An analysis of financial institutions in Black-majority communities: Black borrowers and depositors face considerable challenges in accessing banking services
Introduction
Achieving the American dream—the opportunity to succeed, to provide food and shelter for family members, education for children, hope for a better life, and freedom of opportunity— requires capital. But, in the United States, access to capital for individuals and business owners is uneven based on race. The racial wealth gap remains significant. In 2019, the median net worth of a typical white household, $188,200, was 7.8 times greater than that of a typical Black household, $24,100 (Bhutta et al., 2020). Most houses are bought with a mortgage and most businesses rely on credit to fund their expansion.1
This report documents that, at a local level, there are stark contrasts in access to credit for African Americans: Interest rates on business loans, bank branch density, local banking concentration in the residential mortgage market, and the growth of local businesses are markedly different in majority Black neighborhoods. Several policy approaches are suggested: First, a more granular approach to banking supervision may be needed; microgeographic data in 2021 provides a much closer look at the banking practices of major banks and nonbank lenders than in 1977, when the Community Reinvestment Act was signed into law. Second, the number of African American minority depository institutions (MDIs) has been declining and policy or private-sector support is likely needed (Pike, 2021). Third, as the mobility of Americans is overall declining, geography matters more than ever (Molloy et al 2017). A lack of credit hinders investments in better homes, better schools, better local infrastructure such as roads and public transport, better amenities, and better health care.
Section 1 reviews the history of credit policies. Section 2 presents granular evidence on inequalities in access to banking services, including bank deposits. Section 3 focuses on residential mortgage credit supply. Section 4 turns to small business lending. Section 5 suggests a 21st century agenda for lawmakers and academic researchers.
1. Historical context
Removal of Africans from their rich commercial environments in kingdoms including Ghana, Mali and Songhai through the slave trade between the 14th and 18th centuries did not destroy their proclivity for business and trade (Ammons, 1996). Since the time when Black people in America secured the right to earn capital for their labor following emancipation, they have faced systemic financial discrimination with respect to banking access and fees. Over a century ago, racism and segregation required Black people to pool their resources to support each other, and Black-owned banks played a vital role in the economic health of Black communities (Gerena, 2007). On October 17, 1888, Capitol Savings Bank in Washington, D.C. became the first bank organized and operated by African Americans (Todd, 2019). Within four years of opening, the bank’s deposits had grown to over $300,000 (Partnership for Progress). Between the end of the Reconstruction era and the beginning of the Great Depression, over 130 Black-owned banks opened, providing capital to Black entrepreneurs, businesses, and prospective homeowners (Gerena, 2007).
In the early- to mid-20th century, the federal government took on a large role in the stabilization and financing of the home mortgage market in the United States. In response to the housing market problems brought on by the Great Depression, the Home Owners Loan Corporation (HOLC) purchased and refinanced over one-tenth of all non-farm U.S. mortgages by 1936. The HOLC subsequently created color-coded maps in 200 cities to better understand the risk of the mortgages with the guidance and expertise of local real estate market professionals that reflected long held patterns of racial discrimination, a process that came to be known as redlining. Shortly thereafter, the recently created Federal Housing Administration (FHA), which by the middle of the century covered the insurance of over one-third of the U.S. mortgage market, crafted their own redlining maps to guide decisionmaking. In tandem, the FHA and HOLC helped lock in existing patterns of racial discrimination in the U.S. housing market (Fishback et al, 2020). This period coincided with the Second Great Migration, which witnessed millions of Black people migrating from the rural South to the cities of the North and Midwest. Given the existing market discrimination that non-minority owned banks practiced, their race-based exclusion of Black people from the mortgage market provided an opportunity for minority-owned banks to provide service to a much larger market of Black migrants looking to purchase homes and start businesses. However, Black migrants faced labor market competition with new European immigrants and legacy Black residents in addition to labor market discrimination, which made it difficult for minority-owned banks to finance economic development efforts (Ammons, 1996).
During the seven year period between 1983 and 1989 the number of Black owned banks declined 22%, while the total number of banks in the U.S. declined by only 12% (Price, 1990). Black-owned banks make capital more accessible because they approve a higher percentage of loans to Black applicants than other banks, but their impact is limited by their low numbers and often precarious financial standing (Burton, Scheck, and West, 2020). Compared with white-owned banks, minority-owned banks are more likely to rely more heavily on government deposits, and therefore hold fewer loans and more liquid assets (Price, 1990).
In 2008, the Partnership for Progress was launched by the Board of Governors of the Federal Reserve to help promote and preserve minority-owned banks. But despite its efforts, the number of Black-owned banks has declined, from 48 in 2001 to 18 in 2020. (McKinney, 2019). Banking access in the Black community has not only been limited by the decrease in the number of Black-owned banks, but by an overall decrease in the number of banks in majority Black neighborhoods. Since 2010, the number of banks in majority-black neighborhoods decreased 14.6%, with JPMorgan shrinking its branch footprint in majority-black neighborhoods by 22.8% from 2010 to 2018, compared to a decline of just 0.2% in the rest of the U.S. (Fox, et al., 2019).
The FDIC defines minority depository institutions (MDIs) as federally insured depository institutions for which either “(1) 51% or more of the voting stock is owned by minority individuals; or (2) a majority of the board of directors is minority and the community that the institution serves is predominantly minority. Ownership must be by U.S. citizens or permanent legal U.S. residents to be counted in determining minority ownership.” As of December 31, 2020, the FDIC listed 142 Minority Depository Institutions located in 29 states, Guam, and Puerto Rico with cumulative assets of $287 billion. For context, TIAA had $280 billion in total general account assets in the first quarter of 2021. Of the 142 MDIs, there were only 18 Black or African American owned banks with combined assets of $4.58 billion. The minority status of those 142 financial institutions is presented in Table 1.
2. Racial inequalities in access to banking services and deposits
Today bank customers can access their accounts and perform many banking transactions via the internet. According to Business Insider, this year there will be 196.8 million digital banking users in the U.S., making up 75.4% of the population. But for those who lack financial resources, internet access, or transportation required to bridge the physical and digital distance, brick-and-mortar bank branches are vital—particularly for low-income, inner-city areas (Hegerty, 2015). Racial discrimination and various types of market failure have led to banking and credit deserts in underserved urban and rural communities (Van Tol, 2020). Ergundor (2010) finds a positive correlation between bank branch presence in low-income neighborhoods and mortgage loan originations; that favorable effects of bank branch presence gets stronger as the branch gets closer to the neighborhood; and that in the small-business-lending market, relationships are associated with greater availability of credit.
According to the Fed, in 2019 the majority of U.S. adults had a bank account and relied on traditional banks or credit unions to meet their banking needs, but gaps in banking access existed. Six percent of American adults were unbanked meaning that they did not have a checking, savings, or money market account. Approximately 40% of unbanked adults used an alternative financial service during 2018— such as a money order, check cashing service, pawn shop loan, auto title loan, payday loan, paycheck advance, or tax refund advance. Unbanked and underbanked rates were higher among lower-income households, less-educated households, Black households, Latino or Hispanic households, American Indian or Alaska Native households, working-age disabled households, and households with unstable incomes (FDIC, 2020; Rhine et al., 2006). Hence, the continuing decline in the number of MDIs is especially disconcerting. Table 2 shows the banking status for Black, Latino or Hispanic and white Americans in 2019.
The unbanked and underbanked rates in 2019 were highest for Black adults—making it more difficult for them to accumulate savings. According to 2020 survey data from Bankrate, minorities, millennials, and Northeasterners reported paying higher bank fees. The data showed that the average checking account holder at a bank or credit union paid $8 per month in fees, including routine service charges, ATM fees and overdraft penalties, but fees paid varied by race. White checking account holders reported paying the lowest amount in monthly bank fees, $5, compared to $12 for Black account holders and $16 for Latino or Hispanic account holders.
Majority Black and Latino or Hispanic neighborhoods have fewer options when it comes to financial services than majority white neighborhoods. In 2017, majority Black ZIP codes located in metropolitan areas with over 250,000 people had a median dollar-deposit-based Herfindahl-Hirschman Index (HHI) of 4,584 while non-majority Black ZIP codes had a median HHI of 3,106, where the higher score indicates less competition.2 Similarly, majority Latino or Hispanic ZIP codes had a median HHI of 3,580 compared to a median HHI of 3,157 in non-majority Latino or Hispanic ZIP codes. Access to a wider array of financial services can mean lower interest rates and higher savings rates as banks compete to attract a customer base. Figure 1, below, shows the relationship between the share of Black, Latino or Hispanic, and white residents in a ZIP code and banking competition (as measured by HHI) in ZIP codes located in metropolitan areas with over 250,000 people and after controlling for population. As the share of Black and Latino or Hispanic residents increases, so does the HHI, meaning less banking competition. The reverse is true for the share of white residents in a zip code.
In a world where services, both financial and non-financial, are becoming increasingly available online, one might argue that the physical presence of a brick-and-mortar bank branch in a neighborhood is no longer necessary. Indeed, the biennial FDIC Survey of Household Use of Banking and Financial Services found that the share of banked households in metropolitan areas that used a bank teller as their primary method of accessing their bank account fell from 28% in 2015 to 21% in 2019, as use of mobile and online banking surged. However, the same survey showed that lower-income and less-educated households were twice as likely to use bank branches, and the same was true for elderly adults. Additionally, 23% of urban banked households visited a bank branch 10 or more times a month, demonstrating that a significant number of households still use this service.
While fintech lenders have increased their market share in recent years by increasing the speed of service delivery and efficiency, there is no evidence that they have expanded access to financial services to low-income borrowers in the mortgage market (Fuster et al, 2019). However, during the pandemic, Black-owned businesses were 12 percentage points more likely to obtain PPP loans from fintech lenders, while small banks were much less likely to lend to Black businesses. Howell et al (2021) find that this disparity is largely due to racial discrimination and that when banks automate their lending process, thereby reducing human involvement, their rate of lending to Black businesses increases, especially in localities with high racial animus.
Yet, fintech should not be considered a comprehensive solution to racial disparities in access to capital. There remains a large share of households that lack access to broadband in the U.S. In cities such as Baltimore, over 40% of households or some 96,000 households lack a wired broadband connection, and some 75,000 Baltimore City households, or one in three, do not have either a desktop or laptop computer, making online services more difficult to access (Horrigan, 2020). This is exacerbated by the fact that, as shown in Figure 2, counties with less banking competition (as measured by the Herfindahl Hirschman Index) also have lower shares of households with wired broadband connections.
Finally, the continued importance of brick-and-mortar branches is further evidenced by the crucial role played by local banks in distributing PPP loans during the early months of the COVID-19 pandemic (Li et al, 2020). These more locally oriented banks were better able to discover potential customers in need due to relationship banking and their ability to understand local risk profiles more accurately. In the early stages of the pandemic, counties with the highest numbers of Black-owned businesses received some of the lowest shares of PPP loan coverage, often falling below 20% of eligible firms, possibly reflecting the lack of existing banking relationships in those communities (Mills and Battisto, 2020). Minority-owned depository institutions could play a crucial role in fostering stronger relationships between Black entrepreneurs and the financial system.
From 2010 to 2021, the U.S. lost over 15,500 bank branches. By 2021, majority Black census tracts were much less likely to have a bank branch than non-majority Black neighborhoods. Figure 3 shows a dot density map of Philadelphia census tracts and the share of residents that are Black in 2021. A high number of banks are clustered in the city’s central business district, but immediately outside that area, the city’s majority Black neighborhoods have few, if any, bank branches. Census tracts with a higher share of white residents and tracts that are more suburban have a higher number of branches. Between 2010 and 2021, non-majority Black neighborhoods were more likely to experience a decline in the number of bank branches, but only because they were much more likely to have a bank branch in their neighborhood in the first place. After controlling for the initial number of bank branches in 2010, census tracts with higher shares of Black residents were more likely to experience a bank branch closure by 2021. Figure 4 shows this relationship in the six metropolitan areas of Baltimore, Cleveland, Detroit, Pittsburgh, Philadelphia, and St. Louis.
The financial services industry has expanded beyond banks and credit unions, which are regulated primarily at the federal level. Banks are regulated by the Federal Reserve, while federally chartered credit unions are regulated by the National Credit Union Administration, and state-chartered credit unions are regulated at the state level (Federal Reserve Bank of San Francisco). While the majority of Americans complete their basic financial transactions at banks and credit unions, consumers who operate outside of the formal banking system may be more likely to utilize informal, alternative financial service providers including payday lenders (Dunham, 2018).
