Investments aligned with this Strategic Goal aim to prepare the current and future workforce for the rapid and evolving changes already taking place in economies and the work that people do. Such efforts include developing the skills needed to harness opportunities from new technologies, as well as those related to shifting to net-zero economies and low-carbon means of production and service delivery.
Investors interested in deploying this strategy should consider the scale of the addressable problem, what positive outcomes might be, and how important the change would be to the people (or planet) experiencing it.
Key questions in this dimension include:
Transformative changes affecting work include globalization, the emergence of the platform economy, climate change, rapid urbanization, remote work, and advances in automation and artificial intelligence (AI) (1). AI, automation, and robotics, among other technological advances, will create new jobs in certain sectors and occupations and replace humans’ jobs in others. Workers who perform more routine and lower-skilled tasks may stand at greatest risk of having their jobs automated. Moreover, economic and regulatory changes in light of climate change will likely leave workers in low-skilled, high-carbon industries behind. In general, the negative effects of these transformative trends will fall disproportionately on the most vulnerable.
The current workforce requires opportunities for lifelong learning and training, while new entrants to the workforce need educational systems to prepare and equip them with relevant job skills. A skilled workforce is a prerequisite to make the transition to a greener economy happen – but skills gaps are already recognized as a major bottleneck in a number of sectors, such as renewable energy, energy and resource efficiency. Global skills-mismatches across many countries and sectors have large costs for workers, employers, and societies. Studies have show that skills deficits have important consequences on job satisfaction and wages (21). Over-qualified workers, for example earn less than their equally-qualified and well-matched counterparts and are more likely to leave their job. For employers, skills mismatches reduce productivity and business growth. For society, skills mismatches could reduce the return on investments in education, raise the costs of providing unemployment benefits, and lower income tax revenues (22).
Skills that are relevant to both present and future labor-market needs can improve employability and allow people to find jobs that realize their potential, thereby contributing to individual well-being and societal cohesion (2). Human talent underpins our economic capacity to innovate, catalyzing economic growth.
Investments aiming to improve job skills for the future can:
Target stakeholders can then experience the following commonly sought outcomes, among others:
Note that investments that focus on youth or the school-to-work transition are covered by the Strategic Goal, ‘Improving the Successful Transition of Youth into the Workforce and Society,’ part of the Impact Theme, ‘Access to Quality Education.’
Digitization, automation, and advances in artificial intelligence are already causing changes that disrupt the nature of work. A wide variety of jobs—from blue-collar ‘manual’ jobs to workers in the knowledge economy and white-collar roles—are susceptible to replacement by automation (3). A recent study found that one quarter of U.S. jobs stands at high risk of automation (4). Globally, as many as 375 million workers, or about 14% of the global workforce, may soon need to switch occupational categories and will require retraining and reskilling (5).
Poorly managing transitions related to the future of work risks deepening existing labor market challenges. In developed countries, between 10% and 30% of workers are skills-mismatched—as are 30% to 70% of workers in developing countries (6).
Effects will vary by sector. In agriculture, a critical sector in developing economies employing the largest share of both women (28%) and men (29%), 58% of today’s workforce could be automated (7). Other sectors at high risk of automation include hotels and restaurants, wholesale and retail trade, and construction and manufacturing, all sectors that often form a core part of industrial strategy and plans for economic transformation in developing countries.
Investors interested in deploying this strategy should consider whom they want to target, as almost every strategy has a host of potential beneficiaries. While some investors may target women of color living in a particular rural area, others may set targets more broadly, e.g., women. Investors interested in targeting particular populations should focus on strategies that have been shown to benefit those populations.
Key questions in this dimension include:
Target stakeholders of investments aligned with this Strategic Goal are those who are less able to access the digital tools, knowledge, or resources to successfully upskill and retrain, or those who are already more vulnerable to disruption in employment.
Other stakeholders typically targeted through this Strategic Goal include the following.
Changes in the demand for labor and skills will be felt globally. A recent study by the Brookings Institution, for example, found that one-quarter of U.S. jobs stand at risk of automation (15). The effects of future-of-work transitions may be particularly acute in low- and middle-income countries that already tend to face greater inequalities and skills gaps. ILO evidence shows that robotization in middle-income countries has already led to a significant drop in employment (about 14%) between 2005 and 2014 (16). A study in Cambodia, Indonesia, the Philippines, Thailand, and Vietnam estimated that 56% of all employment in these countries is at high risk of displacement as a result of technology over the next decade (17).
