metadata: authors: 0: author: Das,Jishnu
1: author: Joubert,Clement Jean Edouard
2: author: Tordoir,Sander Florian
count: World
docna: 0: docna: Compensation, diversity and inclusion at the
            World Bank Group
docty: Policy Research Working Paper
subtopic: Human Rights,Labor Markets,ICT Applications,Public Sector Management and Reform,Gender and Development
teratopic: Governance,Gender,Macroeconomics and Economic Growth,Social Protections and Labor
abstracts: cdata!: This paper examines salary gaps by
            gender and nationality at the World Bank Group between 1987
            and 2015 using a unique panel of all employees over this
            period. The paper develops and implements a dynamic
            simulation approach that models existing gaps as arising
            from differences in job composition at entry, entry
            salaries, salary growth and attrition. There are three main
            findings. First, 76 percent of the $27,400 salary gap across
            the average male and female staff at the World Bank Group
            can be attributed to composition effects, whereby men
            entered the World Bank Group at higher paid positions,
            particularly in the earlier half of the sample. Second,
            salary gaps 15 years after joining the World Bank Group can
            favor either men or women depending on their entry position.
            Third, for the most common entry-level professional position
            (known as Grade GF at the World Bank Group) there is a
            gender gap of 3.5 percent in favor of males 15 years after
            entry. The majority of this gap (84 percent) is due to
            differences in salary growth rather than differences in
            entry salaries or attrition. The pattern of these gaps is
            similar for staff from different nationalities. The dynamic
            decomposition method developed here thus identifies specific
            areas of concern and can be widely applied to the analysis
            of salary gaps within firms.
repnme: repnme: Compensation, diversity and inclusion at
            the World Bank Group
display_title: Compensation, diversity and
            inclusion at the World Bank Group
listing_relative_url: /research/2017/05/27458102/compensation-diversity-inclusion-world-bank-group
topicv3: Human Resources m1326304 1211,Gender m1327621 615,Jobs m1503931 2670,PeopleSoft m1505696 2941,Discrimination m1505707 3379
publishtoextweb_dt: 2017-05-12T00:00:00Z
historic_topic: Governance,Gender,Macroeconomics and Economic Growth,Social Protections and Labor
no_of_pages: 69
author_keys: 0: author_key: 439480
1: author_key: 1411340
2: author_key: 1321081
model_extractions: WPS8058 Policy Research Working Paper 8058 Compensation, Diversity and Inclusion at the World Bank Group _Jishnu Das Clement Joubert Sander Florian Tordoir_ Development Research Group Human Development and Public Services Team May 2017
Policy Research Working Paper 8058

# **Abstract**

This paper examines salary gaps by gender and nationality at the World Bank Group between 1987 and 2015 using a unique panel of all employees over this period. The paper develops and implements a dynamic simulation approach that models existing gaps as arising from differences in job composition at entry, entry salaries, salary growth and attrition. There are three main findings. First, 76 percent of the $27,400 salary gap across the average male and female staff at the World Bank Group can be attributed to composition effects, whereby men entered the World Bank Group at higher paid positions, particularly in the earlier half of the sample. Second, salary gaps 15 years after joining the World Bank Group can favor either men or women depending on their entry position. Third, for the most common entry-level professional position (known as Grade GF at the World Bank Group) there is a gender gap of 3.5 percent in favor of males 15 years after entry. The majority of this gap (84 percent) is due to differences in salary growth rather than differences in entry salaries or attrition. The pattern of these gaps is similar for staff from different nationalities. The dynamic decomposition method developed here thus identifies specific areas of concern and can be widely applied to the analysis of salary gaps within firms.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at jdas1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Compensation, Diversity and Inclusion at the World Bank Group<sup>1</sup>

Jishnu Das Development Research Group, The World Bank Clement Joubert Development Research Group, The World Bank Sander Florian Tordoir European Central Bank JEL Codes: J16, J31, J33, J71, L30

> 1 This working paper is the output from a joint task between the Development Research Group, the Gender Cross-Cutting Solution Area (CCSA), the Diversity and Inclusion Office (D&I) and the Human Resources (HR) Compensation Unit at the World Bank Group (WBG). Funding for the task was provided by DEC, the Gender CCSA and the D&I Office. We thank D&I and HR Compensation staff for assistance in putting together the data and clarifying numerous issues that arose during analysis. The work presented here has been guided by an Advisory Committee consisting of Benedicte Leroy De La Briere, Alison C. N. Cave, Shantayanan Devarajan, John T. Giles, Markus Goldstein, Caren Grown, Deon P. Filmer, Asli Demirguc-Kunt, Ana L. Revenga, Maryam Salim, Sudhir Shetty, Yvonne Tsikata, Adam Wagstaff and Dominique Van de Walle. We also thank Carlos Silva, Carolina Sanchez, the Executive Committee of the Staff Association and the HR management team for valuable comments. All visualizations in report were created by Alicia S. Hammond (GCGDR). The findings, interpretations, and conclusions expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments they represent.
# I. Introduction

That women earn considerably less than men, even for the same job, is well established at the level of countries and industries.<sup>2</sup> The focus is now moving to the corporate world and individual firms. Large companies and institutions are looking within themselves and asking whether their diversity and inclusion policies are sufficient to guarantee pay equality: equality both in terms of ensuring that workers who perform similar jobs receive the same pay and that different people have an equal shot at different jobs.<sup>3</sup> In order to determine how the World Bank Group (WBG)—a large multilateral finance institution with a highly diverse workforce—fared in this context, we examined compensation at the institution, focusing on differences between men and women and between citizens of Part 1 and Part 2 countries. The additional emphasis on Part 1 and Part 2 countries is particular to the WBG and the classification roughly groups staff into those who are from higher (Part 1) and lower/middle-income countries (Part 2).<sup>4</sup> Together with the Human Resources group at the WBG we constructed a unique database called the “Human Resource Longitudinal Database” that contains information on all employees (excluding shortterm consultants) between 1987 and 2015.<sup>5</sup> Using this new database we were able to look at both pay differentials and job composition within the institution for those staff who were hired on the U.S. salary based plan, which includes all international hires, regardless of their duty station. To conduct this analysis, we defined and examined two characterizations of salary differences across employee subgroups at the WBG: The **aggregate gap** and the **career gap** .

We define the **aggregate gap** as the difference in mean salaries between men and women (or Part 1 and Part 2) employees at the WBG.<sup>6</sup> Frequently used in the literature on gender gaps in wages, the aggregate gap is sensitive to both occupational sorting and differences in salaries within occupations. In the context of the WBG, the aggregate gap will reflect, in part, the extent to which men and women are hired into

> 2 For recent reviews of the gender earnings gap in the United States, see for instance, Juhn and McCue (2017) and Blau and Kahn (2016). For international data and comparisons see the World Development Report (2012) on Gender Equality and Development. 3 Examples include a recent report by the London School of Economics Equity, Diversity and Inclusion Taskforce (2016) and - - - Facebook’s report on diversity, accessed on March 2017 at <u>https://newsroom.fb.com/news/2016/07/facebook diversity update positive-hiring-trends-show-progress. In addition, Gobillon et al. (2014) and Takao et al. (2013) focus on single large firms.</u>

> 4 Part 1 countries do not borrow from the WBG whereas Part 2 countries are eligible to borrow, a decision that was made by each country upon entering the WBG. As such, the country part classification roughly separates low and middle from high-income economies. This is necessarily a rough classification since a country’s economic status could have changed considerably over time. Appendix Table 1 in the report presents a list of Part 1 and Part 2 countries represented at The WBG.

> 5 Short-Term Consultants range from people working exclusively at the WBG to those on short contracts with permanent jobs at other institutions. Although there is considerable movement of staff from consultancy to staff contracts, the data on consultants are too limited for inclusion in our analysis.

> 6 Although compensation is a broader term, we focus on current salaries where we have complete data for all years and employees. A more complete analysis of compensation would integrate pension and benefits information with this longitudinal database. In addition, as is well known, analysis such as ours reflects one dimension of the overall job experience. Discrimination can be encountered and is experienced in multiple ways, but this remains outside the scope of our current analysis.

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different professional positions (“grades” at the WBG) that are highly correlated with their salaries. There are two reasons for focusing on the aggregate gap. First, summary statistics such as “ _the average woman makes 70 cents on the dollar for the average man_ ” are statements about such gaps, without any conditioning on profession or grade. Second, if productivity distributions are identical for men and women, then any difference in the aggregate gap reflects discrimination either in hiring grades or in salaries conditional on the hiring grade.

Reducing the aggregate gap is an important goal for the WBG, but equally important is examining the career growth of different groups who were hired into the same grade at the same time. For instance, one frequently voiced concern from staff consultations was the imbalance in staffing at different gradelevels within the WBG, with particular concern about the lack of representation of women at higher grades. Therefore in addition to the aggregate gap, we also examined how salaries of different groups of staff hired into the _same_ grade evolved over time, reflecting raises and promotion rates. We label these salary differences the **Career Gap** .

