupational mobility in the UK is high, at a value of 0.5, and Kambourov and Manovskii (2008) and Moscarini and Thomsson, 2007) find that the occupational mobility rate is also high in the US. This high level of mobility is common across labour markets in OECD countries (Davis, 1987; Jolivet et al., 2006). It is generally accepted that the rate of transition is not necessarily constant across the business cycle. However, the dynamics by which recessions affect the reallocation process has long been debated (Barlevy 2002; Caballero and Hammour 1994; Carrillo-Tudela et al. 2016; Lilien 1982; Mortensen and Pissarides 1994).4 Workers’ demographic characteristics affect their occupational transition decisions. Age, marriage status, number of children and gender all play important roles in the occupational transition because they both determine the level of risk presented by each transition decision and the level of risk that the individual is willing to accept (Carrillo-Tudela et al. 2016). The level of education is commonly adopted by the literature as a proxy of a worker’s human capital and is an important factor for workers’ career progression. Alternatively, the accumulation of human capital can also come from job-related activities, as longer employment durations increase workers’ capabilities, whilst unemployment lowers workers’ human capital. Therefore, individuals with records of
In one view, recessions speed up reallocation processes because workers move from declining sectors to expanding sectors. This is called the “cleansing effect” of recessions (Caballero and Hammour, 1994). However, Barlevy (2002) proposes that workers are reallocated less efficiently during contractionary periods due to the lack of opportunities. This is called the “sullying effect” of the recession. 4
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Journal Pre-proof longer and more continuous employment are expected to have higher probabilities of being promoted to management levels. Carrillo-Tudela et al. (2016) were the first to introduce controls on workers’ last occupation, which are generally considered to be the present highpoint of career paths. By controlling for workers’ previous occupations, the reliability of the estimation is increased. Additionally, Carrillo-Tudela et al. (2016) find that methods of job-seeking affect occupational transition. For example, workers who find jobs by responding to specific advertisements have higher probabilities of changing occupations than by other methods. Job type also affects the worker’s likelihood of continuing in a current role. Full-time workers are less likely to change occupations, perhaps because workers can use part-time jobs to build up an awareness of the specific requirements and structure of roles and become more informed before making their next employment decision (Longhi and Taylor 2011). Gender issues have long been discussed within the context of the labour market. Women’s relative lack of representation at higher management levels has been attributed to the existence of a “glass ceiling” (Cornelius and Skinner 2008). However, before analysing gender disparity, we must establish whether differences exist between the performance of female managers compared to male managers. While the impression that women are less able managers was commonplace within many male-dominated business environments, the majority of evidence finds no differences in performance. For example, Liff and Ward (2001) find that women’s management skills do not deviate from the average, but that demographic profiles, job status and employment factors do affect women’s careers. Other studies have found
8
Journal Pre-proof women to be better managers; for example, Arun, Almahrog, and Ali Aribi (2015) find that female directors have better earnings management in low-debt companies, and Dowling and Aribi (2013) suggest that female directors strongly enhance the
management of a company, as women tend to consider decisions more carefully. Women’s representation in top management positions is still a widely studied topic, such that scholars have developed a variety of methods for examining gender disparity in management roles (Dobbin, Schrage, and Kalev 2015; England 2010; Kalev, Dobbin, and Kelly 2006; Stainback and Tomaskovic-Devey 2009). Over recent decades, women’s legal rights at work, employment status, and labour market participation rate have all improved, which augments women’s opportunities for becoming managers. More recent research has analysed whether the working environment for women in general improves when more women occupy important management positions (Cohen, Broschak, and Haveman 1998; Dobbin, Kim, and Kalev 2011; Haveman 1998; Huffman, Cohen, and Pearlman 2010; Kurtulus and TomaskovicDevey 2012; Stainback, Kleiner, and Skaggs 2016). Cohen and Huffman (2007) find that more representation of women in management narrows the gender wage gap and conclude that the promotion of women into management positions can benefit all women. Furthermore, Terjesen and Singh (2008) find that this relationship exists at the state level. Female entrepreneurs are rarer in countries with high fertility rates (Dutta and Mallick 2018). However, female entrepreneurship can be promoted through tertiary education and increasing women’s labour force participation rates. Another route for improving women’s status is the media. Promoting a free and inclusive press can help
9
Journal Pre-proof women to communicate their ideas and gain public recognition, thereby producing a more welcoming environment for female workers (Cooray, Dutta, and Mallick 2017). Due to prejudice and discrimination in the work environment, women must obtain higher qualifications than men to reach the same level. In addition, females are more likely to have to sacrifice family plans due to long working hours (Davidson and Cooper 1987). Female managers have to balance their work commitments with
domestic responsibilities, especially childcare. These difficulties limit women’s career prospects in management (Davidson and Cooper 1987; Hirsh and Jackson 1991).5 While women could feasibly take part-time jobs in order to balance these commitments more easily, Durbin et al. (2010) find that transitions to part-time work limit career development, partly because of the perceived lack of quality part-time jobs at the managerial level. Therefore, women usually have to make a direct choice between career progression and family commitments; when they have children they limit their professional opportunities and also suffer the “parenthood penalty” on their return to work (Scott, Dex, and Joshi 2008). Another gender-related issue which has been widely discussed is the pay gap. Geiler and Renneboog (2015) have shown that female top managers earn about 23% less than that of male top managers. In addition, the gender pay gap grows after marriage and childbirth. Geiler and Renneboog (2015) find this conclusion to be robust after controlling for position, tenure, age, industry, time period, marital status, and parenthood. They also provide a more detailed analysis by disaggregating managers’ positions into categories that include Director, CEO, and COO, finding that The fact that women tend to progress up the career ladder slower that men is due to a range of factors. For example, women may be less active than men in seeking promotions in order to avoid discrimination through “male” selection criteria and subjective assessment methods (Hirsh and Jackson 1991). 5
10
Journal Pre-proof female CEOs do not suffer a pay gap in the UK, but that other female executive directors (e.g. CFOs, COOs, and Deputy CEOs) do receive lower rates of pay than their male equivalents. However, Blau and Devaro (2007) conclude that, while women have lower probabilities of promotion and lower expectations of promotion than men, there is no significant gap in pay. Much of the literature discusses the effect that the proportion of female managers have on payment disparities. However, the literature on the effect of business cycles on gender-disaggregated transitions into management is limited. This paper contributes to filling this gap in the literature. It also examines the genderrelated effects of cyclicality in occupational mobility, following on from the study conducted by Carrillo-Tudela et al. (2016). In addition, the existing literature only covers specific sectors or industries in the UK, such that their samples cannot represent the whole of the UK. By contrast, this paper uses the nationwide Labour Force Survey (LFS) to examine this issue, which provides our results with generalisability.