Dimensions of Impact: CONTRIBUTION
Investors considering investing in a company or portfolio aligned with this strategy should consider whether the effect they want to have compares to what is likely to happen anyway. Is the investment's contribution ‘likely better’ or ‘likely worse’ than what is likely to occur anyway across What, How much and Who?
Key questions in this dimension include:
Organizations can consider contribution at two levels — at the enterprise level and at the investor level. At the enterprise level, contribution is “the extent to which the enterprise contributed to an outcome by considering what would have otherwise happened in absence of their activities (i.e. a counterfactual scenario).” To learn more about methods for assessing counterfactuals, see the Impact Management Project.
At the investor level, investments in projects that improve job skills for the future can contribute toward solutions as follows.
Dimensions of Impact: HOW MUCH
Investors deploying capital into investments aligned with this strategy should think about how significant the investment's effect might be. What is likely to be the change's breadth, depth, and duration?
Key questions in this dimension include:
Investments in line with this Strategic Goal can benefit the approximately 3.3 billion employed people around the world, as well as the roughly 200 million people who are unemployed (18). They can also benefit the millions of new entrants to the global labor force each year.
If retraining and skills development programs build on workers’ current skills, workers can adapt and develop their skills to enhance their relevance to the new industries in which new jobs will be found. The sets of skills that workers can re-use in growing industries include not only soft skills (such as communication and problem-solving) but also semi-technical transferable skills (such as sales and marketing, scheduling, and budgeting), as well as technical transferable skills (such as engineering, repair, and plumbing). Training in such transferable skills potentially has career-long benefits—but greatly depends on the type of support offered. The adoption and dissemination of clean technologies also requires skills in technology application, adaptation and maintenance.
One-off or short-term interventions may lead to no measurable impacts; effects must be tracked to measure career progression over time, including through the use of tracer studies. Also important is to differentiate among the types of gained skills and to evaluate whether they may be transferable beyond the individual (for example, to family members).
Key questions in this dimension include:
Impact risk factors for investments in line with this Strategic Goal include the following:
*For more information on this point, see the Strategic Goal “Improving the Successful Transition of Youth into the Workforce and Society” in the IRIS+ Education theme.
In general, these risks could reduce or eliminate the impact of sound investments, abandoning workers who need new skills to thrive in a future world. Resources invested by all stakeholders in different forms could be lost, hindering their redeployment in future investments.
MWS Technology pioneers technology solutions for vocational training, further education, and welfare-to-work in the UK. Their product, Aptem, is an online employability and vocational training platform with more than 20,000 users. For apprenticeships, for example, Aptem helps reduce the time and effort needed to complete paperwork typically by more than 60%, freeing up time for productive learning. MWS has received investment from 24Haymarket and the Triple Point Impact EIS Service.
Tamboro is an online platform that offers trainings to professional young people who want to obtain better job opportunities as a result of new technologies and globalization. The trainings contribute to the development of essential personal and professional skills. In 2018, they had 659 high school students, more than 56,000 university-enrolled users, and more than 18,000 users who were employed (19). Vox Capital, an investor in Tamboro, invests in businesses that use technology to positively transform society and the environment in Brazil.
Degreed raised USD 32 million (round led by Owl Ventures) to offer free skills training to employees. The company partners with (and charges) employers to offer online training to workers looking for new skills. It has seen a boom of activity from workers at 250 companies amid the pandemic, particularly for soft-skills training, like crisis and change management and mental health (20).
1
ILO Future of Work Commission, Work for a Brighter Future (Geneva: ILO, 2019)
2
“Investing in Europe’s Future - European Investment Bank," EIB, 2018. https://www.eib.org/attachments/efs/investing_in_europes_future_the_role_of_education_and_skills_en.pdf.
3
Alejandro de la Garza, “AI Is About to Spark a Radical Shift in White Collar Work. But There’s Still ‘Plenty of Work for People to Do,’” Time, January 23, 2020.
4
Mark Muro, Robert Maxim, and Jacob Whiton, Automation and Artificial Intelligence: How Machines Affect People and Places (Washington, DC: Brookings, January 2019).