Payday loans, cash advance loans, check advance loans, post-dated check loans, and deferred deposit loans are short-term high interest rate loans provided by check cashers, finance companies, and others to a clientele that mainly consists of low- and moderate-income working people who have bank accounts, but who lack credit cards, have poor credit histories, or have reached their credit limit (Federal Trade Commission). According to the St. Louis Fed, in 2019 the average interest rate on the average payday loan is 391%, compared to 17.8% for the average credit card, and 10.3% for the average personal loan from a commercial bank.
The FICO scoring system, created in 1989, was designed to assess the creditworthiness of consumers (Shift, 2021). Scores range from 300 to 850. The FICO credit score is used by financial institutions as a qualifier to assess financial health. It is not easy for individuals to improve their financial health once their credit score is damaged. Black people are more likely to be excluded from conventional financial services based on their credit scores. Figure 6 shows credit scores by race for 2021. Because Black people are more likely to have lower credit scores, they are more likely to be unbanked or underbanked, causing them to pay higher service fees to receive financial services and making them more likely to depend on alternative financial institutions. Financial institutions rely on FICO credit scores as a screening tool to protect themselves from financial loss due to asymmetric information. However, developing alternative screening methods is necessary to reduce the disparity in banking access and fees.
Black and Latino or Hispanic people are more likely than white people to depend on high interest financial services like check cashing counters and payday lenders because there are fewer banks in Black and Latino or Hispanic neighborhoods. Increasing access to banking services could save Black and Latino or Hispanic Americans up to $40,000 over their lifetime (Moise, 2019). The percentage of Black adults who are not digitally literate, 22%, is twice the percentage of white adults, 11%. Both the disparity in access to banks and digital literacy threaten their ability to grow wealth in the digital economy.
3. Racial inequalities in access to mortgage credit
In the U.S., homeownership is the most common avenue to wealth building and intergenerational wealth transfers. Racial inequality in access to home mortgage loans has a long and troubled history in the country that includes redlining (Aaronson et al. 2017, Fishback et al. 2020), geographically targeted predatory lending (Carr et al. 2001; Agarwal et al. 2014), discrimination in lending standards (Ross et al. 2002), and racial covenants (Gotham, 2000; Sood et al., 2019).3,4
Mortgage lending files collected via the Home Mortgage Disclosure Act display very substantial differences in approval rates, as mortgage lending applications of Black American borrowers are two to three times more likely to be denied. Munnell et al. (1996) compares applicants with similar observable measures of creditworthiness and finds that race plays a statistically and economically significant role in application decisions.5 The authors also note that disparities are likely underestimated, as the creditworthiness controls themselves may be the outcome of other forces described in the previous section. There is no doubt a need for modern studies that identify lending disparities using the granularity of modern datasets.
Mapping the geography of mortgage lending reveals new insights and limitations of CRA examinations. The four maps in Figure 8 below suggest that residents of Baltimore City had access to fewer lenders than other residents of metropolitan Baltimore. The map presents the HHI for each census tract. Again, fewer lenders were present in Baltimore City’s majority Black census tracts than majority white and suburban tracts.
The four maps in Figure 7 suggest that, between 1995 and 2012, residents of the city of Baltimore were granted smaller loans in proportion to their income. The Loan-to-Income (LTI) ratio, a measure of lending standards, is depicted for each census tract. It suggests that lenders have more stringent lending standards in Baltimore City and especially in the city’s majority Black neighborhoods where the LTI ratio is the lowest.
This raises significant questions about the appropriate geographic level of the assessment area of CRA examinations. In a recent report, Johns Hopkins researchers6 describe that large bank lenders are typically assessed based on their lending to low-income census tracts at the state level, rather than at the more granular city or county levels. Channeling the flow of mortgage credit to specific neighborhoods and demographics is key, as across-the-broad increases in mortgage credit supply to all racial groups lead to the growth of urban segregation (Ouazad et al, 2016; Ouazad et al. 2019).
The four panels present maps of the dollar weighted loan-to-income ratio by census tract. Darker colors correspond to lower loan-to-income ratios.
These four figures present the level of competition in census-tract level mortgage origination. The colors correspond to the Herfindahl index (HHI) in mortgage origination, and lighter colors correspond to lower levels of competition. The four panels suggest lower levels of competition in central census tracts.
4. Racial inequalities in access to small business loans
A lower level of business ownership and business assets among Black households is a contributing factor to the racial wealth gap. Limited access to capital is the most important factor that constrains the establishment, expansion and growth of Black-owned businesses (Fairlie, Robb, and Hinson, 2010). According to a 2020 report from The Brookings Institution, “Black people represent 12.7% of the U.S. population but only 4.3% of the nation’s 22.2 million business owners.” Black entrepreneurs face barriers to opening businesses with respect to access to credit. Henderson et al. (2015) examined the influence of racial and gender-related factors on access to business credit lines and found that Black-owned startups receive lower than expected business credit scores and that white-owned startups with the same firm characteristics as Black-owned startups are treated more favorably.
Blanchflower, Levine and Zimmerman (2003) found that between 1993 and 1998, Black-owned small businesses were about twice as likely to be denied credit even after controlling for differences in creditworthiness and other factors, suggesting that the racial disparity in credit availability was likely caused by discrimination. Fairlie, Robb, and Robinson (2020) explored racial differences in capital market outcomes associated with launching a new business and found that Black entrepreneurs are less likely to apply for loans than white entrepreneurs because they expect to be denied credit, even when they have a good credit history.
The COVID-19 pandemic has exacerbated the challenges faced by minority-owned businesses (Marte, 2021). Data from the 2020 Small Business Credit Survey found that 92% of Black-owned businesses reported experiencing financial challenges in 2020, compared to 79% for white-owned firms. According to a survey conducted by Reuters, Black business owners were more likely than any other group to suffer financially during the pandemic—38% borrowed money from a friend or relative, 25% worked a second job, and 74% dipped into their personal funds to cover costs.
Such documented evidence of credit constraints has significant consequences for the availability of local services in Black neighborhoods. For instance, Beaulac et al. (2009) documents the phenomenon of food deserts across the United States. Figure 9 below displays the important differences in the density of local services across Atlanta using the National Establishment Time Series (NETS) dataset. Such a dataset provides the geocoded location of establishments, their sales, and number of employees. Benchmarking using administrative data suggests that NETS is an accurate portrayal of the cross-section distribution of establishments (Barnatchez et al. 2017). Figure 9 suggests a significantly lower density in majority Black neighborhoods of Atlanta.
Credit constraints are likely to play a role in this uneven distribution of economic activity. The upper-right panel of Figure 10 shows a positive correlation between the interest rate on business and commercial loans and the share Black in a census tract. Interest rates are insensitive to racial composition for the share of Black residents in a neighborhood below 25%, and then grow to be 1 percentage point higher in Black neighborhoods.
This may lead to an unrealized potential for business expansion in Black neighborhoods: Figure 11 presents a set of graphs displaying a negative relationship between the number, sales, and employees of service firms and the fraction of Black residents.
This map presents the geocoded location of services in the Atlanta metropolitan area. The boundary is the set of census tracts where the fraction of Black residents is greater than 80%.
The upper-right panel presents the tract-level average interest rate on loans with a commercial or business purpose. Each dot is a census tract of the Atlanta metropolitan area. The average interest rate is the dollar-weighted average. The upper-left panel presents the number of employees in service firms by percentage Black. The lower-left panel presents a similar scatter plot for the dollar sales. The lower-right panel focuses on the number of service firms. Services are the same as those for Figure 8: restaurant and bars, offices of physicians, banks, grocery stores, cinemas, art galleries, and other personal services.
5. Conclusion: A new agenda
New detailed microdata provide descriptive evidence that Black borrowers and depositors are substantially more constrained in their access to banking services. This is visible across a range of services, including deposits, residential mortgage credit, and business loans. This report suggests a new legislative agenda and a new research agenda. First, supervisory tools developed in the aftermath of the 1977 Community Reinvestment Act do not seem adapted to the “big data” of the 21st century. Better information means it’s easier than ever to identify paths to improvement for bank and nonbank lenders. Second, researchers can observe large parts of the balance sheet and income statement of depository institutions, allowing for an understanding of the match between the savings of Black depositors and the flow of loans to Black residents and businesses. This should spark a research agenda that makes financial data science more useful than ever to address 21st century inequalities.
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Status check: Managing debt sustainability and development priorities through a ‘Big Push’
Executive Summary
Emerging market and developing economies (EMDEs) have seen development prospects fade in the two years since the onset of COVID-19. Growth turned negative in 2020, is forecast to snap back in 2021, but then revert to a declining trend.1 Investment levels in Latin America and Africa are forecast to remain in the range of 20-25 percent of GDP in the medium term. Outside of Asia, prospects for growth and for convergence with advanced economies are dim. Unlike in advanced economies, the GDP trajectory in EMDEs post-COVID-19 is significantly lower than pre-COVID-19 estimates; 31 developing countries may have lower levels of GDP per capita in 2025 than in 2019.
Meanwhile, general government debt levels in EMDEs have risen by 9 percentage points of GDP. At current low levels of world interest rates, the debt service implications are manageable for most countries, but risks remain if inflation causes major central banks to raise interest rates. As a result, EMDEs are under pressure to cut public spending, even in face of higher needs to respond to the pandemic.
The present trajectory, therefore, is one of slow growth, low investment and public spending, and rising debt service burdens in many, if not most, EMDEs. There is significant risk that this trajectory will prove unsustainable for economic, social, or political reasons.
The current trajectory is also highly inefficient, with high-return projects in EMDEs left unfunded due to debt overhang considerations, and highly inequitable, with poor and vulnerable countries and populations left to manage the pandemic with limited support.
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Global aspirations for a universal transformation to a low-carbon economy and a “just transition” are not likely to be met in the current baseline scenario for the global economy because EMDEs are central to both objectives and without additional public spending neither transition will happen.
There is another way forward, one that offers better prospects for global growth and equity, with lower risks of systemic debt defaults. Rather than relying on austerity, it is a path that seeks to accelerate green, inclusive and resilient growth. This path takes advantage of historically low prices of energy, made possible by technological advances in renewables, and of historically low interest rates on international capital markets to undertake a “big push” to transform economic structures and accelerate growth.
There are four key ingredients of the “big push” approach.
First, a set of investments needs to be identified to achieve the desired transformations. The country-by-country analytical work on which this paper draws suggests that EMDEs (ex-China) should be increasing their investment rates by about 3-4 percent of GDP above pre-pandemic levels in order to provide adequate growth of zero-carbon energy and infrastructure, sustainable agriculture, forestry, and land use (AFOLU), adaptation and resilience, and human capital. This translates into incremental annual investments of about $1.3 trillion by 2025, and more thereafter.
Second, a financing plan is needed that is aligned with the types of expenditures being considered. The proposal advanced here is for an even split between domestic and external financing. The latter, in turn, can be mobilized from ODA, multilateral and other official financial institutions, and private capital. These are not fully fungible—each has a role to play.
Domestic resource mobilization is a core component of any investment strategy. It is essential for general purpose financing like human capital and recurrent spending on nature and adaptation. Thus, a key part of the big push strategy is improving developing country tax administrative capacity, while reducing fossil fuel subsidies. The needed increase of 2.7 percentage points of GDP is well within the range of possibilities identified by the IMF. Additional revenues may accrue from new regulations governing base erosion and profit shifting (BEPS), but the current G-20 agreement may not yield much for many developing countries in the medium term. Stronger international efforts are also needed to stem illicit financial flows and encourage greater information sharing between tax authorities in advanced and developing countries.
Concessional finance is needed to help poor countries, to promote equity, and to incentivize countries to invest adequately in global public goods that have international spillovers—for example, mitigation, nature, and pandemic preparedness. Bilateral donors have already pledged to double climate finance from $30 billion to $60 billion, and agreement seems likely on a $100 billion IDA20 replenishment by year’s end. However, more is needed. ODA in 2020 from DAC countries amounted to only 0.32 percent of their GDP. A new collective agreement is needed to back the transformational change that is proposed here. Our approach calls for a 50 percent increase in concessional finance relative to 2019 levels, an incremental $96 billion by 2025. This is equivalent to 0.15 percent of donor GDP.
Our proposal is not just a call for more ODA, defined as money designed to promote the welfare of developing countries. As the past year has shown, weak health systems and pandemic surveillance in one country have global repercussions. The point is that provision of concessional finance for implementation of global public goods in developing countries benefits advanced countries as well as developing countries. Our proposal calls for a mixture of ODA and a fair funding of global public goods on concessional terms.
