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The Dynamics of the Early Career Gender Wage Gap among University Graduates: The Case of Russia

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Title: The Dynamics of the Early Career Gender Wage Gap among University Graduates: The Case of Russia
Language: English
Authors: Ksenia Rozhkova (ORCID 0000-0003-1497-5733), Sergey Roshchin (ORCID 0000-0002-2396-6911), Natalya Yemelina (ORCID 0000-0002-0215-7525)
Source: European Journal of Education. 2024 59(4).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 22
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Gender Issues, Salary Wage Differentials, Equal Opportunities (Jobs), Universities, College Graduates, Gender Discrimination, Foreign Countries, Occupational Information, Educational Background, Educational Status Comparison
Geographic Terms: Russia
DOI: 10.1111/ejed.12730
ISSN: 0141-8211
1465-3435
Abstract: This study provides new evidence of the gender wage gap among recent university graduates at different stages of their early career. Using a unique administrative dataset from Russia, we estimate the gender wage gap at means and across wage distribution for a cohort of 2018 university graduates during the first 4 years after graduation. We explore the contribution of educational and job factors to the explanation of the gap. Although a substantial gap of 14% is already present at labour market entry, it is two times smaller compared to the gap for the overall working population. Eighty five percent of the entry wage gap can be explained with differences in fields of study, work experience, and job characteristics. More than 4 years after graduation, the gender wage gap experiences a dramatic increase, reaching 26%. Only 28% of the resulting gap can be explained by the observed characteristics, including industrial and occupational segregation. The size of the gap varies drastically in different parts of the wage distribution, suggesting the existence of a strong glass ceiling effect from the very beginning of graduate careers. The rapidly expanding early career gender wage gap with a growing unexplained component suggests that education policies may have limited ability to promote gender equality in the labour market.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1450692
Database: ERIC
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  Value: <anid>AN0181057333;eje01dec.24;2024Nov26.04:09;v2.2.500</anid> <title id="AN0181057333-1">The dynamics of the early career gender wage gap among university graduates: The case of Russia </title> <p>This study provides new evidence of the gender wage gap among recent university graduates at different stages of their early career. Using a unique administrative dataset from Russia, we estimate the gender wage gap at means and across wage distribution for a cohort of 2018 university graduates during the first 4 years after graduation. We explore the contribution of educational and job factors to the explanation of the gap. Although a substantial gap of 14% is already present at labour market entry, it is two times smaller compared to the gap for the overall working population. Eighty five percent of the entry wage gap can be explained with differences in fields of study, work experience, and job characteristics. More than 4 years after graduation, the gender wage gap experiences a dramatic increase, reaching 26%. Only 28% of the resulting gap can be explained by the observed characteristics, including industrial and occupational segregation. The size of the gap varies drastically in different parts of the wage distribution, suggesting the existence of a strong glass ceiling effect from the very beginning of graduate careers. The rapidly expanding early career gender wage gap with a growing unexplained component suggests that education policies may have limited ability to promote gender equality in the labour market.</p> <p>Keywords: early career; gender wage gap; higher education; Russia</p> <hd id="AN0181057333-2">INTRODUCTION</hd> <p>Higher education is often thought of as a vital instrument to promote gender equality. It provides graduates with a similar level of human capital, thereby enabling them to access high‐paying, qualified jobs, regardless of gender. The results of studies conducted in European countries indicate that higher education is associated with a reduced risk of unemployment and higher wages, with women receiving higher returns to education in general (Psacharopoulos & Patrinos, [<reflink idref="bib41" id="ref1">41</reflink>]) and higher education in particular (DiPrete & Buchmann, [<reflink idref="bib16" id="ref2">16</reflink>]). Furthermore, the proportion of the gender wage gap that remains unexplained is lower for graduates, indicating a reduced likelihood of discrimination for more educated workers (Piazzalunga, [<reflink idref="bib40" id="ref3">40</reflink>]). For a long time, lack of universal access to higher education, as well as institutional and social obstacles for educational attainment of women, were seen as a major contributor to inequality in the labour market. Even now, when these obstacles seem resolved, at least in developed economies, hopes about achieving gender equality in the labour market are still placed on higher education. For instance, recent narrative suggests that education policies aimed at increasing the presence of women in science, technology, engineering, and mathematics (STEM) disciplines, which is likely to lead to gender equality in the labour market (Petrenko & Cadil, [<reflink idref="bib39" id="ref4">39</reflink>]).</p> <p>Early career outcomes are very indicative of whether these hopes are justified. A graduate's early career is an important period, which determines further labour market advancements and may serve as a crucial source of wage disparities (Altonji et al., [<reflink idref="bib1" id="ref5">1</reflink>]). Due to the similar educational and employment background of recent graduates, the early career gender wage gap can be assumed to be insignificant or completely absent. First, both men and women have limited work experience and little time for mobility at labour market entry. Therefore, they tend to work in similar entry jobs during their early career. Second, career interruptions, which are seen as a major driver of the gender wage gap (Blau & Kahn, [<reflink idref="bib7" id="ref6">7</reflink>]), are less common among recent graduates regardless of gender due to their focus on starting a career. However, most of the existing evidence shows that a substantial wage gap can be already observed among recent graduates.</p> <p>Estimates for the early career gender gap vary a lot depending on the country in question. There is no gap in starting wages in the UK and the United States (Gerhart, [<reflink idref="bib22" id="ref7">22</reflink>]; Manning & Swaffield, [<reflink idref="bib31" id="ref8">31</reflink>]), but a large gap in Germany (Behr & Theune, [<reflink idref="bib5" id="ref9">5</reflink>]). A significant expansion of the gap starts five years after college graduation as career outcomes become greatly affected by experience, job mobility, and changes in family status (Stinebrickner et al., [<reflink idref="bib48" id="ref10">48</reflink>]). Focusing on earlier wage dynamics may be more useful, especially with regard to possible policy interventions.</p> <p>This study uses a unique full‐coverage administrative dataset from Russia and explores the dynamic of the gender wage gap of the same cohort of university graduates (class 2018) during the first 6 months and 1–4 years after graduation. The contribution of this study is threefold. First, to our knowledge, this is the first study to explore the dynamics of the early career gender wage gap in starting wages and the changing contributions to the gap during the crucial years in the labour market. Second, we assess the differences across wage distribution, providing evidence for the emerging glass ceiling effect in the first years of labour market experience. Finally, our study is based on rare and rich data, covering the whole graduate population of a large country.</p> <p>Our results show that although the gap already exists in starting wages, and amounts to 14%, it is small relative to the gap estimated for the entire working population. However, it rapidly expands, reaching 26% 4 years after graduation. The increase of the gap is the most dramatic in the lower part of the wage distribution, while the largest gap is observed in the upper part of the wage distribution. This suggests that the glass ceiling[<reflink idref="bib1" id="ref11">1</reflink>] already forms in early career. Focusing on the rapidly expanding gap in starting wages allows us to see the declining contribution of educational characteristics and the growing importance of labour market experiences to the gender wage gap during the early stages of one's career. This finding indicates that policies in higher education may have limited capacity to enforce gender equality in the labour market.</p> <hd id="AN0181057333-3">BACKGROUND</hd> <p></p> <hd id="AN0181057333-4">Factors affecting early career gender wage gap</hd> <p>For recent graduates with limited work experience, educational characteristics serve as a primary signal of potential productivity. Literature suggests that the most important and persistent source of early career wage differentials is gender segregation by university major (Brown & Corcoran, [<reflink idref="bib11" id="ref12">11</reflink>]; Machin & Puhani, [<reflink idref="bib30" id="ref13">30</reflink>]). There are two clearly observed gender divides across fields of study, which can be briefly described as "scientific‐humanistic" and "care‐technical" (Barone, [<reflink idref="bib4" id="ref14">4</reflink>]). Women are generally less presented in highly rewarded STEM majors, including computer science, while they tend to concentrate in arts, humanities, and human‐oriented fields such as health and education (Blau & Kahn, [<reflink idref="bib7" id="ref15">7</reflink>]). Female‐concentrated fields are steadily associated with lower wages in different countries (Altonji et al., [<reflink idref="bib2" id="ref16">2</reflink>]). Choosing less rewarded fields may be associated with anticipated family responsibilities, cultural and social patterns in gender role distribution (England, [<reflink idref="bib17" id="ref17">17</reflink>]), and gender‐specific academic abilities developed early in school (Breda & Napp, [<reflink idref="bib9" id="ref18">9</reflink>]).</p> <p>The university major reportedly accounts for 15%–50% of the gap among university graduates (Brown & Corcoran, [<reflink idref="bib11" id="ref19">11</reflink>]), although larger estimates also exist. McDonald and Thornton ([<reflink idref="bib32" id="ref20">32</reflink>]) suggest that the gap is almost entirely attributable to the fields of study. Education segregation further affects industry segregation, and together, they represent one of the most important explanations for the gender wage gap (Francesconi & Parey, [<reflink idref="bib21" id="ref21">21</reflink>]; Redmond & McGuinness, [<reflink idref="bib43" id="ref22">43</reflink>]).</p> <p>Another possible source of the gender wage gap coming from the side of education is university selectivity. Graduating from a more prestigious school is known to be associated with significant labour market returns (for review, see Milla, [<reflink idref="bib33" id="ref23">33</reflink>]). Women might be attending less prestigious institutions, which leads to their disadvantageous position in the labour market (Davies & Guppy, [<reflink idref="bib15" id="ref24">15</reflink>]).</p> <p>As their career progresses, education starts contributing less to the explanation of the gender wage gap, giving way to differences in work experience. Women are less likely to occupy top management positions (Longarela, [<reflink idref="bib28" id="ref25">28</reflink>]) and systematically demonstrate lower levels of job mobility, receiving less wage gains for switching places of work (Napari, [<reflink idref="bib34" id="ref26">34</reflink>]). Moreover, women have less success in promotion negotiations, often staying away from bargaining (Säve‐Söderbergh, [<reflink idref="bib47" id="ref27">47</reflink>]). Finally, gender differences in working hours are often seen as an important contributor to the wage gap. While male workers are more likely to have longer, full‐time work experience (Blau & Kahn, [<reflink idref="bib6" id="ref28">6</reflink>]) and are often involved in jobs that require overtime work (Leuze & Strauß, [<reflink idref="bib27" id="ref29">27</reflink>]), their female counterparts have higher chances of being employed part‐time, which results in lower wages (Joy, [<reflink idref="bib24" id="ref30">24</reflink>]).</p> <hd id="AN0181057333-5">Russian context</hd> <p>The Russian case is especially relevant in the context of gender wage gap among university graduates. One of the most remarkable achievements of the Soviet Union was providing universal, state‐funded access to higher education. Access for women to higher education was not limited by institutional factors, as early as the 1920s, while the participation of women in education and the labour force was actively promoted by state policy. Contemporary Russia inherited this approach, at least at an institutional level. Women systematically demonstrate higher levels of participation in education than men. In 2019, the share of individuals with higher education in the age group 25–34 reached 47% among women, while only 33.5% among men (Gokhberg et al., [<reflink idref="bib23" id="ref31">23</reflink>]). However, universal access to education and the high level of human capital accumulation of Russian women does not directly translate into gender equality in the labour market. On average, the gap in mean monthly earnings is estimated at 30%–35% for the working population (Oshchepkov, [<reflink idref="bib38" id="ref32">38</reflink>]), which is an extremely high figure compared to other developed economies. The estimated wage disparities were the largest during the economic transition, decreasing to 30% during the period of economic growth of the 2000–2009, and falling below 30% in the 2010s. The unadjusted gap in hourly wages is smaller, estimated at 23.7% (Table 1). Compared with other advanced European economies, Russia has a relatively high proportion of women in tertiary attainment and very similar patterns of gender distribution across fields of study, with a pronounced female dominance in arts and humanities, education, and health and underrepresentation of women in STEM. However, the unadjusted gap in hourly earnings is the highest compared to the selected economies, with the Eastern European countries and the United Kingdom coming closest.</p> <p>1 TABLE A cross‐country comparison of educational indicators and the gender wage gap in selected high‐income OECD countries and Russia.