5
James Manyika, Susan Lund, Michael Chui, Jacques Bughin, Jonathan Woetzel, Parul Batra, Ryan Ko, and Saurabh Sanghvi, Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation (San Fransisco: McKinsey Global Institute, December 2017).
6
“Home - ILOSTAT - The Leading Source of Labour Statistics.” ILOSTAT, 2013. https://ilostat.ilo.org/.
7
8
Valentina Beghini, Umberto Cattaneo, and Emanuela Pozzan, A Quantum Leap for Gender Equality: For a Better Future of Work for All (Geneva: ILO, March 2019).
9
Beghini et al., Quantum Leap.
10
Sonja Hofstetter and Bettina Jenny, “Gender and Vocational Skills Development” (Bern: Swiss Agency for Development and Cooperation SDC, October 2019).
11
OECD, Putting Faces to the Jobs at Risk of Automation, Policy Brief on the Future of Work (Paris: OECD Publishing, 2018).
12
International Labour Conference, Equality at Work: The Continuing Challenge (Geneva: ILO, November 2011).
13
Roger Gomis, Steven Kapsos, Stefan Kühn, and Hannah Liepmann, World Employment and Social Outlook: Trends 2020 (Geneva: ILO, January 2020).
14
Rita K. Almeida and Reyes Aterido, “Investment in Job Training: Why are SMEs lagging so much behind?” (Policy Research Working Paper No. 5358, World Bank, Human Development Network, Social Protection and Labor Unit, July 2010).
15
Muro et al., Automation and Artificial Intelligence.
16
Francesco Carbonero, Ekkehard Ernst, and Enzo Weber, “Robots Worldwide: The Impact of Automation on Employment and Trade” (ILO Research Department Working Paper No. 36, October 2018).
17
Jae-Hee Chang, Gary Rynhart, and Phu Huynh, ASEAN in Transformation: The Future of Jobs at Risk of Automation (Geneva: ILO, July 2016).
18
World Employment and Social Outlook – Trends 2019, 2019. https://www.ilo.org/global/research/global-reports/weso/2019/lang--en/index.htm.
19
Vox Capital Impact Report
20
Natasha Mascarenhas, “Degreed Lands New Cash for Upskilling in a Down Market,” Tech Crunch, June 16, 2020.
21
Quintini, Glenda. “Over-Qualified or Under-Skilled: A Review of Existing Literature, by Glenda Quintini.” OECD Social, Employment and Migration Working Papers. OECD Publishing, September 1, 2011. https://www.oecd-ilibrary.org/social-issues-migration-health/over-qualified-or-under-skilled_5kg58j9d7b6d-en;jsessionid=niSO0orbBqDiaC-CTA5_yaO3.ip-10-240-5-162.
22
"Skills and Jobs Mismatches in Low- and Middle-Income Countries." ILO, 2019. https://www.ilo.org/wcmsp5/groups/public/---ed_emp/documents/publication/wcms_726816.pdf
This mapped evidence shows what outcomes and impacts this strategy can have, based on academic and field research.
Kluve J, Puerto S, Robalino D, Romero J M, Rother F, Stöterau J, Weidenkaff
F, Witte M. Interventions to improve the labour market outcomes of youth: a
systematic review of training, entrepreneurship promotion, employment
services, and subsidized employment interventions
Campbell Systematic Reviews 2017:12
Marjorie Chinen, Thomas de Hoop, María Balarin, Lorena Alcázar, Josh Sennett and Julian Mezarina. Vocational And Business Training To Improve Women’s Labour Market Outcomes In Low- And Middle-Income Countries – A Systematic Review.
Bandiera, Oriana and Burgess, Robin and Das, Narayan and Gulesci, Selim and Rasul, Imran and Sulaiman, Munshi, Can Basic Entrepreneurship Transform the Economic Lives of the Poor?. IZA Discussion Paper No. 7386, Available at SSRN: https://ssrn.com/abstract=2266813
Almeida, Rita K. & de Faria, Marta Lince, 2014. “The Wage Returns to On-the-Job Training: Evidence from Matched Employer-Employee Data,” IZA Discussion Papers 8314, Institute of Labor Economics (IZA).
Mulas-Granados, Carlos and Varghese, Richard and Wallenstein, Judith and Boranova, Vizhdan and deChalendar, Alice, Automation, Skills and the Future of Work: What do Workers Think? (December 2019). IMF Working Paper No. 19/288, Available at SSRN: https://ssrn.com/abstract=3524309
World Bank. 2010. Stepping Up Skills for More Jobs and Higher Productivity. Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/27892 License: CC BY 3.0 IGO.”