Multilateral finance and other official finance. Multilateral development banks (MDBs) are able to offer lower cost loans at longer maturities than other lenders, making debt more sustainable. They could stretch their current balance sheets by making better use of callable capital and reforming statutory lending limits—perhaps freeing up headroom for an additional $750 billion to $1.3 trillion of loans. Other reform efforts, including balance sheet optimization, greater risk pooling, greater use of blended finance and guarantee facilities, and asset sales could also help expand MDB lending. Our proposal calls for MDBs to triple their lending levels, from $63 billion in 2019 to $189 billion by 2025.
Private capital can be attracted into sustainable infrastructure projects, which generate revenue streams to cover equity returns and the debt service associated with the project. There are currently both supply and demand side obstacles that have prevented the scale-up of greater private investment in developing countries: a lack of bankable projects, and a limited appetite for long term investments with perceived high risk. New institutional innovations, such as the development of country platforms, standardized processes, and experience with risk-mitigating official finance suggest that a rapid ramp-up in private finance is now feasible. Our proposal calls for an approximate doubling of the 2019 level of private finance for infrastructure in developing countries by 2025. MDBs and other development partners will need to be more proactive to mobilize and catalyze private finance on this scale.
The third element of the “big push” is policy and institutional reform in countries to ensure that investments generate maximum returns. These reforms are partly sectoral (carbon equivalent taxes and removal of fossil fuel subsidies are clear examples) and partly cross-cutting. Reducing corruption and bureaucratic red tape, increasing voice and citizen participation, and consideration of gender issues are examples. Many countries have the capacity to absorb far larger amounts of investment than they actually receive. To illustrate, the IMF’s public investment management assessment scores African countries at 4.4 compared to 4.5 for Asia. Yet Asian countries profitably invest 15 percentage points of GDP more than African countries. The dispersion in scores is large, suggesting some countries have considerable room to improve, but equally suggesting that other countries have reasonable policy frameworks in place already.
The fourth and final element of the “big push” is international coordination. A “big push” will not work without a concerted global plan and agreement. In some cases, this reflects the need for global policy coordination—the BEPS agreement, proposed measures for carbon taxes and standardized approaches in blended finance are examples. More fundamentally, however, international coordination is needed to change mindsets about appropriate policy. No finance minister in an EMDE will support a big push strategy if this is not approved by the international financial institutions that s/he will rely on to provide needed resources. No private investor will willingly provide funds into a high-debt situation without clarity on how debt work-out mechanisms will operate. No civil society advocate will support a big push without greater transparency on how funds will be spent by their government.
All this suggests the need for a process of consensus building around how to scale up cross-border financing. There are many ongoing discussions and ideas for strengthening parts of the system, but less has been done on forging a common approach so that all parts of the system act in a linked-up fashion. There must be international oversight of core recovery programs, the building of consensus around which individual actors can orient their actions, and the inclusion of regional partners to augment the voice of developing countries. Transparency will be a crucial pillar of any financial architecture reform effort.
The response to COVID-19 thus far has bought us some time, but it is far from enough. The world faces a host of looming medium-term challenges in addition to the short-term imperatives of managing COVID-19 and ensuring recovery. Foremost among these are a low-carbon transition, increasing biodiversity and nature needs, growing adaptation and resilience challenges, and a large deficit in human capital spending. Big problems require big solutions. A stepped-up “big push” investment of around $1.3 trillion in EMDEs by 2025 would enable greater spending on all of these global challenges. A financing strategy must match the right source of financing to each of these needs and deliver this in a timely fashion to enable transformational change over the next decade. The moment is ripe for greater international collaboration and action. In 2020, advanced economies spent over $12 trillion on domestic response efforts. In the next decade, we must mobilize a similarly ambitious effort to tackle a global response.
Download the full working paper
Status check: Managing debt sustainability and development priorities through a ‘Big Push’
Executive Summary
Emerging market and developing economies (EMDEs) have seen development prospects fade in the two years since the onset of COVID-19. Growth turned negative in 2020, is forecast to snap back in 2021, but then revert to a declining trend.1 Investment levels in Latin America and Africa are forecast to remain in the range of 20-25 percent of GDP in the medium term. Outside of Asia, prospects for growth and for convergence with advanced economies are dim. Unlike in advanced economies, the GDP trajectory in EMDEs post-COVID-19 is significantly lower than pre-COVID-19 estimates; 31 developing countries may have lower levels of GDP per capita in 2025 than in 2019.
Meanwhile, general government debt levels in EMDEs have risen by 9 percentage points of GDP. At current low levels of world interest rates, the debt service implications are manageable for most countries, but risks remain if inflation causes major central banks to raise interest rates. As a result, EMDEs are under pressure to cut public spending, even in face of higher needs to respond to the pandemic.
The present trajectory, therefore, is one of slow growth, low investment and public spending, and rising debt service burdens in many, if not most, EMDEs. There is significant risk that this trajectory will prove unsustainable for economic, social, or political reasons.
The current trajectory is also highly inefficient, with high-return projects in EMDEs left unfunded due to debt overhang considerations, and highly inequitable, with poor and vulnerable countries and populations left to manage the pandemic with limited support.
Related Content
Global aspirations for a universal transformation to a low-carbon economy and a “just transition” are not likely to be met in the current baseline scenario for the global economy because EMDEs are central to both objectives and without additional public spending neither transition will happen.
There is another way forward, one that offers better prospects for global growth and equity, with lower risks of systemic debt defaults. Rather than relying on austerity, it is a path that seeks to accelerate green, inclusive and resilient growth. This path takes advantage of historically low prices of energy, made possible by technological advances in renewables, and of historically low interest rates on international capital markets to undertake a “big push” to transform economic structures and accelerate growth.
There are four key ingredients of the “big push” approach.
First, a set of investments needs to be identified to achieve the desired transformations. The country-by-country analytical work on which this paper draws suggests that EMDEs (ex-China) should be increasing their investment rates by about 3-4 percent of GDP above pre-pandemic levels in order to provide adequate growth of zero-carbon energy and infrastructure, sustainable agriculture, forestry, and land use (AFOLU), adaptation and resilience, and human capital. This translates into incremental annual investments of about $1.3 trillion by 2025, and more thereafter.
Second, a financing plan is needed that is aligned with the types of expenditures being considered. The proposal advanced here is for an even split between domestic and external financing. The latter, in turn, can be mobilized from ODA, multilateral and other official financial institutions, and private capital. These are not fully fungible—each has a role to play.
Domestic resource mobilization is a core component of any investment strategy. It is essential for general purpose financing like human capital and recurrent spending on nature and adaptation. Thus, a key part of the big push strategy is improving developing country tax administrative capacity, while reducing fossil fuel subsidies. The needed increase of 2.7 percentage points of GDP is well within the range of possibilities identified by the IMF. Additional revenues may accrue from new regulations governing base erosion and profit shifting (BEPS), but the current G-20 agreement may not yield much for many developing countries in the medium term. Stronger international efforts are also needed to stem illicit financial flows and encourage greater information sharing between tax authorities in advanced and developing countries.
Concessional finance is needed to help poor countries, to promote equity, and to incentivize countries to invest adequately in global public goods that have international spillovers—for example, mitigation, nature, and pandemic preparedness. Bilateral donors have already pledged to double climate finance from $30 billion to $60 billion, and agreement seems likely on a $100 billion IDA20 replenishment by year’s end. However, more is needed. ODA in 2020 from DAC countries amounted to only 0.32 percent of their GDP. A new collective agreement is needed to back the transformational change that is proposed here. Our approach calls for a 50 percent increase in concessional finance relative to 2019 levels, an incremental $96 billion by 2025. This is equivalent to 0.15 percent of donor GDP.
Our proposal is not just a call for more ODA, defined as money designed to promote the welfare of developing countries. As the past year has shown, weak health systems and pandemic surveillance in one country have global repercussions. The point is that provision of concessional finance for implementation of global public goods in developing countries benefits advanced countries as well as developing countries. Our proposal calls for a mixture of ODA and a fair funding of global public goods on concessional terms.
Multilateral finance and other official finance. Multilateral development banks (MDBs) are able to offer lower cost loans at longer maturities than other lenders, making debt more sustainable. They could stretch their current balance sheets by making better use of callable capital and reforming statutory lending limits—perhaps freeing up headroom for an additional $750 billion to $1.3 trillion of loans. Other reform efforts, including balance sheet optimization, greater risk pooling, greater use of blended finance and guarantee facilities, and asset sales could also help expand MDB lending. Our proposal calls for MDBs to triple their lending levels, from $63 billion in 2019 to $189 billion by 2025.
Private capital can be attracted into sustainable infrastructure projects, which generate revenue streams to cover equity returns and the debt service associated with the project. There are currently both supply and demand side obstacles that have prevented the scale-up of greater private investment in developing countries: a lack of bankable projects, and a limited appetite for long term investments with perceived high risk. New institutional innovations, such as the development of country platforms, standardized processes, and experience with risk-mitigating official finance suggest that a rapid ramp-up in private finance is now feasible. Our proposal calls for an approximate doubling of the 2019 level of private finance for infrastructure in developing countries by 2025. MDBs and other development partners will need to be more proactive to mobilize and catalyze private finance on this scale.
The third element of the “big push” is policy and institutional reform in countries to ensure that investments generate maximum returns. These reforms are partly sectoral (carbon equivalent taxes and removal of fossil fuel subsidies are clear examples) and partly cross-cutting. Reducing corruption and bureaucratic red tape, increasing voice and citizen participation, and consideration of gender issues are examples. Many countries have the capacity to absorb far larger amounts of investment than they actually receive. To illustrate, the IMF’s public investment management assessment scores African countries at 4.4 compared to 4.5 for Asia. Yet Asian countries profitably invest 15 percentage points of GDP more than African countries. The dispersion in scores is large, suggesting some countries have considerable room to improve, but equally suggesting that other countries have reasonable policy frameworks in place already.
The fourth and final element of the “big push” is international coordination. A “big push” will not work without a concerted global plan and agreement. In some cases, this reflects the need for global policy coordination—the BEPS agreement, proposed measures for carbon taxes and standardized approaches in blended finance are examples. More fundamentally, however, international coordination is needed to change mindsets about appropriate policy. No finance minister in an EMDE will support a big push strategy if this is not approved by the international financial institutions that s/he will rely on to provide needed resources. No private investor will willingly provide funds into a high-debt situation without clarity on how debt work-out mechanisms will operate. No civil society advocate will support a big push without greater transparency on how funds will be spent by their government.
All this suggests the need for a process of consensus building around how to scale up cross-border financing. There are many ongoing discussions and ideas for strengthening parts of the system, but less has been done on forging a common approach so that all parts of the system act in a linked-up fashion. There must be international oversight of core recovery programs, the building of consensus around which individual actors can orient their actions, and the inclusion of regional partners to augment the voice of developing countries. Transparency will be a crucial pillar of any financial architecture reform effort.
The response to COVID-19 thus far has bought us some time, but it is far from enough. The world faces a host of looming medium-term challenges in addition to the short-term imperatives of managing COVID-19 and ensuring recovery. Foremost among these are a low-carbon transition, increasing biodiversity and nature needs, growing adaptation and resilience challenges, and a large deficit in human capital spending. Big problems require big solutions. A stepped-up “big push” investment of around $1.3 trillion in EMDEs by 2025 would enable greater spending on all of these global challenges. A financing strategy must match the right source of financing to each of these needs and deliver this in a timely fashion to enable transformational change over the next decade. The moment is ripe for greater international collaboration and action. In 2020, advanced economies spent over $12 trillion on domestic response efforts. In the next decade, we must mobilize a similarly ambitious effort to tackle a global response.
Download the full working paper
6 job quality metrics every company should know
Today’s labor shortages make it an auspicious moment for companies ready to measure and improve labor conditions. To do so, corporate boards and management need a clear way to manage retention by understanding its link to job quality.
The economic recovery from the COVID-19 pandemic has created a uniquely tight labor market with millions of unfilled job postings.1 Though shutdowns and business restrictions hit low-wage workers especially hard, evidence suggests that, in this recovery, they have options.2 Many low- and high-wage workers alike have seized the moment and quit their jobs in search of higher quality work and economic mobility.3
Employers who have seen workers leave and who have been unable to rehire feel the costs of attrition. For example, one large food distributor found the labor crunch reduced their ability to respond to customers’ demand, which led to cuts in production, distribution routes, and ultimately lost market share. Stories like this show how dramatic costs can explicitly result—in a short timeframe—from poor working conditions and high turnover.
As a result, many companies find they are playing catch-up to their competitors who made employee retention and well-being a priority years ago.4 Beyond the promise of higher returns to companies with positive culture and high rates of retention, forward-looking companies may benefit from taking steps now in anticipation of the growing call for SEC mandated human capital disclosures.5
Additional disclosures, by way of Environmental, Social and Governance (ESG) indicators, are meant to hold companies accountable toward socially beneficial goals—from reducing carbon footprints to promoting economic inclusion. The indicators proposed here are ideal contributors to the “S” in ESG, social impact, as they track companies’ progress in countering trends that have eroded workers’ well-being: a lack of good paying, stable jobs, and limited and inequitable mobility.