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Country</th><th align="left">Share of women among new first‐time entrants to higher education, %</th><th align="left">Share of tertiary female graduates by field, %</th><th align="left">The unadjusted gender wage gap<xref ref-type="fn" rid="tfn2" />, %</th></tr><tr><th align="left">Arts and humanities</th><th align="left">Education</th><th align="left">Health</th><th align="left">STEM</th></tr></thead><tbody valign="top"><tr><td align="left">Austria</td><td align="left">54</td><td align="left">68</td><td align="left">80</td><td align="left">71</td><td align="left">28</td><td align="char" char=".">18.4</td></tr><tr><td align="left">Belgium</td><td align="left">56</td><td align="left">73</td><td align="left">74</td><td align="left">75</td><td align="left">27</td><td align="char" char=".">5.0</td></tr><tr><td align="left">Czech Republic</td><td align="left">56</td><td align="left">66</td><td align="left">83</td><td align="left">80</td><td align="left">37</td><td align="char" char=".">17.9</td></tr><tr><td align="left">Denmark</td><td align="left">55</td><td align="left">67</td><td align="left">70</td><td align="left">77</td><td align="left">34</td><td align="char" char=".">13.9</td></tr><tr><td align="left">Estonia</td><td align="left">56</td><td align="left">69</td><td align="left">93</td><td align="left">86</td><td align="left">37</td><td align="char" char=".">21.3</td></tr><tr><td align="left">Finland</td><td align="left">54</td><td align="left">74</td><td align="left">84</td><td align="left">84</td><td align="left">30</td><td align="char" char=".">15.5</td></tr><tr><td align="left">France</td><td align="left">54</td><td align="left">69</td><td align="left">76</td><td align="left">74</td><td align="left">32</td><td align="char" char=".">13.9</td></tr><tr><td align="left">Germany</td><td align="left">51</td><td align="left">71</td><td align="left">81</td><td align="left">71</td><td align="left">28</td><td align="char" char=".">17.7</td></tr><tr><td align="left">Greece</td><td align="left">54</td><td align="left">72</td><td align="left">86</td><td align="left">71</td><td align="left">41</td><td align="char" char=".">10.4</td></tr><tr><td align="left">Hungary</td><td align="left">54</td><td align="left">67</td><td align="left">84</td><td align="left">73</td><td align="left">39</td><td align="char" char=".">17.5</td></tr><tr><td align="left">Iceland</td><td align="left">62</td><td align="left">64</td><td align="left">81</td><td align="left">85</td><td align="left">43</td><td align="char" char=".">9.3</td></tr><tr><td align="left">Italy</td><td align="left">55</td><td align="left">87</td><td align="left">93</td><td align="left">67</td><td align="left">39</td><td align="char" char=".">4.3</td></tr><tr><td align="left">Lithuania</td><td align="left">57</td><td align="left">73</td><td align="left">83</td><td align="left">84</td><td align="left">30</td><td align="char" char=".">12.0</td></tr><tr><td align="left">Netherlands</td><td align="left">55</td><td align="left">58</td><td align="left">75</td><td align="left">77</td><td align="left">32</td><td align="char" char=".">13</td></tr><tr><td align="left">Norway</td><td align="left">56</td><td align="left">59</td><td align="left">73</td><td align="left">82</td><td align="left">29</td><td align="char" char=".">14.4</td></tr><tr><td align="left">Poland</td><td align="left">57</td><td align="left">73</td><td align="left">87</td><td align="left">76</td><td align="left">41</td><td align="char" char=".">7.8</td></tr><tr><td align="left">Portugal</td><td align="left">53</td><td align="left">63</td><td align="left">78</td><td align="left">79</td><td align="left">38</td><td align="char" char=".">12.5</td></tr><tr><td align="left">Slovak Republic</td><td align="left">56</td><td align="left">69</td><td align="left">82</td><td align="left">76</td><td align="left">33</td><td align="char" char=".">17.7</td></tr><tr><td align="left">Slovenia</td><td align="left">57</td><td align="left">66</td><td align="left">88</td><td align="left">80</td><td align="left">32</td><td align="char" char=".">8.2</td></tr><tr><td align="left">Spain</td><td align="left">53</td><td align="left">58</td><td align="left">76</td><td align="left">75</td><td align="left">28</td><td align="char" char=".">8.7</td></tr><tr><td align="left">Sweden</td><td align="left">57</td><td align="left">61</td><td align="left">80</td><td align="left">80</td><td align="left">37</td><td align="char" char=".">11.1</td></tr><tr><td align="left">Switzerland</td><td align="left">51</td><td align="left">61</td><td align="left">68</td><td align="left">73</td><td align="left">24</td><td align="char" char=".">17.9</td></tr><tr><td align="left">United Kingdom</td><td align="left">57</td><td align="left">63</td><td align="left">77</td><td align="left">77</td><td align="left">32</td><td align="char" char=".">19.8</td></tr><tr><td align="left">Russia</td><td align="left">59</td><td align="left">72</td><td align="left">79</td><td align="left">69</td><td align="left">33</td><td align="char" char=".">23.7</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note</emph>: (<reflink idref="bib1" id="ref33">1</reflink>) The share of women among new first‐time entrants to higher education for all countries, except for Russia, is based on OECD data for [<reflink idref="bib37" id="ref34">37</reflink>]. Russian statistics is based on Rosstat data for 2020 (Rosstat, [<reflink idref="bib45" id="ref35">45</reflink>]); (<reflink idref="bib2" id="ref36">2</reflink>) the share of tertiary female graduates by field for all countries, except for Russia, is based on OECD data for 2021 (OECD, [<reflink idref="bib37" id="ref37">37</reflink>]). Shares for Russia are calculated on "Monitoring of graduate employment"; (<reflink idref="bib3" id="ref38">3</reflink>) the unadjusted gender wage gap for all countries, except for Russia, Greece, and the United Kingdom, is based on Eurostat data for 2022 (Eurostat, [<reflink idref="bib18" id="ref39">18</reflink>]). Information for Greece and the United Kingdom comes from 2018. Russian statistics is based on Rosstat data for 2021(Rosstat, [<reflink idref="bib45" id="ref40">45</reflink>]).</p> <p>2 a The unadjusted gender wage gap is calculated as the difference between average gross hourly earnings of male and female employees as % of male gross earnings.</p> <p>Job segregation by gender is known to play a significant explanatory role in the Russian labour market, accounting for roughly 45% of the wage gap (Oshchepkov, [<reflink idref="bib38" id="ref41">38</reflink>]). In contrast, education reduces the gap by 11% due to larger female human capital endowments. Family characteristics do not play a key role in the gender wage inequality, explaining about 4% of the gap. A major part of the wage gap (from more than 95% in 1996 to 75% in 2017, depending on a study) remains unexplained. There is a substantial motherhood penalty of 11% in hourly wages (Karabchuk et al., [<reflink idref="bib25" id="ref42">25</reflink>]), as well as a fatherhood premium (Oshchepkov, [<reflink idref="bib38" id="ref43">38</reflink>]), which may serve as an indicator of prevailing traditional gender roles in the society.</p> <p>The early career gender wage gap in Russia remains a rather underexplored topic. The only paper that we are aware of is based on survey data covering a heterogeneous sample of university graduates 2010–2015 whose labour market outcomes are assessed in 2016 (Rudakov et al., [<reflink idref="bib46" id="ref44">46</reflink>]). According to these estimates, the early career gap in monthly wages equals 27%. Horizontal segregation into fields of study and industries plays a key role in explaining the wage gap and constitutes 90% of the explained part. More than half of the gap remains unexplained, neither by educational characteristics, nor by job segregation.</p> <hd id="AN0181057333-6">DATA AND METHOD</hd> <p></p> <hd id="AN0181057333-7">Monitoring of graduate employment</hd> <p>This study is based on a unique nationwide administrative database, collected as a part of the project "Monitoring of graduate employment" by The Ministry of Labour and Social Protection of the Russian Federation and The Federal Service for Labour and Employment. The database contains information for every person who obtained a diploma between 2016 and 2022 in Russia. It covers graduates of various educational levels, including school, vocational training, undergraduate and postgraduate programmes. Information about education is retrieved from the Federal Register of Educational Documents and is merged with employment data from the social security register via individual social security number. The data also include gender, year of birth, and social benefits related to birth and children. The final dataset is depersonalised to ensure data security. This database provides unique information on educational and career outcomes of recent graduates, which is not available from any other sources. The project was launched in 2020 and is planned to become public to provide more information about graduate careers to students and educational organisations.</p> <hd id="AN0181057333-8">Sample</hd> <p>In this study, we focus on graduates with bachelor's and specialist's degrees who finished their studies in 2018. Both bachelor's and specialist's degrees are the 1st cycle programmes. Bachelor's programmes generally require 4 years to accomplish. In turn, specialist's programmes are inherited from the Soviet single‐stage higher education system and generally require 5 years to accomplish. Bachelor's programmes prevail in contemporary Russia and constitute roughly 70% of all the undergraduate programmes. Moreover, the division between these degrees became mostly field‐specific: specialist's programmes are prevailing in health and culture, some engineering majors also award a specialist's degree.</p> <p>First, we limit our sample to those individuals who decided not to pursue further education and exclude those graduates who enrolled in other educational programmes upon graduation (predominantly master's studies or PhD programmes). By restricting our sample to those who are not involved in further education, we solve two important issues. First, we believe that those graduates who went on to further study cannot be directly compared with full‐time workers. Since we do not look at our sample at only one point in time, but follow them throughout the first years of their careers, the outcomes of those graduates who enrolled in other educational programmes, during the 6 months, 1, and 2 years after graduation will not be comparable to the employment outcomes of graduates who dedicated their time exclusively to work. Second, the inclusion of only individuals with bachelor's and specialist's degrees allows our sample to be homogeneous in terms of acquired qualifications, which is important for the decomposition analysis.</p> <p>Second, we focus only on full‐time programmes. There are three forms of higher education in Russia: full‐time form (roughly 60% of all programmes), part‐time form (35%), and a combined form (less than 5% of programmes). Part‐time and combined programmes became widespread with the massification of higher education in Russia and are usually aimed at older students with previous work experience.</p> <p>Third, the sample is limited to individuals older than 20 and younger than 30 years at the time of graduation.</p> <p>Finally, we restrict our sample to childless graduates. Parenthood may contribute to the gender wage gap through a fatherhood premium and a motherhood penalty, associated with child‐related career interruptions (Cukrowska‐Torzewska & Lovasz, [<reflink idref="bib14" id="ref45">14</reflink>]). Children and family may also affect the gender wage gap by limiting the working hours of female graduates and shifting their preferences towards part‐time work (Blau & Kahn, [<reflink idref="bib7" id="ref46">7</reflink>]). Therefore, excluding individuals with children from our sample allows us to focus on drivers of the gender wage gap other than gender differences in family responsibilities.</p> <p>By doing that, we create a rather homogeneous sample of workers with similar educational trajectories and reduce the risk of unobserved variables bias. The final sample consists of approximately 203,000 individuals, 57% of the sample being females.</p> <p>The data contain information for labour income received monthly. Labour market outcomes are assessed during four intervals. First, we look at the average monthly wage received by the graduates in 2018 throughout the first 6 months upon graduation. Second, we look at the average monthly wage received by the graduates in 2019, 2020, 2021, and 2022. As a dependent variable, we use the logarithm of the average monthly wage. The average monthly wage is calculated as the sum of earnings received at the principal place of employment divided by the number of months worked during the year under consideration. To compare wages over time, we deflate the nominal wage by the annual national CPI to 2022 prices.</p> <hd id="AN0181057333-9">Method</hd> <p>We start with estimating three wage regressions via OLS: one regression for each gender and a pooled regression. The wage equation can be written in the following way:1 <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0001" display="block" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>ln</mi><mfenced close=")" open="("><msub><mi>W</mi><mtext mathvariant="italic">year</mtext></msub></mfenced><mo linebreak="goodbreak">=</mo><mi mathvariant="italic">Xβ</mi><mo linebreak="goodbreak">+</mo><msub><mi>Z</mi><mtext mathvariant="italic">year</mtext></msub><mi>γ</mi><mo linebreak="goodbreak">+</mo><mi>ε</mi><mo>,</mo></mrow></semantics></math> </ephtml> where <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0002" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>ln</mi><mfenced close=")" open="("><mi>W</mi></mfenced></mrow></semantics></math> </ephtml> is the dependent variable, <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0003" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>X</mi></mrow></semantics></math> </ephtml> is a vector of educational characteristics (including the constant), <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0004" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>β</mi></mrow></semantics></math> </ephtml> is the corresponding coefficient vector, <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0005" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>Z</mi></mrow></semantics></math> </ephtml> is a vector of job‐related characteristics, <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0006" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>γ</mi></mrow></semantics></math> </ephtml> is the corresponding coefficient vector, <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0007" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="normal">ε</mi></mrow></semantics></math> </ephtml> is the corresponding error term, and <emph>year</emph> identifies year of observation of employment characteristics. The equations are estimated for each year separately. The dependent variable is the natural logarithm of average monthly wage in a particular year (2018, 2019, 2020, 2021, or 2022). Educational characteristics include field of study, a dummy variable for obtaining an honours degree, and a categorical variable for university selectivity. Honours degree (or so‐called "red" diploma) is an academic distinction awarded to top‐performing graduates. To receive it, one should have at least 75% of excellent (A) final grades during the whole process of study, should not have any grades lower than B‐, should receive an A on the final thesis, and pass the final state examination in core professional subjects with A. Honours degree is awarded to roughly 15% of graduate population which means it can be used as a measure of unobserved productivity characteristics, including ability. University selectivity group is determined on the basis of the average points received for 1 examination by admitted students. Admissions are based on the results of the unified state examination, where each examination is graded on a 100‐point scale. The non‐selective group includes universities admitting students with below 60 points for one examination on average. The lower‐middle selectivity group includes universities admitting students with 60–69 points. The upper‐middle selectivity group includes universities admitting students with 70–79 points. Selective universities admit students with over 80 points. Data on university selectivity are obtained from the project "Monitoring of university admission quality" conducted by HSE University.</p> <p>The vector of work characteristics includes industry (a categorical variable composed of 17 industries with Education as a reference category), size of the enterprise (micro as a reference category, small, medium, or large), and region of work (a categorical variable to control for labour market heterogeneity). For those employed in 2022, we also have an opportunity to control for occupations, measured as the first sign of a four‐level hierarchically structured International Standard Classification of Occupations (ISCO) (a categorical variable composed of 9 occupations with Elementary workers as a reference category).