Gail Irvine (Editor), What do we know about digitalisation, productivity and changing work? Carnegie UK Trust, RSA Future of Work Centre, RSA, 2020
Each resource is assigned a rating of rigor according to the NESTA Standards of Evidence.
This starter set of core metrics — chosen from the IRIS catalog with the input of impact investors who work in this area — indicate performance toward objectives within this strategy. They can help with setting targets, tracking performance, and managing toward success.
Number of permanent employees who were promoted within the organization during the reporting period
N/A
Organizations should footnote all assumptions used.
This metric is intended to capture the number of unique individuals promoted by the organization in full- or part-time roles at the point in time defined by the reporting end date. This metric excludes Temporary Employees (OI9028). Organizations are encouraged to note what roles and levels employees promoted began and ended the reported period in.
To understand the key indicator that will be used to measure the outcome (improved career progression opportunities), which is a critical step in measuring progress toward the Strategic Goal.
Number of training hours provided for employees (full-time, part-time, or temporary) during the reporting period
N/A
Organizations should footnote the types of training provided, particularly those that lead to recognized certifications
This metric is intended to capture the sum of all training hours provided to employees. It is not intended to capture the average number of training hours per employee. Trainings may include both internal and external opportunities provided by the organization.
Training can be categorized as: (1) skills-based training to advance core job responsibilities (enhancing employees’ ability to do their jobs effectively); (2) skills-based training on cross-job functions (training beyond regular job responsibilities, enabling employees’ to advance in their professions); (3) training on literacy, communications, and other life skills; or (4) trainings related to diversity and inclusion (for example, training on implicit bias or sexual harassment). Organizations should footnote details on the training(s) provided, including the type.
To understand quantity of training provided.
Number of employees (full-time, part-time, or temporary) who were trained through programs provided by the organization (both internally and externally) during the reporting period.
N/A
Organizations should footnote the types of training provided and duration of training(s), with particular emphasis on those that lead to recognized certifications.
Organizations should further footnote whether the employees who received the training are low skilled, medium skilled or high skilled employees.
Trainings can be categorized as skills-based trainings to advance core job responsibilities (i.e., enhance the employee’s ability to do his/her job effectively), skills-based trainings on cross-job functions (e.g., training beyond regular job responsibilities, enabling the employee’s ability to advance his/her profession), or trainings on literacy, communications and other life skills (e.g., enhancing the employee’s ability to enhance his/her livelihood). Organizations should footnote details on the training(s) provided, including type.
To understand how much training was provided by the organization and to whom.
Describes the type and scope of programs implemented and assistance provided to upgrade employee skills.
N/A
Organizations should footnote all assumptions used.
This metric is qualitative and is sourced from GRI-404-2.
To understand the types of training, skill development, and transition programs provided to employees by the organization.
While the above core metrics provide a starter set of measurements that can show outcomes of a portfolio targeted toward this goal, the additional metrics below — or others from the IRIS catalog — can provide more nuance and depth to understanding your impact.
Number of full-time equivalent employees working for enterprises financed or supported by the organization as of the end of the reporting period.
N/A
Organizations should footnote all assumptions used. See usage guidance for further information.
While this metric requests full-time equivalent job information, organizations are encouraged to also provide the breakdown of this data for full-time and part-time positions supported/financed.
In calculating the number of full-time equivalent jobs, part-time jobs should be converted to full-time equivalent jobs on a pro rata basis, based on local definition (e.g., if the standard working week equals 40 hours, a 20 hour per week job would be equal to a 0.5 FTE job). Both full-time and part-time jobs should be calculated based on the number of employees employed as of the end of the reporting period. Seasonal or short-term jobs should be prorated based on the time worked throughout the reporting period (e.g., a full-time position for three months at any point during the reporting period would be equal to a 0.25 FTE job). Note that in the United States, the U.S. Treasury Department defines a working week as 35 hours. See glossary definition for more information on how to calculate full-time equivalent.
To understand number of jobs in enterprises directly supported by the organization, which can be helpful in assessing employees’ ability to find, grow and retain employment or work.