3. Methodology 3.1 Data and Variables The LFS is a unique source of quarterly longitudinal data on the UK labour market. It uses international definitions of employment, unemployment, and economic inactivity, and incorporates related factors such as occupation, industry, wage, training activity, and hours worked. The LFS is widely used in the analysis of social, economic and employment policies. The sample size of the LFS is around 80,000 individuals and 35,000 households. Its large sample size and level of detail makes the LFS an appropriate dataset to realise the research objective of this paper on analysing workers’
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Journal Pre-proof occupational movements. This paper therefore uses two-quarter longitudinal data from 2001 Q1 to 2017 Q1 to investigate gender differences across career paths in the UK. This study adopts the definitions detailed in Standard Occupation Classification (SOC) 2000 and 2010. SOC2010 was modified in 2010 to replace SOC2000. However, this modification did not allow SOC 2000 to be fully compatible with SOC 2010. To conform with past research conducted by Carrillo-Tudela et al. (2016), we use major occupational groups to reduce potential incompatibility errors. These major groups are listed in Table 1. Table 1. Occupational codes Codes
SOC2000
SOC2010
1
Managers and senior officers
Managers, directors and senior officers
2
Professional occupations
Professional occupations
3
Associate professional and technical occupations
Associate professional and technical occupations
4
Administrative and secretarial occupations
Administrative and secretarial occupations
5
Skilled trades occupations
Skilled trades occupations
6
Personal service occupations
7
Sales and customer service occupations
Sales and customer service occupations
8
Process, plant and machine operatives
Process, plant and machine operatives
9
Elementary occupations
Elementary occupations
Caring, leisure and other service occupations
The aim of this research is to investigate workers’ occupational movements from non-management to management roles. The dependent variable is a dummy indicating whether workers switch their occupation from non-manager to manger when they change jobs. If a worker takes a management role when their previous role
12
Journal Pre-proof was non-management, the value of the variable is 1, and 0 otherwise. The transition from one management role to another is excluded from the sample, as it does not capture the transition of interest. In any case, the manager to manager transition only accounts for 5% of the sample. We only investigate transitions across different employers, and not promotions within the same organisation, as the transition to management within a firm is more likely to be explained by unobservable factors, such as relationships with line managers and company culture, such that we exclude these cases to avoid bias. This study controls for several important explanatory variables, including the participant’s demographic characteristics, employment status, education, and job history. Additionally, the one-quarter-lagged term of unemployment rate (UNEMPR) is included to measure the cyclicality of occupational transitions into management; this variable contributes to the model by considering macroeconomic aspects. Whether the occupational movement is affected by the business cyclical due to the cleansing or sullying effect is discussed in detail by the literature (Barlevy 2002; Caballero and Hammour 1994; Carrillo-Tudela et al. 2016; Lilien 1982; Mortensen and Pissarides 1994). Age and educational level are widely used to measure workers’ experience and human capital. Workers’ ability to change occupations is generally considered to depend upon their human capital (Carrillo-Tudela et al. 2016; Cooray et al. 2017). The speed of human capital accumulation depends on the type of job, such as whether it is part- or full-time, permanent or temporary (Longhi and Taylor 2011). The literature considers unemployment to represent a depreciation of human capital, while the duration of employment is related to a worker’s human capital (Edin and Gustavsson 2008; Pavoni 2009). Most importantly, gender discrimination in the labour market, such as women’s expectation to get married and raise children, can
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Journal Pre-proof impede women’s careers in many ways (Cooray et al. 2017; Davidson and Cooper 1987; Durbin et al. 2010; Liff and Ward 2001). Table 1 displays the explanatory variables. UNEMPR is the unemployment rate, which captures the stage of the business cycle. It is worth to noting that the unemployment rate incorporates a lag of one period to capture the cyclicality of occupational transition to management. AGE represents the participant’s age. AGESQ is the square of participant’s age, which is included to test whether there is non-linear relationship between workers’ age and the probability of being promoted to management. In addition, the gender term, FEMALE, higher education qualification, H-EDU, secondary education certificates, M-EDU, the number of children, CHILD, marriage or cohabitation, MARID, the length of employment EMPDUR, the length of unemployment, UMPDUR, and hours worked per week, FTJOB, are included in the estimation. The methods of seeking a job, previous occupation, and residential area are also controlled in each model.6
Variable
Table 2. Independent variables Description
UNEMPR
Unemployment rate
AGE
Respondent’s age
AGESQ
AGE*AGE/1000
MARID
Married or cohabiting (dummy: equal to 1 if respondent lives with his/her spouse, 0 otherwise)
CHILD
Respondent’s number of children
FEMALE
Gender (dummy: equal to 1 if respondent is female, 0 otherwise)
UNEMPD
Unemployed (dummy: equal to 1 if respondent is unemployed, 0 otherwise; reference group: employed)
The methods of job seeking consist of five options: job centre, advertisement searches, directly approaching employers, asking family or friends, and other methods. 6
14
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H-EDU
Higher education (dummy: equal to 1 if respondent has a higher-level degree, 0 otherwise; reference group: low education)
M-EDU
Middle education (dummy: equal to 1 if respondent’s highest qualification is an A-level/O-level, GCSE or equivalent qualification, 0 otherwise; reference group: low education)
EMPDUR
The duration of employment with current employer
UMPDUR
The duration of unemployment
FTJOB
Full-time employment (dummy: equal to 1 if respondent’s previous job was full time, 0 otherwise)
Promoted UNEMPR AGE AGESQ MARID CHILD FMALE H-EDU M-EDU L-EDU Observations
Table 3. Descriptive statistics Mean SD. Min 0.0405 0.197 0 0.0585 0.0122 0.0472 32.22 11.31 16 1.166 0.779 0.256 0.350 0.477 0 0.652 0.938 0 0.506 0.500 0 0.302 0.459 0 0.558 0.497 0 0.139 0.346 0 38555
Max 1 0.0883 55 3.025 1 8 1 1 1 1
Table 3 provides the descriptive statistics for the aggregate data (the descriptive statistics of the EE, UE, male and female samples are provided in the Appendix). There are 38,000 workers moving from non-management to management occupations. The sample is composed of an equal number of men and women. The rate of becoming a manager is 4%, with a rate of 4.4% for men and 3.7% for women (see Appendix). The average unemployment rate across the sample period is 5.9%. In the sample population, 30% are highly educated, 56% are middle-educated, and 14% are lower-educated, while 55% are married. Regarding the transition channel, 66% become managers through an EE transition, and 34% following unemployment. 15
Journal Pre-proof 3.2 Empirical Model A Probit model is a type of regression that accounts for a binary dependent variable. This model is designed to estimate the probability of a particular respondent’s potential outcomes depending on their particular characteristics. Specifically, the model takes the form: Prob (𝑌 = 1|𝑋) = Φ(𝑋 '𝛽) where Prob denotes the probability that an observation falls into a specified category, and Φ denotes the cumulative distribution function of the standard normal distribution. Therefore, Prob has a non-linear relationship with the regressors. The coefficient β is estimated using maximum likelihood. The Probit model can also be rewritten as a latent variable model in the following form: y ∗ = 𝑋'𝛽 + 𝜖 Where 𝑦 ∗ is not observed, such that it is characterised as a “latent” variable. This produces the dummy variable 𝑌, as defined by:
{
1 𝑖𝑓 𝑦 ∗ > 0 Y = 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 In the Probit model, we assume the existence of latent variables which determine the binary outcome. Therefore, we can use these latent variables to measure the probability of a worker’s promotion from a non-management to management role. Therefore, our econometric model can be written as: 𝑃𝑀𝑖,𝑡 = 𝛼 + ∑𝑢𝛽𝑢𝑥𝑢𝑖,𝑡 + 𝜀𝑖
(1)
where the binary variable PM denotes whether the individual i undergoes an occupational transition in time t. If a worker becomes a manager, PM is 1, otherwise it is 0. u is the number of explanatory variables.
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Journal Pre-proof In this study, we define the occupational transition to management by using the standard occupational classification listed in Table 1. If a worker’s occupation at time t-1 is not a “manager” or “senior officer” in SOC2000 or a “manager”, “director” or “senior officer” in SOC2010 and their occupation in time t falls into these categories, then the worker will be defined as having transferred into a management role. To avoid redundant wording, this study adopts the term “promotion” to represent “promotion to manager” or “transferred to management” in the results. This study uses five models to investigate the determinants of a worker’s likelihood of occupational transition to management. We estimate using the entire sample, but also split the sample according to the method of transition to a new job, including employment-to-employment (EE) and unemployment-to-employment (UE) transitions. If an employee was hired by another employer without any spell of unemployment, the transition is defined as an EE transition. If the worker was reemployed in a management role following unemployment, then the transition is defined as a UE transition. These samples are further separated into EE and UE sub-samples, because a spell of employment/unemployment affects workers’ occupational transitions (Carrillo-Tudela et al. 2016; Longhi and Taylor 2011). The unemployment duration is considered as the depreciation of a worker’s human capital and capabilities, whereas the employment duration is considered to reflect the accumulation of human capital (Edin and Gustavsson 2008; Pavoni 2009). By splitting these samples into EE and UE subsamples, this study is able to investigate how the employment/unemployment duration affects the possibility of being promoted to management.
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Journal Pre-proof Given the aim of this study the sample is further disaggregated into two subgroups: male and female. This allows us to identify the differences between men and women.
4. Results Table 4 presents the results of the Probit model on occupational transitions into management positions in the UK. Column 1 shows the aggregate results, which include both EE and UE transitions. Column 2 shows the results for EE transitions, and Column 3 presents the results of UE transitions. Column 4 shows the results with only males included, and Column shows the results for the female sample. Column 1 displays the determinants of occupational transition to management roles. A worker’s age has a concave relationship with this probability, such that both young and old workers are less likely to become managers than middle-aged workers. This is understandable, since young workers lack the appropriate experience, whereas older workers lack of time they might be in the job before retirement or a reprioritisation of work commitments could be more influential on their reduced tendency of taking up management roles. Additionally, dependent children impede workers’ EE transitions, such that a worker with dependent children has a lower probability of becoming a manager when changing employers. Notably, this effect is more pronounced for female workers. We also note that, once unemployed, a worker has a lower probability of becoming a manager and that higher education has a significant and positive effect on the likelihood of being promoted to management. Most importantly, female workers are less likely to be promoted than male workers, and the overall probability of being promoted to management is procyclical.