</p> <p>Separate regressions estimation is needed to perform a decomposition (Blinder, [<reflink idref="bib8" id="ref47">8</reflink>]; Oaxaca, [<reflink idref="bib36" id="ref48">36</reflink>]). For this study, we use the Neumark ([<reflink idref="bib35" id="ref49">35</reflink>]) modification of the Blinder‐Oaxaca methodology to decompose the wage gap at means. The decomposition can be written as follows:2 <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0008" display="block" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>ln</mi><mfenced close=")" open="("><msup><mover accent="true"><mi>W</mi><mo>¯</mo></mover><mi>m</mi></msup></mfenced><mo linebreak="goodbreak">−</mo><mi>ln</mi><mfenced close=")" open="("><msup><mover accent="true"><mi>W</mi><mo>¯</mo></mover><mi>f</mi></msup></mfenced><mo linebreak="goodbreak">=</mo><mfenced close=")" open="("><mrow><msup><mover accent="true"><mi>X</mi><mo>¯</mo></mover><mi>m</mi></msup><mo linebreak="goodbreak">−</mo><msup><mover accent="true"><mi>X</mi><mo>¯</mo></mover><mi>f</mi></msup></mrow></mfenced><msup><mover accent="true"><mi>β</mi><mo>̂</mo></mover><mi>t</mi></msup><mo linebreak="goodbreak">+</mo><msup><mover accent="true"><mi>X</mi><mo>¯</mo></mover><mi>m</mi></msup><mfenced close=")" open="("><mrow><msup><mover accent="true"><mi>β</mi><mo>̂</mo></mover><mi>m</mi></msup><mo linebreak="goodbreak">−</mo><msup><mover accent="true"><mi>β</mi><mo>̂</mo></mover><mi>t</mi></msup></mrow></mfenced><mo linebreak="goodbreak">+</mo><msup><mover accent="true"><mi>X</mi><mo>¯</mo></mover><mi>f</mi></msup><mfenced close=")" open="("><mrow><msup><mover accent="true"><mi>β</mi><mo>̂</mo></mover><mi>f</mi></msup><mo linebreak="goodbreak">−</mo><msup><mover accent="true"><mi>β</mi><mo>̂</mo></mover><mi>t</mi></msup></mrow></mfenced></mrow></semantics></math> </ephtml> where ln(W) is the logarithm of average monthly wage for a given period, <emph>X</emph> is a set of explanatory variables, <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0009" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mover accent="true"><mi>β</mi><mo>̂</mo></mover></mrow></semantics></math> </ephtml> are the coefficients obtained from wage regressions, <emph>m</emph> denotes men, <emph>f</emph> denotes women, and <emph>t</emph> denotes total sample. <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0010" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mfenced close=")" open="("><mrow><msup><mover accent="true"><mi>X</mi><mo>¯</mo></mover><mi>m</mi></msup><mo>−</mo><msup><mover accent="true"><mi>X</mi><mo>¯</mo></mover><mi>f</mi></msup></mrow></mfenced><msup><mover accent="true"><mi>β</mi><mo>̂</mo></mover><mi>t</mi></msup></mrow></semantics></math> </ephtml> represents the explained part of the gap, which arises due to gender differences in the observed characteristics. <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0011" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msup><mover accent="true"><mi>X</mi><mo>¯</mo></mover><mi>m</mi></msup><mfenced close=")" open="("><mrow><msup><mover accent="true"><mi>β</mi><mo>̂</mo></mover><mi>m</mi></msup><mo>−</mo><msup><mover accent="true"><mi>β</mi><mo>̂</mo></mover><mi>t</mi></msup></mrow></mfenced></mrow></semantics></math> </ephtml> and <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0012" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msup><mover accent="true"><mi>X</mi><mo>¯</mo></mover><mi>f</mi></msup><mfenced close=")" open="("><mrow><msup><mover accent="true"><mi>β</mi><mo>̂</mo></mover><mi>f</mi></msup><mo>−</mo><msup><mover accent="true"><mi>β</mi><mo>̂</mo></mover><mi>t</mi></msup></mrow></mfenced></mrow></semantics></math> </ephtml> represent the unexplained part of the gap, which can be attributed to gender‐based differences in returns to characteristics, and unobserved characteristics, which are not controlled for in wage estimation.</p> <p>However, the traditional Oaxaca‐Blinder approach does not allow for a decomposition in different quantiles of the wage distribution. For this, we use a method proposed by Firpo et al. ([<reflink idref="bib19" id="ref50">19</reflink>], [<reflink idref="bib20" id="ref51">20</reflink>]). This is an unconditional quantile regression (UQR) approach, which is based on the recentred influence functions (RIF). For τth quantile, the influence function can be written as:3 <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0013" display="block" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>RIF</mtext><mfenced close=")" open="("><mrow><mi>ln</mi><mfenced close=")" open="("><mi>W</mi></mfenced><mo>,</mo><msub><mi>q</mi><mi>τ</mi></msub></mrow></mfenced><mo>=</mo><mfrac><mrow><mi>τ</mi><mo>−</mo><mi>I</mi><mfenced close="}" open="{"><mrow><mi>ln</mi><mfenced close=")" open="("><mi>W</mi></mfenced><mo>≤</mo><msub><mi>q</mi><mi>τ</mi></msub></mrow></mfenced></mrow><mrow><msub><mi>f</mi><mi>Y</mi></msub><mfenced close=")" open="("><mrow><msub><mi>q</mi><mi>τ</mi></msub></mrow></mfenced></mrow></mfrac><mo>,</mo></mrow></semantics></math> </ephtml> where <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0014" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>q</mi><mi>τ</mi></msub></mrow></semantics></math> </ephtml> is the τth quantile of the dependent variable ln<emph>(W)</emph>, <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0015" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>I</mi><mfenced close="}" open="{"><mrow><mi>Y</mi><mo>≤</mo><msub><mi>q</mi><mi>τ</mi></msub></mrow></mfenced></mrow></semantics></math> </ephtml> is the condition indicator, which takes the value 0 or 1, and <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0016" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>f</mi><mi>Y</mi></msub><mfenced close=")" open="("><msub><mi>q</mi><mi>τ</mi></msub></mfenced></mrow></semantics></math> </ephtml> is the density function of the marginal distribution at point <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0017" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>q</mi><mi>τ</mi></msub></mrow></semantics></math> </ephtml> . To obtain RIF, it is necessary to add the quantile of interest <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0018" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>q</mi><mi>τ</mi></msub></mrow></semantics></math> </ephtml> to the influence function:4 <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0019" display="block" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>RIF</mi><mfenced close=")" open="(" separators=","><mrow><mi>ln</mi><mfenced close=")" open="("><mi>W</mi></mfenced></mrow><msub><mi>q</mi><mi>τ</mi></msub></mfenced><mo linebreak="goodbreak">=</mo><msub><mi>q</mi><mi>τ</mi></msub><mo linebreak="goodbreak">+</mo><mfrac><mrow><mi>τ</mi><mo>−</mo><mi>I</mi><mfenced close="}" open="{"><mrow><mi>ln</mi><mfenced close=")" open="("><mi>W</mi></mfenced><mo>≤</mo><msub><mi>q</mi><mi>τ</mi></msub></mrow></mfenced></mrow><mrow><msub><mi>f</mi><mi>W</mi></msub><mfenced close=")" open="("><msub><mi>q</mi><mi>τ</mi></msub></mfenced></mrow></mfrac></mrow></semantics></math> </ephtml></p> <p>Next, to estimate the RIF, we need to estimate the sample quantile <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0020" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mover accent="true"><mi>q</mi><mo>̂</mo></mover><mi>τ</mi></msub></mrow></semantics></math> </ephtml> and the density function at point <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0021" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mover accent="true"><mi>q</mi><mo>̂</mo></mover><mi>τ</mi></msub></mrow></semantics></math> </ephtml> via kernel methods. We choose bandwidth to minimise the mean integrated squared error. <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0022" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mover accent="true"><mi mathvariant="italic">RIF</mi><mo>̂</mo></mover><mfenced close=")" open="(" separators=","><mrow><mi>ln</mi><mfenced close=")" open="("><mi>W</mi></mfenced></mrow><msub><mover accent="true"><mi>q</mi><mo>̂</mo></mover><mi>τ</mi></msub></mfenced></mrow></semantics></math> </ephtml> is estimated for each observation, for a given quantile of interest, and then, it is used in place of the dependent variable. The coefficients of the RIF regression can be estimated via OLS because the expectation of RIF equals the value of the distribution quantile of interest. The wage equation at quantile <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0023" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>τ</mi></mrow></semantics></math> </ephtml> can be written as:5 <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0024" display="block" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="italic">RIF</mi><mfenced close=")" open="(" separators=","><mrow><mi mathvariant="italic">ln</mi><mfenced close=")" open="("><mi>W</mi></mfenced></mrow><msub><mi>q</mi><mi>τ</mi></msub></mfenced><mo linebreak="goodbreak">=</mo><mi>X</mi><msup><mi>β</mi><mi>τ</mi></msup><mo linebreak="goodbreak">+</mo><msup><mi>ε</mi><mi>τ</mi></msup><mo>,</mo></mrow></semantics></math> </ephtml> where <emph>ln(W)</emph> is the natural logarithm of monthly wages, <emph>X</emph> is a vector of explanatory variables (including the constant), <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0025" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msup><mi>β</mi><mi>τ</mi></msup></mrow></semantics></math> </ephtml> is the corresponding coefficient vector, and <ephtml> <math altimg="urn:x-wiley:01418211:media:ejed12730:ejed12730-math-0026" display="inline" overflow="scroll" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msup><mi>ε</mi><mi>τ</mi></msup></mrow></semantics></math> </ephtml> is the corresponding error term. We estimate the RIF regressions separately for male and female samples. Based on the obtained estimates, we can decompose the gender wage gap in an Oaxaca‐Blinder manner into the explained and unexplained components (Firpo et al., [<reflink idref="bib19" id="ref52">19</reflink>], [<reflink idref="bib20" id="ref53">20</reflink>]).</p> <p>Decomposition methods, based on UQR, have several advantages over those based on classical (conditional) quantile regressions (CQR, Koenker & Bassett, [<reflink idref="bib26" id="ref54">26</reflink>]). First, UQR rely on the unconditional distribution of wages, which is independent of the regressors included into the model. In other words, the focus is placed on high or low values of wages rather than individuals similar in characteristics. This is especially important for investigating the glass ceiling in the labour market. Second, the decomposition method, based on CQR proposed by Machado and Mata ([<reflink idref="bib29" id="ref55">29</reflink>]), does not allow for the unconditional mean interpretation, which is used in Oaxaca‐Blinder type decompositions. Finally, although conditional quantile decomposition can be used to decompose the gap into explained and unexplained parts, the contribution of particular covariates to the gap cannot be identified. The advantages of the decomposition based on UQR lead to its increasing popularity in empirical literature. This method is regularly applied to studies in the fields of labour and education (Briel et al., [<reflink idref="bib10" id="ref56">10</reflink>]) and it is the most appropriate tool for our analysis.</p> <hd id="AN0181057333-10">Limitations</hd> <p>Despite the uniqueness and coverage of our data, it still has several limitations. First, we are only aware of formal employment and earnings. Second, we lack information regarding occupations for years of employment before 2022. This potentially can drive the explained part of the gap for earlier years of employment down due to possible vertical segregation. Since we observe graduates at the labour market entry, we assume that similar levels of education allow young workers to occupy similar jobs in terms of qualifications. Restricting our sample solely to higher education graduates smoothens possible biases due to lack of occupation information. As an additional robustness check, we provide similar estimates for a cohort of 2021 university graduates employed in 2022, for whom information on occupations is available. The results suggest that controlling for occupations does not significantly change the results for early career gender wage gap (see Table 11A in the Appendix). Third, we observe monthly wages but have no information on hours worked. This may also drive the explained part of the gap down. Male workers, on average, work longer hours than their female counterparts (Blau & Kahn, [<reflink idref="bib7" id="ref57">7</reflink>]). Therefore, if female graduates are more likely than males to take part‐time jobs, then the heterogeneity in wages attributed to differences in working hours will remain unexplained in our regressions. However, evidence on recent graduates shows that males and females tend to have equal working hours in their early careers (Francesconi & Parey, [<reflink idref="bib21" id="ref58">21</reflink>]). Furthermore, we assume that restricting our sample to yet childless graduates makes the gender difference in working hours insignificant. Finally, we do not directly control for abilities, which may bias our results. However, we assume that controlling for such educational characteristics as university selectivity and honours degree allows us to resolve the problem.</p> <hd id="AN0181057333-11">FINDINGS</hd> <p></p> <hd id="AN0181057333-12">Descriptive statistics</hd> <p>For both men and women, the mean age at graduation is approximately 23 years. 60% of graduates were employed shortly after university, but the proportion gradually increased, reaching 73% after 4 years. In 2022, or 4 years after graduation, both male and female graduates demonstrate similar duration of work experience of nearly 4 years. Table 2 shows the average monthly wage obtained by graduates throughout their early careers. Mean wage demonstrates gradual increase throughout the first years in the labour market. The average starting wage equals 35.9 thousand rubles and reaches 70.4 thousand four years after graduation.</p> <p>2 TABLE Descriptive statistics for selected variables.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left"><italic>N</italic></th><th align="left">Mean</th><th align="left">SD</th><th align="left">Min</th><th align="left">Max</th></tr></thead><tbody valign="top"><tr><td align="left">Age at graduation (years)</td><td align="left">203,387</td><td align="char" char=".">22.70</td><td align="char" char=".">1.31</td><td align="char" char=".">20</td><td align="char" char=".">30</td></tr><tr><td align="left">Experience in 2022 (years)</td><td align="left">203,387</td><td align="char" char=".">4.01</td><td align="char" char=".">2.41</td><td align="char" char=".">0</td><td align="char" char=".">11.25</td></tr><tr><td align="left">Average wage in 2018 (thousand rubles, 2022 prices)</td><td align="left">120,185</td><td align="char" char=".">35.94</td><td align="char" char=".">26.31</td><td align="char" char=".">1.22</td><td align="char" char=".">170.4</td></tr><tr><td align="left">Average wage in 2019 (thousand rubles, 2022 prices)</td><td align="left">143,721</td><td align="char" char=".">43.63</td><td align="char" char=".">32.03</td><td align="char" char=".">1.80</td><td align="char" char=".">212.0</td></tr><tr><td align="left">Average wage in 2020 (thousand rubles, 2022 prices)</td><td align="left">153,536</td><td align="char" char=".">54.24</td><td align="char" char=".">43.05</td><td align="char" char=".">1.45</td><td align="char" char=".">273.3</td></tr><tr><td align="left">Average wage in 2021 (thousand rubles, 2022 prices)</td><td align="left">153,033</td><td align="char" char=".">64.67</td><td align="char" char=".">52.16</td><td align="char" char=".">1.10</td><td align="char" char=".">333.5</td></tr><tr><td align="left">Average wage in 2022 (thousand rubles, 2022 prices)</td><td align="left">147,427</td><td align="char" char=".">70.41</td><td align="char" char=".">59.09</td><td align="char" char=".">2.00</td><td align="char" char=".">383.2</td></tr></tbody></table> </ephtml> </p> <p>Table 3 further demonstrates wage distribution across genders. First, we observe large wage disparities in both genders, which rapidly increase during early career. The wages in the lowest 10th percentile of the income distribution increased only slightly over the span of 4 years, while wages in the top 90th percentile tended to grow at a considerably faster rate.