Related Content
This report provides simple, evidence-based outcome metrics that promote good quality jobs. Different companies will tackle the metrics differently depending on their industry structure and creativity of their management. Regardless, an important starting point is clarity about which metrics matter when it comes to improving job quality and how to measure progress against them.
In the U.S. labor market, many workers churn from one low-paid, low-quality job to the next.6 Despite their willingness to work hard, workers find it difficult or impossible to advance. And across all sectors of the economy, historical inequities continue to drive down wages and economic mobility of female, Black, and Hispanic workers.7
Data gathering can help determine whether progress toward these challenges, captured in these metrics, also leads to cost savings in hiring, retention, and overall performance. This way, progress at the firm level can also lead toward greater shared prosperity; the metrics below are a first step in that direction.
Key metrics: Job quality, mobility, and equity
Metrics overview and rationale
Job quality
In the last decades, low wage work has become increasingly pervasive and precarious, and at the same time stagnant wages have left many full-time workers unable to afford basic living expenses, forcing them to work multiple jobs. Many have little cushion for emergencies, leading to constant churn and short job tenures.
To track their social impact on job quality, companies can measure the share of their workforce paid living wages and with healthcare. The living wage depends on the cost of housing, health insurance, childcare, and other necessities. The national living wage is $16.54 an hour.8 We estimate that in 2019, 29 percent of workers earned a living wage, accounting for geographical differences in the cost of living.9
Likewise, unexpected health-related expenses can spell disaster for uninsured workers. Healthcare is an important component of job quality as it promotes stability and facilitates mobility. Nine percent of workers in 2019 did not have health insurance.10
Improving wages may seem like an intractable proposition in tight margin industries. However, some companies have made bold commitments, such as Bank of America, which last year raised its minimum wage to $20 an hour.11 The bank and other companies such as Google and Microsoft have also shown willingness to raise wages of workers employed by their vendors, allowing companies that provide food and janitorial services, for example, to pass on the cost of wage increases to their more community focused customers. Other companies are experimenting with profit sharing agreements for employees across the wage spectrum.12 Profit sharing strategies can increase worker engagement particularly when workers already earn more than a living wage. Whatever the strategy, and despite perceived limited flexibility when it comes to wages, measuring outcomes can help identify strategies for progress that also make business sense.
Job quality metrics
1. What percent of workers earn a living wage, as defined by their geographical location?13 How many have healthcare?
2. How many new jobs are created each year in each quintile with a living wage?
Economic mobility
Many low-wage workers churn from one low-wage job to the next, seeing little wage growth. Research shows that ‘stepping stone’ jobs that have historically helped workers transition from low to high wages are becoming a smaller share of all jobs.14 Companies that have more of these stepping stone jobs are not only contributing to a robust middle class and more stable workforce, but also can benefit from a more diverse pipeline of entry level workers and improved retention. Increasing internal mobility can also be good for a company’s bottom line, since internal hires are often less expensive and more productive than external hires.15
Internal promotions are not the only way workers move up. The organizational structure of a company may make it impractical to expect every frontline worker be able to move up internally. This will be increasingly the case as firms outsource low-wage work to contract firms that can offer little mobility to their workers. Companies that help low-wage workers transition to higher wages even when they leave the company can also make a company more attractive to prospective employees. Walmart’s recent program offering trade skills (among them plumbing certificates) is a bet in that direction.16
Companies can improve mobility rates by paying higher wages, offering training opportunities and unlocking barriers to more promising jobs.17 Doing so can attract talent, alleviate workers’ competing stressors and increase their skillsets; in turn, they can work more autonomously and be more productive.18
Rapidly churning through jobs makes it difficult for workers to accumulate the know-how required for experience to translate to better opportunities. Rapid churn also makes workers expensive to companies. Increasing a worker’s minimum tenure at a company can translate into upward economic mobility—though very long tenures can also result in stagnation. Mobility rates tend to grow over a low-wage worker’s first three years with a company before falling thereafter.19 In turn, lower turnover rates, decreased absenteeism, and lower replacement costs all help a company’s bottom line.20
Economic mobility metrics
3. What percentage of workers that started in the lowest paid quintile (those that make less than $12.31 an hour) moved to above living wage ($16.54 an hour) each year?21
4. What percentage of your lowest paid workers left before the one-year mark? How many left before the two year mark?
Job equity
Gender and racial inequities appear in both the share of workers in high-quality, high-paying jobs and in the share of workers who see economic mobility.
Research into “occupational segregation” has long shown that women, Black, and Hispanic workers are often disproportionately underrepresented in higher-paying occupations. Due to their underrepresentation in the highest quintile of occupations, we estimate that women, Black, and Hispanic workers annually underearn by $106 billion, $153 billion, and $310 billion, respectively.22
Many companies have set representation goals for themselves for diversity at the board and different levels of the organization. But they often find themselves competing for a small pool of diverse talent within their company. Using the right metrics can help by breaking down the problem to look at the talent building process.
For example, to unlock diversity at the top, managers may want to look at mobility rates in their company across demographics. Across the economy, we find race and gender mobility gaps hold some workers back. When female, Black, and Hispanic workers switch occupations, they move up less often than their male, white, and Asian counterparts. These mobility gaps are only partly explained by workers’ education levels, for the gaps persist even among highly educated workers.
This type of analysis can also help firms diversify their highest paid occupations, specifically if they can diversify the pipeline of entry-level positions and unlock bottlenecks to make sure all workers progress through the company. Our research shows that some barriers to economic mobility exist along specific pathways from one occupation to the next. Even well-traveled pathways from low- to mid- and high-wage work are marked by racial, ethnic, and gender disparities. Pinpointing exactly where these barriers exist within a company is the first step to alleviating them. (See example below.)
How companies can make mobility gaps actionable: Unlocking pathways to high-paying jobs in healthcare
Companies can measure the most frequent sources of promotion for their workforce. For example, a grocery retailer might examine the pathway from grocery bagger to cashier to assistant manager. They can look at the rates of promotion at each step and then compare the rates of different groups. The example below shows a common pathway in the healthcare sector. It shows how mobility rates vary between white, Black, and Hispanic workers as they transition from licensed practical nurses (LPNs) to registered nurses. White LPNs move up to nurses at twice the rate of Black and Hispanic LPNs.
Job equity metrics
5. What is the demographic composition in the company’s high-wage occupations? How does it compare to that of the labor force in your region?
6. What are the mobility gaps in each of the company’s wage quintiles?
The key metrics highlighted above are important for companies to track because they connect directly to labor market trends that affect workers’ access to good jobs, mobility, and equity. Managers that are able to measure and track progress across them are likely to see increased job quality and the engagement and loyalty it engenders. Broad adoption of the metrics will also add to the evidence base for successful practices and their impact on performance—ultimately contributing to an effective human capital disclosure scheme that constantly adapts and improves with the times.
The Job Quality Metrics project includes Ethan Rouen, Natalie Geismar, Jay Garg, and Ian Seyal. It is informed by a working group in 17 Rooms and part of Leadership Now’s Business for Racial Equity Pledge signed by more than 1,000 private sector leaders.
Expanded list of workforce composition questions and company metrics
To get started, below is a tailored set of practical and actionable metrics that most companies can realistically begin measuring and tracking. Compiling workforce metrics will allow firms to track metrics on job quality, mobility, and equity; assess their baseline, measure impact, and set goals accordingly.
Workforce composition metrics
Total number of workers (including full-time, part-time, and contract workers)
Percent of workers, by wage quintile, who are full-time employees, part-time employees, and contract workers
Gender and racial breakdown of employees at each wage quintile
Gender and racial breakdown of employees in each job title, occupation, or job level
Breakdown of educational attainment for each wage quintile
Corporate EBITDA and revenue (to assess impact of metrics on firm performance)
Job quality metrics
Percent of workers, by wage quintile, with a living wage, as defined by their geographical location, and employer-sponsored healthcare benefits
Number of new jobs created by wage quintile.
Number of new jobs created with a living wage and healthcare benefits by wage quintile
Median annual wage, by wage quintile
Median training expenditure per year per employee, by wage quintile
Median training expenditure per employee, excluding job-related trainings (e.g., compliance training), by wage quintile
Economic mobility and job equity metrics
What percentage of workers that started in the lowest paid quintile (those that make less than $12.31 an hour) moved to above living wage ($16.54 an hour) each year?
What percentage of your lowest paid workers crossed the one-year mark? How many crossed the two-year mark?
Internal promotion rate, by wage quintile, race, and gender
Horizontal job change rate, by wage quintile
Average wage gain per promotion, by wage quintile
Voluntary and involuntary turnover rate, by wage quintile, race, and gender
Number of jobs posted that do not require a bachelor’s degree (BA) and the percentage of those postings actually filled by workers without BAs
Qualitative practices
Presence of rotational or cross-training programs to promote learning new skills
Presence of internship, mentorship, and/or apprenticeship programs
Presence of career development programming, through HR or other sources
Does your company consider the work practices of contracted (B2B) companies and/or vendors? If so, how?
Are open positions promoted internally through internal job boards or other mechanisms?
Tracking common occupation pathways within the company (see the example above on unlocking pathways in healthcare)
Do senior employees participate in creating curricula with external reskilling organizations (community colleges, non-profits, vendors) for entry-level positions or mid-level roles?
Do you follow workers after they leave, did they receive a wage upgrade?
6 job quality metrics every company should know
Today’s labor shortages make it an auspicious moment for companies ready to measure and improve labor conditions. To do so, corporate boards and management need a clear way to manage retention by understanding its link to job quality.
The economic recovery from the COVID-19 pandemic has created a uniquely tight labor market with millions of unfilled job postings.1 Though shutdowns and business restrictions hit low-wage workers especially hard, evidence suggests that, in this recovery, they have options.2 Many low- and high-wage workers alike have seized the moment and quit their jobs in search of higher quality work and economic mobility.3
Employers who have seen workers leave and who have been unable to rehire feel the costs of attrition. For example, one large food distributor found the labor crunch reduced their ability to respond to customers’ demand, which led to cuts in production, distribution routes, and ultimately lost market share. Stories like this show how dramatic costs can explicitly result—in a short timeframe—from poor working conditions and high turnover.
As a result, many companies find they are playing catch-up to their competitors who made employee retention and well-being a priority years ago.4 Beyond the promise of higher returns to companies with positive culture and high rates of retention, forward-looking companies may benefit from taking steps now in anticipation of the growing call for SEC mandated human capital disclosures.5
Additional disclosures, by way of Environmental, Social and Governance (ESG) indicators, are meant to hold companies accountable toward socially beneficial goals—from reducing carbon footprints to promoting economic inclusion. The indicators proposed here are ideal contributors to the “S” in ESG, social impact, as they track companies’ progress in countering trends that have eroded workers’ well-being: a lack of good paying, stable jobs, and limited and inequitable mobility.
Related Content
This report provides simple, evidence-based outcome metrics that promote good quality jobs. Different companies will tackle the metrics differently depending on their industry structure and creativity of their management. Regardless, an important starting point is clarity about which metrics matter when it comes to improving job quality and how to measure progress against them.
In the U.S. labor market, many workers churn from one low-paid, low-quality job to the next.6 Despite their willingness to work hard, workers find it difficult or impossible to advance. And across all sectors of the economy, historical inequities continue to drive down wages and economic mobility of female, Black, and Hispanic workers.7
Data gathering can help determine whether progress toward these challenges, captured in these metrics, also leads to cost savings in hiring, retention, and overall performance. This way, progress at the firm level can also lead toward greater shared prosperity; the metrics below are a first step in that direction.
Key metrics: Job quality, mobility, and equity
Metrics overview and rationale
Job quality
In the last decades, low wage work has become increasingly pervasive and precarious, and at the same time stagnant wages have left many full-time workers unable to afford basic living expenses, forcing them to work multiple jobs. Many have little cushion for emergencies, leading to constant churn and short job tenures.