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Journal Pre-proof The results for EE transition and UE transition, presented in Columns 2 and 3, show that the channel of transition affects the likelihood of transitioning to management. We find that longer periods of employment increase the probability of promotion (as captured by the variable EMPDUR), whilst unemployment tends to hinder a worker’s promotion chances (as captured by the variable UMPDUR). In addition, a previously employed full-time worker has a higher probability of being promoted to management than a part-time worker. Marriage and the number of children have significant and positive effects on the probability of being promoted to management in the EE transition, implying that marriage pushes an employee to be more responsible and encourages them to climb the career ladder. For unemployed individuals, obtaining a management role is not their priority, since they are more likely to see a non-management job as a means of getting on the career ladder. Columns 4 and 5, which detail the male and female samples, respectively, show that the business cycle has no significant effect on women’s promotion, while men’s promotion is procyclical. The purpose of splitting samples according to gender is to investigate the difference of occupational transition to management for men and women, respectively. This sample splitting is according to the finding of column1 that women have less probability to be promoted. This sample splitting is also helpful to compare the difference of explainable variables’ effects on occupational transition. It is interestingly to find that the possibility of being promoted for women is actually not affected by business cyclical. In addition, we find that marriage has a positive effect on promotion for men, but no effect for women, and that higher numbers of children impede women’s
19
Journal Pre-proof probability of being promoted to management positions. This is because women are normally responsible for the majority of childcare provision, thus impeding women’s career paths and reducing their promotion opportunities.7 By contrast, men’s promotion chances are not affected by the number of children. In summary, this paper finds several interesting results. Higher education significantly increases the probability of becoming a manager. Additionally, full-time workers have higher probabilities of becoming managers than those in part-time roles. Keeping other conditions (e.g. education level, age, working hours, etc.) stable, women are significantly less likely to become mangers than men, and, linking this to the business cycle, men’s occupational transition to management is procyclical. In other words, men have a lower probability of becoming a manger during recession periods but a higher probability during economic expansion. However, the business cycle does not affect women’s likelihood of being promoted to management positions. Moreover, we find no evidence that marriage hampers workers’ promotion chances. Instead, we find that marriage is significantly and positively related to employed workers’ and men’s ability to climb the career ladder.
We also analyse the sample of workers who left the labour market due to family commitments in order to investigate whether female workers have higher likelihoods of giving up their career prospects for their family. The result suggests that female workers are more likely to take career breaks than male workers. Meanwhile, workers are more willing to leave the labour market for family reasons during recessions. Both marriage and the number of children are related to the probability that workers leave the labour market. These findings imply that female workers are more likely to give up potential promotions for their family. The results are available upon request. 7
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Table 4. Results of Probit models AGG EE UE UNEMPR -0.153*** -0.167*** -0.0890* (-3.52) (-2.66) (-1.84) AGE 0.00436*** 0.00469*** 0.00250*** (9.49) (7.29) (4.43) AGESQ -0.0527*** -0.0558*** -0.0313*** (-8.41) (-6.38) (-4.05) MARID 0.00381*** 0.00469** 0.00189 (2.61) (2.35) (1.04) * CHILD 0.0000965 0.00167 -0.000463 (0.15) (1.82) (-0.57) *** *** FMALE -0.00844 -0.00607 -0.00634*** (-6.77) (-3.42) (-4.11) *** UNEMPD -0.00872 (-7.82) H-EDU 0.0242*** 0.0243*** 0.0187*** (6.35) (5.00) (3.25) *** *** M-EDU 0.0146 0.0144 0.0110*** (6.31) (4.58) (3.78) EMPDUR 0.000825** (2.54) FTJOB 0.0121*** (6.91) UMPDUR -0.00179*** (-2.71) Regions Yes Yes Yes S-Method Yes Yes Yes Last OCC Yes Yes Yes N 38555 25452 12282 Pseudo R2 0.093 0.094 0.104 Log lik. -5672.7 -4211.3 -1283.9
Male -0.207*** (-3.48) 0.00370*** (5.76) -0.0441*** (-5.11) 0.00962*** (3.92) 0.00146* (1.66)
Female -0.0912 (-1.49) 0.00533*** (8.32) -0.0664*** (-7.47) -0.00187 (-1.11) -0.00213** (-2.20)
-0.00606*** (-3.86) 0.0296*** (4.95) 0.0154*** (4.86)
-0.0106*** (-6.96) 0.0174*** (3.80) 0.0132*** (4.10)
Yes Yes Yes 19061 0.110 -2921.8
Yes Yes Yes 19494 0.086 -2694.2
Notes: Unemployment rate is lagged by one quarter. Marginal effects; t-statistics in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01
5. Conclusion and Recommendations The UK has the highest childcare costs in the OECD, and parents spend an average of 33.8% of their net income on full-time childcare.8 Many women take on this role in order to reduce their expenditures. However, this action restricts women’s potential to http://www.dailymail.co.uk/news/article-3831626/UK-childcare-expensive-world-Families-spendincome-nurseries-childminders.html 8
21
Journal Pre-proof advance to management positions. In September 2017, the UK government extended the length of free childcare from 15 to 30 hours per week over 38 weeks a year. Whether this 30 hours of childcare effectively helps women recoup their loss of opportunity is an issue that should be closely investigated. In addition to this policy, the government could encourage companies to provide the option of working-from-home and promote flexible working hours. For example, workers could be excused in the early afternoon in order to pick-up the children from nursery or school, then finish the rest of their workload at home. This would help women to manage competing pressures from work and family. As Cooray et al. (2017) suggest, a media that promotes women’s equality of choice can also help in this effort – for example, the media demonstrating that “looking after the family is not the duty for women only, it is for both men and women”. This research excludes internal promotion, as the data on this phenomenon is not clearly outlined in LFS. However, an analysis of intra-organisational promotion could further aid our understanding of the determinants of promotion, especially in regards to gender. Future research might also investigate whether promotion prospects differ between private and public employers, or consider the company culture as an important factor and develop a mechanism for measuring the impact of a company’s culture on promotions.
22
Journal Pre-proof Appendix Table A1. Descriptive statistics for four sub-set models EE
UE
Mean
SD.
Min
Max
Mean
SD.
Min
Max
Promoted
0.0476
0.213
0
1
0.0262
0.16
0
1
UNEMPR
0.0576
0.0116
0.0472
0.0883
0.0602
0.0132
0.0472
0.0883
AGE
31.92
11.22
16
55
32.76
11.48
16
55
AGESQ
1.145
0.769
0.256
3.025
1.205
0.799
0.256
3.025
MARID
0.363
0.481
0
1
0.322
0.467
0
1
CHILD
0.65
0.925
0
8
0.652
0.958
0
7
FMALE
0.524
0.499
0
1
0.463
0.499
0
1
H-EDU
0.315
0.464
0
1
0.271
0.445
0
1
M-EDU
0.567
0.496
0
1
0.546
0.498
0
1
L-EDU
0.119
0.324
0
1
0.182
0.386
0
1
Observation
25452
12282 Male
Female
Mean
SD.
Min
Max
Mean
SD.