Number of full-time equivalent employees working for enterprises financed or supported by the organization at the beginning of the reporting period who remain at the organization as of the end of the reporting period.
N/A
Organizations should footnote all assumptions used. Organizations that report data using more than one reporting period should make very apparent which reporting period they are using for this metric. See usage guidance for further information.
Self employed individuals and owners of businesses should be counted as employees.
While this metric requests data on full-time equivalents, organizations are encouraged to also provide the breakdown of these data for full- and part-time positions supported/financed. Organizations can refer to the full-time equivalent glossary term for more detail, including notes on its calculation. In brief, in calculating the number of full-time equivalent jobs, part-time jobs should be converted to full-time equivalent jobs on a pro rata basis based on local definitions (for example, if the standard working week equals 40 hours, a 20-hour-per-week job would be 0.5 FTE). Both full- and part-time jobs should be calculated based on the number employed as of the end of the reporting period. Seasonal or short-term jobs should be prorated based on the time worked throughout the reporting period. (For example, a full-time position for three months at any point during the reporting period would be 0.25 FTE.) Note that, in the United States, the U.S. Treasury Department defines a working week as 35 hours.
Organizations may choose to disclose employment by function (examples: operations and maintenance, construction, land conservation).
To understand number of jobsmaintained in enterprises directly supported by the organization, which can be helpful in assessing employees’ ability to find, grow and retain employment or work.
Net number of new full-time equivalent employees working for enterprises financed or supported by the organization between the beginning and end of the reporting period.
Many organizations may choose the beginning of the reporting period to be the time when the organization began its support/investment.
N/A
Organizations should footnote all assumptions used. Organizations that report data using more than one reporting period should make very apparent which reporting period they are using for this metric. See usage guidance for further information.
While this metric requests data on full-time equivalents, organizations are encouraged to also provide the breakdown of these data for full- and part-time positions supported/financed. Organizations can refer to the full-time equivalent glossary term for more detail, including notes on its calculation. In brief, in calculating the number of full-time equivalent jobs, part-time jobs should be converted to full-time equivalent jobs on a pro rata basis based on local definitions (for example, if the standard working week equals 40 hours, a 20-hour-per-week job would be 0.5 FTE). Both full- and part-time jobs should be calculated based on the number employed as of the end of the reporting period. Seasonal or short-term jobs should be prorated based on the time worked throughout the reporting period. (For example, a full-time position for three months at any point during the reporting period would be 0.25 FTE.) Note that, in the United States, the U.S. Treasury Department defines a working week as 35 hours.
Organizations may choose to disclose employment by function (examples: operations and maintenance, construction, land conservation).
To understand number of jobs created in enterprises directly supported by the organization, which can be helpful in assessing employees’ ability to find, grow and retain employment or work.
Percentage of the organization’s clients who were placed in part-time, full-time, temporary, or permanent jobs during the reporting period.
Number of clients placed in jobs / Number of clients assisted
Organizations should footnote all assumptions used. See usage guidance for further information.
This metric is most applicable to organizations providing clients (e.g., students or trainees) with vocational/technical training.
This metric is intended to capture the percentage of clients (for example, job services clients or students in vocational or technical training) who are placed in jobs during the reporting period. Organizations should include only those clients who were placed in and began jobs during the reporting period. They should exclude clients who were placed in jobs but did not begin work.
To understand the degree to which target stakeholders experienced an increased ability to find, grow and retain employment or work.
Number of the organization’s clients who were placed in part-time, full-time, temporary, or permanent jobs during the reporting period.
N/A
Organizations should footnote all assumptions used. See usage guidance for further information.
This metric is most applicable to organizations providing clients (e.g., students) with vocational/technical training.
Organizations should include only clients who were placed in and began jobs during the reporting period, and should exclude clients who were placed in roles but did not begin work.
To understand the degree to which target stakeholders experienced an increased ability to find, grow and retain employment or work. This metric is also the core outcome metric under the Impact Theme “Access to Quality Education” and Strategic Goal “Improving The Successful Transition of Youth Into the Workforce and Society.”
Value of the costs incurred by the organization as a result of training provided to employees (full-time, part-time, or temporary) during the reporting period
N/A
Organizations should footnote all assumptions used.
These costs should not include salary/payroll expenses that are incurred during the training hours.