To exploit the longitudinal nature of these data, we developed and implemented a novel dynamic simulation approach that relates current salary gaps to hiring, promotion and attrition patterns from 1987 to the present.<sup>7</sup> We use this dynamic simulation approach to decompose the gaps into differences arising from the following four factors:<sup>8</sup>

- Staff Composition Effects: Are women hired in systematically different grades relative to men? This is relevant only for the aggregate gap, as the career gap is conditioned on the grade at entry.

- Entry Salaries: For the same position, are women hired at lower salaries relative to men?

- Attrition:  Is the distribution of salaries among women who leave different from that of men? If so, it would potentially alter the salary distribution of those who remain due to selection effects.

- Salary Growth: Do the salaries of women grow at different rates relative to those of men?

Our first result is that there has been substantial catch-up over time, although aggregate salary gaps persist. In 1987, the average woman employee earned 52 cents on the dollar compared to the average

> 7 The decomposition relies on simulating counterfactuals in the spirit of the structural labor literature (see for example Keane and Wolpin’s (2010) decomposition of the white female-black female pay gap). In contrast to these studies, our simulations do not attempt to capture endogenous responses by agents to the counterfactual change.

> 8 The pay gaps observed in 1987, the first year of our data, reflect pre-1987 HR policies and thus cannot be decomposed. This “legacy” pay gap shows up as a residual in our decomposition.

3
man at the WBG; in 2015 this had increased to 77 cents.<sup>9</sup> The average Part 2 employee earned 84 cents on the dollar compared to the average Part 1 employee in 1987 and this increased to 87 cents in 2015.

The aggregate gap reflects, in part, how women and men (and Part 1 versus Part 2) are hired into different grades. At the WBG grades run from GA to GL. Grades GA-GD are the grade levels for Administrative and Client Services (ACS) staff. GE corresponds to analyst-level staff. GF and GG contain the bulk of professional technical staff. Staff in the GH level, the first leadership position at the WBG, can be either in a technical or managerial role. GI (Director) through GK (Vice President) refers to increasingly senior management positions. GL is the president of the WBG.

Over the period of our data, the fractions of men and women hired at each grade have converged. However, even in 2015 women were hired into lower grades on average: 78.1% of GA-GD hires were female whereas 62.3% of GG hires were male. Further, the historical differences in hiring created a pipeline to higher paid jobs that contain more men. Compositional differences between Part 1 and Part 2 are qualitatively similar to those between men and women. Hires at GE and above are 40.4% Part 2 employees, while hires at GA-GD are 62.5% Part 2. In contrast to the gender differences, however, there is no pattern of convergence toward parity over time in the shares of Part 1 and Part 2 staff hired at different grades.

Consistent with these patterns, the decomposition of the aggregate gap shows that 76% of the aggregate gender gap in 2015 and 61% of the aggregate country part gap in 2015 can be attributed to differences in grade composition at entry. In addition, differences in salary growth and differences in entry salaries account for 12% of the aggregate gap across men and women and 16% across Part 1 and Part 2 staff. Finally, attrition is a more important contributor (14%) for the aggregate gap across Part 1 and Part 2 employees relative to the aggregate gap across women and men (1%).

Career gaps varied across entry grades, which is an important finding in itself. In grades GE and GG, 15 years after joining the WBG any existing gaps in salaries by sex or nationality were small. In all other grades besides GF, there were insufficient hires in each year for every subgroup to offer meaningful estimates. For instance, the largest number of men in the GA-GD cohort were hired in grade GB. But even there, only 209 men were hired (an average of 8 men per year) over the duration of our data and there were 12 years with 3 or fewer men hired into this grade. Similarly, for grade GH, there were 11 years with fewer than 5 women hired into the grade; for higher grades, the numbers were even smaller.

> 9 As a comparison, the aggregate gender pay gap in the United States declined from 26 cents on the dollar in 1989 to 20.7 cents on the dollar in 2010 (Blau and Kahn, 2016).

4
For employees who entered at GF, there were both sizeable salary differences and sufficient sample to further decompose career gaps. We therefore focus our decomposition of the career gap for staff who joined the WBG in grade GF between 1987 and 2001. This results in a sample of 1,763 staff who had been hired as a GF in 2001 or earlier, representing 37.1 percent of all hires at grades GE+ among those cohorts.

Relative to male Part 1 employees, in this sample 15 years after entering the WBG, the annual salary of female Part 1 employees was $5,036 lower. It was $5,178 lower for male Part 2 employees and $4,139 lower for female Part 2 employees. In percentage terms, for every dollar that male Part 1 employees (who entered as a GF) earned after 15 years, female Part 1 employees earned 96.52 cents, male Part 2 employees earned 96.42 cents and female Part 2 employees earned 97.14 cents. Our decomposition results indicate that the bulk of these differences is explained by differences in salary growth (rather than entry salaries or attrition), which reflects a longer lead time for promotions to grades GG and GH.

Although the data thus show a career gap after 15 years for those who entered as GF, we should caution that attrition from the WBG may confound any interpretation of this gap. About 8-10 percent of staff leave the WBG every year, and therefore within 7-9 years, half of original staff hires exit the sample. The decomposition analysis assumes that, had they not left the WBG, leavers and stayers in the same cohort would receive the same salaries, conditional on their last salary and entry grade. Our analysis did not find differences—in the means or distributions of either performance or salaries—of stayers versus leavers, but it could be sensitive to that assumption.<sup>10</sup> These relatively modest career gaps are somewhat surprising given previous analysis by Filmer et al. (2005), which showed a consistent salary premium for Part 1 men compared to women and Part 2 employees.<sup>11</sup> It also runs contrary to the perceptions of staff at the institution. To address this concern, we first re-examined the Filmer et al. (2005) results and found that the gender salary gap becomes small in their analysis once starting grades are accounted for—information that was not incorporated in the Filmer et al. (2005) salary decomposition. The results presented here are therefore consistent with those of Filmer et al. (2005) as both our analysis and theirs points to entry grades as a critical determinant of current salary gaps.

> 10 In addition to examining compensation metrics prior to staff leaving the WBG, we also tried to follow-up a small group of (randomly) selected staff who had left the Bank and tracked down where they were currently employed using Social Networks such as Facebook and LinkedIn. Current employment appears to be very diverse, with some staff employed in other international organizations, academia, the public or private sector, or staying on in a consultancy role at the WBG.

> 11 Filmer et al. (2005) used a cross-section of salaries among what were in 1997 known as professional staff. They identified salary deficits for women and Part 2 employees at the World Bank, only half of which could be explained by differences in staff characteristics.

5
We then examined whether the smaller contributions of entry salaries and salary growth to the aggregate gap reflects a flat compensation system with a strong preference for equity that seeks to close gaps where they exist. For instance, at the WBG, the compensation methodology seeks to promote equity within each grade by accelerating increases for those staff positioned below the midpoint and moderating for staff positioned above the midpoint, so that over time, equal performance is compensated in an equitable way. This is supported by the 2015 introduction of a four-zone salary band.

In fact, we found that salary responds strongly to performance ratings at the WBG. Among the cohort of GF entrants between 2000 and 2005, staff in the lowest performance decile gained 26 percent in real salaries over a 10-year period while those in the highest performance decile gained 83 percent. We also did not find evidence for the systematic application of informal management practices that could limit performance-related pay increases, such as not awarding staff the highest performance rating in two successive years. The hiring and compensation system at the WBG is thus reasonably successful at restricting subgroup differences among staff hired in the same grade, while maintaining salary incentives for high performance. However, it is not as successful in retaining high performing staff as exits from the institution are not correlated with historical performance.

Our econometric approach to diversity and inclusion issues in a large firm contrasts with a complementary human resource approach, where individual cases are examined on the basis of cross-sectional data to ensure compliance with equal pay policies. Our attempt here is to apply the tools available in the labor literature to the internal labor market of a single firm to generate policy insights for compensation policies in such environments. The combination of a data-based simulation approach thus bridges the large literature on subgroup differences in the labor market and growing interest in the organization of large firms.

In addition, the empirical approach we follow also complements, but is conceptually different from, a literature that decomposes wage differences across subgroups into those arising from employee characteristics and those arising from the returns to these characteristics. Given the longitudinal data available to us, our decomposition allows us to identify the sources of wage gaps and provides a starting point for further institutional efforts. For instance, relative to the cross-sectional data used by Filmer et al. (2005), these data allow us to incorporate employee turnover and pipeline effects (historical hiring will affect gaps today), which could mask or artificially amplify salary gaps in cross-sections. One key finding is that pay gaps arise at different points in staff careers depending on their entry grade. The organizational structure that leads to different gaps in different grades merits further debate and it is in understanding 6
these very specific gaps that incorporating employee characteristics will likely become a critical part of the analytical work.<sup>12</sup> The remainder of this document is as follows. In Section II, we discuss the construction of the data set and broad patterns in the data that illustrate the institutional context for our findings. Section III provides the key descriptive findings on salary gaps by gender and nationality in terms of each individual contributor— composition, entry salaries, salary growth and turnover. Section IV discusses the results of our dynamic accounting framework, which allows us to decompose the aggregate and career gaps into each of their sub-components using simulations. Section V concludes.