Min
Max
Promoted
0.0439
0.205
0
1
0.0372
0.189
0
1
UNEMPR
0.0586
0.0124
0.0472
0.0883
0.0584
0.0121
0.0472
0.0883
AGE
32.14
11.38
16
55
32.29
11.24
16
55
AGESQ
1.162
0.787
0.256
3.025
1.169
0.772
0.256
3.025
MARID
0.351
0.477
0
1
0.349
0.477
0
1
CHILD
0.617
0.945
0
8
0.687
0.929
0
6
FMALE
0
0
0
0
1
0
1
1
H-EDU
0.279
0.449
0
1
0.325
0.468
0
1
M-EDU
0.566
0.496
0
1
0.551
0.497
0
1
L-EDU
0.155
0.362
0
1
0.124
0.33
0
1
Observation
19061
19494
23
Journal Pre-proof References Arun, Thankom Gopinath, Yousf Ebrahem Almahrog, and Zakaria Ali Aribi. 2015. “Female Directors and Earnings Management: Evidence from UK Companies.” International Review of Financial Analysis 39:137–46. Barlevy, Gadi. 2002. “The Sullying Effect of Recessions.” The Review of Economic Studies 69(1):65–96. Blau, Francine D. and Jed Devaro. 2007. “New Evidence on Gender Differences in Promotion Rates: An Empirical Analysis of a Sample of New Hires.” Industrial Relations: A Journal of Economy and Society 46(3):511–50. Caballero, Ricardo J. and Mohamad L. Hammour. 1994. “The Cleansing Effect of Recessions.” The American Economic Review 84(5):1350–68. Carrillo-Tudela, Carlos, Bart Hobijn, Powen She, and Ludo Visschers. 2016. “The Extent and Cyclicality of Career Changes: Evidence for the U.K.” European Economic Review 84:18–41. Cohen, Lisa E., Joseph P. Broschak, and Heather A. Haveman. 1998. “And Then There Were More? The Effect of Organizational Sex Composition on the Hiring and Promotion of Managers.” American Sociological Review 711–27. Cohen, Philip N. and Matt L. Huffman. 2007. “Working for the Woman? Female Managers and the Gender Wage Gap.” American Sociological Review 72(5):681–704. Cooray, Arusha, Nabamita Dutta, and Sushanta Mallick. 2017. “The Right to Be Free: Is Media Freedom Good News for Women’s Rights?” Journal of Institutional Economics 13(2):327–55. Cornelius, Nelarine and Denise Skinner. 2008. “The Careers of Senior Men and Women – A Capabilities Theory Perspective.” British Journal of Management 19:S141–49. Davidson, Marilyn J. and Cary L. Cooper. 1987. “Female Managers in Britain – A Comparative Perspective.” Human Resource Management 26(2):217–42. Davis, Steven J. 1987. “Fluctuations in the Pace of Labor Reallocation.” CarnegieRochester Conference Series on Public Policy 27:335–402. Dobbin, Frank, Soohan Kim, and Alexandra Kalev. 2011. “You Can’t Always Get What You Need: Organizational Determinants of Diversity Programs.” American Sociological Review 76(3):386–411. Dobbin, Frank, Daniel Schrage, and Alexandra Kalev. 2015. “Rage against the Iron Cage: The Varied Effects of Bureaucratic Personnel Reforms on Diversity.” American Sociological Review 80(5):1014–44.
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Journal Pre-proof Dowling, Michael and Zakaria Ali Aribi. 2013. “Female Directors and UK Company Acquisitiveness.” International Review of Financial Analysis 29:79–86. Durbin, S., J. Tomlinson, S. Durbin, and J. Tomlinson. 2010. “Female Part-Time Managers: Networks and Career Mobility.” Work, Employment and Society 24:621–40. Dutta, Nabamita and Sushanta Mallick. 2018. “Enabling Women Entrepreneurs: Exploring Factors That Mitigate the Negative Impact of Fertility Rates on Female Entrepreneurship.” Kyklos 71(3):402–32. Edin, Per-Anders and Magnus Gustavsson. 2008. “Time out of Work and Skill Depreciation.” Industrial and Labor Relations Review 61(2):163–80. England, Paula. 2010. “The Gender Revolution: Uneven and Stalled.” Gender & Society 24(2):149–66. Geiler, Philipp and Luc Renneboog. 2015. “Are Female Top Managers Really Paid Less?” Journal of Corporate Finance 35:345–69. Haveman, Heather A. 1998. “And Then There Were More? The Effect of Organizational Sex Composition on the Hiring and Promotion of Managers.” American Sociological Review 711–27. Hirsh, Wendy and Charles Jackson. 1991. “Women Managers and Career Progression: The British Experience.” Women in Management Review 6(2). Huffman, Matt L., Philip N. Cohen, and Jessica Pearlman. 2010. “Engendering Change: Organizational Dynamics and Workplace Gender Desegregation, 1975–2005.” Administrative Science Quarterly 55(2):255–77. Jolivet, Grégory, Fabien Postel-Vinay, and Jean-Marc Robin. 2006. “The Empirical Content of the Job Search Model: Labor Mobility and Wage Distributions in Europe and the US.” European Economic Review 50(4):877–907. Kalev, Alexandra, Frank Dobbin, and Erin Kelly. 2006. “Best Practices or Best Guesses? Assessing the Efficacy of Corporate Affirmative Action and Diversity Policies.” American Sociological Review 71(4):589–617. Kambourov, Gueorgui and Iourii Manovskii. 2008. “Rising Occupational and Industry Mobility in the United States: 1968-97.” International Economic Review 49(1):41–79. Kurtulus, Fidan Ana and Donald Tomaskovic-Devey. 2012. “Do Female Top Managers Help Women to Advance? A Panel Study Using EEO-1 Records.” The ANNALS of the American Academy of Political and Social Science 639(1):173–97. Liff, Sonia and Kate Ward. 2001. “Distorted Views Through the Glass Ceiling: The Construction of Women’s Understandings of Promotion and Senior Management Positions.” Gender, Work & Organization 8(1):19–36.
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Journal Pre-proof Lilien, David M. 1982. “Sectoral Shifts and Cyclical Unemployment.” Journal of Political Economy 90(4):777–93. Longhi, Simonetta and Mark P. Taylor. 2011. Occupational Change and Mobility among Employed and Unemployed Job Seekers. ISER Working Paper Series. 2011–25. Institute for Social and Economic Research. Mortensen, Dale T. and Christopher A. Pissarides. 1994. “Job Creation and Job Destruction in the Theory of Unemployment.” The Review of Economic Studies 61(3):397–415. Moscarini, Giuseppe and Kaj Thomsson. 2007. “Occupational and Job Mobility in the US*.” Scandinavian Journal of Economics 109(4):807–36. Pavoni, Nicola. 2009. “Optimal Unemployment Insurance, with Human Capital Depreciation, and Duration Dependence.” International Economic Review 50(2):323–62. Scott, Jacqueline, Shirley Dex, and Heather Joshi, eds. 2008. Women and Employment: Changing Lives and New Challenges. Cheltenham, UK ; Northampton, MA: Edward Elgar Publishing Ltd. Stainback, Kevin, Sibyl Kleiner, and Sheryl Skaggs. 2016. “Women in Power: Undoing or Redoing the Gendered Organization?” Gender & Society 30(1):109–35. Stainback, Kevin and Donald Tomaskovic-Devey. 2009. “Intersections of Power and Privilege: Long-Term Trends in Managerial Representation.” American Sociological Review 74(5):800–820. Terjesen, Siri and Val Singh. 2008. “Female Presence on Corporate Boards: A MultiCountry Study of Environmental Context.” Journal of Business Ethics 83(1):55–63.
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Journal Pre-proof Highlights
1. Female workers are less likely to become managers than male workers. 2. The probability of becoming a manager by age is represented by an inverted U-curve. 3. Workers with more human capital are more likely to become managers. 4. Female workers with more dependent children are less likely to become managers. 5. Male workers with more dependent children are more likely to become managers.
Congmin peng, Po-wen she PII:
S0264-9993(18)31729-2
DOI:
https://doi.org/10.1016/j.econmod.2019.10.021
Reference:
ECMODE 5044
To appear in:
Economic Modelling
Received Date:
28 November 2018
Accepted Date:
22 October 2019
Please cite this article as: Congmin peng, Po-wen she, Are Women Less Likely to be Managers in the UK Labour Market? 1 1We would like to thank the Editor and two anonymous referees for their suggested revisions. We would also like to thank the audience of the “Twelfth Biennial Asian Consumer and Family Economics Association (ACFEA) Conference” in China for their valuable comments., Economic Modelling (2019), https://doi.org/10.1016/j.econmod.2019.10.021
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Journal Pre-proof
Are Women Less Likely to be Managers in the UK Labour Market?
CONGMIN PENG Anglia Ruskin University Correspondence: [email protected] PO-WEN SHE University of Cambridge
Journal Pre-proof
Are Women Less Likely to be Managers in the UK Labour Market? *
Abstract This paper investigates the gender gap within management roles in the UK. We find that female workers are less likely to become managers than male workers because women are traditionally expected to provide the majority of care for their family. This implies that women are pressured to do more informal work than men, which limits their formal working experience and impedes their career development. We also find that the probability of becoming a manager adopts an inverted U-shape relationship with age. This likelihood of becoming a manager also increases with employment duration and educational attainment due to the accumulation of relevant experience and human capital.