To track their social impact on job quality, companies can measure the share of their workforce paid living wages and with healthcare. The living wage depends on the cost of housing, health insurance, childcare, and other necessities. The national living wage is $16.54 an hour.8 We estimate that in 2019, 29 percent of workers earned a living wage, accounting for geographical differences in the cost of living.9
Likewise, unexpected health-related expenses can spell disaster for uninsured workers. Healthcare is an important component of job quality as it promotes stability and facilitates mobility. Nine percent of workers in 2019 did not have health insurance.10
Improving wages may seem like an intractable proposition in tight margin industries. However, some companies have made bold commitments, such as Bank of America, which last year raised its minimum wage to $20 an hour.11 The bank and other companies such as Google and Microsoft have also shown willingness to raise wages of workers employed by their vendors, allowing companies that provide food and janitorial services, for example, to pass on the cost of wage increases to their more community focused customers. Other companies are experimenting with profit sharing agreements for employees across the wage spectrum.12 Profit sharing strategies can increase worker engagement particularly when workers already earn more than a living wage. Whatever the strategy, and despite perceived limited flexibility when it comes to wages, measuring outcomes can help identify strategies for progress that also make business sense.
Job quality metrics
1. What percent of workers earn a living wage, as defined by their geographical location?13 How many have healthcare?
2. How many new jobs are created each year in each quintile with a living wage?
Economic mobility
Many low-wage workers churn from one low-wage job to the next, seeing little wage growth. Research shows that ‘stepping stone’ jobs that have historically helped workers transition from low to high wages are becoming a smaller share of all jobs.14 Companies that have more of these stepping stone jobs are not only contributing to a robust middle class and more stable workforce, but also can benefit from a more diverse pipeline of entry level workers and improved retention. Increasing internal mobility can also be good for a company’s bottom line, since internal hires are often less expensive and more productive than external hires.15
Internal promotions are not the only way workers move up. The organizational structure of a company may make it impractical to expect every frontline worker be able to move up internally. This will be increasingly the case as firms outsource low-wage work to contract firms that can offer little mobility to their workers. Companies that help low-wage workers transition to higher wages even when they leave the company can also make a company more attractive to prospective employees. Walmart’s recent program offering trade skills (among them plumbing certificates) is a bet in that direction.16
Companies can improve mobility rates by paying higher wages, offering training opportunities and unlocking barriers to more promising jobs.17 Doing so can attract talent, alleviate workers’ competing stressors and increase their skillsets; in turn, they can work more autonomously and be more productive.18
Rapidly churning through jobs makes it difficult for workers to accumulate the know-how required for experience to translate to better opportunities. Rapid churn also makes workers expensive to companies. Increasing a worker’s minimum tenure at a company can translate into upward economic mobility—though very long tenures can also result in stagnation. Mobility rates tend to grow over a low-wage worker’s first three years with a company before falling thereafter.19 In turn, lower turnover rates, decreased absenteeism, and lower replacement costs all help a company’s bottom line.20
Economic mobility metrics
3. What percentage of workers that started in the lowest paid quintile (those that make less than $12.31 an hour) moved to above living wage ($16.54 an hour) each year?21
4. What percentage of your lowest paid workers left before the one-year mark? How many left before the two year mark?
Job equity
Gender and racial inequities appear in both the share of workers in high-quality, high-paying jobs and in the share of workers who see economic mobility.
Research into “occupational segregation” has long shown that women, Black, and Hispanic workers are often disproportionately underrepresented in higher-paying occupations. Due to their underrepresentation in the highest quintile of occupations, we estimate that women, Black, and Hispanic workers annually underearn by $106 billion, $153 billion, and $310 billion, respectively.22
Many companies have set representation goals for themselves for diversity at the board and different levels of the organization. But they often find themselves competing for a small pool of diverse talent within their company. Using the right metrics can help by breaking down the problem to look at the talent building process.
For example, to unlock diversity at the top, managers may want to look at mobility rates in their company across demographics. Across the economy, we find race and gender mobility gaps hold some workers back. When female, Black, and Hispanic workers switch occupations, they move up less often than their male, white, and Asian counterparts. These mobility gaps are only partly explained by workers’ education levels, for the gaps persist even among highly educated workers.
This type of analysis can also help firms diversify their highest paid occupations, specifically if they can diversify the pipeline of entry-level positions and unlock bottlenecks to make sure all workers progress through the company. Our research shows that some barriers to economic mobility exist along specific pathways from one occupation to the next. Even well-traveled pathways from low- to mid- and high-wage work are marked by racial, ethnic, and gender disparities. Pinpointing exactly where these barriers exist within a company is the first step to alleviating them. (See example below.)
How companies can make mobility gaps actionable: Unlocking pathways to high-paying jobs in healthcare
Companies can measure the most frequent sources of promotion for their workforce. For example, a grocery retailer might examine the pathway from grocery bagger to cashier to assistant manager. They can look at the rates of promotion at each step and then compare the rates of different groups. The example below shows a common pathway in the healthcare sector. It shows how mobility rates vary between white, Black, and Hispanic workers as they transition from licensed practical nurses (LPNs) to registered nurses. White LPNs move up to nurses at twice the rate of Black and Hispanic LPNs.
Job equity metrics
5. What is the demographic composition in the company’s high-wage occupations? How does it compare to that of the labor force in your region?
6. What are the mobility gaps in each of the company’s wage quintiles?
The key metrics highlighted above are important for companies to track because they connect directly to labor market trends that affect workers’ access to good jobs, mobility, and equity. Managers that are able to measure and track progress across them are likely to see increased job quality and the engagement and loyalty it engenders. Broad adoption of the metrics will also add to the evidence base for successful practices and their impact on performance—ultimately contributing to an effective human capital disclosure scheme that constantly adapts and improves with the times.
The Job Quality Metrics project includes Ethan Rouen, Natalie Geismar, Jay Garg, and Ian Seyal. It is informed by a working group in 17 Rooms and part of Leadership Now’s Business for Racial Equity Pledge signed by more than 1,000 private sector leaders.
Expanded list of workforce composition questions and company metrics
To get started, below is a tailored set of practical and actionable metrics that most companies can realistically begin measuring and tracking. Compiling workforce metrics will allow firms to track metrics on job quality, mobility, and equity; assess their baseline, measure impact, and set goals accordingly.
Workforce composition metrics
Total number of workers (including full-time, part-time, and contract workers)
Percent of workers, by wage quintile, who are full-time employees, part-time employees, and contract workers
Gender and racial breakdown of employees at each wage quintile
Gender and racial breakdown of employees in each job title, occupation, or job level
Breakdown of educational attainment for each wage quintile
Corporate EBITDA and revenue (to assess impact of metrics on firm performance)
Job quality metrics
Percent of workers, by wage quintile, with a living wage, as defined by their geographical location, and employer-sponsored healthcare benefits
Number of new jobs created by wage quintile.
Number of new jobs created with a living wage and healthcare benefits by wage quintile
Median annual wage, by wage quintile
Median training expenditure per year per employee, by wage quintile
Median training expenditure per employee, excluding job-related trainings (e.g., compliance training), by wage quintile
Economic mobility and job equity metrics
What percentage of workers that started in the lowest paid quintile (those that make less than $12.31 an hour) moved to above living wage ($16.54 an hour) each year?
What percentage of your lowest paid workers crossed the one-year mark? How many crossed the two-year mark?
Internal promotion rate, by wage quintile, race, and gender
Horizontal job change rate, by wage quintile
Average wage gain per promotion, by wage quintile
Voluntary and involuntary turnover rate, by wage quintile, race, and gender
Number of jobs posted that do not require a bachelor’s degree (BA) and the percentage of those postings actually filled by workers without BAs
Qualitative practices
Presence of rotational or cross-training programs to promote learning new skills
Presence of internship, mentorship, and/or apprenticeship programs
Presence of career development programming, through HR or other sources
Does your company consider the work practices of contracted (B2B) companies and/or vendors? If so, how?
Are open positions promoted internally through internal job boards or other mechanisms?
Tracking common occupation pathways within the company (see the example above on unlocking pathways in healthcare)
Do senior employees participate in creating curricula with external reskilling organizations (community colleges, non-profits, vendors) for entry-level positions or mid-level roles?
Do you follow workers after they leave, did they receive a wage upgrade?
Strengthening international cooperation on AI
Executive Summary
International cooperation on artificial intelligence—why, what, and how
Since 2017, when Canada became the first country to adopt a national AI strategy, at least 60 countries have adopted some form of policy for artificial intelligence (AI). The prospect of an estimated boost of 16 percent, or US$13 trillion, to global output by 2030 has led to an unprecedented race to promote AI uptake across industry, consumer markets, and government services. Global corporate investment in AI has reportedly reached US$60 billion in 2020 and is projected to more than double by 2025.
Cameron F. Kerry
Ann R. and Andrew H. Tisch Distinguished Visiting Fellow – Governance Studies, Center for Technology Innovation
Twitter
@Cam_Kerry
Joshua P. Meltzer
Senior Fellow – Global Economy and Development
Twitter
@JoshuaPMeltzer
Andrea Renda
Senior Research Fellow and Head of Global Governance, Regulation, Innovation and the Digital Economy (GRID) – Center for European Policy Studies (CEPS)
Alex Engler
Fellow – Governance Studies
Twitter
@AlexCEngler
R
Rosanna Fanni
Associate Research Assistant and Digital Forum Coordinator, Global Governance, Regulation, Innovation and the Digital Economy (GRID) – CEPS
At the same time, the work on developing global standards for AI has led to significant developments in various international bodies. These encompass both technical aspects of AI (in standards development organizations (SDOs) such as the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC), and the Institute of Electrical and Electronics Engineers (IEEE) among others) and the ethical and policy dimensions of responsible AI. In addition, in 2018 the G-7 agreed to establish the Global Partnership on AI, a multistakeholder initiative working on projects to explore regulatory issues and opportunities for AI development. The Organization for Economic Cooperation and Development (OECD) launched the AI Policy Observatory to support and inform AI policy development. Several other international organizations have become active in developing proposed frameworks for responsible AI development.
In addition, there has been a proliferation of declarations and frameworks from public and private organizations aimed at guiding the development of responsible AI. While many of these focus on general principles, the past two years have seen efforts to put principles into operation through fully-fledged policy frameworks. Canada’s directive on the use of AI in government, Singapore’s Model AI Governance Framework, Japan’s Social Principles of Human-Centric AI, and the U.K. guidance on understanding AI ethics and safety have been frontrunners in this sense; they were followed by the U.S. guidance to federal agencies on regulation of AI and an executive order on how these agencies should use AI. Most recently, the EU proposal for adoption of regulation on AI has marked the first attempt to introduce a comprehensive legislative scheme governing AI.
Global corporate investment in AI has reportedly reached US$60 billion in 2020 and is projected to more than double by 2025.
In exploring how to align these various policymaking efforts, we focus on the most compelling reasons for stepping up international cooperation (the “why”); the issues and policy domains that appear most ready for enhanced collaboration (the “what”); and the instruments and forums that could be leveraged to achieve meaningful results in advancing international AI standards, regulatory cooperation, and joint R&D projects to tackle global challenges (the “how”). At the end of this report, we list the topics that we propose to explore in our forthcoming group discussions.
Why international cooperation on AI is important
Even more than many domains of science and engineering in the 21st century, the international AI landscape is deeply collaborative, especially when it comes to research, innovation, and standardization. There are several reasons to sustain and enhance international cooperation.
AI research and development is an increasingly complex and resource-intensive endeavor, in which scale is an important advantage. Cooperation among governments and AI researchers and developers across national boundaries can maximize the advantage of scale and exploit comparative advantages for mutual benefit. An absence of international cooperation would lead to competitive and duplicative investments in AI capacity, creating unnecessary costs and leaving each government worse off in AI outcomes. Several essential inputs used in the development of AI, including access to high-quality data (especially for supervised machine learning) and large-scale computing capacity, knowledge, and talent, benefit from scale.
International cooperation based on commonly agreed democratic principles for responsible AI can help focus on responsible AI development and build trust. While much progress has been made aligning on responsible AI, there remain differences—even among Forum for Cooperation on AI (FCAI) participants. The next steps in AI governance involve translating AI principles into policy, regulatory frameworks, and standards. These will require deeper understanding of how AI works in practice and working through the operation of principles in specific contexts and in the face of inevitable tradeoffs, such as may arise when seeking AI that is both accurate and explainable. Effective cooperation will require concrete steps in specific areas, which the recommendations of this report aim to suggest.
When it comes to regulation, divergent approaches can create barriers to innovation and diffusion. Governments’ efforts to boost domestic AI development around concepts of digital sovereignty can have negative spillovers, such as restrictions on access to data, data localization, discriminatory investment, and other requirements. Likewise, diverging risk classification regimes and regulatory requirements can increase costs for businesses seeking to serve the global AI market. Varying governmental AI regulations may necessitate building variations of AI models that can increase the work necessary to build an AI system, leading to higher compliance costs that disproportionately affect smaller firms. Differing regulations may also force variation in how data sets are collected and stored, creating additional complexity in data systems and reducing the general downstream usefulness of the data for AI. Such additional costs may apply to AI as a service as well as hardware-software systems that embed AI solutions, such as autonomous vehicles, robots, or digital medical devices. Enhanced cooperation is key to create a larger market in which different countries can try to leverage their own competitive advantage. For example, the EU seeks to achieve a competitive advantage in “industrial AI”: EU enterprises could exploit that AI without the prospect of having to engage in substantial reengineering to meet requirements of another jurisdiction.