# II. Data

The World Bank Group's Human Resource Longitudinal Database was constructed in order to better understand salary dynamics and career differences across subgroups such as gender and nationality. The data set is structured in a panel from 1987 to 2015 with staff uniquely identified through a universal personnel identifier (UPI) that never changes for an individual, even across disjointed employment spells. The data are gathered from two human resource databases at the WBG—PeopleSoft/Business Intelligence and Talent Management—with data from each year taken as a snapshot on June 30.

PeopleSoft/Business Intelligence contains information on the staff’s universal personnel identified (UPI); compensation and benefits (e.g. salaries); personal backgrounds (e.g. gender, age); professional situation (e.g. professional grade); location (e.g. HQ or country-office based) and role and movements within the organization (e.g. promotions and lateral moves). In addition to these, information on the yearly performance rating (SRIs), which is available from 2000 onwards, is drawn from the Talent Management database.

Unfortunately, PeopleSoft does not contain reliable data going back to 1987 and multiple changes in the WBG ranging from types and grades of employees to corrections in the employment spells had to be carefully dealt with. A brief description of the types of employees and how they have changed over time is necessary to interpret the results; the accompanying data Appendix and codebook provides further details.

The most important broad distinction between types of employees at the WBG is between staff members and consultants. Our data contain all staff members. The data set has no information on Short-Term

> 12 We caution that the WBG’s data on pre-entry characteristics of staff are incomplete. Some of this is because we have staff members in our data who were hired as far back as 1955, but this is also because pre-entry data on staff are not standardized. They are based on individual CVs submitted by staff and the information contained in these CVs can vary dramatically. As one example, the variable that captures the highest educational degree is missing for 55 percent of staff in the data.

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Consultants, who can range from people working exclusively at the WBG to those on short contracts, but with permanent jobs at other institutions. Although there is considerable movement of staff from consultancy to staff contracts, the data on consultants are too limited for inclusion in our analysis.

Among staff members, the WBG grades run from GA to GL (the president of the WBG). Grades GA-GD are the grade levels for Administrative and Client Services (ACS) staff. GE corresponds to analyst-level. GF and GG contain the bulk of professional technical staff. Staff in the GH level, the first leadership position at the WBG, can be either in a technical or managerial role. GI (Director) through GK (Vice Presidents) and GL (President) refer to senior management positions. Staff entering the WBG can do so through multiple channels. Staff can be recruited through international recruitment by different units, which advertise positions and hire new employees at the relevant grade level. Staff can be hired through local recruitment, which does not include international benefits. In addition, staff can also be recruited through the “Young Professional” process, which is the flagship recruitment program of the institution.<sup>13</sup> Apart from these grades, our data also contain information on employees with “Unassigned or Ungraded” grades. These are of two types. First, a small number are staff outside the salary and promotion structure of the WBG, such as executive directors and their advisers. Second, prior to a reform in 1998 (which we discuss in Footnote 15 below) a large fraction of employees without grades were “long-term consultants”, who were not considered staff members but nevertheless held full time jobs at the WBG. With the reform, many of them were converted to graded employees, and a new category of ungraded employees with specific 2-3 year contracts was introduced called Extended-Term Consultants. In 2016, these posts were abolished as well; our data stop a year prior to this last change. Before 1999, therefore, the unclassified grade was a highly heterogeneous category, including country managers and Executive Directors (EDs). After 1999, the grade became more homogeneous, with most regular staff being slotted into normal grade levels.

# 1. Sample

Even though the WBG has more than 100 country offices, we restricted our sample to staff in the Washington D.C. headquarters hired on a US dollar salary plan, commonly known as “internationally recruited staff”. The main reason for the restriction is the substantial country-specific expertise required to convert local salaries to dollar equivalents. Given the starting date of 1987 in our data, the dissolution of the Soviet Union and the emergence of local currencies, the emergence of the euro and the dissolution

> 13 Since 2015, there is an additional recruitment program at the GE-level called the Analyst Program. Before that, there was a multitude of different youth recruitment programs which were phased in and out over time. Most of these were graded as Unclassified.

8
Additional sample restrictions are as follows:

- Among the 259,618 records corresponding to the universe of World Bank Group headquarters employees between 1987 and 2015, we exclude the 4,689 records of Executive Directors and their staff, and of “secondments”.<sup>16</sup>

- We also exclude 229 records because they had missing or anomalous grades (6 records); recorded gender changed over time (97 records); recorded salary was 0 (88 records) and; recorded salary is clearly outside the grade range in the corresponding year (38 records).<sup>17</sup>

Finally, given that different data are available in different time periods and that we will examine career gaps after 15 years of service, the samples for our analysis will differ depending on the specific analysis. In particular:

- For results on the aggregate gap, we use the entire sample subject to the restrictions discussed previously.

- For results on the career gap, we use data on staff who entered as GF between the years of 1987 and 2000. As discussed in the introduction, this is the only group with sufficient gaps and hiring in each subgroup to allow for meaningful decompositions. The time period is determined by the need for data 15 years after entry, which limits the last entry date to 2000.

- For questions related to pay and performance, we focus on staff who entered the WBG between 2000 and 2005. The performance system that is used started in 2000 and the last entry date of 2005 allows us to examine staff performance over a 10- year period.

# 2. Institutional Features

Like other multinational firms, the WBG is a large institution with central headquarters in Washington DC and country offices in over 100 countries. Unlike other multinational firms, however, special arrangements with the U.S. government allow the WBG to hire and bring in staff to central headquarters from all around the world. In fact, as Figure 1 shows, U.S. citizens are a minority in the Washington D.C. office. Compensation at the WBG therefore reflects multiple objectives, balancing the need to incentivize performance, allow managerial discretion and ensuring equity. For instance, at the WBG, salary bands for different grades as well as mean increases each year are decided with reference to a “comparator group”

> 16 Executive Directors are shareholder-appointed members of the supervisory board of the World Bank Group. The United States government, for example, appoints an Executive Director. Although EDs and their staff are paid by the WBG, their special role and manner of appointment sets them apart from staff. People on secondments are also excluded because they are not paid by the WBG.

> 17 This was defined as (i) less than half the 10th percentile for that year and grade or (ii) more than twice the 90th percentile for that year and grade.

10
that includes a mix of other international organizations, private sector firms and public sector salaries. However, to allow for managerial discretion and performance incentives, compensation bands within each grade are quite wide and in theory, raises can vary substantially around the average increase, depending both on the performance rating of the employee in the last year as well as their relative position within the salary band for their grade. To promote equity, employees with salaries above the midpoint of their grade receive a lower raise for the same level of performance.

These practices have not remained static over the period of our data. In fact, multiple institutional changes and HR policies have been enacted to further one or more of these objectives. These, in turn could have affected hiring and turnover as well as the salary structure.<sup>18</sup> It is therefore useful to examine basic summary statistics that deepen our understanding of the underlying dynamics in the data.

# 3. Summary Statistics

A first characterization of the data is in terms of compositional changes. Figure 2 shows that over the period of our data, there was a secular increase in the fraction of GE+ level staff as a fraction of total staff from 64 percent in 1987 to 85 percent in 2015, consistent both with increasing automatization of routine tasks and shifting of routine tasks from GA-GD to GE+ staff.<sup>19</sup> The proportional increase in GE+ staff was primarily in the technical grades of GE, GF and GH; no change was seen in the proportion of managers to staff between the years of 2000 and 2015.<sup>20</sup>

> 18 An important policy change was a reform in 1998 that changed the pension regimes (from defined benefits to a combination of defined benefit and defined contribution), the grading system (from ‘narrow’ to ‘broad banding’ thus shifting the WBG from smaller bands and more frequent promotions to larger bands with fewer promotions) and, importantly for our analysis, eliminated long-term consultant contracts. Appendix Figure 1 shows all new hires between 1990 and 2005 at the WBG for ungraded, GA-GD and GE+ staff. The term “ungraded” are those with “unassigned” or “unclassified” grades at the institution— prior to 1998 these included staff on long-term consultant contracts and country managers. After 1998 these were mostly (see Technical Appendix 1) staff hired on Extended Term Consultant or Extended Term Temporary contracts with a fixed duration. New hiring in the ungraded category collapsed immediately after the reform and then picked up, but at much lower levels than before with the coming of ETCs and ETTs. Many ungraded staff were converted to “regular” staff at different grade-levels where there is a corresponding spike in “new” hires at those grades in 2008: 59 percent of those who were ungraded in 1998 were converted over the next 3 years. Managers appeared to have been forward-looking in their hiring decisions with a “dip” in regular hiring and a large increase in ungraded hiring prior to the reform: between 1987 and 1998, the fraction of all employees who are ungraded rises from 11.9 percent to 29.3 percent. Prior to the reform, the salary distribution among the ungraded is quite wide with a difference of over $72,000 (285%) between those in the bottom and top 10% in 1997. In our analysis, we always treat those who were ungraded prior to 1987 as separate and then re-analyze them as if they had no history with the WBG if we see them as converted in 1998. That is, we treat a new employee at the institution who enters (say) as GF in 1998 precisely the same as those who were converted from ungraded to GF. We have checked, in sensitivity analysis, whether this is a valid assumption and can confirm that the experiences of these staff are no different from those of new entrants at that grade in 1998.