Training cost varies enormously depending on level of services provided to participants, whether an organization has staff dedicated to engage employer networks, the level of need in the community where the program is located requiring more outreach, or provision of financial support to participants, which can make comparisons in employee training costs across organizations problematic if not accompanied by additional metrics that specify level of services provided as well as level of local need. To address this issue, the IRIS+ system offers a core set of metrics than can help guide more comprehensive and industry-validated IMM.
To understand the financial investment of the organization in training provided to employees.
Average tenure of employees of the organization as of the end of the reporting period.
Sum of each employee's tenure / Number of employees
Organizations should footnote all assumptions used. See usage guidance for further information.
Where applicable, footnote the date used as a start date for the tenure calculation such as: immediately upon employment, upon satisfactory completion of a probation period, etc.
To understand the average length of time an employee works at the organization, which can be helpful in assessing employees’ ability to find, grow and retain employment or work.
Ratio of the number of involuntarily departing permanent (full-time and part-time) employees, compared to the average number of permanent (full-time and part-time) employees at the organization during the reporting period.
(Departed Permanent Employees: Involuntary) / (Average Number of Permanent Employees during the reporting period)
Organizations should footnote all assumptions used. See usage guidance for further information.
The number of departed permanent employees should only include employees departing the organization involuntarily. Organizations can refer to Departed Permanent Employees: Involuntary (OI7225) for additional information on involuntary departures.
The average number of permanent employees can be calculated a number of ways. One example for organizations reporting on a yearly basis is to calculate the average number of permanent employees on a monthly basis.
To understand the rate of involuntary departures from the organization, which can be helpful in assessing employees’ ability to find, grow and retain employment or work.
Ratio of the number of permanent (full-time and part-time) employees that departed voluntarily, compared to the average number of permanent (full-time and part-time) employees at the organization during the reporting period.
(Departing Permanent Employees: Voluntary) / (Average Number of Permanent Employees during the reporting period)
Organizations should footnote all assumptions used. See usage guidance for further information.
The number of departed permanent employees should only include employees departing the organization voluntarily. Organizations can refer to Departed Permanent Employees: Voluntary (OI8431) for additional information on voluntary departures.
The average number of permanent employees can be calculated a number of ways. One example for organizations reporting on a yearly basis is to calculate the average number of permanent employees on a monthly basis.
To understand the rate of voluntary departures from the organization, which can be helpful in assessing employees’ ability to find, grow and retain employment or work.
Indicates whether the organization has a written policy to support progression/promotion of employees fairly and equitably and a system to monitor compliance with this policy.
Yes/No
Organizations should footnote details on how they determine the advancement of employees.
Organizations are encouraged to align these policies with state or international standards. For example, the United States Equal Employment Commission (EEOC) prohibits any employer from making decisions about job assignments and promotions based on an employee’s race, color, religion, sex (including gender identity, sexual orientation, and pregnancy), national origin, age (40 or older), disability or genetic information.
To understand whether organizations have policies in place to support improved career progression opportunities.
Percentage of total employees who received a regular performance and career development review during the reporting period.
(Employees who received performance and career development review during the reporting period) / (Permanent Employees: Total (OI8869) + Temporary Employees (OI9028))
Organizations should footnote all assumptions used.
This metric is sourced from GRI-403.
Organizations are encouraged to disaggregate this data by gender and by employee category.
To understand the percent of employees receiving performance and career development review, which can be helpful in assessing employees’ ability to find, grow and retain employment or work and their career progression opportunities.
Number of employees who are female and who were promoted within the organization during the reporting period.
N/A
Organizations should footnote all assumptions used.
This metric is intended to capture the number of unique female individuals promoted by the organization in full- or part-time roles at the point in time defined by the reporting end date. This metric excludes Temporary Employees (OI9028). Organizations are encouraged to note what roles and levels employees promoted began and ended the reported period in.
To understand the degree to number of women promoted during the reporting period, which can be helpful in assessing whether career progression opportunities are equally available to all employees.
Net number of new full-time equivalent employees living in low income areas working for enterprises financed or supported by the organization between the beginning and end of the reporting period.
Many organizations may choose the beginning of the reporting period to be the time when the organization began its support/investment.
N/A
Organizations should footnote all assumptions used, including how low income areas are identified. Organizations that report data using more than one reporting period should make very apparent which reporting period they are using for this metric. See usage guidance for further information.