Keywords: gender discrimination, occupational transition, UK labour market
We would like to thank the Editor and two anonymous referees for their suggested revisions. We would also like to thank the audience of the “Twelfth Biennial Asian Consumer and Family Economics Association (ACFEA) Conference” in China for their valuable comments. *
1
Journal Pre-proof 1. Introduction In recent decades, the gender gaps in employment rates, pay and working hours have been widely discussed by the general public.1 In part because of societal pressure, in 1997 the UK government introduced several labour market policies that were designed to help low-income families and groups which were isolated from the labour market, such as women with young children. However, these gender gaps are still common in the labour market, especially at higher levels. In 2013, the Office for National Statistics (ONS) found that only 35% of senior management roles were occupied by women.2 Furthermore, women only held a sixth of all senior roles in top UK companies.3 The intention of this paper is to identify the mechanisms that underly these findings. Managers
require
professional
expertise,
which
places
women
in
disadvantaged positions. In many cultures, women are traditionally expected to be responsible for performing duties such as cooking, cleaning, and raising children. This restricts women’s capacity to enter the workforce and impairs their competitiveness. Due to the general requirement to perform time-consuming housework, women have less time to devote to their careers, which can impede their accumulation of work experience and technical expertise. Women, therefore, often experience more difficulties at work than men, especially in management roles. Figure 1 shows the employment rates of men and women with and without dependent children. The solid line with square data points is the employment rate for men with children. The solid line with triangular data points is the employment rate https://timewise.co.uk/article/article-real-reasons-behind-gender-pay-gap/ This is slightly higher than the EU average of 33%. Source: http://webarchive.nationalarchives.gov.uk/20160108153731/http://www.ons.gov.uk/ons/rel/lmac/wome n-in-the-labour-market/2013/sty-women-in-work.html 3 Source: https://www.ft.com/content/0713fe70-18f8-11e6-bb7d-ee563a5a1cc1 1 2
2
Journal Pre-proof for men without children. The dashed line with square data points is the employment rate for women without children. The dashed line with square data points is the employment rate for women with children. Figure 1 provides some important insights. Firstly, the employment rate of mothers has increased by 11.8% to 73.7% over the past two decades. Secondly, women’s employment rate is considerably lower than that for men. Thirdly, the employment rate of fathers is 20% higher than that of mothers.
%
Figure 1. Employment rates of men and women with and without dependent children 95 90 85 80 75 70 65 60 1995
2000
2005
Men - with dependent children Women - with dependent children
2010
Year
2015
Men - without dependent children Women - without dependent children
Source: Labour Force Survey Figure 2 presents the employment rates of men and women with and without dependent children across age groups. It shows that mothers aged under 50 are less likely to be employed than women under 50 without dependent children, but fathers are more likely to be employed than men without dependent children.
3
Journal Pre-proof Figure 2. Employment rates of men and women with and without dependent children 100 90 80 70 60 50 40 30 20 10 0 16 to 24 women with children
25 to 34
35 to 49
50 to 64
women without children
men with children
men without children
Source: Labour Force Survey 2017
Figure 3 displays the differences in employment rates and the number of dependent children in couple-based families and single parent families. It shows that the more children couple-based families have, the less likely it is that both parents work full-time. Three categories form the majority: both parents working full-time, both parents employed (full-time job for the man and part-time job for the woman), and only the father working full-time. Figure 3 also shows that when a family has more dependent children, the mother is more likely to be unemployed. This is consistent with the societal expectation that women should raise children and be responsible for housework, and are thereby more likely to sacrifice potential careers.
4
Journal Pre-proof Figure 3: Percentage of coupled-families by employment composition and number of dependent children 3 or more children
Two children
One child 0
20
40
60
80
100
Both employed full time Both employed (female full-time; male part-time) Both employed (male full-time; female part-time) Both employed part time Both not employed Father only employed - full time
Source: Labour Force Survey 2017 Figure 4 presents the proportion of managers for each gender in the UK. The solid line with diamond data points is the proportion of male managers, and the dashed line with triangle data points is the proportion of female managers. Male managers accounted for 67.1% of the total and female managers accounted for 32.9% in 2001. In 2017, the proportion of female managers was 37.6%, while the proportion
Figure 4: Proportion of managers by gender (%) 70.00 65.00 60.00 55.00 50.00 45.00 40.00 35.00 30.00 25.00
Male
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
20.00
Female
5
Journal Pre-proof of male managers was 62.4%. Therefore, the proportion of female managers increased by 4.7% over these 15 years, a relatively small rate of change. This study investigates why women still have a lower probability of becoming mangers than men. Recent studies have examined the cyclicality of occupational and sectoral mobility in the UK labour market, which motivates this paper to examine whether the business cycle affects the likelihood of being promoted (Carrillo-Tudela et al., 2016). Carrillo-Tudela et al. (2016) note that promotions to management positions is a specific
type
of
occupational
mobility.
Therefore,
this
paper
integrates
macroeconomic factors with individuals’ demographic information and employment status in order to contribute to the existing literature. This study is the first to document workers’ occupational movements from non-management to management positions by disaggregating movements into employment-to-employment (EE) transitions and unemployment-to-employment (UE) transitions in the UK labour market. We investigate gender differences within occupational movements and the effects of children on the probability of becoming a manger. This study is structured as follows. Section 2 provides a literature review of gender imbalances within management roles. Section 3 introduces an econometric model to investigate the determinants of occupational transitions into management. Section 4 provides the results of the empirical analysis. Section 5 concludes.
2. Literature Review A successfully functioning labour market pairs workers’ expertise with vacant positions. However, this process of pairing is slowed by frictions, which include factors such as moving costs and re-training. These frictions cause inefficiency in 6
Journal Pre-proof reallocation. The occupational transition rate is used to measure the extent of reallocation and pairing. Longhi and Taylor (2011) find that occupational mobility in the UK is high, at a value of 0.5, and Kambourov and Manovskii (2008) and Moscarini and Thomsson, 2007) find that the occupational mobility rate is also high in the US. This high level of mobility is common across labour markets in OECD countries (Davis, 1987; Jolivet et al., 2006). It is generally accepted that the rate of transition is not necessarily constant across the business cycle. However, the dynamics by which recessions affect the reallocation process has long been debated (Barlevy 2002; Caballero and Hammour 1994; Carrillo-Tudela et al. 2016; Lilien 1982; Mortensen and Pissarides 1994).4 Workers’ demographic characteristics affect their occupational transition decisions. Age, marriage status, number of children and gender all play important roles in the occupational transition because they both determine the level of risk presented by each transition decision and the level of risk that the individual is willing to accept (Carrillo-Tudela et al. 2016). The level of education is commonly adopted by the literature as a proxy of a worker’s human capital and is an important factor for workers’ career progression. Alternatively, the accumulation of human capital can also come from job-related activities, as longer employment durations increase workers’ capabilities, whilst unemployment lowers workers’ human capital. Therefore, individuals with records of
In one view, recessions speed up reallocation processes because workers move from declining sectors to expanding sectors. This is called the “cleansing effect” of recessions (Caballero and Hammour, 1994). However, Barlevy (2002) proposes that workers are reallocated less efficiently during contractionary periods due to the lack of opportunities. This is called the “sullying effect” of the recession. 4
7
Journal Pre-proof longer and more continuous employment are expected to have higher probabilities of being promoted to management levels. Carrillo-Tudela et al. (2016) were the first to introduce controls on workers’ last occupation, which are generally considered to be the present highpoint of career paths. By controlling for workers’ previous occupations, the reliability of the estimation is increased. Additionally, Carrillo-Tudela et al. (2016) find that methods of job-seeking affect occupational transition. For example, workers who find jobs by responding to specific advertisements have higher probabilities of changing occupations than by other methods. Job type also affects the worker’s likelihood of continuing in a current role. Full-time workers are less likely to change occupations, perhaps because workers can use part-time jobs to build up an awareness of the specific requirements and structure of roles and become more informed before making their next employment decision (Longhi and Taylor 2011). Gender issues have long been discussed within the context of the labour market. Women’s relative lack of representation at higher management levels has been attributed to the existence of a “glass ceiling” (Cornelius and Skinner 2008). However, before analysing gender disparity, we must establish whether differences exist between the performance of female managers compared to male managers. While the impression that women are less able managers was commonplace within many male-dominated business environments, the majority of evidence finds no differences in performance. For example, Liff and Ward (2001) find that women’s management skills do not deviate from the average, but that demographic profiles, job status and employment factors do affect women’s careers. Other studies have found