> 19 There are small differences in the year-to-year changes depending on how we treat the ungraded staff prior to the 1998 reform. Here we exclude the ungraded staff from the figure, which implicitly assumes the fraction of professional to support staff in the ungraded and graded staff prior to the 1998 reform was the same.

> 20 The data do not allow us to identify managers prior to 2000.

11
# **Figure 2: Grade Composition at the WBG between 1987 and 2015**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>GA-GD GE-GH GE-GH Non-Managers GE-GH Managers GI+<br>Share of Employees (%)<br><!-- End of picture text --> A second characterization recognizes that the composition of employees depends both on hiring and exits. Figure 3 plots annual exits from the institution as a percentage of regular staff, excluding staff exits due to mandatory retirement. Exits at the WBG cycle around an average of 9 percent a year.<sup>21</sup> Exits were higher in 1987 and 1988 and then declined to a low of 6 percent before rising again to a peak with a large institutional reform in 1998 followed by a subsequent trough of 6 percent in 2000. Since then exits increased again with an 11 percent exit rate in 2015, the last year of our data. One hypothesis consistent with these patterns is that institutional reforms such as those seen in 1998 and 2012-2013 accelerate the exits of those who would have left within 2-3 years. Consequently, exits peak in reform years (which are usually associated with new presidential terms) but then drop because reforms bring exits “forward”. A second hypothesis—more clearly seen in the first half of our data—is that exits track economic performance in the U.S., rising when the economy is strong. Regardless of the specific hypothesis, the 9% exit rate implies that 50% of staff leave the institution every 8 years. These high rates of attrition leave substantial room for interpreting the remaining salary gaps of those who choose to remain at the

> 21 Of independent interest, higher exits are not associated with higher age at exit with a mean age at exit of 43.5 years

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progressions over the span of our data, in contrast to lower grades, which is similar to trends in senior management pay observed in the United States over the period.

**Table 1: Summary of Salaries by Grade**

|||||**Fisc**|**al Year**|||
|---|---|---|---|---|---|---|---|
|**Grade**||1987-90|1991-95|1996-00|2001-05|2006-10|2011-15|
|**GA**|mean salary (GA in 1987 = 100)|100|103|96|100|101|99|
||Number of staff|122|62|72|74|34|8|
||p90-p10 % difference|36%|27%|30%|29%|26%|13%|
|**GB**|mean salary (GA in 1987 = 100)|112|116|116|118|121|119|
||Number of staff|2600|3099|1828|1068|381|253|
||p90-p10 % difference within grade|32%|27%|25%|23%|26%|26%|
|**GC**|mean salary (GA in 1987 = 100)|141|150|148|149|154|153|
||Number of staff|4,859|6,212|6,159|5,156|4155|3718|
||p90-p10 % difference within grade|41%|38%|38%|33%|33%|36%|
|**GD**|mean salary (GA in 1987 = 100)|171|185|185|182|188|189|
||Number of staff|1093|2088|2478|3642|3506|3084|
||p90-p10 % difference within grade|36%|33%|38%|39%|36%|36%|
|**GE**|mean salary (GA in 1987 = 100)|193|218|217|214|219|220|
||Number of staff|2618|2719|2917|4000|3917|4007|
||p90-p10 % difference within grade|37%|32%|40%|38%|37%|33%|
|**GF**|mean salary (GA in 1987 = 100)|256|275|273|278|282|284|
||Number of staff|1822|2944|3522|6,017|7,084|8,615|
||p90-p10 % difference within grade|33%|23%|26%|29%|28%|28%|
|**GG**|mean salary (GA in 1987 = 100)|362|378|372|377|389|390|
||Number of staff|7,644|10,746|10,323|9,973|11,813|13,842|
||p90-p10 % difference within grade|43%|42%|39%|32%|33%|36%|
|**GH**|mean salary (GA in 1987 = 100)|457|492|484|501|534|539|
||Number of staff|2901|4428|5407|6,161|6,741|8,008|
||p90-p10 % difference within grade|24%|26%|33%|31%|31%|35%|
|**GI**|mean salary (GA in 1987 = 100)|547|610|610|644|698|712|
||Number of staff|548|743|920|1168|1160|1224|
||p90-p10 % difference within grade|20%|17%|21%|22%|22%|26%|
|**GJ**|mean salary (GA in 1987 = 100)|619|706|704|782|851|877|
||Number of staff|69|100|160|188|152|163|
||p90-p10 % difference within grade|23%|9%|16%|19%|17%|17%|

Figure 4 shows the evolution of mean salaries over time for different grades at the WBG more clearly, normalizing each salary to a base of 100 in 1987. Like in Table 1, mean real salaries have increased more for grades GI, GJ and GK compared to GF and GG staff. Annual real salaries increased by 3/10th of 1 14
percent between 1987 and 2014 among GB-GD and GG staff, 7/10th of 1 percent for GE and GH level staff and 1.1 to 1.6 percent for GI-GK level staff.<sup>24</sup>

**Figure 4: Salary Trends for Staff at the WBG, 1987-2015**

<!-- Start of picture text --> 160<br>150<br>GB<br>140<br>GC<br>GD<br>GE<br>130<br>GF<br>GG<br>GH<br>120<br>GI<br>GJ<br>GK<br>110<br>100<br>1987 1990 1995 2000 2005 2010 2014<br>Average Real Salary (1987=100)<br><!-- End of picture text --> Each of these “macro-changes” over the period of our data can affect the salary gap across subgroups. For instance, the decline in GA-GD level staff, who are predominantly women in jobs with lower salaries implies that the average salary of women relative to men will rise in the institution. Similarly, differences in the profile of staff leaving the WBG will affect the salaries of those who remain. Finally, differential increases in salaries for different grades can affect both aggregate and career gaps. First, as GA-GD staff tend to be women, their lower salary growth over time will imply that the aggregate gap will also increase. Second, staff are promoted over time. If men are promoted faster to GH (for instance) relative to women and GH salaries are growing faster, this will again induce an increase in both the aggregate and career

> 24 These are salaries of all staff in a given grade, and thus include tenure effects; with zero turnover, salaries will grow with experience. A second option, in Appendix Figure 2 focuses just on entry-level salaries for different grades over this time period. Like in Figure 4, with the exception of GI+ staff, real entry salaries have increased slowly over this period with the largest increases of 15-20% for GH level staff.

15
gaps over time.<sup>25</sup> Taken as aggregates, there is considerable room both in the composition of employees and how they are compensated for differences by subgroups to arise. We turn to this next.

# III. Descriptive Findings

In 1987, the mean salary of a female staff member at the WBG was 52% that of a male staffer. By 2015, this had increased to 77%. The male-female difference can be further separated by country-part and a clear ordering emerges with the highest salaries for Part 1 males throughout the period of our data (Figure 5). The salaries of Part 2 males are lower, but the large differences are between men and women, with salaries of both Part 1 and Part 2 females significantly below those of males. Among females, salaries of Part 1 are again higher than those of Part 2 employees throughout the entire data period.

**Figure 5: The Aggregate Gap: Mean Salaries by Subgroups over Time**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>Women: Part 2 Women: Part 1 Men: Part 2 Men: Part 1<br>Salary differences relative to Men Part 1 (%)<br><!-- End of picture text --> In Section IV, we develop a dynamic accounting framework to decompose this gap into its respective components---differences in staff composition (both historical and present); differences in entry salaries; differences in salary growth over time and; differences in attrition. Here we treat each of these individually to understand how they could contribute to the aggregate and career gaps at the institution.

> 25 It would be useful to understand these changes in the context of changes in other institutions, but this is easier said than done. The WBG does have some claim to exceptionality and includes some of the best trained and educated staffers in any institution from multiple nations in the same location. In addition, data from private firms are usually not available. Finally, the IADB and IMF have not assembled their HR data in a similar fashion.

16
<!-- Start of picture text --> A A<br>A<br>A (A rs<br>y o eo<br>eo A =— ——<br>a a<br>A<br>A<br>. od<br>° o<br>Sd<br>4<br><!-- End of picture text -->

## o

# A ~~=—— ——~~

These graphs show the proportion of women (respectively Part 2 employees) among employees hired at each given grade over time. Gender equity has improved significantly as the fractions of men and women hired at each grade have converged over time. However, women are still hired into lower grades, for instance, 78.1% of GA-GD hires are female whereas 62.3% of GG hires are male. Hires at GE and above are only 40.4% Part 2 employees, while hires at GA-GD are 62.5% Part 2. There is no pattern of convergence toward parity over time in the shares of Part 1 and Part 2 hired at different grades.
It is worth emphasizing that in most grades, hiring from all subgroups was so low that sufficient samples for analysis at the grade-level are hard to obtain.<sup>26</sup> Figure 7 shows hiring in each year for Grades GB and GH; among grades GA to GD, GB has the _highest_ number of male hires and among grades GH+, GH has the highest number of female hires over the period of our data. Even here, the number of male hires (GB) and female hires (GH) is small, exceeding 10 hires in only a couple of years (GB) and never more than 10 for GH. There are several years where fewer than 2 GB males or 2 GH males are hired. Figure 7 also shows new GF and GG hires. Again, there is a female disadvantage (more so for GG) but the gap narrows over the period of our data and the number of hires across males and females is now sufficiently large that we can recover a sizeable sample by aggregating a small number of years into a single cohort analysis.