Aligning key aspects of AI regulation can enable specialized firms in AI development to thrive. Such companies generate business by developing expertise in a specialized AI system, then licensing these to other companies as one part of a broader tool. As AI becomes more ubiquitous, complex stacks of specialized AI systems may emerge in many sectors. A more open global market would allow a company to take advantage of digital supply chains, using a single product with a natural language model built in Canada, a video analysis algorithm trained in Japan, and network analysis developed in France. Enabling global competition by such specialized firms will encourage healthier markets and more AI innovation.
Enhanced cooperation in trade is essential to avoid unjustified restrictions to the flow of goods and data, which would substantially reduce the prospective benefits of AI diffusion. While the strategic importance of data and sovereignty has in many countries given rise to legitimate industrial policy initiatives aimed at mapping and reducing dependencies on the rest of the world, protectionist measures can jeopardize global cooperation, impinge on global value chains, and negatively affect consumer choice, thereby reducing market size and overall incentives to invest in meaningful AI solutions.
Enhanced cooperation is needed to tap the potential of AI solutions to address global challenges. No country can “go it alone” in AI, especially when it comes to sharing data and applying AI to tackle global challenges like climate change or pandemic preparedness. The governments involved in the FCAI share interests in deploying AI for global social, humanitarian, and environmental benefit. For example, the EU is proposing to employ AI to support its Green Deal, and the G-7 and GPAI have called for harnessing AI for U.N. Sustainable Development Goals. Collaborative “moonshots” can pool resources to leverage the potential of AI and related technologies to address key global problems in domains such as health care, climate science, or agriculture at the same time as they provide a way to test approaches to responsible AI together.
Cooperation among likeminded countries is important to reaffirm key principles of openness and protection of democracy, freedom of expression, and other human rights. The risks associated with the unconstrained use of AI solutions by techno-authoritarian regimes— such as China’s—expose citizens to potential violations of human rights and threaten to split cyberspace into incompatible technology stacks and fragment the global AI R&D process.
The fact that international cooperation is an element of most governments’ AI strategies indicates that governments appreciate the connection between AI development and collaboration across borders. This report is about concrete ways to realize this connection.
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At the same time, international cooperation should not be interpreted as complete global harmonization: countries legitimately differ in national strategic priorities, legal traditions, economic structures, demography, and geography. International collaboration can nonetheless create the level playing field that would enable countries to engage in fruitful “co-opetition” in AI: agreeing on basic principles and when possible seeking joint outcomes, but also competing for the best solutions to be scaled up at the global level. Robust cooperation based on common principles and values is a foundation for successful national development of AI.
Rules, standards, and R&D projects: Key areas for collaboration
Our exploration of international AI governance through roundtables, other discussions, and research led us to identify three main areas where enhanced collaboration would provide fruitful: regulatory policies, standard-setting, and joint research and development (R&D) projects. Below, we summarize ways in which cooperation may unfold in each of these areas, as well as the extent of collaboration conceivable in the short term as well as in the longer term.
Cooperation on regulatory policy
AI policy development is in the relatively early stages in all countries, and so timely and focused international cooperation can help align AI policies and regulations.
International regulatory cooperation has the potential to reduce regulatory burdens and barriers to trade, incentivize AI development and use, and increase market competition at the global level. That said, countries differ in legal tradition, economic structure, comparative advantage in AI, weighing of civil and fundamental rights, and balance between ex ante regulation and ex post enforcement and litigation systems. Such differences will make it difficult to achieve complete regulatory convergence. Indeed, national AI strategies and policies reflect differences in countries’ willingness to move towards a comprehensive regulatory framework for AI. Despite these differences, AI policy development is in the relatively early stages in all countries, and so timely and focused international cooperation can help align AI policies and regulations.
Against this backdrop, it is reasonable to assume that AI policy development is less embedded in pre-existing legal tradition or frameworks at this stage, and thus that international cooperation in this field can achieve higher levels of integration. The following areas for cooperation emerged from the FCAI dialogues and our other explorations.
Building international cooperation into AI policies. FCAI governments should give effect to their recognition of the need for international engagement on AI by committing to pursue coordination with each other and other international partners prior to adopting domestic AI initiatives.
A common, technology-neutral definition of AI for regulatory purposes. Based on the definitions among FCAI participants and the work of the OECD expert group, converging on a common definition of AI and working together to gradually update the description of an AI system, and its possible configurations and techniques, appears feasible and already partly underway. A common definition is important to guide future cooperation in AI and determines the level of ambition that can be reached by such a process.
Building on a risk-based approach to AI regulation. A variety of governments and other bodies have endorsed a risk-based approach to AI in national strategies and in bilateral or multilateral contexts. Most notably, a risk-based approach is central to the policy frameworks of the two most prominent exemplars of AI policy development—the U.S. and the EU. These recent, broadly parallel developments have opened the door to developing international cooperation on ways to address risks while maximizing benefits. However, there remain challenges to convergence on a risk-based approach. Dialogue on clear identification and classification of risks, approaches to benefit-risk analysis, possible convergence on cases in which the risks are too high to be mitigated, and the type of risk assessment to be performed and who should perform it, would greatly benefit cooperation on a risk-based approach.
Sharing experiences and developing common criteria and standards for auditing AI systems. The field of accountability in AI and algorithms has been the subject of wide and valuable work by civil society organizations as well as governments. The exchange of good practices and—ultimately—a common, or at least a compatible, framework for AI auditing would eliminate significant barriers to the development of a truly international market for AI solutions. It also would facilitate the emergence of third-party auditing standards and an international market for AI auditing, with potential benefits in terms of quality, price, and access for auditing services for deployers of AI. Additionally, exchange of practices and international standards for AI auditing, monitoring, and oversight would significantly help the policy community keep up to speed in market monitoring.
A joint platform for regulatory sandboxes. Even without convergence on risk assessments or regulatory measures, an international platform for regulatory learning involving all governments that participate in FCAI and possibly others is a promising avenue for deepening international cooperation on AI. Such a platform could host an international repository of ongoing experiments on AI-enabled innovations, including regulatory sandboxes. As use of sandboxes becomes a more common way for governments to test the viability and conformity of new AI solutions under legislative and regulatory requirements, updating information on ongoing government initiatives could save resources and inform AI developers and policymakers. Aligning the criteria and overall design of AI sandboxes in different administrations could also increase the prospective benefits and impact of these processes, as developers willing to enter the global market might be able to go through the sandbox process in a single participating country.
Cooperation on AI use in government: procurement and accountability. A natural candidate for further exchange and cooperation in FCAI is the adoption of AI solutions in government, including both “back office” solutions and more public-facing applications. The sharing of good practices and overall lessons on what works when deploying AI in government would also be an important achievement. Important areas in this respect are procurement and effective oversight of deployment.
Sectoral cooperation on AI use cases. A sector-specific approach can ensure higher levels of regulatory certainty. In sectors like finance, key criteria such as fairness, discrimination, and transparency have long been subject to extensive regulatory intervention, and sectoral regulation must ensure continuity while accounting for the increasing use of AI. In health and pharmaceuticals, the use of AI both as a stand-alone solution and embedded in medical devices has prompted a very specific, technical discussion regarding the risk-based approach to be adopted and has already enabled valuable sectoral initiatives. The adoption of different standards and criteria in sectoral regulation may increase regulatory costs for developers willing to serve more than one sector and country with their AI solutions. In such a cross-cutting framework, examples from mature areas of regulation such as finance and health can also become a form of regulatory sandbox to model regulation for other sectors in the future.
Cooperation on sharing data across borders
Data governance is a focal area for international cooperation on AI because of the importance of data as an input for AI R&D and because of the added complexity of regulatory regimes already in place that restrict certain information flows, including data protection and intellectual property laws. Effective international cooperation on AI needs a robust and coherent framework for data protection and data sharing. There are a variety of channels addressing these issues including the Asia-Pacific Economic Cooperation group, the working group on data governance of the Global Partnership on AI, and bilateral discussions between the EU and U.S. Nonetheless, the potential impact of such laws on data available for AI-driven medical and scientific research requires specific focus as the EU both reviews its General Data Protection Regulation and considers new legislation on private and public sector data sharing.
There are other significant data governance issues that may benefit from pooled efforts across borders that, by and large, are the subject of international cooperation. Key areas in this respect include opening government data including international data sharing, improving data interoperability, and promoting technologies for trustworthy data sharing.
Cooperation on international standards for AI
As countries move from developing frameworks and policies to more concrete efforts to regulate AI, demand for AI standards will grow. These include standards for risk management, data governance, and technical documentation that can establish compliance with emerging legal requirements. International AI standards will also be needed to develop commonly accepted labeling practices that can facilitate business-to-business (B2B) contracting and to demonstrate conformity with AI regulations; address the ethics of AI systems (transparency, neutrality/lack of bias, etc.); and maximize the harmonization and interoperability for AI systems globally. International standards from standards development organizations like the ISO/IEC and IEEE can help ensure that global AI systems are ethically sound, robust, and trustworthy, that opportunities from AI are widely distributed, and that standards are technically sound and research-driven regardless of sector or application.
International standards from standards development organizations like the ISO/IEC and IEEE can help ensure that global AI systems are ethically sound, robust, and trustworthy, that opportunities from AI are widely distributed, and that standards are technically sound and research-driven regardless of sector or application.
The governments participating in the FCAI recognize and support industry-led standards setting. While there are differences in how the FCAI participants engage with industry-led standards bodies, a common element is support for the central role of the private sector in driving standards. That said, there is a range of steps that FCAI participants can take to strengthen international cooperation in AI standards. The approach of FCAI participants that emphasizes an industry-led approach to developing international AI standards contrasts with the overall approach of other countries, such as China, where the state is at the center of standards making activities. The more direct involvement by the Chinese government in setting standards, driving the standards agenda, and aligning these with broader Chinese government priorities requires attention by all FCAI participants with the aim of encouraging Chinese engagement in international AI standard-setting consistent with outcomes that are technically robust and industry driven.
Sound AI standards can also support international trade and investment in AI, expanding AI opportunity globally and increasing returns to investment in AI R&D. The World Trade Organization (WTO) Technical Barriers to Trade (TBT) Agreement’s relevance to AI standards is limited by its application only to goods, whereas many AI standards will apply to services. Recent trade agreements have started to address AI issues, including support for AI standards, but more is needed. An effective international AI standards development process is also needed to avoid bifurcated AI standards—centered around China on the one hand and the West on the other. Which outcome prevails will to some extent depend on progress in effective international AI standards development.
R&D cooperation: Selecting international AI projects
Productive discussion of AI ethics, regulation, risks, and benefits requires use cases because the issues are highly contextual. As a result, AI policy development has tended to move from broad principles to specific sectors or use cases. Considering this need, we suggest that developing international cooperation on AI would benefit from putting cooperation into operation with specific use cases. To this end, we propose that FCAI participants expand efforts to deploy AI on important global problems collectively by working toward agreement on joint research aimed at a specific development project (or projects). Such an effort could stimulate development of AI for social benefit and also provide a forcing function for overcoming differences in approaches to AI policy and regulation.
Criteria for the kinds of goals or projects to consider include the following:
Global significance. The project should be aimed at important global issues that demand transnational solutions. The shared importance of the issues should give all participants a common stake and, if successful, could contribute toward global welfare.
Global scale. The problem and the scope of the project should require resources on a large enough scale that the pooled support of leading governments and institutions adds significant value.
A public good. Given its significance and scale, the project would amount to a public good. In turn, the output of the project should also be a public good and both the project and the output should be available to all participants and less developed countries.
A collaborative test bed. Governance of the project is likely to necessitate addressing regulatory, ethical, and risk questions in a context that is concrete and in which the participants have incentives to achieve results. It would amount to a very large and shared regulatory sandbox.
Assessable impact. The project will need to be monitored commensurately with its scale, public visibility, and experimental nature. Participants will need to assess progress toward both defined project goals and broader impact.
A multistakeholder effort. Considering its public importance and the resources it should marshal, the project will need to be government-initiated. But the architecture and governance should be open to nongovernmental participation on a shared basis.
This proposal could be modeled on several large-scale international scientific collaborations: CERN, the Human Genome Project, or the International Space Station. It would also build on numerous initiatives toward collaborative research and development on AI. Similar global collaboration will be more difficult in a world of increased geopolitical and economic competition, nationalism, nativism, and protectionism among governments that have been key players in these efforts.