</p> <p>3 TABLE Average wages among recent graduates along wage distribution.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">Female sample</th><th align="left">Male sample</th></tr><tr><th align="left">Mean</th><th align="left">SD</th><th align="left">10th</th><th align="left">50th</th><th align="left">90th</th><th align="left">Mean</th><th align="left">SD</th><th align="left">10th</th><th align="left">50th</th><th align="left">90th</th></tr></thead><tbody valign="top"><tr><td align="left">Average wage in 2018 (thousand rubles, 2022 prices)</td><td align="char" char=".">33.32</td><td align="char" char=".">22.94</td><td align="char" char=".">11.22</td><td align="char" char=".">28.16</td><td align="char" char=".">61.02</td><td align="char" char=".">40.22</td><td align="char" char=".">30.57</td><td align="char" char=".">11.43</td><td align="char" char=".">32.45</td><td align="char" char=".">78.36</td></tr><tr><td align="left">Average wage in 2019 (thousand rubles, 2022 prices)</td><td align="char" char=".">40.28</td><td align="char" char=".">27.50</td><td align="char" char=".">13.76</td><td align="char" char=".">34.21</td><td align="char" char=".">74.11</td><td align="char" char=".">48.93</td><td align="char" char=".">37.50</td><td align="char" char=".">14.36</td><td align="char" char=".">39.50</td><td align="char" char=".">93.95</td></tr><tr><td align="left">Average wage in 2020 (thousand rubles, 2022 prices)</td><td align="char" char=".">49.60</td><td align="char" char=".">37.68</td><td align="char" char=".">14.64</td><td align="char" char=".">40.43</td><td align="char" char=".">95.40</td><td align="char" char=".">60.58</td><td align="char" char=".">48.73</td><td align="char" char=".">16.67</td><td align="char" char=".">47.02</td><td align="char" char=".">120.21</td></tr><tr><td align="left">Average wage in 2021 (thousand rubles, 2022 prices)</td><td align="char" char=".">58.06</td><td align="char" char=".">44.61</td><td align="char" char=".">15.78</td><td align="char" char=".">47.41</td><td align="char" char=".">112.58</td><td align="char" char=".">73.42</td><td align="char" char=".">59.63</td><td align="char" char=".">18.23</td><td align="char" char=".">57.54</td><td align="char" char=".">145.90</td></tr><tr><td align="left">Average wage in 2022 (thousand rubles, 2022 prices)</td><td align="char" char=".">62.13</td><td align="char" char=".">49.31</td><td align="char" char=".">16.56</td><td align="char" char=".">49.78</td><td align="char" char=".">121.11</td><td align="char" char=".">81.33</td><td align="char" char=".">68.40</td><td align="char" char=".">19.00</td><td align="char" char=".">62.75</td><td align="char" char=".">164.00</td></tr></tbody></table> </ephtml> </p> <p>3 <emph>Note:</emph> 10th, 50th, and 90th represent 10th, 50th, and 90th percentiles of wage distribution, respectively.</p> <p>Table 1A in the Appendix shows the gender distribution of various educational characteristics. First, there is a clear "care‐technical" and "science‐humanities" gender divide in the fields of study. "Female"[<reflink idref="bib2" id="ref59">2</reflink>] majors are Education, Social sciences, Arts and culture, Humanities, Health, and Economics and management. Second, women generally show better academic performance and are more likely to receive an honours degree (78% of such degrees are obtained by female graduates). Moreover, women are slightly less likely to go to non‐selective universities and are more represented in universities of upper‐middle selectivity. Previous literature suggests that there is a large wage premium associated with university prestige in the Russian labour market (Roshchin & Rudakov, [<reflink idref="bib44" id="ref60">44</reflink>]).</p> <p>Table 2A in the Appendix further explores the differences in wages associated with various fields of study. The largest difference is observed in the highest‐paying major – mathematics and computer science. 6 months after graduation, the raw gap in this field reaches 33%. Significant gender differences are also present in natural sciences, engineering, and arts and culture. Four years after graduation, the raw wage gap exceeds 30% among graduates in most of STEM majors. The difference in early career wages remains rather small in social sciences (10%) and law (5%).</p> <p>Table 3A in the Appendix shows the gender distribution by industry of principal employment. 6 months after graduation, females dominated in Trade, Finance, Real estate activities, Accommodation and food services, Administrative and support service activities, Public administration, Education, and Health. Male‐dominated industries were Agriculture, Manufacturing, Mining, Utility supply, Construction, Transportation, and Information and communication. The dynamic in gender distribution across industries was not very noticeable.</p> <p>Table 4A in the Appendix provides detailed information on wage distribution across industries. 6 months after graduation, the highest raw gap is observed in high‐paying Mining (22%), Information and communication (27%), Finance (33%), and Science (31%). 4 years after graduation, the highest gap is in Finance (70%). Large raw gaps are also observed in the same high‐paying industries: Mining (45 per cent) and Information and communication (46%), as well as Transportation (49%) and Education (51%). Therefore, experiencing a large gap at labour market entry results in further expansion of pay inequality.</p> <p>For the most part, industry distribution depends on the choice of college major. However, even graduating in the same field, females tend to be employed in less rewarded industries (Tables 5A and 6A in the Appendix). The most prominent example is employment of Education majors: women are more likely to be employed in low‐paying Education than men (50% compared to 28%). Majoring in the same field and working in the same industry, women still have lower early career wages (see Tables 7A and 8A in the Appendix).</p> <hd id="AN0181057333-13">Decomposition results</hd> <p>We start off with analysing the Oaxaca‐Blinder decomposition at means. The detailed results of the decomposition are presented in Table 4. First, we observe a rapid widening of the gender wage gap throughout the early stages of the career. The raw gap starts from 14% in 2018, or 6 months after graduation, then slightly increases to 16% in 2019, 21% in 2020, and reaches 26% in 2021 and 2022. For reference, the gap for the general population is estimated at 30% (Oshchepkov, [<reflink idref="bib38" id="ref61">38</reflink>]). Therefore, only three‐four years after graduation, early career workers face a similar gap as experienced workers with different educational backgrounds. A relatively small gap at labour market entry can illustrate the equalising power of higher education, and its success in producing a homogeneous group of professionals. However, a rapid increase in the gender wage gap during the first years of market experience indicates that post‐educational choices and job sorting prevail in the explanation of the labour market inequalities. Although higher education remains a powerful instrument of social mobility with equalising properties, universal access to it does not fully imply gender equality in the labour market. A recent paper for Russia (Rudakov et al., [<reflink idref="bib46" id="ref62">46</reflink>]) reports a significantly higher wage gap of 23% among labour market entrants and a higher gap of 31% five years after graduation. The difference in the estimates obtained in both cases can arise due to differences in the used samples. The previous study does not differentiate between the levels and forms of higher education and includes female graduates who already have children into the analysis. Moreover, it focuses on graduates of five different cohorts (2010–2015) whose employment is assessed at the same point of time (2016). In contrast, we follow the same cohort of graduates throughout their early career and limit our sample to full‐time undergraduates to make it relatively more homogeneous.</p> <p>4 TABLE The Oaxaca‐Blinder decomposition results (at means) of the early career GWG, cohort of 2018 graduates.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">2018</th><th align="left">2019</th><th align="left">2020</th><th align="left">2021</th><th align="left">2022</th></tr><tr><th align="left">(1)</th><th align="left">(2)</th><th align="left">(3)</th><th align="left">(4)</th><th align="left">(5)</th></tr></thead><tbody valign="top"><tr><td align="left">Difference</td><td align="left">0.132<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.145<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.193<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.230<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.234<xref ref-type="fn" rid="tfn5" /></td></tr><tr><td align="left">(0.00493)</td><td align="left">(0.00445)</td><td align="left">(0.00440)</td><td align="left">(0.00459)</td><td align="left">(0.00578)</td></tr><tr><td align="left">Explained</td><td align="left">0.112<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.110<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0563<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0458<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0658<xref ref-type="fn" rid="tfn5" /></td></tr><tr><td align="left">(0.00309)</td><td align="left">(0.00294)</td><td align="left">(0.00304)</td><td align="left">(0.00322)</td><td align="left">(0.00432)</td></tr><tr><td align="left">Unexplained</td><td align="left">0.0204<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0354<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.136<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.184<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.168<xref ref-type="fn" rid="tfn5" /></td></tr><tr><td align="left">(0.00481)</td><td align="left">(0.00411)</td><td align="left">(0.00402)</td><td align="left">(0.00417)</td><td align="left">(0.00497)</td></tr><tr><td align="left">Explained</td></tr><tr><td align="left">Experience</td><td align="left">0.0245<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0152<xref ref-type="fn" rid="tfn5" /></td><td align="left">−0.0101<xref ref-type="fn" rid="tfn5" /></td><td align="left">−0.00945<xref ref-type="fn" rid="tfn5" /></td><td align="left">−0.00453<xref ref-type="fn" rid="tfn5" /></td></tr><tr><td align="left">(0.00102)</td><td align="left">(0.000963)</td><td align="left">(0.000899)</td><td align="left">(0.000915)</td><td align="left">(0.00112)</td></tr><tr><td align="left">Field of study</td><td align="left">0.0426<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0465<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0314<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0286<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0323<xref ref-type="fn" rid="tfn5" /></td></tr><tr><td align="left">(0.00207)</td><td align="left">(0.00175)</td><td align="left">(0.00176)</td><td align="left">(0.00187)</td><td align="left">(0.00212)</td></tr><tr><td align="left">Industry</td><td align="left">0.0244<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0230<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0200<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0198<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0379<xref ref-type="fn" rid="tfn5" /></td></tr><tr><td align="left">(0.00131)</td><td align="left">(0.00114)</td><td align="left">(0.00118)</td><td align="left">(0.00123)</td><td align="left">(0.00148)</td></tr><tr><td align="left">Region of work</td><td align="left">0.00769<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.00814<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.00396<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.00345<xref ref-type="fn" rid="tfn6" /></td><td align="left">0.00403<xref ref-type="fn" rid="tfn6" /></td></tr><tr><td align="left">(0.00121)</td><td align="left">(0.00115)</td><td align="left">(0.00112)</td><td align="left">(0.00118)</td><td align="left">(0.00154)</td></tr><tr><td align="left">Size of enterprise</td><td align="left">0.0158<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0240<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0301<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0253<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0255<xref ref-type="fn" rid="tfn5" /></td></tr><tr><td align="left">(0.00114)</td><td align="left">(0.00123)</td><td align="left">(0.00129)</td><td align="left">(0.00138)</td><td align="left">(0.00175)</td></tr><tr><td align="left">Honours degree</td><td align="left">−0.00640<xref ref-type="fn" rid="tfn5" /></td><td align="left">−0.00733<xref ref-type="fn" rid="tfn5" /></td><td align="left">−0.0130<xref ref-type="fn" rid="tfn5" /></td><td align="left">−0.0144<xref ref-type="fn" rid="tfn5" /></td><td align="left">−0.0134<xref ref-type="fn" rid="tfn5" /></td></tr><tr><td align="left">(0.000642)</td><td align="left">(0.000582)</td><td align="left">(0.000646)</td><td align="left">(0.000688)</td><td align="left">(0.000776)</td></tr><tr><td align="left">University selectivity</td><td align="left">0.00317<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.000232</td><td align="left">−0.00601<xref ref-type="fn" rid="tfn5" /></td><td align="left">−0.00749<xref ref-type="fn" rid="tfn5" /></td><td align="left">−0.00683<xref ref-type="fn" rid="tfn5" /></td></tr><tr><td align="left">(0.000700)</td><td align="left">(0.000636)</td><td align="left">(0.000669)</td><td align="left">(0.000716)</td><td align="left">(0.000878)</td></tr><tr><td align="left">Occupation</td><td align="left">–</td><td align="left">–</td><td align="left">–</td><td align="left">–</td><td align="left">−0.00915<xref ref-type="fn" rid="tfn5" /></td></tr><tr><td align="left">–</td><td align="left">–</td><td align="left">–</td><td align="left">–</td><td align="left">(0.00143)</td></tr><tr><td align="left">Unexplained</td></tr><tr><td align="left">Experience</td><td align="left">0.112<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0680<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0792<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.109<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0139</td></tr><tr><td align="left">(0.0221)</td><td align="left">(0.0186)</td><td align="left">(0.0188)</td><td align="left">(0.0182)</td><td align="left">(0.0201)</td></tr><tr><td align="left">Field of study</td><td align="left">−0.00119</td><td align="left">0.00773</td><td align="left">0.0166<xref ref-type="fn" rid="tfn6" /></td><td align="left">0.0299<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0156<xref ref-type="fn" rid="tfn7" /></td></tr><tr><td align="left">(0.00706)</td><td align="left">(0.00574)</td><td align="left">(0.00564)</td><td align="left">(0.00615)</td><td align="left">(0.00730)</td></tr><tr><td align="left">Industry</td><td align="left">0.00183</td><td align="left">0.0104<xref ref-type="fn" rid="tfn6" /></td><td align="left">0.00706</td><td align="left">−0.00594</td><td align="left">0.00363</td></tr><tr><td align="left">(0.00430)</td><td align="left">(0.00372)</td><td align="left">(0.00392)</td><td align="left">(0.00436)</td><td align="left">(0.00531)</td></tr><tr><td align="left">Region of work</td><td align="left">−0.00876</td><td align="left">−0.0129</td><td align="left">−0.0326<xref ref-type="fn" rid="tfn5" /></td><td align="left">−0.0154<xref ref-type="fn" rid="tfn7" /></td><td align="left">−0.0212<xref