For the decomposition exercise, the stock of employees at different grades reflects both pipeline effects and new hiring. For example, suppose an employee can rise to GH only after 12-15 years of service at the Bank if starting as a GF and 5-7 years if starting as GG. In that case, if GHs are predominantly promoted within the institution, they will be naturally constrained by the number of GFs and GGs 7-15 years in the past. But in 2000, there were 1,112 female GFs and GGs, relative to just under 2,000 male GFs and GGs. Although a crude measure of the pipeline (where tenure effects, exits and grade-year interactions will all become relevant), the example emphasizes that that there is a long lead time between policy changes and current staffing patterns. These lead times will be longer at the higher grades and will be longer if promotions rather than external hires form the bulk of employees at these higher grades.

> 26 There were 260 male GF’s, 1634 male GGs and 673 male GH’s in 1987 relative to 177 (GF), 285 (GG) and 25 (GH) females. In 1987, there were 684 female GBs, 1074 GCs and 190 GDs compared to 84 (GB), 114 (GC) and 52 (GD) for men. By 2015, the number of female GBs and GCs had reduced to 26 and 587 while GDs had increased to 469. And there were only 12 male GBs, 101 male GCs and 84 male GDs.

18
# **Figure 7: Number of Female and Male New Hires in Grades GB, GH and GF, GG: 1988-2015**

<!-- Start of picture text --> 200<br>180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Male Female<br>No. of People Hired<br>1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> <!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Male Female<br>250<br>200<br>150<br>100<br>50<br>0<br>19<br>Male Female<br>No. of People Hired<br>1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>No. of People Hired<br>1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> <!-- Start of picture text --> 200<br>180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Male Female<br>No. of People Hired<br>1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->
# 2. Differences in Salaries

In contrast to the large differences in hiring across grades, salary differences by sex and country part are smaller in the data. Two figures demonstrate this. Figure 8 presents entry salaries for Part 1 women as well as Part 2 employees compared to Men Part 1 employees.<sup>27</sup> To simplify the comparison we de-trend the Part 1 male entry salary as the constant blue-line, and compare other groups to this salary. The basic pattern, abstracting from fluctuations over time that arise due to the small number of hires in specific grade-year combinations, is that entry salaries have _always_ been fairly equal at the WBG. With the exception of GG hires where women and Part 2 employees enter at a salary deficit of $2,000 to $5,000, there is little evidence of systematic gaps in entry salaries over the period of our data. This is a sharp contrast with a number of findings from the U.S. literature demonstrating significant gaps in entry salaries for women compared to men.

**Figure 8: Entry Salaries by Grade and Year of Entry, Selected Grades, 1987-2015, Expressed as Differences with the Men – Part 1 Average**

<!-- Start of picture text --> GA-GD Staff<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>1985 1990 1995 2000 2005 2010 2015<br>Men: Part 1 Men: Part 2 Women: Part 1 Women: Part 2<br>Salary differences relative to Men Part 1 ($'000)<br><!-- End of picture text -->

> 27 In the recent literature on gender and salaries, entry salaries have played an important role with building evidence that women are less likely to negotiate over salaries when they first take a job.

20
**Figure 8: Entry Salaries by Grade and Year of Entry, Selected Grades, 1987-2015, Expressed as Differences with the Men – Part 1 Average (cont’d)**

<!-- Start of picture text --> GE Staff<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>1985 1990 1995 2000 2005 2010 2015<br>Men: Part 1 Men: Part 2 Women: Part 1 Women: Part 2<br>GG Staff<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>-10<br>-12<br>-14<br>1985 1990 1995 2000 2005 2010 2015<br>Men: Part 1 Men: Part 2 Women: Part 1 Women: Part 2<br>Salary differences relative to Men Part 1 ($'000)<br>Salary differences relative to Men Part 1 ($'000)<br><!-- End of picture text --> <!-- Start of picture text --> GF Staff<br>5<br>4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>-4<br>-5<br>1985 1990 1995 2000 2005 2010 2015<br>Men: Part 1 Men: Part 2 Women: Part 1 Women: Part 2<br>Salary differences relative to Men Part 1 ($'000)<br><!-- End of picture text --> <!-- Start of picture text --> GH+ Staff<br><!-- End of picture text --> <!-- Start of picture text --> 80<br>60<br>40<br>20<br>0<br>-20<br>-40<br>-60<br>1985 1990 1995 2000 2005 2010 2015<br>Men: Part 1 Men: Part 2 Women: Part 1 Women: Part 2<br>21<br>Salary differences relative to Men Part 1 ($'000)<br><!-- End of picture text -->
Figure 9 shows salary growth for employees at the WBG. The horizontal axis in each figure shows the number of years that the staff has worked at the WBG, and we aggregate staff with the same number of tenure years, irrespective of the year in which they joined the institution.<sup>28</sup> As with entry salaries, Grades GC and GE appear to have little difference in salary growth over time. In contrast, Grades GB and GF show clear declines over time, reaching a $10,000 difference in annual salary after 20 years for GF employees. Grade GG starts off with a salary deficit for all groups relative to Part 1 males, also seen in the salary at entry, but there appears to be some catch-up over time. More generally, Figure 10 shows the existing salary gaps after 15 years of tenure at the WBG, highlighting the larger deficits among staff who entered as GB or GF, but not GC, GD, GE or GG.

**Figure 9: Salary Growth at the WBG for Selected Grades**

<!-- Start of picture text --> Staff Hired at GB Level (31.9% of Hires)<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>-4<br>-5<br>-6<br>-7<br>-8<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Years of Tenure<br>Men: Part 1 Men: Part 2 Women: Part 1 Women: Part 2<br>Salary difference relative to Men Part 1 ($'000)<br><!-- End of picture text -->

> 28 For instance, people who joined in 1990 and 1995 will have 10 years of tenure in 2000 and 2005. Therefore, the salary pertaining to (say) 10 years of tenure is the average salary of those who joined in 1990, but observed in 2000 and those who joined in 1995, but observed in 2005.

22
**Figure 9: Salary Growth at the WBG f or Selected Grades (cont’d)**

<!-- Start of picture text --> Staff Hired at GE Level (4.8% of Hires)<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Years of Tenure<br>Men: Part 1 Men: Part 2 Women: Part 1 Women: Part 2<br>Salary difference relative to Men Part 1 ($'000)<br><!-- End of picture text --> <!-- Start of picture text --> Staff Hired at GC Level (2.2% of Hires)<br>20<br>15<br>10<br>5<br>0<br>-5<br>-10<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Years of Tenure<br>Men: Part 1 Men: Part 2 Women: Part 1 Women: Part 2<br>Staff Hired at GF Level (23.5% of Hires)<br>0<br>-1<br>-2<br>-3<br>-4<br>-5<br>-6<br>-7<br>-8<br>-9<br>-10<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Years of Tenure<br>Men: Part 1 Men: Part 2 Women: Part 1 Women: Part 2<br>Salary difference relative to Men Part 1 ($'000)<br>Salary difference relative to Men Part 1 ($'000)<br><!-- End of picture text --> <!-- Start of picture text --> Staff Hired at GG Level (33.3% of Hires)<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20<br>Years of Tenure<br>Men: Part 1 Men: Part 2 Women: Part 1 Women: Part 2<br>Salary difference relative to Men Part 1 ($'000)<br><!-- End of picture text --> Men: Part 1 Men: Part 2 Women: Part 1 Women: Part 2 23
<!-- Start of picture text --> ——— __..)<br>a<br>_—E<br>in<br>= #&£8§8§=~—<br><!-- End of picture text -->
# 3. Differences in Exits

Figure 11 shows the fraction of staff at each grade who were still at the WBG 15 years after they joined across men and women and Part 1 and Part 2 employees. The joining years here are 1988-2000. Reflecting the exit rates that we documented previously, less than 60 percent of staff remain at the WBG 15 years or longer. Several other patterns are noteworthy. First, among Part 1 employees, the fraction of employees who remain this long is never higher than 50 percent and is substantially lower for Grades GA, GB and GG. Second, Part 2 employees remain at the WG longer than Part 1 employees, a difference that is particularly pronounced among the GA-GD level staff, but also clear among starting GE and GF staff. Finally, among Part 2 staff, in most grades women are less likely to leave than men. Among Part 1 staff, the differences vary by grades.