8
Journal Pre-proof women to be better managers; for example, Arun, Almahrog, and Ali Aribi (2015) find that female directors have better earnings management in low-debt companies, and Dowling and Aribi (2013) suggest that female directors strongly enhance the
management of a company, as women tend to consider decisions more carefully. Women’s representation in top management positions is still a widely studied topic, such that scholars have developed a variety of methods for examining gender disparity in management roles (Dobbin, Schrage, and Kalev 2015; England 2010; Kalev, Dobbin, and Kelly 2006; Stainback and Tomaskovic-Devey 2009). Over recent decades, women’s legal rights at work, employment status, and labour market participation rate have all improved, which augments women’s opportunities for becoming managers. More recent research has analysed whether the working environment for women in general improves when more women occupy important management positions (Cohen, Broschak, and Haveman 1998; Dobbin, Kim, and Kalev 2011; Haveman 1998; Huffman, Cohen, and Pearlman 2010; Kurtulus and TomaskovicDevey 2012; Stainback, Kleiner, and Skaggs 2016). Cohen and Huffman (2007) find that more representation of women in management narrows the gender wage gap and conclude that the promotion of women into management positions can benefit all women. Furthermore, Terjesen and Singh (2008) find that this relationship exists at the state level. Female entrepreneurs are rarer in countries with high fertility rates (Dutta and Mallick 2018). However, female entrepreneurship can be promoted through tertiary education and increasing women’s labour force participation rates. Another route for improving women’s status is the media. Promoting a free and inclusive press can help
9
Journal Pre-proof women to communicate their ideas and gain public recognition, thereby producing a more welcoming environment for female workers (Cooray, Dutta, and Mallick 2017). Due to prejudice and discrimination in the work environment, women must obtain higher qualifications than men to reach the same level. In addition, females are more likely to have to sacrifice family plans due to long working hours (Davidson and Cooper 1987). Female managers have to balance their work commitments with
domestic responsibilities, especially childcare. These difficulties limit women’s career prospects in management (Davidson and Cooper 1987; Hirsh and Jackson 1991).5 While women could feasibly take part-time jobs in order to balance these commitments more easily, Durbin et al. (2010) find that transitions to part-time work limit career development, partly because of the perceived lack of quality part-time jobs at the managerial level. Therefore, women usually have to make a direct choice between career progression and family commitments; when they have children they limit their professional opportunities and also suffer the “parenthood penalty” on their return to work (Scott, Dex, and Joshi 2008). Another gender-related issue which has been widely discussed is the pay gap. Geiler and Renneboog (2015) have shown that female top managers earn about 23% less than that of male top managers. In addition, the gender pay gap grows after marriage and childbirth. Geiler and Renneboog (2015) find this conclusion to be robust after controlling for position, tenure, age, industry, time period, marital status, and parenthood. They also provide a more detailed analysis by disaggregating managers’ positions into categories that include Director, CEO, and COO, finding that The fact that women tend to progress up the career ladder slower that men is due to a range of factors. For example, women may be less active than men in seeking promotions in order to avoid discrimination through “male” selection criteria and subjective assessment methods (Hirsh and Jackson 1991). 5
10
Journal Pre-proof female CEOs do not suffer a pay gap in the UK, but that other female executive directors (e.g. CFOs, COOs, and Deputy CEOs) do receive lower rates of pay than their male equivalents. However, Blau and Devaro (2007) conclude that, while women have lower probabilities of promotion and lower expectations of promotion than men, there is no significant gap in pay. Much of the literature discusses the effect that the proportion of female managers have on payment disparities. However, the literature on the effect of business cycles on gender-disaggregated transitions into management is limited. This paper contributes to filling this gap in the literature. It also examines the genderrelated effects of cyclicality in occupational mobility, following on from the study conducted by Carrillo-Tudela et al. (2016). In addition, the existing literature only covers specific sectors or industries in the UK, such that their samples cannot represent the whole of the UK. By contrast, this paper uses the nationwide Labour Force Survey (LFS) to examine this issue, which provides our results with generalisability.
3. Methodology 3.1 Data and Variables The LFS is a unique source of quarterly longitudinal data on the UK labour market. It uses international definitions of employment, unemployment, and economic inactivity, and incorporates related factors such as occupation, industry, wage, training activity, and hours worked. The LFS is widely used in the analysis of social, economic and employment policies. The sample size of the LFS is around 80,000 individuals and 35,000 households. Its large sample size and level of detail makes the LFS an appropriate dataset to realise the research objective of this paper on analysing workers’
11
Journal Pre-proof occupational movements. This paper therefore uses two-quarter longitudinal data from 2001 Q1 to 2017 Q1 to investigate gender differences across career paths in the UK. This study adopts the definitions detailed in Standard Occupation Classification (SOC) 2000 and 2010. SOC2010 was modified in 2010 to replace SOC2000. However, this modification did not allow SOC 2000 to be fully compatible with SOC 2010. To conform with past research conducted by Carrillo-Tudela et al. (2016), we use major occupational groups to reduce potential incompatibility errors. These major groups are listed in Table 1. Table 1. Occupational codes Codes
SOC2000
SOC2010
1
Managers and senior officers
Managers, directors and senior officers
2
Professional occupations
Professional occupations
3
Associate professional and technical occupations
Associate professional and technical occupations
4
Administrative and secretarial occupations
Administrative and secretarial occupations
5
Skilled trades occupations
Skilled trades occupations
6
Personal service occupations
7
Sales and customer service occupations
Sales and customer service occupations
8
Process, plant and machine operatives
Process, plant and machine operatives
9
Elementary occupations
Elementary occupations
Caring, leisure and other service occupations
The aim of this research is to investigate workers’ occupational movements from non-management to management roles. The dependent variable is a dummy indicating whether workers switch their occupation from non-manager to manger when they change jobs. If a worker takes a management role when their previous role
12
Journal Pre-proof was non-management, the value of the variable is 1, and 0 otherwise. The transition from one management role to another is excluded from the sample, as it does not capture the transition of interest. In any case, the manager to manager transition only accounts for 5% of the sample. We only investigate transitions across different employers, and not promotions within the same organisation, as the transition to management within a firm is more likely to be explained by unobservable factors, such as relationships with line managers and company culture, such that we exclude these cases to avoid bias. This study controls for several important explanatory variables, including the participant’s demographic characteristics, employment status, education, and job history. Additionally, the one-quarter-lagged term of unemployment rate (UNEMPR) is included to measure the cyclicality of occupational transitions into management; this variable contributes to the model by considering macroeconomic aspects. Whether the occupational movement is affected by the business cyclical due to the cleansing or sullying effect is discussed in detail by the literature (Barlevy 2002; Caballero and Hammour 1994; Carrillo-Tudela et al. 2016; Lilien 1982; Mortensen and Pissarides 1994). Age and educational level are widely used to measure workers’ experience and human capital. Workers’ ability to change occupations is generally considered to depend upon their human capital (Carrillo-Tudela et al. 2016; Cooray et al. 2017). The speed of human capital accumulation depends on the type of job, such as whether it is part- or full-time, permanent or temporary (Longhi and Taylor 2011). The literature considers unemployment to represent a depreciation of human capital, while the duration of employment is related to a worker’s human capital (Edin and Gustavsson 2008; Pavoni 2009). Most importantly, gender discrimination in the labour market, such as women’s expectation to get married and raise children, can
13
Journal Pre-proof impede women’s careers in many ways (Cooray et al. 2017; Davidson and Cooper 1987; Durbin et al. 2010; Liff and Ward 2001). Table 1 displays the explanatory variables. UNEMPR is the unemployment rate, which captures the stage of the business cycle. It is worth to noting that the unemployment rate incorporates a lag of one period to capture the cyclicality of occupational transition to management. AGE represents the participant’s age. AGESQ is the square of participant’s age, which is included to test whether there is non-linear relationship between workers’ age and the probability of being promoted to management. In addition, the gender term, FEMALE, higher education qualification, H-EDU, secondary education certificates, M-EDU, the number of children, CHILD, marriage or cohabitation, MARID, the length of employment EMPDUR, the length of unemployment, UMPDUR, and hours worked per week, FTJOB, are included in the estimation. The methods of seeking a job, previous occupation, and residential area are also controlled in each model.6