ref-type="fn" rid="tfn6" /></td></tr><tr><td align="left">(0.00854)</td><td align="left">(0.00735)</td><td align="left">(0.00788)</td><td align="left">(0.00694)</td><td align="left">(0.00817)</td></tr><tr><td align="left">Size of enterprise</td><td align="left">0.000230</td><td align="left">−0.00791<xref ref-type="fn" rid="tfn6" /></td><td align="left">−0.0000</td><td align="left">0.0223<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0283<xref ref-type="fn" rid="tfn5" /></td></tr><tr><td align="left">(0.00342)</td><td align="left">(0.00307)</td><td align="left">(0.00316)</td><td align="left">(0.00353)</td><td align="left">(0.00527)</td></tr><tr><td align="left">Honours degree</td><td align="left">0.00526<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.00644<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.00697<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.00789<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.00539<xref ref-type="fn" rid="tfn5" /></td></tr><tr><td align="left">(0.00152)</td><td align="left">(0.00135)</td><td align="left">(0.00123)</td><td align="left">(0.00127)</td><td align="left">(0.00159)</td></tr><tr><td align="left">University selectivity</td><td align="left">−0.0255<xref ref-type="fn" rid="tfn5" /></td><td align="left">−0.0162<xref ref-type="fn" rid="tfn5" /></td><td align="left">−0.0246<xref ref-type="fn" rid="tfn5" /></td><td align="left">−0.0160<xref ref-type="fn" rid="tfn5" /></td><td align="left">−0.00728</td></tr><tr><td align="left">(0.00386)</td><td align="left">(0.00346)</td><td align="left">(0.00341)</td><td align="left">(0.00347)</td><td align="left">(0.00386)</td></tr><tr><td align="left">Occupation</td><td align="left">–</td><td align="left">–</td><td align="left">–</td><td align="left">–</td><td align="left">0.00363</td></tr><tr><td align="left">–</td><td align="left">–</td><td align="left">–</td><td align="left">–</td><td align="left">(0.00531)</td></tr><tr><td align="left">Constant</td><td align="left">−0.0634<xref ref-type="fn" rid="tfn7" /></td><td align="left">−0.0201</td><td align="left">0.0836<xref ref-type="fn" rid="tfn5" /></td><td align="left">0.0523<xref ref-type="fn" rid="tfn7" /></td><td align="left">0.115<xref ref-type="fn" rid="tfn5" /></td></tr><tr><td align="left">(0.0253)</td><td align="left">(0.0215)</td><td align="left">(0.0216)</td><td align="left">(0.0210)</td><td align="left">(0.0262)</td></tr><tr><td align="left">Observations</td><td align="left">107,922</td><td align="left">129,124</td><td align="left">138,492</td><td align="left">138,134</td><td align="left">81,022</td></tr></tbody></table> </ephtml> </p> <ulist> <item>4 <emph>Note</emph>: Robust standard errors in parentheses.</item> <item>5 *** <emph>p</emph> < .001;</item> <item>6 ** <emph>p</emph> < .01;</item> <item>7 * <emph>p</emph> < .05.</item> </ulist> <p>Second, though a significant gap exists already in entry wages 6 months after graduation, 85% of it can be attributed to the observed characteristics of education and employment, with educational factors accounting for 53% of the explained part (see Figure 1). The highest contribution to the gap is provided by the fields of study, which explain 32% of the differences in the starting wages (Table 4). University selectivity makes a small contribution to the gap, explaining 2% of the observed inequality. A portion of the gap is explained by the regional heterogeneity of jobs (6%). Industry additionally explains 18% of the initial gap, while job characteristics jointly (industry, work experience, size of the enterprise, region of work) explain 55% of the gap.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/EJE/01dec24/ejed12730-fig-0001.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="ejed12730-fig-0001.jpg" title="1 The structure of the explained part of GPG at means (in %)." /> </p> <p></p> <p>Third, the input of educational and job characteristics into the gender wage gap runs primarily through the difference in endowments rather than returns, with a few notable exceptions (see Figure 2). For instance, academic performance, which is shown to be positively associated with wages in regressions (see Tables 12A–16A in the Appendix), provides a significant contribution to the unexplained part of the gap. This means that women are paid less compared to men for their honours degree and abilities implied by it.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/EJE/01dec24/ejed12730-fig-0002.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="ejed12730-fig-0002.jpg" title="2 The structure of the unexplained part of GPG at means (in %)." /> </p> <p></p> <p>Finally, the more time passes since graduation, the larger the gender wage gap becomes, the lower portion of it can be explained by the explanatory variables (see Figure 3). In 2022, four years after graduation, educational characteristics explain only 3% of the observed gender wage gap. Only 20% of the gap can be jointly explained by the observed differences in educational and job characteristics, including occupational segregation.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/EJE/01dec24/ejed12730-fig-0003.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="ejed12730-fig-0003.jpg" title="3 The size of the explained and unexplained part of the early career GWG after the Oaxaca‐Blinder decomposition." /> </p> <p></p> <hd id="AN0181057333-17">RIF‐decomposition results</hd> <p>We continue by analysing the results of the RIF decomposition (see Tables 5 and 6). First, the size of the raw gap varies drastically in different parts of the earnings distribution. While we observe no gender gap in the starting wages 6 months after graduation for the 10th percentile of distribution, there is already a pronounced gap of 15% in the 50th percentile and a huge, 28% gap in the 90th percentile. At the same time, there is no significant differences in the initial distribution of male and female graduates across occupations, with most recent graduates working as professionals (see Table 9A in the Appendix). While the gap at means is entirely explained by educational and job characteristics, only 52% of the inequality observed at the top can be attributed to them. The increase of the gap towards the top and the diminishing ability to explain it by the observed factors serves as evidence of a strong glass ceiling effect, which emerges from the very beginning of a graduate career. This result is consistent with the findings obtained for other countries with different institutional settings, for example Colombia (Cepeda‐Emiliani & Barón‐Rivera, [<reflink idref="bib12" id="ref63">12</reflink>]), as well as with previous evidence for Russia (Rudakov et al., [<reflink idref="bib46" id="ref64">46</reflink>]).</p> <p>5 TABLE RIF‐decomposition results of the early career GWG, cohort of 2018 graduates.</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">2018</th><th align="left">2019</th><th align="left">2020</th><th align="left">2021</th></tr><tr><th align="left">10th percentile</th><th align="left">50th percentile</th><th align="left">90th percentile</th><th align="left">10th percentile</th><th align="left">50th percentile</th><th align="left">90th percentile</th><th align="left">10th percentile</th><th align="left">50th percentile</th><th align="left">90th percentile</th><th align="left">10th percentile</th><th align="left">50th percentile</th><th align="left">90th percentile</th></tr><tr><th align="left">(1)</th><th align="left">(2)</th><th align="left">(3)</th><th align="left">(4)</th><th align="left">(5)</th><th align="left">(6)</th><th align="left">(7)</th><th align="left">(8)</th><th align="left">(9)</th><th align="left">(10)</th><th align="left">(11)</th><th align="left">(12)</th></tr></thead><tbody valign="top"><tr><td align="left">Difference</td><td align="char" char=".">0.0135</td><td align="char" char=".">0.137<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.245<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0457<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.152<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.239<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.140<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.153<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.236<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.151<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.197<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.264<xref ref-type="fn" rid="tfn9" /></td></tr><tr><td align="char" char=".">(0.0119)</td><td align="char" char=".">(0.00538)</td><td align="char" char=".">(0.00692)</td><td align="char" char=".">(0.00766)</td><td align="char" char=".">(0.00487)</td><td align="char" char=".">(0.00624)</td><td align="char" char=".">(0.00747)</td><td align="char" char=".">(0.00486)</td><td align="char" char=".">(0.00679)</td><td align="char" char=".">(0.00822)</td><td align="char" char=".">(0.00494)</td><td align="char" char=".">(0.00684)</td></tr><tr><td align="left">Explained</td><td align="char" char=".">0.164<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.140<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.128<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0984<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.128<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.121<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0705<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0754<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0402<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0688<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0599<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0461<xref ref-type="fn" rid="tfn9" /></td></tr><tr><td align="char" char=".">(0.0102)</td><td align="char" char=".">(0.00480)</td><td align="char" char=".">(0.00591)</td><td align="char" char=".">(0.00636)</td><td align="char" char=".">(0.00425)</td><td align="char" char=".">(0.00537)</td><td align="char" char=".">(0.00586)</td><td align="char" char=".">(0.00413)</td><td align="char" char=".">(0.00563)</td><td align="char" char=".">(0.00684)</td><td align="char" char=".">(0.00414)</td><td align="char" char=".">(0.00572)</td></tr><tr><td align="left">Unexplained</td><td align="char" char=".">−0.151<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.00336</td><td align="char" char=".">0.116<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.0527<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0241<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.119<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0694<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0779<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.196<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0821<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.137<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.218<xref ref-type="fn" rid="tfn9" /></td></tr><tr><td align="char" char=".">(0.0162)</td><td align="char" char=".">(0.00633)</td><td align="char" char=".">(0.00743)</td><td align="char" char=".">(0.0100)</td><td align="char" char=".">(0.00553)</td><td align="char" char=".">(0.00669)</td><td align="char" char=".">(0.00908)</td><td align="char" char=".">(0.00524)</td><td align="char" char=".">(0.00766)</td><td align="char" char=".">(0.00990)</td><td align="char" char=".">(0.00519)</td><td align="char" char=".">(0.00766)</td></tr><tr><td align="left">Explained</td></tr><tr><td align="left">Experience</td><td align="char" char=".">0.0424<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0288<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0255<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0171<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0185<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0165<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.0137<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.00757<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.00123</td><td align="char" char=".">−0.0139<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.00692<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.00280<xref ref-type="fn" rid="tfn10" /></td></tr><tr><td align="char" char=".">(0.00253)</td><td align="char" char=".">(0.00125)</td><td align="char" char=".">(0.00135)</td><td align="char" char=".">(0.00137)</td><td align="char" char=".">(0.00107)</td><td align="char" char=".">(0.00104)</td><td align="char" char=".">(0.00124)</td><td align="char" char=".">(0.00102)</td><td align="char" char=".">(0.000977)</td><td align="char" char=".">(0.00133)</td><td align="char" char=".">(0.00101)</td><td align="char" char=".">(0.000920)</td></tr><tr><td align="left">Field of study</td><td align="char" char=".">0.0678<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0547<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0747<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0445<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0605<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0888<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0300<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0445<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0599<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0222<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0375<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0704<xref ref-type="fn" rid="tfn9" /></td></tr><tr><td align="char" char=".">(0.00996)</td><td align="char" char=".">(0.00389)</td><td align="char" char=".">(0.00467)</td><td align="char" char=".">(0.00605)</td><td align="char" char=".">(0.00331)</td><td align="char" char=".">(0.00417)</td><td align="char" char=".">(0.00538)</td><td align="char" char=".">(0.00314)</td><td align="char" char=".">(0.00434)</td><td align="char" char=".">(0.00600)</td><td align="char" char=".">(0.00315)</td><td align="char" char=".">(0.00454)</td></tr><tr><td align="left">University selectivity</td><td align="char" char=".">0.00356<xref ref-type="fn" rid="tfn11" /></td><td align="char" char=".">0.00281<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">0.00814<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.00265<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">−0.00105</td><td align="char" char=".">0.00419<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">−0.00245<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">−0.00634<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.0104<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.00373<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.00745<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.0131<xref ref-type="fn" rid="tfn9" /></td></tr><tr><td align="char" char=".">(0.00153)</td><td align="char" char=".">(0.000878)</td><td align="char" char=".">(0.00154)</td><td align="char" char=".">(0.000857)</td><td align="char" char=".">(0.000721)</td><td align="char" char=".">(0.00143)</td><td align="char" char=".">(0.000809)</td><td align="char" char=".">(0.000764)</td><td align="char" char=".">(0.00163)</td><td align="char" char=".">(0.000915)</td><td align="char" char=".">(0.000762)</td><td align="char" char=".">(0.00171)</td></tr><tr><td align="left">Industry</td><td align="char" char=".">0.0350<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0351<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0164<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0129<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0290<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0151<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0205<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0266<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.00389</td><td align="char" char=".">0.0237<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0284<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0153<xref ref-type="fn" rid="tfn9" /></td></tr><tr><td align="char" char=".">(0.00571)</td><td align="char" char=".">(0.00238)</td><td align="char" char=".">(0.00311)</td><td align="char" char=".">(0.00358)</td><td