**Figure 11: Fraction of Staff Who Remain at the WBG after 15 Years**

<!-- Start of picture text --> 70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>GA GB GC GD GE GF GG<br>Male - Part I Male - Part II Female - Part I Female - Part II<br><!-- End of picture text --> Figure 12 and Table 2 show how these exit rates affect the interpretation of the career gap (exit rates may differ from those in Figure 11, since the last joining date for these data are 1995, rather than 2000). Here, we follow GF entrants into the WBG, hired between 1988 and 1996, with “cohort snapshots” after 5, 10, 15 and 20 years. For each of these “cohort snapshots”, we first provide exit rates (Figure 12) and then career trajectories (Table 2) for Part 1 men, Part 2 men, Part 1 women and Part 2 women separately. Figure 12 highlights three patterns for this GF entry cohort. First, exit rates rise steadily through the years, reaching between 35 and 50% for staff with 15 years of experience at the WBG. At 20 years, close to 60 25
percent of men and 65 percent of women have left the institution. Second, till 10 years, men tend to leave the WBG at faster rates than women, but after 10 years this pattern reverses, which may be linked to increasing demand for household care responsibilities. Third, for all the snapshots, Part 1 staff are more likely to leave than Part 2 staff, with differences that are more pronounced till 15 years after joining the WBG.

**Figure 12: The Experience of the GF Cohort (1988-1996)**

<!-- Start of picture text --> 20 yrs.<br>REMAIN<br><!-- End of picture text --> <!-- Start of picture text --> 15 yrs.<br><!-- End of picture text --> <!-- Start of picture text --> 10 yrs.<br><!-- End of picture text --> <!-- Start of picture text --> 10 yrs.<br>5 yrs.<br>LEAVE<br><!-- End of picture text --> Given these exit rates, without strong assumptions on selection we lack certitude both on the direction and the magnitude of career gaps across subgroups. If we were to follow a bounds approach, we could choose to assign the 27% of Part 1 men who have left the WBG at 5 years to the highest grade possible while distributing the remaining subgroups according to the proportions in the data among those who remained. This would maximize the advantage for Part 1 men. Alternatively, we could assign this 27% to GF, which would _minimize_ the advantage among Part 1 men. Although both are unlikely, there is nothing in the data that would invalidate this assumption. But such `assumption free’ allocations will fundamentally change the career gaps we observe, and it is quite obvious that the further we move out from the entry date the less informative these bounds would become. Depending on how we allocate grades among those who have left will lead us to virtually any conclusion that we wish to draw. The point here is not to support or reject these assignments, but to claim that without substantial information on the leavers, even with a 5-year career trajectory, the results are consistent with multiple interpretations.

26
Having said that, as an alternative to the bounding approach we can choose to adopt more stringent assumptions on the composition of those who leave and those who remain at the Bank. These assumptions are necessarily untested, since we can never know what the career trajectory of those who left the Bank would have been had they chosen to stay, but we can examine whether in their _past_ performance the leavers and stayers looked very different. Appendix Table 2 shows the relative salaries and mean performance ratings of stayers and leavers at different parts of the tenure profile. Strikingly, we find few differences in their salaries at the point they left, a finding that is consistent with the hypothesis that leavers are a combination of high and low performers, who are either `pulled-away’ or `pushed-out’. In the decomposition exercise that follows we will assume therefore that conditional on their salary before leaving, leavers are identical to stayers. This assumption makes more sense if performance is a “type of person”, but not if performance is “effort on the job”. If people leave in anticipation of future shocks to their productivity, this is a particularly poor assumption, but one that will plague any attempt to analyze performance within a single firm.

Table 2 then shows the career trajectories of those who chose to remain. Rather than focus on salaries, we examine how the original GF cohort is subsequently promoted to GG, GH and higher as grades and salaries are tightly tied at the WBG. Several patterns are immediately obvious.

**Table 2:  The Experience of the GF Cohort (1988-1996)**

|**Duration**|**Type**|**Remain**|**GF**|**GG**|**GH**|**GI+**|
|---|---|---|---|---|---|---|
|**5-years**|Men P1|73|15|55|2|0|
|**5-years**|Men P2|82|21|59|2|0|
|**5-years**|Female P1|78|19|58|1|0|
|**5-years**|Female P2|84|31|52|1|0|
|**10-years**|Men P1|58|7|31|19|1|
|**10-years**|Men P2|66|6|38|23|0|
|**10-years**|Female P1|57|4|35|17|1|
|**10-years**|Female P2|64|10|36|17|0|
|**15-years**|Men P1|54|3|18|26|6|
|**15-years**|Men P2|56|3|18|32|4|
|**15-years**|Female P1|45|2|20|21|3|
|**15-years**|Female P2|48|2|16|29|1|
|**20-years**|Men P1|41|1|10|21|10|
|**20-years**|Men P2|44|1|10|27|7|
|**20-years**|Female P1|35|1|10|18|6|
|**20-years**|Female P2|36|1|8|21|7|

27
First, the answer to which group is being promoted faster (leaving aside the question of how to assign counterfactual grades to those who have left) depends on which snapshot we look at. Although Part 2 men perform the best at each cohort snapshot, with 5 years tenure, Part 1 women are very close behind followed by Part 1 men and Part 2 women. After 10 years, Part 1 and Part 2 women look quite similar in terms of promotions to GG and GH with Part 1 performing the worst. However, if we look at the fraction still remaining in their original grade, GF, Part 2 women appear to be performing the worst, followed by Part 1 and Part 2 _men_ with Part 1 women performing best. After 20 years, Part 2 men are still doing the best with the highest promotions to GH and GI (31 percent of the original cohort) with Part 1 men close behind followed by Part 1 and Part 2 women.

### _Summary_

In terms of employee composition, there were key historical differences between men and women and Part 1 and Part 2 employees at the WBG in terms of the jobs that they were hired into. In 1987, women were hired into grades GA-GD and men into GE+. Over time, the representation of women and Part 2 nationals into GE+ grades has increased, although, even in 2014, as a fraction of all GE+ staff, they are still below 50 percent in new hiring. In terms of entry salaries, we find systematic and continuing differences in grade GG and this deficit is approximately $2,000-5,000 among new hires from 2010-2015. When it comes to salary growth, there is evidence that even though they enter at similar salaries, the return to tenure is lower for staff who are not Part 1 males and enter as GF (but not for GG or GE cohorts) and this deficit increases to $4,000-5,000 after 15 years. In addition to these differences, there are aggregate changes at the institution over the period of our data, whereby entry salaries have increased more for higher grades, GA-GD hiring has declined and exit rates have fluctuated in periodic cycles. In what proportion each of these contributes to the aggregate and career gaps cannot be addressed by examining each of these components separately. We therefore turn to a dynamic decomposition approach.

# IV: Decomposition of Salary Gaps

## 1. Overview of the Methodology

Our decomposition is based on the following accounting relationship. One can compute the distribution of salaries in a given year _y_ by combining four pieces of information: (i) the salaries of incumbent employees in year _y-1_ , (ii) the salary raises received by these incumbent employees, (iii) the entry salaries received by new hires, (iv) the distribution of salaries among staff who chose to leave the organization in that year.

28
An important consideration in applying this decomposition concept to the WBG salary data is that employees are hired at a specific grade. This suggests that our “entry salaries” component should be subdivided into the grades at which men and women are hired, and the entry salaries they receive _conditional on their hiring grade._ This leaves us with five components: attrition, salary growth, entry salaries, grade composition of hires and legacy.

Appendix 2 describes in detail how the decomposition is implemented using simulations. In brief, the procedure involves the following steps. First we estimate from our data simple parametric models for each decomposition component, in each year and for each group of employee that we are concerned with. For example, we estimate the probability of leaving the bank in 1995 for a Part 1 male employee hired as a GF, as a function of his current salary. Next, taking as given the initial 1987 distribution of salaries, we use the estimated models to simulate the salary raise that each employee receives in each year, whether they leave the WBG in each year, as well as the salaries of new hires, all the way to 2015. After verifying that the simulations for 2015 reproduce accurately the 2015 data, we can use the simulation model to produce counterfactual salary distributions in which, for example, the parameters governing the salary growth for women are replaced by the parameters governing salary growth for men.

The procedure described above decomposes the difference in salaries between two groups of employees in a given year, which we have called the “aggregate gap”. However, the same method can also be adapted to conduct an analysis by tenure. The object of interest in that case is salary gaps as they develop over the employee's careers, which we call the “career gap”. To do so, the data are arranged by years of tenure instead of by calendar year. Hiring salary, attrition, and salary growth processes are also estimated for each year of tenure. The simulation algorithm starts by drawing a distribution of entry salaries and proceeds to simulate salary growth and exits for each year of tenure.