Organizations can refer to the glossary for additional information on defining low-income areas.
While this metric requests data on full-time equivalents, organizations are encouraged to also provide the breakdown of these data for full- and part-time positions supported/financed. Organizations can refer to the full-time equivalent glossary term for more detail, including notes on its calculation. In brief, in calculating the number of full-time equivalent jobs, part-time jobs should be converted to full-time equivalent jobs on a pro rata basis based on local definitions (for example, if the standard working week equals 40 hours, a 20-hour-per-week job would be 0.5 FTE). Both full- and part-time jobs should be calculated based on the number employed as of the end of the reporting period. Seasonal or short-term jobs should be prorated based on the time worked throughout the reporting period. (For example, a full-time position for three months at any point during the reporting period would be 0.25 FTE.) Note that, in the United States, the U.S. Treasury Department defines a working week as 35 hours.
Organizations may choose to disclose employment by function (examples: operations and maintenance, construction, land conservation).
To understand number of jobs created in low-income areas in enterprises directly supported by the organization, which can be helpful in assessing employees’ ability to find, grow and retain employment or work.
Value of wages (including bonuses, excluding benefits) paid to all full-time and part-time employees of the organization during the reporting period.
Temporary Employee Wages (OI4202) + Permanent Employee Wages: Total (OI9677)
Organizations should footnote all assumptions used.
This metric is intended to capture pre-tax wages/salaries paid to the organization’s temporary employees and should not include benefits nor include payroll expenses.
To understand total wages paid by the organization, which can be helpful in assessing employees’ incomes and livelihood opportunities.
Ratio that compares the additional average wage paid to employees of the organization, to the average wage paid for a similar job in a similar industry/category in the local market, at the end of the reporting period.
(Average wage paid to employees in a specified position−Average wage paid to employees in a comparable position at a similar organization) / Average wage paid to employees in a comparable position at a similar organization
Organizations should footnote the specific position(s) for which the wage premium applies, as well as assumptions and sources related to the comparable position at a similar organization. See usage guidance for further information.
To source data points for average wages paid to employees in comparable positions at similar organizations, organizations can refer to resources issued by independent, third-party research bodies within their respective countries of operation. For example, organizations operating in the United States may refer to wage data by area and occupation published by the U.S. Bureau of Labor Statistics. Additional resources include: WDR2013 Occupational Wages around the World report, WageIndicator.org , PayScale.com, and others.
To understand how wages paid by the organization compare in the local or regional context, which can be helpful in understanding employees' incomes and livelihood opportunities.
Ratio of the wages paid during the reporting period to the highest compensated full-time employee (inclusive of bonus, excluding benefits), compared to the lowest paid full-time employee.
(Wages of highest paid full time employee during the reporting period) / (Wages of lowest paid full time employee during the reporting period)
Organizations should footnote all assumptions used, including details about the positions used for the calculation(s). See usage guidance for further information.
Organizations with operations in many countries are encouraged to report multiple ratios, where each compares wages of two employees located in the same country. Organizations should footnote the details about the types of positions used when calculating this metric.
This ratio can be used for salaried employees with fixed wages or for employees with variable salaries (e.g., hourly, daily, weekly, other specified time cycle, or other specified parameter).
To understand how wages paid by the organization compare across the organization, which can be helpful in understanding employees’ incomes and livelihood opportunities.
Ratio of the average wage paid during the reporting period to female employees of the organization for a specified position, compared to the average wage paid to male employees of the organization for the same position.
(Average wage paid to female employees in a specified position) / (Average wage paid to male employees in the same position)
Organizations should footnote all assumptions used, including details about the positions used for the calculation(s). See usage guidance for further information.
Organizations should footnote the specific position(s) they use to calculate this ratio, including whether the position is a managerial or non-managerial role. For example, organizations reporting this metric for multiple positions are encouraged to report various data points for gender wage equity and specify the position to which the data points correspond.
While this metric helps organizations begin to understand gender wage equity in their operations, organizations are cautioned that other factors may affect the data collected. For example, the average wages reported for this metric may only be meaningful if there are multiple individuals of both genders in a similar position within the organization. Additionally, factors such as employee education, experience, tenure at the organization, and others may also influence wage disparities.
To understand how wages paid within the organization compare between female and male employees, which can be helpful in understanding employees’ incomes and livelihood opportunities (and to what degree they are equitable).
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