align="char" char=".">(0.00209)</td><td align="char" char=".">(0.00290)</td><td align="char" char=".">(0.00337)</td><td align="char" char=".">(0.00215)</td><td align="char" char=".">(0.00327)</td><td align="char" char=".">(0.00390)</td><td align="char" char=".">(0.00220)</td><td align="char" char=".">(0.00341)</td></tr><tr><td align="left">Region of work</td><td align="char" char=".">0.00442<xref ref-type="fn" rid="tfn11" /></td><td align="char" char=".">0.00743<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.00944<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.00335<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">0.00768<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.00948<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.00145</td><td align="char" char=".">0.00316<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">0.00749<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.00156</td><td align="char" char=".">0.00335<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">0.00586<xref ref-type="fn" rid="tfn9" /></td></tr><tr><td align="char" char=".">(0.00180)</td><td align="char" char=".">(0.00138)</td><td align="char" char=".">(0.00141)</td><td align="char" char=".">(0.00123)</td><td align="char" char=".">(0.00129)</td><td align="char" char=".">(0.00131)</td><td align="char" char=".">(0.00114)</td><td align="char" char=".">(0.00122)</td><td align="char" char=".">(0.00139)</td><td align="char" char=".">(0.00148)</td><td align="char" char=".">(0.00129)</td><td align="char" char=".">(0.00149)</td></tr><tr><td align="left">Size of enterprise</td><td align="char" char=".">0.0182<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0218<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.00912<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0248<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0249<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0111<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0411<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0298<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0142<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0452<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0243<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0101<xref ref-type="fn" rid="tfn9" /></td></tr><tr><td align="char" char=".">(0.00246)</td><td align="char" char=".">(0.00140)</td><td align="char" char=".">(0.00105)</td><td align="char" char=".">(0.00184)</td><td align="char" char=".">(0.00127)</td><td align="char" char=".">(0.000885)</td><td align="char" char=".">(0.00227)</td><td align="char" char=".">(0.00129)</td><td align="char" char=".">(0.000889)</td><td align="char" char=".">(0.00303)</td><td align="char" char=".">(0.00127)</td><td align="char" char=".">(0.000784)</td></tr><tr><td align="left">Honours degree</td><td align="char" char=".">−0.00723<xref ref-type="fn" rid="tfn11" /></td><td align="char" char=".">−0.0104<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.0149<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.00167</td><td align="char" char=".">−0.0117<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.0245<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.00639<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.0149<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.0336<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.00633<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">−0.0193<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.0397<xref ref-type="fn" rid="tfn9" /></td></tr><tr><td align="char" char=".">(0.00354)</td><td align="char" char=".">(0.00161)</td><td align="char" char=".">(0.00260)</td><td align="char" char=".">(0.00228)</td><td align="char" char=".">(0.00142)</td><td align="char" char=".">(0.00251)</td><td align="char" char=".">(0.00192)</td><td align="char" char=".">(0.00137)</td><td align="char" char=".">(0.00286)</td><td align="char" char=".">(0.00216)</td><td align="char" char=".">(0.00135)</td><td align="char" char=".">(0.00297)</td></tr><tr><td align="left">Unexplained</td></tr><tr><td align="left">Experience</td><td align="char" char=".">0.0995</td><td align="char" char=".">0.114<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0648<xref ref-type="fn" rid="tfn11" /></td><td align="char" char=".">0.000591</td><td align="char" char=".">0.0332</td><td align="char" char=".">0.0586<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">−0.0534</td><td align="char" char=".">0.0356</td><td align="char" char=".">0.0779<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">0.141<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0282</td><td align="char" char=".">0.0247</td></tr><tr><td align="char" char=".">(0.0637)</td><td align="char" char=".">(0.0209)</td><td align="char" char=".">(0.0254)</td><td align="char" char=".">(0.0406)</td><td align="char" char=".">(0.0178)</td><td align="char" char=".">(0.0207)</td><td align="char" char=".">(0.0414)</td><td align="char" char=".">(0.0182)</td><td align="char" char=".">(0.0251)</td><td align="char" char=".">(0.0425)</td><td align="char" char=".">(0.0179)</td><td align="char" char=".">(0.0236)</td></tr><tr><td align="left">Field of study</td><td align="char" char=".">0.00598</td><td align="char" char=".">−0.0177<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">−0.0384<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.00246</td><td align="char" char=".">−0.00888</td><td align="char" char=".">−0.0241<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">0.00386</td><td align="char" char=".">0.000449</td><td align="char" char=".">−0.000879</td><td align="char" char=".">0.0159</td><td align="char" char=".">0.00801</td><td align="char" char=".">0.00565</td></tr><tr><td align="char" char=".">(0.0183)</td><td align="char" char=".">(0.00669)</td><td align="char" char=".">(0.00975)</td><td align="char" char=".">(0.0107)</td><td align="char" char=".">(0.00574)</td><td align="char" char=".">(0.00829)</td><td align="char" char=".">(0.0108)</td><td align="char" char=".">(0.00598)</td><td align="char" char=".">(0.00926)</td><td align="char" char=".">(0.0120)</td><td align="char" char=".">(0.00596)</td><td align="char" char=".">(0.00927)</td></tr><tr><td align="left">University selectivity</td><td align="char" char=".">−0.0336<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.0244<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.0293<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.00188</td><td align="char" char=".">−0.0120<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">−0.0403<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.00789</td><td align="char" char=".">−0.0221<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.0497<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.000870</td><td align="char" char=".">−0.0149<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.0449<xref ref-type="fn" rid="tfn9" /></td></tr><tr><td align="char" char=".">(0.00982)</td><td align="char" char=".">(0.00426)</td><td align="char" char=".">(0.00770)</td><td align="char" char=".">(0.00632)</td><td align="char" char=".">(0.00378)</td><td align="char" char=".">(0.00710)</td><td align="char" char=".">(0.00593)</td><td align="char" char=".">(0.00366)</td><td align="char" char=".">(0.00771)</td><td align="char" char=".">(0.00622)</td><td align="char" char=".">(0.00358)</td><td align="char" char=".">(0.00767)</td></tr><tr><td align="left">Industry</td><td align="char" char=".">−0.00917</td><td align="char" char=".">−0.00648</td><td align="char" char=".">0.0184<xref ref-type="fn" rid="tfn11" /></td><td align="char" char=".">0.0114</td><td align="char" char=".">0.0123<xref ref-type="fn" rid="tfn11" /></td><td align="char" char=".">0.0193<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">0.00360</td><td align="char" char=".">0.00610</td><td align="char" char=".">0.00242</td><td align="char" char=".">−0.0115</td><td align="char" char=".">−0.000818</td><td align="char" char=".">−0.0121</td></tr><tr><td align="char" char=".">(0.0140)</td><td align="char" char=".">(0.00557)</td><td align="char" char=".">(0.00798)</td><td align="char" char=".">(0.00864)</td><td align="char" char=".">(0.00499)</td><td align="char" char=".">(0.00706)</td><td align="char" char=".">(0.00945)</td><td align="char" char=".">(0.00536)</td><td align="char" char=".">(0.00783)</td><td align="char" char=".">(0.0107)</td><td align="char" char=".">(0.00559)</td><td align="char" char=".">(0.00770)</td></tr><tr><td align="left">Region of work</td><td align="char" char=".">0.0191</td><td align="char" char=".">0.0111</td><td align="char" char=".">−0.0575<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.00135</td><td align="char" char=".">0.0135</td><td align="char" char=".">−0.0515<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.00651</td><td align="char" char=".">−0.00522</td><td align="char" char=".">−0.0512<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0263</td><td align="char" char=".">−0.0147</td><td align="char" char=".">−0.0696<xref ref-type="fn" rid="tfn9" /></td></tr><tr><td align="char" char=".">(0.0243)</td><td align="char" char=".">(0.00963)</td><td align="char" char=".">(0.0119)</td><td align="char" char=".">(0.0168)</td><td align="char" char=".">(0.00865)</td><td align="char" char=".">(0.0103)</td><td align="char" char=".">(0.0180)</td><td align="char" char=".">(0.00880)</td><td align="char" char=".">(0.0114)</td><td align="char" char=".">(0.0138)</td><td align="char" char=".">(0.00947)</td><td align="char" char=".">(0.0110)</td></tr><tr><td align="left">Size of enterprise</td><td align="char" char=".">−0.0123</td><td align="char" char=".">0.00848<xref ref-type="fn" rid="tfn11" /></td><td align="char" char=".">−0.0152<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.0190<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.00133</td><td align="char" char=".">−0.0125<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0215<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.000938</td><td align="char" char=".">−0.0136<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.103<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.00327</td><td align="char" char=".">−0.00439</td></tr><tr><td align="char" char=".">(0.00832)</td><td align="char" char=".">(0.00335)</td><td align="char" char=".">(0.00430)</td><td align="char" char=".">(0.00577)</td><td align="char" char=".">(0.00307)</td><td align="char" char=".">(0.00377)</td><td align="char" char=".">(0.00637)</td><td align="char" char=".">(0.00317)</td><td align="char" char=".">(0.00407)</td><td align="char" char=".">(0.00819)</td><td align="char" char=".">(0.00345)</td><td align="char" char=".">(0.00437)</td></tr><tr><td align="left">Honours degree</td><td align="char" char=".">0.00803</td><td align="char" char=".">0.00742<xref ref-type="fn" rid="tfn11" /></td><td align="char" char=".">0.0110<xref ref-type="fn" rid="tfn11" /></td><td align="char" char=".">0.00314</td><td align="char" char=".">0.00800<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">0.0274<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.000705</td><td align="char" char=".">0.00561<xref ref-type="fn" rid="tfn11" /></td><td align="char" char=".">0.0313<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.00198</td><td align="char" char=".">0.0104<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0390<xref ref-type="fn" rid="tfn9" /></td></tr><tr><td align="char" char=".">(0.00692)</td><td align="char" char=".">(0.00308)</td><td align="char" char=".">(0.00488)</td><td align="char" char=".">(0.00439)</td><td align="char" char=".">(0.00268)</td><td align="char" char=".">(0.00452)</td><td align="char" char=".">(0.00389)</td><td align="char" char=".">(0.00257)</td><td align="char" char=".">(0.00502)</td><td align="char" char=".">(0.00422)</td><td align="char" char=".">(0.00254)</td><td align="char" char=".">(0.00516)</td></tr><tr><td align="left">Constant</td><td align="char" char=".">−0.228<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">−0.0953<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.163<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.0469</td><td align="char" char=".">−0.0232</td><td align="char" char=".">0.142<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.0946</td><td align="char" char=".">0.0565<xref ref-type="fn" rid="tfn10" /></td><td align="char" char=".">0.200<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">−0.196<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.117<xref ref-type="fn" rid="tfn9" /></td><td align="char" char=".">0.280<xref ref-type="fn" rid="tfn9" /></td></tr><tr><td align="char" char=".">(0.0747)</td><td align="char" char=".">(0.0248)</td><td align="char" char=".">(0.0314)</td><td align="char" char=".">(0.0479)</td><td align="char" char=".">(0.0213)</td><td align="char" char=".">(0.0264)</td><td align="char" char=".">(0.0483)</td><td align="char" char=".">(0.0214)</td><td align="char" char=".">(0.0301)</td><td align="char" char=".">(0.0489)</td><td align="char" char=".">(0.0213)</td><td align="char" char=".">(0.0286)</td></tr></tbody></table> </ephtml> </p> <ulist> <item>8 <emph>Note</emph>: Standard errors in parentheses.</item> <item>9 *** <emph>p</emph> < .001;</item> <item>10 ** <emph>p</emph> < .01;</item> <item>11 * <emph>p</emph> < .05.</item> <item>6 TABLE RIF‐decomposition results of the early career GWG, controlling for occupational segregation, cohort of 2018 graduates.</item> </ulist> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="left">2022</th></tr><tr><th align="left">10th percentile</th><th align="left">50th percentile</th><th align="left">90th percentile</th></tr><tr><th align="left">(4)</th><th align="left">(5)</th><th align="left">(6)</th></tr></thead><tbody valign="top"><tr><td align="left">Difference</td><td align="char" char=".">0.131<xref ref-type="fn" rid="tfn13" /></td><td align="char" char=".">0.233<xref ref-type="fn" rid="tfn13" /></td><td align="char" char=".">0.304<xref ref-type="fn" rid="tfn13" /></td></tr><tr><td align="char" char=".">(0.0102)</td><td align="char" char=".">(0.00647)</td><td align="char" char=".">(0.00954)</td></tr><tr><td align="left">Explained</td><td align="char" char=".">0.0699<xref ref-type="fn" rid="tfn13" /></td><td align="char" char=".">0.0699<xref ref-type="fn" rid="tfn13" /></td><td align="char" char=".">0.0848<xref ref-type="fn" rid="tfn13" /></td></tr><tr><td align="char" char=".">(0.00899)</td><td align="char" char=".">(0.00544)</td><td align="char" char=".">(0.00789)</td></tr><tr><td align="left">Unexplained</td><td align="char" char=".">0.0616<xref ref-type="fn" rid="tfn13" /></td><td align="char" char=".">0.163<xref ref-type="fn" rid="tfn13" /></td><td align="char" char=".">0.220<xref ref-type="fn" rid="tfn13" /></td></tr><tr><td align="char" char=".">(0.0125)</td><td align="char" char=".">(0.00673)</td><td align="char" char=".">(0.0104)</td></tr><tr><td align="left">Explained</td></tr><tr><td align="left">Experience</td><td align="char" char=".">−0.00681<xref ref-type="fn" rid="tfn13" /></td><td align="char" char=".">−0.00237<xref ref-type="fn" rid="tfn15" /></td><td align="char" char=".">0.0000</td></tr><tr><td align="char" char=".">(0.00147)</td><td align="char" char=".">(0.00110)</td><td align="char" char=".">(0.00118)</td></tr><tr><td align="left">Field of study</td><td align="char" char=".">0.0224<xref ref-type="fn" rid="tfn14" /></td><td align="char" char=".">0.0400<xref ref-type="fn" rid="tfn13" /></td><td align="char" char=".">0.0795<xref ref-type="fn" rid="tfn13" /></td></tr><tr><td align="char" char=".">(0.00740)</td><td align="char" char=".">(0.00393)</td><td align="char" char=".">(0.00614)</td></tr><tr><td align="left">University selectivity</td><td align="char" char=".">−0.00384<xref ref-type="fn" rid="tfn13" /></td><td align="char" char=".">−0.00618<xref ref-type="fn" rid="tfn13" /></td><td align="char" char=".">−0.0123<xref ref-type="fn" rid="tfn13" /></td></tr><tr><td align="char" char=".">(0.00114)</td><td align="char" char=".">(0.000904)</td><td align="char" char=".">(0.00223)</td></tr><tr><td