Recommendations
Below, we present recommendations for developing international cooperation on AI based on our discussions and work to date.
R1. Commit to considering international cooperation in drafting and implementing national AI policies.
This recommendation could be implemented within a relatively short timeframe and initially would take the form of firm declarations by individual countries. Ultimately this could lead to a joint declaration with clear commitments on the part of the governments involved.
R2. Refine a common approach to responsible AI development.
This type of recommendation requires enhanced cooperation between FCAI governments, which can then provide a good basis for incremental forms of cooperation.
R3. Agree on a common, technology-neutral definition of AI systems.
FCAI governments should work on a common definition of AI that is technology-neutral and broad. This recommendation can be implemented in a relatively short term and requires joint action by FCAI governments. The time to act is short, as the rather broad definition given in the EU AI Act is still undergoing the legislative process in the EU and many other countries are still shaping their AI policy frameworks.
R4. Agree on the contours of a risk-based approach.
Alignment on this key element of AI policy would be an important step towards an interoperable system of responsible AI. It would also facilitate cooperation among FCAI governments, industry, and civil society working on AI standards in international SDOs. General agreement on a risk-based approach could be achieved in the short term; developing the contours of a risk-based classification system would probably take more time and require deeper cooperation among FCAI governments as well as stakeholders.
R5. Establish “redlines” in developing and deploying AI.
This may entail an iterative process. FCAI governments could agree on an initial, limited list of redlines such as certain AI uses for generalized social scoring by governments; and then gradually expand the list over time to include emerging AI uses on which there is substantial agreement on the need to prohibit use.
R6. Strengthen sectoral cooperation, starting with more developed policy domains.
Sectoral cooperation can be organized on relatively short timeframes starting from sectors that have well-developed regulatory systems and present higher risks, such as health care, transport and finance, in which sectoral regulation already exists, and its adaptation to AI could be achieved relatively swiftly.
R7. Create a joint platform for regulatory learning and experiments.
A joint repository could stimulate dialogue on how to design and implement sandboxes and secure sound governance, transparency, and reproducibility of results, and aid their transferability across jurisdictions and categories of users. This recommended action is independent of others and is feasible in the short term. It requires soft cooperation, in the form of a structured exchange of good practices. Over time, the repository should become richer in terms of content, and therefore more useful.
R8. Step up cooperation and exchange of practices on the use of AI in government.
FCAI governments could set up, either as a stand-alone initiative or in the context of a broader framework for cooperation, a structured exchange on government uses of AI. The dialogue may involve AI applications to improve the functioning of public administration such as the administration of public benefits or health care; AI-enabled regulation and regulatory governance practices; or other decision-making and standards and procedures for AI procurement. This recommended action could be implemented in the short term, although collecting all experiences and setting the stage for further cooperation would require more time.
R9. Step up cooperation on accountability.
FCAI governments could profit from enhanced cooperation on accountability, whether through market oversight and enforcement, auditing requirements, or otherwise. This could combine with sectoral cooperation and possibly also with standards development for auditing AI systems.
R10. Assess the impact of AI on international data governance.
There is a need for a common understanding of how data governance rules affect AI R&D in areas such as health research and other scientific research, and whether they inhibit the exploration that is an essential part of both scientific discovery and machine learning. There is also need for a critical look at R&D methods to develop a deeper understanding of appropriate boundaries on use of personal data or other protected information. In turn, there is also a need to expand R&D and understanding in privacy-protecting technologies that can enable exploration and discovery while protecting personal information.
R11. Adopt a stepwise, inclusive approach to international AI standardization.
A stepwise approach to standards development is needed to allow time for technology development and experimentation and to gather the data and use cases to support robust standards. It also would ensure that discussions at the international level happen once technology has reached a certain level of maturity or where a regulatory environment is adopted. To support such an approach, it would be helpful to establish a comprehensive database of AI standards under development at national and international levels.
R12. Develop a coordinated approach to AI standards development that encourages Chinese participation consistent with an industry-led, research-driven approach.
There is currently a risk of disconnect between growing concern among governments and national security officials alarmed by Chinese engagement in the standards process on the one hand, and industry participants’ perceptions of the impact of Chinese participation in SDOs on the other. To encourage constructive involvement and discourage self-serving standards, FCAI participants (and likeminded countries) should encourage Chinese engagement in international standards setting while also agreeing on costs for actions that use SDOs strategically to slow down or stall standards making. This can be accomplished through trade and other measures but will require cooperation among FCAI participants to be effective.
R13. Expand trade rules for AI standards.
The rules governing use of international standards in the WTO TBT Agreement and free trade agreements are limited to goods only, whereas AI standards will apply mainly to services. New trade rules are needed that extend rules on international standards to services. As a starting point, such rules should be developed in the context of bilateral free trade agreements or plurilateral agreements, with the aim to make them multilateral in the WTO. Trade rules are also needed to support data free flow with trust and to reduce barriers and costs to AI infrastructure. Consideration also should be given to linking participation in the development of AI standards in bodies such as ISO/IEC, with broader trade policy goals and compliance with core WTO commitments.
R14. Increase funding for participation in SDOs.
Funding should be earmarked for academics and industry participation in SDOs, as well as for SDO meetings in FCAI countries and more broadly in less developed countries. Broadened participation is important to democratize the standards making process and strengthen the legitimacy and adoption of the resulting standards. Hosting meetings of standards bodies in diverse countries can broaden exposure to standards-setting processes around AI and critical technology.
R15. Develop common criteria and governance arrangements for international large-scale R&D projects.
Joint research and development applying to large-scale global problems such as climate change or disease prevention and treatment can have two valuable effects: It can bring additional resources to the solution of pressing global challenges, and the collaboration can help to find common ground in addressing differences in approaches to AI. FCAI will seek to incubate a concrete roadmap on such R&D for adoption by FCAI participants as well as other governments and international organizations. Using collaboration on R&D as a mechanism to work through matters that affect international cooperation on AI policy means that this recommendation should play out in the near term.
Proposed future topics for FCAI dialogues
– Scaling R&D cooperation on AI projects.
– China and AI: what are the risks, opportunities, and ways forward?
– Government use of AI: developing common approaches.
– Regulatory cooperation and harmonization: issues and mechanisms.
– A suitable international framework for data governance.
– Standards development.
– An AI trade agreement: partners, content, and strategy.
Download the full report
Strengthening international cooperation on AI
Executive Summary
International cooperation on artificial intelligence—why, what, and how
Since 2017, when Canada became the first country to adopt a national AI strategy, at least 60 countries have adopted some form of policy for artificial intelligence (AI). The prospect of an estimated boost of 16 percent, or US$13 trillion, to global output by 2030 has led to an unprecedented race to promote AI uptake across industry, consumer markets, and government services. Global corporate investment in AI has reportedly reached US$60 billion in 2020 and is projected to more than double by 2025.
At the same time, the work on developing global standards for AI has led to significant developments in various international bodies. These encompass both technical aspects of AI (in standards development organizations (SDOs) such as the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC), and the Institute of Electrical and Electronics Engineers (IEEE) among others) and the ethical and policy dimensions of responsible AI. In addition, in 2018 the G-7 agreed to establish the Global Partnership on AI, a multistakeholder initiative working on projects to explore regulatory issues and opportunities for AI development. The Organization for Economic Cooperation and Development (OECD) launched the AI Policy Observatory to support and inform AI policy development. Several other international organizations have become active in developing proposed frameworks for responsible AI development.
In addition, there has been a proliferation of declarations and frameworks from public and private organizations aimed at guiding the development of responsible AI. While many of these focus on general principles, the past two years have seen efforts to put principles into operation through fully-fledged policy frameworks. Canada’s directive on the use of AI in government, Singapore’s Model AI Governance Framework, Japan’s Social Principles of Human-Centric AI, and the U.K. guidance on understanding AI ethics and safety have been frontrunners in this sense; they were followed by the U.S. guidance to federal agencies on regulation of AI and an executive order on how these agencies should use AI. Most recently, the EU proposal for adoption of regulation on AI has marked the first attempt to introduce a comprehensive legislative scheme governing AI.
Global corporate investment in AI has reportedly reached US$60 billion in 2020 and is projected to more than double by 2025.
In exploring how to align these various policymaking efforts, we focus on the most compelling reasons for stepping up international cooperation (the “why”); the issues and policy domains that appear most ready for enhanced collaboration (the “what”); and the instruments and forums that could be leveraged to achieve meaningful results in advancing international AI standards, regulatory cooperation, and joint R&D projects to tackle global challenges (the “how”). At the end of this report, we list the topics that we propose to explore in our forthcoming group discussions.
Why international cooperation on AI is important
Even more than many domains of science and engineering in the 21st century, the international AI landscape is deeply collaborative, especially when it comes to research, innovation, and standardization. There are several reasons to sustain and enhance international cooperation.
AI research and development is an increasingly complex and resource-intensive endeavor, in which scale is an important advantage. Cooperation among governments and AI researchers and developers across national boundaries can maximize the advantage of scale and exploit comparative advantages for mutual benefit. An absence of international cooperation would lead to competitive and duplicative investments in AI capacity, creating unnecessary costs and leaving each government worse off in AI outcomes. Several essential inputs used in the development of AI, including access to high-quality data (especially for supervised machine learning) and large-scale computing capacity, knowledge, and talent, benefit from scale.
International cooperation based on commonly agreed democratic principles for responsible AI can help focus on responsible AI development and build trust. While much progress has been made aligning on responsible AI, there remain differences—even among Forum for Cooperation on AI (FCAI) participants. The next steps in AI governance involve translating AI principles into policy, regulatory frameworks, and standards. These will require deeper understanding of how AI works in practice and working through the operation of principles in specific contexts and in the face of inevitable tradeoffs, such as may arise when seeking AI that is both accurate and explainable. Effective cooperation will require concrete steps in specific areas, which the recommendations of this report aim to suggest.
When it comes to regulation, divergent approaches can create barriers to innovation and diffusion. Governments’ efforts to boost domestic AI development around concepts of digital sovereignty can have negative spillovers, such as restrictions on access to data, data localization, discriminatory investment, and other requirements. Likewise, diverging risk classification regimes and regulatory requirements can increase costs for businesses seeking to serve the global AI market. Varying governmental AI regulations may necessitate building variations of AI models that can increase the work necessary to build an AI system, leading to higher compliance costs that disproportionately affect smaller firms. Differing regulations may also force variation in how data sets are collected and stored, creating additional complexity in data systems and reducing the general downstream usefulness of the data for AI. Such additional costs may apply to AI as a service as well as hardware-software systems that embed AI solutions, such as autonomous vehicles, robots, or digital medical devices. Enhanced cooperation is key to create a larger market in which different countries can try to leverage their own competitive advantage. For example, the EU seeks to achieve a competitive advantage in “industrial AI”: EU enterprises could exploit that AI without the prospect of having to engage in substantial reengineering to meet requirements of another jurisdiction.
Aligning key aspects of AI regulation can enable specialized firms in AI development to thrive. Such companies generate business by developing expertise in a specialized AI system, then licensing these to other companies as one part of a broader tool. As AI becomes more ubiquitous, complex stacks of specialized AI systems may emerge in many sectors. A more open global market would allow a company to take advantage of digital supply chains, using a single product with a natural language model built in Canada, a video analysis algorithm trained in Japan, and network analysis developed in France. Enabling global competition by such specialized firms will encourage healthier markets and more AI innovation.
Enhanced cooperation in trade is essential to avoid unjustified restrictions to the flow of goods and data, which would substantially reduce the prospective benefits of AI diffusion. While the strategic importance of data and sovereignty has in many countries given rise to legitimate industrial policy initiatives aimed at mapping and reducing dependencies on the rest of the world, protectionist measures can jeopardize global cooperation, impinge on global value chains, and negatively affect consumer choice, thereby reducing market size and overall incentives to invest in meaningful AI solutions.
Enhanced cooperation is needed to tap the potential of AI solutions to address global challenges. No country can “go it alone” in AI, especially when it comes to sharing data and applying AI to tackle global challenges like climate change or pandemic preparedness. The governments involved in the FCAI share interests in deploying AI for global social, humanitarian, and environmental benefit. For example, the EU is proposing to employ AI to support its Green Deal, and the G-7 and GPAI have called for harnessing AI for U.N. Sustainable Development Goals. Collaborative “moonshots” can pool resources to leverage the potential of AI and related technologies to address key global problems in domains such as health care, climate science, or agriculture at the same time as they provide a way to test approaches to responsible AI together.