Variable
Table 2. Independent variables Description
UNEMPR
Unemployment rate
AGE
Respondent’s age
AGESQ
AGE*AGE/1000
MARID
Married or cohabiting (dummy: equal to 1 if respondent lives with his/her spouse, 0 otherwise)
CHILD
Respondent’s number of children
FEMALE
Gender (dummy: equal to 1 if respondent is female, 0 otherwise)
UNEMPD
Unemployed (dummy: equal to 1 if respondent is unemployed, 0 otherwise; reference group: employed)
The methods of job seeking consist of five options: job centre, advertisement searches, directly approaching employers, asking family or friends, and other methods. 6
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Journal Pre-proof
H-EDU
Higher education (dummy: equal to 1 if respondent has a higher-level degree, 0 otherwise; reference group: low education)
M-EDU
Middle education (dummy: equal to 1 if respondent’s highest qualification is an A-level/O-level, GCSE or equivalent qualification, 0 otherwise; reference group: low education)
EMPDUR
The duration of employment with current employer
UMPDUR
The duration of unemployment
FTJOB
Full-time employment (dummy: equal to 1 if respondent’s previous job was full time, 0 otherwise)
Promoted UNEMPR AGE AGESQ MARID CHILD FMALE H-EDU M-EDU L-EDU Observations
Table 3. Descriptive statistics Mean SD. Min 0.0405 0.197 0 0.0585 0.0122 0.0472 32.22 11.31 16 1.166 0.779 0.256 0.350 0.477 0 0.652 0.938 0 0.506 0.500 0 0.302 0.459 0 0.558 0.497 0 0.139 0.346 0 38555
Max 1 0.0883 55 3.025 1 8 1 1 1 1
Table 3 provides the descriptive statistics for the aggregate data (the descriptive statistics of the EE, UE, male and female samples are provided in the Appendix). There are 38,000 workers moving from non-management to management occupations. The sample is composed of an equal number of men and women. The rate of becoming a manager is 4%, with a rate of 4.4% for men and 3.7% for women (see Appendix). The average unemployment rate across the sample period is 5.9%. In the sample population, 30% are highly educated, 56% are middle-educated, and 14% are lower-educated, while 55% are married. Regarding the transition channel, 66% become managers through an EE transition, and 34% following unemployment. 15
Journal Pre-proof 3.2 Empirical Model A Probit model is a type of regression that accounts for a binary dependent variable. This model is designed to estimate the probability of a particular respondent’s potential outcomes depending on their particular characteristics. Specifically, the model takes the form: Prob (𝑌 = 1|𝑋) = Φ(𝑋 '𝛽) where Prob denotes the probability that an observation falls into a specified category, and Φ denotes the cumulative distribution function of the standard normal distribution. Therefore, Prob has a non-linear relationship with the regressors. The coefficient β is estimated using maximum likelihood. The Probit model can also be rewritten as a latent variable model in the following form: y ∗ = 𝑋'𝛽 + 𝜖 Where 𝑦 ∗ is not observed, such that it is characterised as a “latent” variable. This produces the dummy variable 𝑌, as defined by:
{
1 𝑖𝑓 𝑦 ∗ > 0 Y = 0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 In the Probit model, we assume the existence of latent variables which determine the binary outcome. Therefore, we can use these latent variables to measure the probability of a worker’s promotion from a non-management to management role. Therefore, our econometric model can be written as: 𝑃𝑀𝑖,𝑡 = 𝛼 + ∑𝑢𝛽𝑢𝑥𝑢𝑖,𝑡 + 𝜀𝑖
(1)
where the binary variable PM denotes whether the individual i undergoes an occupational transition in time t. If a worker becomes a manager, PM is 1, otherwise it is 0. u is the number of explanatory variables.
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Journal Pre-proof In this study, we define the occupational transition to management by using the standard occupational classification listed in Table 1. If a worker’s occupation at time t-1 is not a “manager” or “senior officer” in SOC2000 or a “manager”, “director” or “senior officer” in SOC2010 and their occupation in time t falls into these categories, then the worker will be defined as having transferred into a management role. To avoid redundant wording, this study adopts the term “promotion” to represent “promotion to manager” or “transferred to management” in the results. This study uses five models to investigate the determinants of a worker’s likelihood of occupational transition to management. We estimate using the entire sample, but also split the sample according to the method of transition to a new job, including employment-to-employment (EE) and unemployment-to-employment (UE) transitions. If an employee was hired by another employer without any spell of unemployment, the transition is defined as an EE transition. If the worker was reemployed in a management role following unemployment, then the transition is defined as a UE transition. These samples are further separated into EE and UE sub-samples, because a spell of employment/unemployment affects workers’ occupational transitions (Carrillo-Tudela et al. 2016; Longhi and Taylor 2011). The unemployment duration is considered as the depreciation of a worker’s human capital and capabilities, whereas the employment duration is considered to reflect the accumulation of human capital (Edin and Gustavsson 2008; Pavoni 2009). By splitting these samples into EE and UE subsamples, this study is able to investigate how the employment/unemployment duration affects the possibility of being promoted to management.
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Journal Pre-proof Given the aim of this study the sample is further disaggregated into two subgroups: male and female. This allows us to identify the differences between men and women.
4. Results Table 4 presents the results of the Probit model on occupational transitions into management positions in the UK. Column 1 shows the aggregate results, which include both EE and UE transitions. Column 2 shows the results for EE transitions, and Column 3 presents the results of UE transitions. Column 4 shows the results with only males included, and Column shows the results for the female sample. Column 1 displays the determinants of occupational transition to management roles. A worker’s age has a concave relationship with this probability, such that both young and old workers are less likely to become managers than middle-aged workers. This is understandable, since young workers lack the appropriate experience, whereas older workers lack of time they might be in the job before retirement or a reprioritisation of work commitments could be more influential on their reduced tendency of taking up management roles. Additionally, dependent children impede workers’ EE transitions, such that a worker with dependent children has a lower probability of becoming a manager when changing employers. Notably, this effect is more pronounced for female workers. We also note that, once unemployed, a worker has a lower probability of becoming a manager and that higher education has a significant and positive effect on the likelihood of being promoted to management. Most importantly, female workers are less likely to be promoted than male workers, and the overall probability of being promoted to management is procyclical.
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Journal Pre-proof The results for EE transition and UE transition, presented in Columns 2 and 3, show that the channel of transition affects the likelihood of transitioning to management. We find that longer periods of employment increase the probability of promotion (as captured by the variable EMPDUR), whilst unemployment tends to hinder a worker’s promotion chances (as captured by the variable UMPDUR). In addition, a previously employed full-time worker has a higher probability of being promoted to management than a part-time worker. Marriage and the number of children have significant and positive effects on the probability of being promoted to management in the EE transition, implying that marriage pushes an employee to be more responsible and encourages them to climb the career ladder. For unemployed individuals, obtaining a management role is not their priority, since they are more likely to see a non-management job as a means of getting on the career ladder. Columns 4 and 5, which detail the male and female samples, respectively, show that the business cycle has no significant effect on women’s promotion, while men’s promotion is procyclical. The purpose of splitting samples according to gender is to investigate the difference of occupational transition to management for men and women, respectively. This sample splitting is according to the finding of column1 that women have less probability to be promoted. This sample splitting is also helpful to compare the difference of explainable variables’ effects on occupational transition. It is interestingly to find that the possibility of being promoted for women is actually not affected by business cyclical. In addition, we find that marriage has a positive effect on promotion for men, but no effect for women, and that higher numbers of children impede women’s
19
Journal Pre-proof probability of being promoted to management positions. This is because women are normally responsible for the majority of childcare provision, thus impeding women’s career paths and reducing their promotion opportunities.7 By contrast, men’s promotion chances are not affected by the number of children. In summary, this paper finds several interesting results. Higher education significantly increases the probability of becoming a manager. Additionally, full-time workers have higher probabilities of becoming managers than those in part-time roles. Keeping other conditions (e.g. education level, age, working hours, etc.) stable, women are significantly less likely to become mangers than men, and, linking this to the business cycle, men’s occupational transition to management is procyclical. In other words, men have a lower probability of becoming a manger during recession periods but a higher probability during economic expansion. However, the business cycle does not affect women’s likelihood of being promoted to management positions. Moreover, we find no evidence that marriage hampers workers’ promotion chances. Instead, we find that marriage is significantly and positively related to employed workers’ and men’s ability to climb the career ladder.