align="left">Industry</td><td align="char" char=".">0.0158<xref ref-type="fn" rid="tfn14" /></td><td align="char" char=".">0.0373<xref ref-type="fn" rid="tfn13" /></td><td align="char" char=".">0.0466<xref ref-type="fn" rid="tfn13" /></td></tr><tr><td align="char" char=".">(0.00492)</td><td align="char" char=".">(0.00280)</td><td align="char" char=".">(0.00462)</td></tr><tr><td align="left">Region of work</td><td align="char" char=".">0.00608<xref ref-type="fn" rid="tfn14" /></td><td align="char" char=".">0.00347<xref ref-type="fn" rid="tfn15" /></td><td align="char" char=".">0.00395<xref ref-type="fn" rid="tfn15" /></td></tr><tr><td align="char" char=".">(0.00203)</td><td align="char" char=".">(0.00169)</td><td align="char" char=".">(0.00186)</td></tr><tr><td align="left">Size of enterprise</td><td align="char" char=".">0.0486<xref ref-type="fn" rid="tfn13" /></td><td align="char" char=".">0.0227<xref ref-type="fn" rid="tfn13" /></td><td align="char" char=".">0.0102<xref ref-type="fn" rid="tfn13" /></td></tr><tr><td align="char" char=".">(0.00400)</td><td align="char" char=".">(0.00152)</td><td align="char" char=".">(0.00106)</td></tr><tr><td align="left">Honours degree</td><td align="char" char=".">−0.00435</td><td align="char" char=".">−0.0161<xref ref-type="fn" rid="tfn13" /></td><td align="char" char=".">−0.0416<xref ref-type="fn" rid="tfn13" /></td></tr><tr><td align="char" char=".">(0.00254)</td><td align="char" char=".">(0.00165)</td><td align="char" char=".">(0.00390)</td></tr><tr><td align="left">Occupation</td><td align="char" char=".">−0.00815<xref ref-type="fn" rid="tfn15" /></td><td align="char" char=".">−0.00895<xref ref-type="fn" rid="tfn13" /></td><td align="char" char=".">−0.00163</td></tr><tr><td align="char" char=".">(0.00361)</td><td align="char" char=".">(0.00203)</td><td align="char" char=".">(0.00212)</td></tr><tr><td align="left">Unexplained</td></tr><tr><td align="left">Experience</td><td align="char" char=".">0.0456</td><td align="char" char=".">−0.0275</td><td align="char" char=".">0.0202</td></tr><tr><td align="char" char=".">(0.0495)</td><td align="char" char=".">(0.0213)</td><td align="char" char=".">(0.0290)</td></tr><tr><td align="left">Field of study</td><td align="char" char=".">−0.0152</td><td align="char" char=".">−0.00680</td><td align="char" char=".">−0.0000</td></tr><tr><td align="char" char=".">(0.0155)</td><td align="char" char=".">(0.00769)</td><td align="char" char=".">(0.0127)</td></tr><tr><td align="left">University selectivity</td><td align="char" char=".">0.00938</td><td align="char" char=".">−0.0112<xref ref-type="fn" rid="tfn14" /></td><td align="char" char=".">−0.0261<xref ref-type="fn" rid="tfn14" /></td></tr><tr><td align="char" char=".">(0.00712)</td><td align="char" char=".">(0.00430)</td><td align="char" char=".">(0.00965)</td></tr><tr><td align="left">Industry</td><td align="char" char=".">0.0166</td><td align="char" char=".">0.0159<xref ref-type="fn" rid="tfn15" /></td><td align="char" char=".">−0.0216</td></tr><tr><td align="char" char=".">(0.0141)</td><td align="char" char=".">(0.00759)</td><td align="char" char=".">(0.0111)</td></tr><tr><td align="left">Region of work</td><td align="char" char=".">0.0404</td><td align="char" char=".">−0.0311<xref ref-type="fn" rid="tfn14" /></td><td align="char" char=".">−0.0628<xref ref-type="fn" rid="tfn13" /></td></tr><tr><td align="char" char=".">(0.0210)</td><td align="char" char=".">(0.00973)</td><td align="char" char=".">(0.0145)</td></tr><tr><td align="left">Size of enterprise</td><td align="char" char=".">0.143<xref ref-type="fn" rid="tfn13" /></td><td align="char" char=".">−0.00310</td><td align="char" char=".">−0.0108</td></tr><tr><td align="char" char=".">(0.0136)</td><td align="char" char=".">(0.00515)</td><td align="char" char=".">(0.00679)</td></tr><tr><td align="left">Honours degree</td><td align="char" char=".">−0.00170</td><td align="char" char=".">0.00797<xref ref-type="fn" rid="tfn15" /></td><td align="char" char=".">0.0418<xref ref-type="fn" rid="tfn13" /></td></tr><tr><td align="char" char=".">(0.00497)</td><td align="char" char=".">(0.00319)</td><td align="char" char=".">(0.00696)</td></tr><tr><td align="left">Occupation</td><td align="char" char=".">0.0457</td><td align="char" char=".">−0.0134</td><td align="char" char=".">−0.0165</td></tr><tr><td align="char" char=".">(0.0355)</td><td align="char" char=".">(0.0192)</td><td align="char" char=".">(0.0206)</td></tr><tr><td align="left">Constant</td><td align="char" char=".">−0.222<xref ref-type="fn" rid="tfn13" /></td><td align="char" char=".">0.232<xref ref-type="fn" rid="tfn13" /></td><td align="char" char=".">0.296<xref ref-type="fn" rid="tfn13" /></td></tr><tr><td align="char" char=".">(0.0672)</td><td align="char" char=".">(0.0295)</td><td align="char" char=".">(0.0397)</td></tr></tbody></table> </ephtml> </p> <ulist> <item>12 <emph>Note</emph>: Standard errors in parentheses.</item> <item>13 *** <emph>p</emph> < .001;</item> <item>14 ** <emph>p</emph> < .01;</item> <item>15 * <emph>p</emph> < .05.</item> </ulist> <p>Second, despite its absence at labour market entry, the gender gap in the bottom develops at a faster rate than in other parts of earnings distribution. After 2 years in the labour market, the observed raw gap grows to 15%. The proportion of the gap explained by education‐ and job‐related characteristics declines to 53%.</p> <p>Third, the field of study remains the most influential single contributor to the early career wage gap in all parts of earnings distribution, especially at the top. However, we observe a diminishing role of educational characteristics in explaining the gender wage gap as time after graduation progresses. Educational characteristics account for 53% of the explained part of the gap at the 90th percentile, while only 39% at the 10th and 34% in the 50th percentiles immediately after graduation (see Table 5). 4 years after, the contribution of education falls to approximately 18% in the bottom, 18% in the middle, and 38% of the explained part in the upper part of earnings distribution. This means that there are certain majors, which immediately provide access to the most rewarded jobs. In particular, a large difference in wages observed among mathematics and computer science majors is likely to make a substantial contribution to the gender gap at the top. Industry and size of the enterprise contribute more to the understanding of wage differentials at the bottom and at means of the wage distribution. Figure 4 provides a concise overview of the dynamics in the structure of the explained part of the gender wage gap for top earners.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/EJE/01dec24/ejed12730-fig-0004.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="ejed12730-fig-0004.jpg" title="4 The structure of the explained part of GPG (%) in the top 90th percentile wage distribution." /> </p> <p></p> <p>The structure of the unexplained part seems more volatile (see Figure 5). Education mostly acts in favour of female graduates, jointly reducing the unexplained part of the gap. An honours degree is an exception. University selectivity is reducing the unexplained part throughout the whole early career, increases the explained part at the start and then conversely reduces it with career progression.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/EJE/01dec24/ejed12730-fig-0005.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="ejed12730-fig-0005.jpg" title="5 The structure of the unexplained part of GPG (%) in the top 90th percentile wage distribution." /> </p> <p></p> <hd id="AN0181057333-20">DISCUSSION AND CONCLUSIONS</hd> <p>This study covers the dynamics of the early career gender wage gap among university graduates, looking at their employment outcomes during the first 6 months and 1–4 years after graduation. We explore the early career wage gap in the context of Russia, a country of mass universal access to higher education and a high level of human capital acquisition among women.</p> <p>First, we show that the gap in starting wages already exists and constitutes 14%, which is two times less compared to the gap among the general population. However, the gap rapidly expands during the first years in the labour market, almost doubling (26%) and almost reaching the figure observed for the general population.</p> <p>Second, we find that the main contributor to the gap, at the initial career stage, is the gender differences in the fields of study. Given the lack of any institutional restrictions in access to education, this suggests that men and women might have different individual preferences when it comes to career choice (Atrostic, [<reflink idref="bib3" id="ref65">3</reflink>]).</p> <p>Third, we demonstrate that the proportion of the gap which can be explained by the observed parameters of education and job, drastically decreases during the early career. 85% of the gap in entry wages can be attributed to the observed characteristics of education and employment immediately after graduation, while only 20% three years later. The observed changes are not explained by gender‐based sorting into different jobs, vertical or horizontal segregation. Unfortunately, we do not have sufficient details related to job characteristics to control for the level of risk associated with different jobs (and, therefore, leading to compensating differentials).</p> <p>Fourth, RIF decompositions further show that at the beginning, graduates experience equality in poverty but a rapid divergence in the upper‐end of income distribution, suggesting that the glass ceiling already forms in early career. Four years after graduation, the gap at the 90th percentile already reaches 36%, with vertical segregation being statistically insignificant as a contributor.</p> <p>Summing up, although higher education continues to serve as an important social mechanism, facilitating social mobility and forming a relatively homogeneous group of professionals with comparable results at the labour market entry, further post‐educational experiences, choices, and labour market institutions significantly influence the labour market position of graduates and extend the gender wage gap. In developed countries, the largest gaps are no longer concentrated among low‐educated workers. Rather, highly educated women experience higher gender wage gaps. Therefore, it is unlikely that changes in female education in terms of fields of study or the attainment of higher credentials will result in the closure of the gender wage gap, as supported by recent research (Quadlin et al., [<reflink idref="bib42" id="ref66">42</reflink>]).</p> <p>This research may also be viewed as a part of a broader topic of graduate employability as a policy issue. Society and governments exert greater pressure on higher education institutions, suggesting that they should be more responsive to labour market demand for particular types of labour. However, the changing dynamic between higher education and the labour market, arising due to massification of higher education, indicates that there is no direct matching between the two as university graduates exist in a wider economic and social context. The successful integration of graduates may be more dependent on their early experiences in the labour market than on their skills and educational credentials. Even well‐developed employability policies may not necessarily translate into better graduates' labour market experience and outcomes (Tomlinson, [<reflink idref="bib49" id="ref67">49</reflink>]). The observed growth in the early career gender wage gap is one of the explicit examples. Consequently, educational policy in higher education may have limited ability to narrow the gap in the labour market. Instead, greater focus should be placed on the process of forming preferences, which occurs during the earlier stages of socialisation.</p> <p>We contribute to the existing literature in multiple ways. First, we provide estimates of the gender wage gap at the very beginning of graduate careers, where the results are little affected by labour market experiences, mobility, and family responsibilities. This remains rare, since the existing literature mostly focuses on early career outcomes from 5 to 10 years after college graduation (Manning & Swaffield, [<reflink idref="bib31" id="ref68">31</reflink>]; Napari, [<reflink idref="bib34" id="ref69">34</reflink>]; Triventi, [<reflink idref="bib50" id="ref70">50</reflink>]). Second, we trace the dynamics of the gap on the same cohort of graduates, which allows us to avoid biases arising in previous papers due to cross‐cohort comparisons. Third, we add to the limited literature, exploring early career gender wage gaps in dynamic rather than at one point of time. Finally, the richness of our administrative dataset, especially in terms of the characteristics of educational background, allows us to explore a rather homogeneous sample and to receive more accurate estimates. Administrative data are extremely rarely explored in the context of the early gender wage gap (the only exception we are aware of is Cepeda‐Emiliani & Barón‐Rivera, [<reflink idref="bib12" id="ref71">12</reflink>]).</p> <hd id="AN0181057333-21">FUNDING INFORMATION</hd> <p>This work was funded by the Program for Basic Research of the National Research University Higher School of Economics (Moscow, Russia).</p> <hd id="AN0181057333-22">CONFLICT OF INTEREST STATEMENT</hd> <p>The authors have no competing interests to declare that are relevant to the content of this article.</p> <hd id="AN0181057333-23">DATA AVAILABILITY STATEMENT</hd> <p>Data cannot be made publicly available.</p> <p>GRAPH: Data S1.</p> <ref id="AN0181057333-24"> <title> Footnotes </title> <blist> <bibl id="bib1" idref="ref5" type="bt">1</bibl> <bibtext> The term glass ceiling may refer either to gender differences in career advances or to increasing gender wage gaps across the wage distribution (e.g. Cotter et al., [13]).</bibtext> </blist> <blist> <bibl id="bib2" idref="ref16" type="bt">2</bibl> <bibtext> "Female" major requires the proportion of females concentrated in the field to exceed the proportion of females in higher education by 5% (i.e. 57 + 5 = 62% in the respective field are women). Similarly, to be considered male dominated, the respective field should have at least 48% of male graduates. The same logic applies to the determination of "male" and "female" industries.