This decomposition method has several strengths. First, the factors identified in the decomposition correspond to well-defined Human Resources levers. Namely, the policies concerning raises and promotions, the policies regarding new hires and the policies regarding retention of employees. An important point regarding the interpretation of the results, is that this decomposition is not concerned with determining why each of these policies have favored men over women or some nationalities over others. For example, if male hires receive higher salaries than female hires, we do not attempt to separate whether this is due to discrimination or to objective differences in qualifications among male and female candidates. Instead, the type of statement that we can make is for example: “80 percent of salary 30
differences between men and women originate from differences in the salaries negotiated upon joining the WBG.” A second advantage of this method is that it accounts for the role of attrition, which, as documented earlier, is sizeable. This is because the data allow us to observe employees who have left the Bank and infer what current salaries would look like if they had stayed in the institution. Note that this is done at the cost of the assumption that they would have received the same salaries on average as the employees of the same group, entry grade and cohort who ended up staying _and who had the same salary level as them at the time of their departure._ In addition, the decomposition can handle the pervasive nonstationary or cohort-specific trends exhibited by salaries and employee composition in the data, as documented in the earlier sections of this report.

The following sections describe the results of our decomposition of the aggregate gender gap, the aggregate country part gap, the career gender gap and the career country part gap.

# 2. Decomposition Results: The Aggregate Gender Gap

The aggregate gender gap is defined as the difference between the average salaries of male and female employees in 2015. The quality of the simulation fit is presented in Figure 14. Both the aggregate gender gap and the aggregate country part gap for all grades in 2015 is well approximated by the simulations (bars 1 and 2 and bars 5 and 6). We also implement the decomposition separately for hiring grades GE and higher. That conditional gap is also well approximated in the simulations (bars 3 and 4 and bars 7 and 8 in Figure 14). We will also decompose the career gap further below into similar components; the final set of four bars shows the simulation fit for the career gap, by gender and by country part.

**Figure 14: Actual vs Simulated Salary Gaps**

<!-- Start of picture text --> 30 27.426.9<br>25<br>20<br>15.6<br>14.614.1 14.8<br>15<br>10 7.5 8.5<br>5.1<br>5 3.9 3.5 2.6<br>0<br>All Grades GE+ All Grades GE+ Gender Country part<br>Aggregate Gender Gap Aggregate Country Part Gap GF Career Gap<br>Total Gap Simulated Gap<br>Salary Gaps ('$'000)<br><!-- End of picture text --> 31
Figure 15 shows how the decomposition of the aggregate gender gap is obtained by the methodology described in the previous section. Equating attrition probabilities across men and women closes 0.6 percent of the gap. Further equating salary growth closes an additional 5.4 percent and equating entry salaries accounts for another 7.3 percent. These three factors explain together only 13.3 percent of the gap. 76.2 percent of the gap corresponds to hiring grade composition effects whereby women are disproportionately hired at lower grades. The remainder of the gap (10.5 percent) can be attributed to differences between men and women hired before 1987 (the ``initial'' distribution).

For grades GE and higher, the decomposition factors are shown in Figure 15, right graph. The composition of hires remains the most important factor to explain the gender pay gap. This is not surprising when one considers, as we have noted in previous sections, that the WBG hired many more men than women at the higher grades such as GG or GH throughout the period. Note that differential attrition has the effect of bringing male and female salaries closer together, i.e. it contributes negatively to the gender gap.

**Figure 15: Decomposition of the Aggregate Gender Gaps**

<!-- Start of picture text --> 80 76.2 76.9<br>70<br>60<br>50<br>40<br>30<br>20 16.5<br>10.5 9.8<br>7.3 7.4<br>10 5.4<br>0.6<br>0<br>All staff (27.4k) GE+ staff (14.6k)<br>-10<br>-9.7<br>-20<br>Attrition Salary Growth Entry Salary Grade Composition Pre-1987<br>Contribution to the Gap (%)<br><!-- End of picture text -->

# 3. Decomposition Results: The Aggregate Country Part Gap

The results of the decomposition of the pay gaps between Part 1 and Part 2 employees are presented in Figure 16. Equating attrition probabilities across Part 1 and Part 2 employees closes 14.5 percent of the gap. Further equating salary growth closes an additional 6.2 percent and equating entry salaries accounts for another 10.5 percent. These three factors explain together 31.2 percent of the gap. The bulk of the 32
gap (60.7 percent) again corresponds to the fact that Part 2 employees are on average hired at lower grades. The remainder of the gap can be attributed to differences predating 1987. Within the GE+ category (Figure 16, right graph), the composition of hires again explains the majority (52 percent) of the gap.

**Figure 16: Decomposition of Aggregate Gaps by Country Grouping**

<!-- Start of picture text --> 70<br>60.7<br>60<br>52<br>50<br>40<br>30<br>20 16.9<br>14.5<br>12.5<br>10.5 10.7<br>8.1 7.9<br>10 6.2<br>0<br>All staff (14.8k) GE+ staff (7.5k)<br>Attrition Salary Growth Entry Salary Grade Composition Pre-1987<br>Contribution to the Gap (%)<br><!-- End of picture text -->

# 4. Decomposition Results:  The GF Career Gap

We now decompose the pay gaps as they develop over the course of a career. We simulate the salaries of employees hired between 1987 and 2001 onwards for 15 years. The choice of 15 years reflects a tradeoff between capturing career dynamics at higher grades, which necessitates a long span, and obtaining results that are relevant to a large fraction of employees, rather than the small subgroup that accumulates 20 years of tenure at the WBG or more. As we discuss in section III.2, most hiring grades are not amenable to this decomposition, either because the salary gaps are too small, or because there are not enough individuals of each subgroup hired at that grade. We thus focus on GF hires, a group that includes large enough gender-by-country part subsamples and exhibits sizeable pay gaps among them. Note that since grade GF is one of the most common entry grades, we still cover 37.1 percent of all employees in grades GE+ despite this restriction.

To examine the gender career gap, we first compare male Part 1 and female Part 1 employees hired at the WBG headquarters at grade GF between 1987 and 2001. As seen in Figure 17, left graph, this gap is mostly due to differences in salary growth (84.5 percent), while attrition, salary growth and entry salaries 33
conditional on grade explain only 15.5 percent of it. A similar story emerges when comparing male Part 1 employees with male Part 2 employees. In this case (Figure 17, right graph), salary growth more than explains the total gap (123 percent), and differential attrition works in the opposite direction.

**Figure 17: Decomposition of the Career Gaps**

<!-- Start of picture text --> 140<br>123.1<br>120<br>100<br>84.5<br>80<br>60<br>40<br>20 10.3 11.5<br>5.2<br>0 0 0 0<br>0<br>Male Part 1 vs. Female Part 1 (5.1k) Male Part 1 vs. Male Part 2 (3.5k)<br>-20<br>-40<br>-34.6<br>-60<br>Attrition Salary Growth Entry Salary Grade Composition Pre-1987<br>Contribution to the Gap (%)<br><!-- End of picture text -->

# 5. Discussion of the Results

The main insight from the decomposition exercise is that in terms of the aggregate gap, the historical and continuing differences in entry grades are the major contributors. Career gaps arise sporadically across grades and GF is the only entry grade with sufficient sample and sufficient salary gaps after 15 years for a meaningful decomposition exercise. These results are at odds with the prior results of Filmer et al. (2005) and with a prevailing view in the institution that there is considerable difference in the pay and promotion by sex and nationality at the WBG.

We therefore conclude our analysis with a brief discussion of two further issues. First, we reconcile the prior results of Filmer et al. (2005) with our findings. Second, we assess whether equity across subgroups comes at the “cost” of rewarding performance. That is, if salary increases are arbitrary, there is little reason for differences to arise across subgroups. Whether the WBG _both_ rewards performance _and_ limits differences by sex and nationality or whether it limits differences by subgroups by _limiting_ rewards to performance is an important issue in its own right.

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To reconcile our results with Filmer et al. (2005), Table 3 replicates their gender gaps, noting that these numbers do not account for differences in the entry grades of employees. We consider the 1997 crosssection in our sample and apply the same sample restrictions, to the extent that we could identify them. While our samples do not perfectly match, we obtain very similar gender and country part gaps, with large differences in salaries in favor of Part 1 men and against Part 1 women and all Part 2 employees. We then look at the subsample of staff hired after 1987, for whom we are able to identify the grade at which they were hired. The pay gaps are a bit smaller (probably because they are from younger cohorts) but still large. We then consider subgroups that are homogeneous with respect to their entry grade and these differences become much smaller and sometimes disappear altogether. In other words, our results are fully compatible with those of Filmer et al. (2005) and suggest again that differences in entry grades are a key component of differences in salaries between subgroups at the WBG.