We also analyse the sample of workers who left the labour market due to family commitments in order to investigate whether female workers have higher likelihoods of giving up their career prospects for their family. The result suggests that female workers are more likely to take career breaks than male workers. Meanwhile, workers are more willing to leave the labour market for family reasons during recessions. Both marriage and the number of children are related to the probability that workers leave the labour market. These findings imply that female workers are more likely to give up potential promotions for their family. The results are available upon request. 7
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Journal Pre-proof
Table 4. Results of Probit models AGG EE UE UNEMPR -0.153*** -0.167*** -0.0890* (-3.52) (-2.66) (-1.84) AGE 0.00436*** 0.00469*** 0.00250*** (9.49) (7.29) (4.43) AGESQ -0.0527*** -0.0558*** -0.0313*** (-8.41) (-6.38) (-4.05) MARID 0.00381*** 0.00469** 0.00189 (2.61) (2.35) (1.04) * CHILD 0.0000965 0.00167 -0.000463 (0.15) (1.82) (-0.57) *** *** FMALE -0.00844 -0.00607 -0.00634*** (-6.77) (-3.42) (-4.11) *** UNEMPD -0.00872 (-7.82) H-EDU 0.0242*** 0.0243*** 0.0187*** (6.35) (5.00) (3.25) *** *** M-EDU 0.0146 0.0144 0.0110*** (6.31) (4.58) (3.78) EMPDUR 0.000825** (2.54) FTJOB 0.0121*** (6.91) UMPDUR -0.00179*** (-2.71) Regions Yes Yes Yes S-Method Yes Yes Yes Last OCC Yes Yes Yes N 38555 25452 12282 Pseudo R2 0.093 0.094 0.104 Log lik. -5672.7 -4211.3 -1283.9
Male -0.207*** (-3.48) 0.00370*** (5.76) -0.0441*** (-5.11) 0.00962*** (3.92) 0.00146* (1.66)
Female -0.0912 (-1.49) 0.00533*** (8.32) -0.0664*** (-7.47) -0.00187 (-1.11) -0.00213** (-2.20)
-0.00606*** (-3.86) 0.0296*** (4.95) 0.0154*** (4.86)
-0.0106*** (-6.96) 0.0174*** (3.80) 0.0132*** (4.10)
Yes Yes Yes 19061 0.110 -2921.8
Yes Yes Yes 19494 0.086 -2694.2
Notes: Unemployment rate is lagged by one quarter. Marginal effects; t-statistics in parentheses * p < 0.10, ** p < 0.05, *** p < 0.01
5. Conclusion and Recommendations The UK has the highest childcare costs in the OECD, and parents spend an average of 33.8% of their net income on full-time childcare.8 Many women take on this role in order to reduce their expenditures. However, this action restricts women’s potential to http://www.dailymail.co.uk/news/article-3831626/UK-childcare-expensive-world-Families-spendincome-nurseries-childminders.html 8
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Journal Pre-proof advance to management positions. In September 2017, the UK government extended the length of free childcare from 15 to 30 hours per week over 38 weeks a year. Whether this 30 hours of childcare effectively helps women recoup their loss of opportunity is an issue that should be closely investigated. In addition to this policy, the government could encourage companies to provide the option of working-from-home and promote flexible working hours. For example, workers could be excused in the early afternoon in order to pick-up the children from nursery or school, then finish the rest of their workload at home. This would help women to manage competing pressures from work and family. As Cooray et al. (2017) suggest, a media that promotes women’s equality of choice can also help in this effort – for example, the media demonstrating that “looking after the family is not the duty for women only, it is for both men and women”. This research excludes internal promotion, as the data on this phenomenon is not clearly outlined in LFS. However, an analysis of intra-organisational promotion could further aid our understanding of the determinants of promotion, especially in regards to gender. Future research might also investigate whether promotion prospects differ between private and public employers, or consider the company culture as an important factor and develop a mechanism for measuring the impact of a company’s culture on promotions.
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Journal Pre-proof Appendix Table A1. Descriptive statistics for four sub-set models EE
UE
Mean
SD.
Min
Max
Mean
SD.
Min
Max
Promoted
0.0476
0.213
0
1
0.0262
0.16
0
1
UNEMPR
0.0576
0.0116
0.0472
0.0883
0.0602
0.0132
0.0472
0.0883
AGE
31.92
11.22
16
55
32.76
11.48
16
55
AGESQ
1.145
0.769
0.256
3.025
1.205
0.799
0.256
3.025
MARID
0.363
0.481
0
1
0.322
0.467
0
1
CHILD
0.65
0.925
0
8
0.652
0.958
0
7
FMALE
0.524
0.499
0
1
0.463
0.499
0
1
H-EDU
0.315
0.464
0
1
0.271
0.445
0
1
M-EDU
0.567
0.496
0
1
0.546
0.498
0
1
L-EDU
0.119
0.324
0
1
0.182
0.386
0
1
Observation
25452
12282 Male
Female
Mean
SD.
Min
Max
Mean
SD.
Min
Max
Promoted
0.0439
0.205
0
1
0.0372
0.189
0
1
UNEMPR
0.0586
0.0124
0.0472
0.0883
0.0584
0.0121
0.0472
0.0883
AGE
32.14
11.38
16
55
32.29
11.24
16
55
AGESQ
1.162
0.787
0.256
3.025
1.169
0.772
0.256
3.025
MARID
0.351
0.477
0
1
0.349
0.477
0
1
CHILD
0.617
0.945
0
8
0.687
0.929
0
6
FMALE
0
0
0
0
1
0
1
1
H-EDU
0.279
0.449
0
1
0.325
0.468
0
1
M-EDU
0.566
0.496
0
1
0.551
0.497
0
1
L-EDU
0.155
0.362
0
1
0.124
0.33
0
1
Observation
19061
19494
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Journal Pre-proof References Arun, Thankom Gopinath, Yousf Ebrahem Almahrog, and Zakaria Ali Aribi. 2015. “Female Directors and Earnings Management: Evidence from UK Companies.” International Review of Financial Analysis 39:137–46. Barlevy, Gadi. 2002. “The Sullying Effect of Recessions.” The Review of Economic Studies 69(1):65–96. Blau, Francine D. and Jed Devaro. 2007. “New Evidence on Gender Differences in Promotion Rates: An Empirical Analysis of a Sample of New Hires.” Industrial Relations: A Journal of Economy and Society 46(3):511–50. Caballero, Ricardo J. and Mohamad L. Hammour. 1994. “The Cleansing Effect of Recessions.” The American Economic Review 84(5):1350–68. Carrillo-Tudela, Carlos, Bart Hobijn, Powen She, and Ludo Visschers. 2016. “The Extent and Cyclicality of Career Changes: Evidence for the U.K.” European Economic Review 84:18–41. Cohen, Lisa E., Joseph P. Broschak, and Heather A. Haveman. 1998. “And Then There Were More? The Effect of Organizational Sex Composition on the Hiring and Promotion of Managers.” American Sociological Review 711–27. Cohen, Philip N. and Matt L. Huffman. 2007. “Working for the Woman? Female Managers and the Gender Wage Gap.” American Sociological Review 72(5):681–704. Cooray, Arusha, Nabamita Dutta, and Sushanta Mallick. 2017. “The Right to Be Free: Is Media Freedom Good News for Women’s Rights?” Journal of Institutional Economics 13(2):327–55. Cornelius, Nelarine and Denise Skinner. 2008. “The Careers of Senior Men and Women – A Capabilities Theory Perspective.” British Journal of Management 19:S141–49. Davidson, Marilyn J. and Cary L. Cooper. 1987. “Female Managers in Britain – A Comparative Perspective.” Human Resource Management 26(2):217–42. Davis, Steven J. 1987. “Fluctuations in the Pace of Labor Reallocation.” CarnegieRochester Conference Series on Public Policy 27:335–402. Dobbin, Frank, Soohan Kim, and Alexandra Kalev. 2011. “You Can’t Always Get What You Need: Organizational Determinants of Diversity Programs.” American Sociological Review 76(3):386–411. Dobbin, Frank, Daniel Schrage, and Alexandra Kalev. 2015. “Rage against the Iron Cage: The Varied Effects of Bureaucratic Personnel Reforms on Diversity.” American Sociological Review 80(5):1014–44.
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Journal Pre-proof Highlights
1. Female workers are less likely to become managers than male workers. 2. The probability of becoming a manager by age is represented by an inverted U-curve. 3. Workers with more human capital are more likely to become managers. 4. Female workers with more dependent children are less likely to become managers. 5. Male workers with more dependent children are more likely to become managers.