</bibtext> </blist> </ref> <ref id="AN0181057333-25"> <title> REFERENCES </title> <blist> <bibtext> Altonji, J. G., Arcidiacono, P., & Maurel, A. (2015). The analysis of field choice in college and graduate school. In Handbook of the economics of education (pp. 305 – 396). Elsevier BV. https://doi.org/10.1016/b978‐0‐444‐63459‐7.00007‐5</bibtext> </blist> <blist> <bibtext> Altonji, J. G., Kahn, L. B., & Speer, J. D. (2016, January 1). Cashier or consultant? Entry labor market conditions, field of study, and career success. Journal of Labor Economics, 34 (S1), S361 – S401. https://doi.org/10.1086/682938</bibtext> </blist> <blist> <bibl id="bib3" idref="ref38" type="bt">3</bibl> <bibtext> Atrostic, B. (1982). The demand for leisure and nonpecuniary job characteristics. American Economic Review, 72 (3), 428 – 440. https://<ulink href="http://www.jstor.org/stable/1831542">www.jstor.org/stable/1831542</ulink></bibtext> </blist> <blist> <bibl id="bib4" idref="ref14" type="bt">4</bibl> <bibtext> Barone, C. (2011, March 22). Some things never change. Sociology of Education, 84 (2), 157 – 176. https://doi.org/10.1177/0038040711402099</bibtext> </blist> <blist> <bibl id="bib5" idref="ref9" type="bt">5</bibl> <bibtext> Behr, A., & Theune, K. (2018, March 1). The gender pay gap at labour market entrance: Evidence from Germany. International Labour Review, 157 (1), 83 – 100. https://doi.org/10.1111/ilr.12037</bibtext> </blist> <blist> <bibl id="bib6" idref="ref28" type="bt">6</bibl> <bibtext> Blau, F. D., & Kahn, L. M. (1997, January 1). Swimming upstream: Trends in the gender wage differential in the 1980s. Journal of Labor Economics, 15 (1, Part 1), 1 – 42. https://doi.org/10.1086/209845</bibtext> </blist> <blist> <bibl id="bib7" idref="ref6" type="bt">7</bibl> <bibtext> Blau, F. D., & Kahn, L. M. (2017, September 1). The gender wage gap: Extent, trends, and explanations. Journal of Economic Literature, 55 (3), 789 – 865. https://doi.org/10.1257/jel.20160995</bibtext> </blist> <blist> <bibl id="bib8" idref="ref47" type="bt">8</bibl> <bibtext> Blinder, A. S. (1973, January 1). Wage discrimination: Reduced form and structural estimates. Journal of Human Resources, 8 (4), 436. https://doi.org/10.2307/144855</bibtext> </blist> <blist> <bibl id="bib9" idref="ref18" type="bt">9</bibl> <bibtext> Breda, T., & Napp, C. (2019, July 30). Girls' comparative advantage in reading can largely explain the gender gap in math‐related fields. Proceedings of the National Academy of Sciences, 116 (31), 15435 – 15440. https://doi.org/10.1073/pnas.1905779116</bibtext> </blist> <blist> <bibtext> Briel, S., Osikominu, A., Pfeifer, G., Reutter, M., & Satlukal, S. (2021, March 27). Gender differences in wage expectations: The role of biased beliefs. Empirical Economics, 62 (1), 187 – 212. https://doi.org/10.1007/s00181‐021‐02044‐0</bibtext> </blist> <blist> <bibtext> Brown, C. R., & Corcoran, M. A. (1997, July 1). Sex‐based differences in school content and the male‐female wage gap. Journal of Labor Economics, 15 (3), 431 – 465. https://doi.org/10.1086/209867</bibtext> </blist> <blist> <bibtext> Cepeda‐Emiliani, L., & Barón‐Rivera, J. D. (2012, March 8). Educational Segregation and the Gender Wage Gap for Recent College Graduates in Colombia. https://doi.org/10.32468/be.695</bibtext> </blist> <blist> <bibtext> Cotter, D. A., Hermsen, J. M., Ovadia, S., & Vanneman, R. (2001). The glass ceiling effect. Social Forces, 80 (2), 655 – 681. https://doi.org/10.1353/sof.2001.0091</bibtext> </blist> <blist> <bibtext> Cukrowska‐Torzewska, E., & Lovasz, A. (2020). The role of parenthood in shaping the gender wage gap–a comparative analysis of 26 European countries. Social Science Research, 85, 102355. https://doi.org/10.1016/j.ssresearch.2019.102355</bibtext> </blist> <blist> <bibtext> Davies, S., & Guppy, N. (1997, June 1). Fields of study, college selectivity, and student inequalities in higher education. Social Forces, 75 (4), 1417 – 1438. https://doi.org/10.1093/sf/75.4.1417</bibtext> </blist> <blist> <bibtext> DiPrete, T. A., & Buchmann, C. (2006). Gender‐specific trends in the value of education and the emerging gender gap in college completion. Demography, 43 (1), 1 – 24. https://doi.org/10.1353/dem.2006.0003</bibtext> </blist> <blist> <bibtext> England, P. (2005, July 1). Gender inequality in labor markets: The role of motherhood and segregation. Social Politics, 12 (2), 264 – 288. https://doi.org/10.1093/sp/jxi014</bibtext> </blist> <blist> <bibtext> Eurostat. (2022). Gender pay gap in unadjusted form. https://ec.europa.eu/eurostat/databrowser/view/sdg_05_20/default/table?lang=en</bibtext> </blist> <blist> <bibtext> Firpo, S., Fortin, N. M., & Lemieux, T. (2009, May 1). Unconditional Quantile Regressions. Econometrica, 77 (3), 953 – 973. https://doi.org/10.3982/ecta6822</bibtext> </blist> <blist> <bibtext> Firpo, S., Fortin, N. M., & Lemieux, T. (2018, May 25). Decomposing wage distributions using recentered influence function regressions. Econometrics, 6 (2), 28. https://doi.org/10.3390/econometrics6020028</bibtext> </blist> <blist> <bibtext> Francesconi, M., & Parey, M. (2018). Early gender gaps among university graduates. European Economic Review, 109, 63 – 82. https://doi.org/10.1016/j.euroecorev.2018.02.004</bibtext> </blist> <blist> <bibtext> Gerhart, B. (1990, July 1). Gender differences in current and starting salaries: The role of performance, college major, and job title. Industrial and Labor Relations Review, 43 (4), 418 – 433. https://doi.org/10.1177/001979399004300406</bibtext> </blist> <blist> <bibtext> Gokhberg, L., Ozerova, O., Sautina, E., & Shugal, N. (2021). Education in figures: Pocket data book. National Research University Higher School of Economics. https://<ulink href="http://www.hse.ru/en/primarydata/oc2021/">www.hse.ru/en/primarydata/oc2021/</ulink></bibtext> </blist> <blist> <bibtext> Joy, L. (2000, May 1). Do colleges shortchange women? Gender differences in the transition from college to work. American Economic Review, 90 (2), 471 – 475. https://doi.org/10.1257/aer.90.2.471</bibtext> </blist> <blist> <bibtext> Karabchuk, T., Trach, T., & Pankratova, V. (2021). Motherhood wage penalty in Russia: Empirical study on RLMS‐HSE data. In: T. Karabchuk, K. Kumo, K. Gatskova, & E. Skoglund (Eds.), Gendering Post‐Soviet Space (pp. 235 – 255). Sringer. https://doi.org/10.1007/978‐981‐15‐9358‐1_11</bibtext> </blist> <blist> <bibtext> Koenker, R., & Bassett, G. W. (1978, January 1). Regression quantiles. Econometrica, 46 (1), 33. https://doi.org/10.2307/1913643</bibtext> </blist> <blist> <bibtext> Leuze, K., & Strauß, S. (2016, February 1). Why do occupations dominated by women pay less? How 'female‐typical' work tasks and working‐time arrangements affect the gender wage gap among higher education graduates. Work, Employment & Society, 30 (5), 802 – 820. https://doi.org/10.1177/0950017015624402</bibtext> </blist> <blist> <bibtext> Longarela, I. R. (2017). Explaining vertical gender segregation: a research agenda. Work, Employment and Society, 31 (5), 861 – 871. https://doi.org/10.1177/0950017016668133</bibtext> </blist> <blist> <bibtext> Machado, J., & Mata, J. (2005, May 1). Counterfactual decomposition of changes in wage distributions using quantile regression. Journal of Applied Econometrics, 20 (4), 445 – 465. https://doi.org/10.1002/jae.788</bibtext> </blist> <blist> <bibtext> Machin, S., & Puhani, P. A. (2003, June 1). Subject of degree and the gender wage differential: Evidence from the UK and Germany. Economics Letters, 79 (3), 393 – 400. https://doi.org/10.1016/s0165‐1765(03)00027‐2</bibtext> </blist> <blist> <bibtext> Manning, A., & Swaffield, J. K. (2008, July 1). The gender gap in early‐career wage growth. The Economic Journal, 118 (530), 983 – 1024. https://doi.org/10.1111/j.1468‐0297.2008.02158.x</bibtext> </blist> <blist> <bibtext> McDonald, J. A., & Thornton, R. J. (2007, January 1). Do new male and female college graduates receive unequal pay? Journal of Human Resources, XLII (1), 32 – 48. https://doi.org/10.3368/jhr.xlii.1.32</bibtext> </blist> <blist> <bibtext> Milla, J. (2017, January 1). The context‐bound university selectivity premium. IZA Discussion Paper, no. 11025. https://doi.org/10.2139/ssrn.3045720</bibtext> </blist> <blist> <bibtext> Napari, S. (2009, April 1). Gender differences in early‐career wage growth. Labour Economics, 16 (2), 140 – 148. https://doi.org/10.1016/j.labeco.2008.08.005</bibtext> </blist> <blist> <bibtext> Neumark, D. (1988, January 1). Employers' discriminatory behavior and the estimation of wage discrimination. Journal of Human Resources, 23 (3), 279. https://doi.org/10.2307/145830</bibtext> </blist> <blist> <bibtext> Oaxaca, R. L. (1973, October 1). Male‐female wage differentials in urban labor markets. International Economic Review, 14 (3), 693. https://doi.org/10.2307/2525981</bibtext> </blist> <blist> <bibtext> OECD. (2021). Distribution of graduates and new entrants by field: Share of graduates by gender in fields of education. https://stats.oecd.org/index.aspx?queryid=108601</bibtext> </blist> <blist> <bibtext> Oshchepkov, A. (2021). Gender Pay Gap in Russia: Literature Review and New Decomposition Results. In: T. Karabchuk, K. Kumo, K. Gatskova, E. Skoglund (Eds.), Gendering Post‐Soviet Space. Springer. https://doi.org/10.1007/978‐981‐15‐9358‐1_10</bibtext> </blist> <blist> <bibtext> Petrenko, O., & Cadil, J. (2024). Can successful female STEM graduates contribute to narrowing the gender pay gap in the EU? European Journal of Education, 59 (2), e12641. https://doi.org/10.1111/ejed.12641</bibtext> </blist> <blist> <bibtext> Piazzalunga, D. (2018). The gender wage gap among college graduates in Italy. Italian Economic Journal, 4 (1), 33 – 90. https://doi.org/10.1007/s40797‐017‐0069‐8</bibtext> </blist> <blist> <bibtext> Psacharopoulos, G., & Patrinos, H. A. (2018, June 7). Returns to investment in education: A decennial review of the global literature. Education Economics, 26 (5), 445 – 458. https://doi.org/10.1080/09645292.2018.1484426</bibtext> </blist> <blist> <bibtext> Quadlin, N., VanHeuvelen, T., & Ahearn, C. E. (2023). Higher education and high‐wage gender inequality. Social Science Research, 112, 102873. https://doi.org/10.1016/j.ssresearch.2023.102873</bibtext> </blist> <blist> <bibtext> Redmond, P., & McGuinness, S. (2019, June 1). The gender wage gap in Europe: Job preferences, gender convergence and distributional effects. Oxford Bulletin of Economics and Statistics, 81 (3), 564 – 587. https://doi.org/10.1111/obes.12282</bibtext> </blist> <blist> <bibtext> Roshchin, S., & Rudakov, V. (2016, August 20). The effect of university quality on graduates' wages. Voprosy Economiki, 8, 74 – 95. https://doi.org/10.32609/0042‐8736‐2016‐8‐74‐95</bibtext> </blist> <blist> <bibtext> Rosstat. (n.d.) https://rosstat.gov.ru/statistic</bibtext> </blist> <blist> <bibtext> Rudakov, V., Kiryushina, M. F., Figueiredo, H., & Teixeira, P. (2022, May 30). Early career gender wage gaps among university graduates in Russia. International Journal of Manpower, 44 (6), 1046 – 1070. https://doi.org/10.1108/ijm‐03‐2021‐0206</bibtext> </blist> <blist> <bibtext> Säve‐Söderbergh, J. (2019, May 1). Gender gaps in salary negotiations: Salary requests and starting salaries in the field. Journal of Economic Behavior and Organization, 161, 35 – 51. https://doi.org/10.1016/j.jebo.2019.01.019</bibtext> </blist> <blist> <bibtext> Stinebrickner, T. R., Stinebrickner, R., & Sullivan, P. M. (2018, July 13). Job Tasks and the Gender Wage Gap among College Graduates. https://doi.org/10.3386/w24790</bibtext> </blist> <blist> <bibtext> Tomlinson, M. (2012, May 29). Graduate employability: A review of conceptual and empirical themes. Higher Education Policy, 25, 407 – 431. https://doi.org/10.1057/hep.2011.26</bibtext> </blist> <blist> <bibtext> Triventi, M. (2013, May 20). The gender wage gap and its institutional context: A comparative analysis of European graduates. Work, Employment & Society, 27 (4), 563 – 580. https://doi.org/10.1177/0950017012460322</bibtext> </blist> </ref> <aug> <p>By Ksenia Rozhkova; Sergey Roshchin and Natalya Yemelina</p> <p>Reported by Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib41" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib16" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib40" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib39" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib22" firstref="ref7"></nolink> <nolink nlid="nl6" bibid="bib31" firstref="ref8"></nolink> <nolink nlid="nl7" bibid="bib48" firstref="ref10"></nolink> <nolink nlid="nl8" bibid="bib11" firstref="ref12"></nolink> <nolink nlid="nl9" bibid="bib30" firstref="ref13"></nolink> <nolink nlid="nl10" bibid="bib17" firstref="ref17"></nolink> <nolink nlid="nl11" bibid="bib32" firstref="ref20"></nolink> <nolink nlid="nl12" bibid="bib21" firstref="ref21"></nolink> <nolink nlid="nl13" bibid="bib43" firstref="ref22"></nolink> <nolink nlid="nl14" bibid="bib33" firstref="ref23"></nolink> <nolink nlid="nl15" bibid="bib15" firstref="ref24"></nolink> <nolink nlid="nl16" bibid="bib28" firstref="ref25"></nolink> <nolink nlid="nl17" bibid="bib34" firstref="ref26"></nolink> <nolink nlid="nl18" bibid="bib47" firstref="ref27"></nolink> <nolink nlid="nl19" bibid="bib27" firstref="ref29"></nolink> <nolink nlid="nl20" bibid="bib24" firstref="ref30"></nolink> <nolink nlid="nl21" bibid="bib23" firstref="ref31"></nolink> <nolink nlid="nl22" bibid="bib38" firstref="ref32"></nolink> <nolink nlid="nl23" bibid="bib37" firstref="ref34"></nolink> <nolink nlid="nl24" bibid="bib45" firstref="ref35"></nolink> <nolink nlid="nl25" bibid="bib18" firstref="ref39"></nolink> <nolink nlid="nl26" bibid="bib25" firstref="ref42"></nolink> <nolink nlid="nl27" bibid="bib46" firstref="ref44"></nolink> <nolink nlid="nl28" bibid="bib14" firstref="ref45"></nolink> <nolink nlid="nl29" bibid="bib36" firstref="ref48"></nolink> <nolink nlid="nl30" bibid="bib35" firstref="ref49"></nolink> <nolink nlid="nl31" bibid="bib19" firstref="ref50"></nolink> <nolink nlid="nl32" bibid="bib20" firstref="ref51"></nolink> <nolink nlid="nl33" bibid="bib26" firstref="ref54"></nolink> <nolink nlid="nl34" bibid="bib29" firstref="ref55"></nolink> <nolink nlid="nl35" bibid="bib10" firstref="ref56"></nolink> <nolink nlid="nl36" bibid="bib44" firstref="ref60"></nolink> <nolink nlid="nl37" bibid="bib12" firstref="ref63"></nolink> <nolink nlid="nl38" bibid="bib42" firstref="ref66"></nolink> <nolink nlid="nl39" bibid="bib49" firstref="ref67"></nolink> <nolink nlid="nl40" bibid="bib50" firstref="ref70"></nolink>
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  Data: <searchLink fieldCode="DE" term="%22Russia%22">Russia</searchLink>
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  Label: Abstract
  Group: Ab
  Data: This study provides new evidence of the gender wage gap among recent university graduates at different stages of their early career. Using a unique administrative dataset from Russia, we estimate the gender wage gap at means and across wage distribution for a cohort of 2018 university graduates during the first 4 years after graduation. We explore the contribution of educational and job factors to the explanation of the gap. Although a substantial gap of 14% is already present at labour market entry, it is two times smaller compared to the gap for the overall working population. Eighty five percent of the entry wage gap can be explained with differences in fields of study, work experience, and job characteristics. More than 4 years after graduation, the gender wage gap experiences a dramatic increase, reaching 26%. Only 28% of the resulting gap can be explained by the observed characteristics, including industrial and occupational segregation. The size of the gap varies drastically in different parts of the wage distribution, suggesting the existence of a strong glass ceiling effect from the very beginning of graduate careers. The rapidly expanding early career gender wage gap with a growing unexplained component suggests that education policies may have limited ability to promote gender equality in the labour market.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2024
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1450692
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1450692
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/ejed.12730
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 22
    Subjects:
      – SubjectFull: Gender Issues
        Type: general
      – SubjectFull: Salary Wage Differentials
        Type: general
      – SubjectFull: Equal Opportunities (Jobs)
        Type: general
      – SubjectFull: Universities
        Type: general
      – SubjectFull: College Graduates
        Type: general
      – SubjectFull: Gender Discrimination
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Occupational Information
        Type: general
      – SubjectFull: Educational Background
        Type: general
      – SubjectFull: Educational Status Comparison
        Type: general
      – SubjectFull: Russia
        Type: general
    Titles:
      – TitleFull: The Dynamics of the Early Career Gender Wage Gap among University Graduates: The Case of Russia
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Ksenia Rozhkova
      – PersonEntity:
          Name:
            NameFull: Sergey Roshchin
      – PersonEntity:
          Name:
            NameFull: Natalya Yemelina
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 12
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 0141-8211
            – Type: issn-electronic
              Value: 1465-3435
          Numbering:
            – Type: volume
              Value: 59
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: European Journal of Education
              Type: main
ResultId 1