**Table 3: Comparison to the Salary Gaps Obtained by Filmer Et Al. (2005)**

|||**Salary in 1997**|**Entry Salary**|**Tenure in 1997**|**N**|
|---|---|---|---|---|---|
|**Filmer et al.**|**Male - Part I**|100|100|11.9|1356|
|**(2005), table 1**|**Male - Part II**|95.3|91.4|12.5|857|
||**Female - Part I**|86.6|86.4|10.7|537|
||**Female - Part II**|82.5|80.6|11.2|250|
|**Replication:**|**Male - Part I**|100|N/A|11|1745|
|**Employees hired at**|**Male - Part II**|94.3|N/A|11.6|1143|
|**GE+ grades**|**Female - Part I**|84.1|N/A|10.6|752|
||**Female - Part II**|74.7|N/A|11.8|440|
|**Employees**<br>**hired**|**Male - Part I**|100|N/A|4.7|886|
|**after 1987 at GE+**|**Male - Part II**|92.5|N/A|5.3|516|
|**grades**|**Female - Part I**|88.8|N/A|4.3|405|
||**Female - Part II**|80.6|N/A|4.4|200|
|**Employees hired at**|**Male - Part I**|100|100|4.5|264|
|**GF after 1987**|**Male - Part II**|100.6|98.3|5.2|209|
||**Female - Part I**|99.1|101.5|4.6|190|
||**Female - Part II**|96.3|94.2|4.3|112|
|**Employees hired at**|**Male - Part I**|100|100|5|546|
|**GG after 1987**|**Male - Part II**|97.6|94|5.4|254|
||**Female - Part I**|93.5|94.5|3.8|157|

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|||**Salary in 1**|**997**<br>**Entry Salary**|**Tenure in 1997**|**N**|
|---|---|---|---|---|---|
||**Female - Part II**|92.5|90.4|3.9|49|
|**Employees hired as**|**Male - Part I**|100|100|5|117|
|**YP after 1987**|**Male - Part II**|96.6|100.1|5.6|74|
||**Female - Part I**|90|97.1|4.4|83|
||**Female - Part II**|90.6|89.3|4.7|46|

We then examine the link between pay and performance. In 2001, performance ratings were introduced and staff were graded on a scale of 1 to 5. The system was strengthened in 2005 and again in 2010 to increase the payoff to higher ratings. Nevertheless, a common belief in the institution is that specific management practices may limit the ability of the WBG to reward high performers. One such practice is that staff should not receive ratings of 5 in two consecutive years and they should receive a 3 in the year that they are promoted.

If we assume that such ratings accurately reflect performance, we can use the performance data available from 2001 onwards in order to look at the link between compensation and performance. Between 2001 and 2015, with some changes, SRIs in our data are graded from 1 to 5, with 60 percent of staff in any given year falling in the average grade of 3, and 40 percent in grades 4 and 5. Grades 1 and 2 are for particularly poor performance, but are seldom used over the period of our data, accounting for less than 0.5 percent of all performance records.

**Table 4: Transition Fractions for SRIs in 2 Continuous Years**

|||**Fraction wh**<br>|**o received a SRI**<br>|**of _____ the next**<br>|**year**<br>|
|---|---|---|---|---|---|
|**Of staff who received a**|**1**|**2**|**3**|**4**|**5**|
|**SRI of__ thisyear**||||||
|**2**|0.10%|3.90%|67.80%|21.80%|6.50%|
|**3**|0.00%|0.40%|72.50%|22.80%|4.30%|
|**4**|0.00%|0.00%|43.00%|43.20%|13.70%|
|**5**|0.00%|0.00%|30.30%|45.40%|24.20%|
|**Total**|0.00%|0.38%|60.00%|30.80%|8.81%|

Patterns in the data are consistent both with rewards to performance and a preference for equality. For instance, of all staff who received a 5 in a given year, 24 percent went on to receive a 5 the next year (Table 4). Similar patterns obtain for GA-GD staff, GE to GH staff and GI+ staff and if we restrict our data only to the 2010-2015 period when SRI budgets tightened (tables available on request). On the other 36
hand, as a staff member’s salary increases within the same grade, their subsequent salary increase slows down. For those who are above the “midpoint” of the salary band at their grade-level, high performance yields marked compensation increases only if the staff member is promoted. For instance, in our data there are two variables that show the salary raise in a given year for an employee—the raise that an employee would receive based on his/her SRI alone and the actual raise that takes into account the relative compensation of the staff at his/her grade level. In a regression context, the “midpoint” penalty implies that, after conditioning on grade, an increase in the SRI implies a 1.9 percentage point salary increase, but there is a 0.6 percentage point penalty for every $10,000 increase in the initial salary.

Similarly, large policies appear not to be reflected in performance ratings. For instance, in 2010, a significant change was introduced, whereby new hires would be given 3 year contracts with renewal based on performance. This could have incentivized performance, but we do not see higher SRIs on average for staff hired in 2011 and 2012. The SRI obtained over the first three years at the WBG remains the same for staff hired in 2008 (SRI=3.31), 2009 (SRI=3.31), 2010 (SRI=3.34), 2011 (SRI=3.32) and 2012 (SRI=3.25).

An alternate way to assess performance rewards is to examine the diversity in performance of cohorts over time. Take for instance the 2005-2015 period, where the SRI system has been in place for 10 years and consider the salary structures of those who were hired between 2000 and 2005. The worst 10 percent of performers among staff who entered as GF between 2000 and 2005 in a balanced panel over this period has an average SRI of 3.22; the _best_ 10 percent of performers has an average SRI of 4.30.

These differences in average SRIs led to large variation in pay (Table 5). By 2015, the best 10 percent saw their salary increase by 83% against 26% for the worst 10 percent. Nevertheless, these rewards are not seen as clearly through the entire distribution of performance. There is a clear penalty for staff in the lowest decile, and to a lesser extent, to deciles 2, 3 and 4, and a clear reward for those in the top decile with muted differentiation among staff in deciles 5 through 9. A hypothesis consistent with these data is that most of the difference in salaries is due to promotions rather than annual salary raises within the same grade. The fraction of employees promoted to GH or more within 10 years is lower than 17 percent for the first 4 deciles, between 30 and 40 percent for deciles 5 through 9, while 5 percent of employees in the top decile are already at grade GI by that time and 50 percent are GH.

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**Table 5: Salaries and Promotions by Mean Performance Ratings Aggregated over 10 Years (staff hired at GF in 2000-2005)**

||**Bottom**<br>**decile**|**2**<sup>**nd**</sup>|**3**<sup>**rd**</sup>|**4**<sup>**th**</sup>|**Middle**<br>**decile**|**6**<sup>**th**</sup>|**7**<sup>**th**</sup>|**8**<sup>**th**</sup>|**9**<sup>**th**</sup>|**Top**<br>**decile**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Start**<br>**Salary**|100|98|99|99|98|99|98|98|101|99|
|**10 year**<br>**salary**<br>**growth**|26%|38%|45%|49%|55%|58%|65%|68%|69%|83%|
|**%GG**<br>**after 10**<br>**yrs**|60|74|79|71|59|63|61|58|63|45|
|**%GH**<br>**after 10**<br>**yrs**|1|4|12|16|26|29|33|38|37|53|
|**%GI**<br>**after 10**<br>**yrs**|0|0|0|0|0|0|1|0|0|3|

# V. Conclusion

We have analyzed salary differences at the WBG between 1987 and 2015 in terms of differences by sex and by nationality. Our analysis begins with the idea that aggregate and career gaps can be examined as an interplay between composition and compensation effects and our decomposition results suggest that composition effects play the major role in explaining the aggregate gap in 2015. The career gap in general—and especially among GE+ staff where sample sizes are sufficient for meaningful comparisons— tends to be small at the WBG with entry salaries, salary growth and exits contributing in different proportions depending on the grade at which the employee was originally hired.

As a research paper, the objective is to establish basic facts that can inform discussion and debate at the WBG. One general theme that emerges from this line of work is that pay distributions today are linked in complex ways to historical hiring and compensation patterns. Conversely, policies today will have longrun effects that need to be carefully considered. For instance, attempts to increase the number of women at the GH level through greater hiring at the GF level and promotions would have to account for the fact that men tend to leave the institution at a faster rate than women and that overall exits are highly cyclical. Consequently, boosting hiring at the bottom of an exit-cycle can have very different long-term effects relative to additional hiring at the peak of the cycle. Similarly, one-time salary raises to staff who are below a benchmark would have to be evaluated both in terms of their effects on the balance between pay and performance as well as the design of the compensation system.

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Two specific issues stand out for further attention. First, compositional differences continue to affect the distribution of staff at the WBG. We do not know whether these differences emerge at the point of hiring (equally qualified men and women have applied, but men are chosen more often) or at the point of job applications (fewer qualified women apply relative to men). Data on job applicants are currently not available in the HR system and further, even for those who are successfully hired into the WBG, data on personal characteristics are incomplete. For instance, education levels measured as the highest degree is missing for 55 percent of staff in the data. If compositional differences arise at the application stage, a very different kind of policy would be required (such as an outreach program) relative to compositional differences that arise at the hiring stage given an equal application pool across all subgroups.

Second, exit rates from the institution are such that 50% of staff leave within 7-9 years. Little is known about why staff leave the institution and what jobs they receive outside the WBG. Further, managers may have flexibility to negotiate, but new tools may need to be leveraged better to retain staff who plan to leave. Our analysis revealed that those who leave and those who stay look very similar on their performance at the WBG prior to their exit. Therefore, the institution loses both high and low performing staff whenever the number of exits rises. Appropriate actions that can be taken to retain high-performing staff—especially women and Part 2 staff—may be a useful new area for policy or simply be an area that needs to be better managed within the institution.

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