Cooperation among likeminded countries is important to reaffirm key principles of openness and protection of democracy, freedom of expression, and other human rights. The risks associated with the unconstrained use of AI solutions by techno-authoritarian regimes— such as China’s—expose citizens to potential violations of human rights and threaten to split cyberspace into incompatible technology stacks and fragment the global AI R&D process.
The fact that international cooperation is an element of most governments’ AI strategies indicates that governments appreciate the connection between AI development and collaboration across borders. This report is about concrete ways to realize this connection.
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At the same time, international cooperation should not be interpreted as complete global harmonization: countries legitimately differ in national strategic priorities, legal traditions, economic structures, demography, and geography. International collaboration can nonetheless create the level playing field that would enable countries to engage in fruitful “co-opetition” in AI: agreeing on basic principles and when possible seeking joint outcomes, but also competing for the best solutions to be scaled up at the global level. Robust cooperation based on common principles and values is a foundation for successful national development of AI.
Rules, standards, and R&D projects: Key areas for collaboration
Our exploration of international AI governance through roundtables, other discussions, and research led us to identify three main areas where enhanced collaboration would provide fruitful: regulatory policies, standard-setting, and joint research and development (R&D) projects. Below, we summarize ways in which cooperation may unfold in each of these areas, as well as the extent of collaboration conceivable in the short term as well as in the longer term.
Cooperation on regulatory policy
AI policy development is in the relatively early stages in all countries, and so timely and focused international cooperation can help align AI policies and regulations.
International regulatory cooperation has the potential to reduce regulatory burdens and barriers to trade, incentivize AI development and use, and increase market competition at the global level. That said, countries differ in legal tradition, economic structure, comparative advantage in AI, weighing of civil and fundamental rights, and balance between ex ante regulation and ex post enforcement and litigation systems. Such differences will make it difficult to achieve complete regulatory convergence. Indeed, national AI strategies and policies reflect differences in countries’ willingness to move towards a comprehensive regulatory framework for AI. Despite these differences, AI policy development is in the relatively early stages in all countries, and so timely and focused international cooperation can help align AI policies and regulations.
Against this backdrop, it is reasonable to assume that AI policy development is less embedded in pre-existing legal tradition or frameworks at this stage, and thus that international cooperation in this field can achieve higher levels of integration. The following areas for cooperation emerged from the FCAI dialogues and our other explorations.
Building international cooperation into AI policies. FCAI governments should give effect to their recognition of the need for international engagement on AI by committing to pursue coordination with each other and other international partners prior to adopting domestic AI initiatives.
A common, technology-neutral definition of AI for regulatory purposes. Based on the definitions among FCAI participants and the work of the OECD expert group, converging on a common definition of AI and working together to gradually update the description of an AI system, and its possible configurations and techniques, appears feasible and already partly underway. A common definition is important to guide future cooperation in AI and determines the level of ambition that can be reached by such a process.
Building on a risk-based approach to AI regulation. A variety of governments and other bodies have endorsed a risk-based approach to AI in national strategies and in bilateral or multilateral contexts. Most notably, a risk-based approach is central to the policy frameworks of the two most prominent exemplars of AI policy development—the U.S. and the EU. These recent, broadly parallel developments have opened the door to developing international cooperation on ways to address risks while maximizing benefits. However, there remain challenges to convergence on a risk-based approach. Dialogue on clear identification and classification of risks, approaches to benefit-risk analysis, possible convergence on cases in which the risks are too high to be mitigated, and the type of risk assessment to be performed and who should perform it, would greatly benefit cooperation on a risk-based approach.
Sharing experiences and developing common criteria and standards for auditing AI systems. The field of accountability in AI and algorithms has been the subject of wide and valuable work by civil society organizations as well as governments. The exchange of good practices and—ultimately—a common, or at least a compatible, framework for AI auditing would eliminate significant barriers to the development of a truly international market for AI solutions. It also would facilitate the emergence of third-party auditing standards and an international market for AI auditing, with potential benefits in terms of quality, price, and access for auditing services for deployers of AI. Additionally, exchange of practices and international standards for AI auditing, monitoring, and oversight would significantly help the policy community keep up to speed in market monitoring.
A joint platform for regulatory sandboxes. Even without convergence on risk assessments or regulatory measures, an international platform for regulatory learning involving all governments that participate in FCAI and possibly others is a promising avenue for deepening international cooperation on AI. Such a platform could host an international repository of ongoing experiments on AI-enabled innovations, including regulatory sandboxes. As use of sandboxes becomes a more common way for governments to test the viability and conformity of new AI solutions under legislative and regulatory requirements, updating information on ongoing government initiatives could save resources and inform AI developers and policymakers. Aligning the criteria and overall design of AI sandboxes in different administrations could also increase the prospective benefits and impact of these processes, as developers willing to enter the global market might be able to go through the sandbox process in a single participating country.
Cooperation on AI use in government: procurement and accountability. A natural candidate for further exchange and cooperation in FCAI is the adoption of AI solutions in government, including both “back office” solutions and more public-facing applications. The sharing of good practices and overall lessons on what works when deploying AI in government would also be an important achievement. Important areas in this respect are procurement and effective oversight of deployment.
Sectoral cooperation on AI use cases. A sector-specific approach can ensure higher levels of regulatory certainty. In sectors like finance, key criteria such as fairness, discrimination, and transparency have long been subject to extensive regulatory intervention, and sectoral regulation must ensure continuity while accounting for the increasing use of AI. In health and pharmaceuticals, the use of AI both as a stand-alone solution and embedded in medical devices has prompted a very specific, technical discussion regarding the risk-based approach to be adopted and has already enabled valuable sectoral initiatives. The adoption of different standards and criteria in sectoral regulation may increase regulatory costs for developers willing to serve more than one sector and country with their AI solutions. In such a cross-cutting framework, examples from mature areas of regulation such as finance and health can also become a form of regulatory sandbox to model regulation for other sectors in the future.
Cooperation on sharing data across borders
Data governance is a focal area for international cooperation on AI because of the importance of data as an input for AI R&D and because of the added complexity of regulatory regimes already in place that restrict certain information flows, including data protection and intellectual property laws. Effective international cooperation on AI needs a robust and coherent framework for data protection and data sharing. There are a variety of channels addressing these issues including the Asia-Pacific Economic Cooperation group, the working group on data governance of the Global Partnership on AI, and bilateral discussions between the EU and U.S. Nonetheless, the potential impact of such laws on data available for AI-driven medical and scientific research requires specific focus as the EU both reviews its General Data Protection Regulation and considers new legislation on private and public sector data sharing.
There are other significant data governance issues that may benefit from pooled efforts across borders that, by and large, are the subject of international cooperation. Key areas in this respect include opening government data including international data sharing, improving data interoperability, and promoting technologies for trustworthy data sharing.
Cooperation on international standards for AI
As countries move from developing frameworks and policies to more concrete efforts to regulate AI, demand for AI standards will grow. These include standards for risk management, data governance, and technical documentation that can establish compliance with emerging legal requirements. International AI standards will also be needed to develop commonly accepted labeling practices that can facilitate business-to-business (B2B) contracting and to demonstrate conformity with AI regulations; address the ethics of AI systems (transparency, neutrality/lack of bias, etc.); and maximize the harmonization and interoperability for AI systems globally. International standards from standards development organizations like the ISO/IEC and IEEE can help ensure that global AI systems are ethically sound, robust, and trustworthy, that opportunities from AI are widely distributed, and that standards are technically sound and research-driven regardless of sector or application.
International standards from standards development organizations like the ISO/IEC and IEEE can help ensure that global AI systems are ethically sound, robust, and trustworthy, that opportunities from AI are widely distributed, and that standards are technically sound and research-driven regardless of sector or application.
The governments participating in the FCAI recognize and support industry-led standards setting. While there are differences in how the FCAI participants engage with industry-led standards bodies, a common element is support for the central role of the private sector in driving standards. That said, there is a range of steps that FCAI participants can take to strengthen international cooperation in AI standards. The approach of FCAI participants that emphasizes an industry-led approach to developing international AI standards contrasts with the overall approach of other countries, such as China, where the state is at the center of standards making activities. The more direct involvement by the Chinese government in setting standards, driving the standards agenda, and aligning these with broader Chinese government priorities requires attention by all FCAI participants with the aim of encouraging Chinese engagement in international AI standard-setting consistent with outcomes that are technically robust and industry driven.
Sound AI standards can also support international trade and investment in AI, expanding AI opportunity globally and increasing returns to investment in AI R&D. The World Trade Organization (WTO) Technical Barriers to Trade (TBT) Agreement’s relevance to AI standards is limited by its application only to goods, whereas many AI standards will apply to services. Recent trade agreements have started to address AI issues, including support for AI standards, but more is needed. An effective international AI standards development process is also needed to avoid bifurcated AI standards—centered around China on the one hand and the West on the other. Which outcome prevails will to some extent depend on progress in effective international AI standards development.
R&D cooperation: Selecting international AI projects
Productive discussion of AI ethics, regulation, risks, and benefits requires use cases because the issues are highly contextual. As a result, AI policy development has tended to move from broad principles to specific sectors or use cases. Considering this need, we suggest that developing international cooperation on AI would benefit from putting cooperation into operation with specific use cases. To this end, we propose that FCAI participants expand efforts to deploy AI on important global problems collectively by working toward agreement on joint research aimed at a specific development project (or projects). Such an effort could stimulate development of AI for social benefit and also provide a forcing function for overcoming differences in approaches to AI policy and regulation.
Criteria for the kinds of goals or projects to consider include the following:
Global significance. The project should be aimed at important global issues that demand transnational solutions. The shared importance of the issues should give all participants a common stake and, if successful, could contribute toward global welfare.
Global scale. The problem and the scope of the project should require resources on a large enough scale that the pooled support of leading governments and institutions adds significant value.
A public good. Given its significance and scale, the project would amount to a public good. In turn, the output of the project should also be a public good and both the project and the output should be available to all participants and less developed countries.
A collaborative test bed. Governance of the project is likely to necessitate addressing regulatory, ethical, and risk questions in a context that is concrete and in which the participants have incentives to achieve results. It would amount to a very large and shared regulatory sandbox.
Assessable impact. The project will need to be monitored commensurately with its scale, public visibility, and experimental nature. Participants will need to assess progress toward both defined project goals and broader impact.
A multistakeholder effort. Considering its public importance and the resources it should marshal, the project will need to be government-initiated. But the architecture and governance should be open to nongovernmental participation on a shared basis.
This proposal could be modeled on several large-scale international scientific collaborations: CERN, the Human Genome Project, or the International Space Station. It would also build on numerous initiatives toward collaborative research and development on AI. Similar global collaboration will be more difficult in a world of increased geopolitical and economic competition, nationalism, nativism, and protectionism among governments that have been key players in these efforts.
Recommendations
Below, we present recommendations for developing international cooperation on AI based on our discussions and work to date.
R1. Commit to considering international cooperation in drafting and implementing national AI policies.
This recommendation could be implemented within a relatively short timeframe and initially would take the form of firm declarations by individual countries. Ultimately this could lead to a joint declaration with clear commitments on the part of the governments involved.
R2. Refine a common approach to responsible AI development.
This type of recommendation requires enhanced cooperation between FCAI governments, which can then provide a good basis for incremental forms of cooperation.
R3. Agree on a common, technology-neutral definition of AI systems.
FCAI governments should work on a common definition of AI that is technology-neutral and broad. This recommendation can be implemented in a relatively short term and requires joint action by FCAI governments. The time to act is short, as the rather broad definition given in the EU AI Act is still undergoing the legislative process in the EU and many other countries are still shaping their AI policy frameworks.
R4. Agree on the contours of a risk-based approach.
Alignment on this key element of AI policy would be an important step towards an interoperable system of responsible AI. It would also facilitate cooperation among FCAI governments, industry, and civil society working on AI standards in international SDOs. General agreement on a risk-based approach could be achieved in the short term; developing the contours of a risk-based classification system would probably take more time and require deeper cooperation among FCAI governments as well as stakeholders.
R5. Establish “redlines” in developing and deploying AI.
This may entail an iterative process. FCAI governments could agree on an initial, limited list of redlines such as certain AI uses for generalized social scoring by governments; and then gradually expand the list over time to include emerging AI uses on which there is substantial agreement on the need to prohibit use.
R6. Strengthen sectoral cooperation, starting with more developed policy domains.
Sectoral cooperation can be organized on relatively short timeframes starting from sectors that have well-developed regulatory systems and present higher risks, such as health care, transport and finance, in which sectoral regulation already exists, and its adaptation to AI could be achieved relatively swiftly.
R7. Create a joint platform for regulatory learning and experiments.
A joint repository could stimulate dialogue on how to design and implement sandboxes and secure sound governance, transparency, and reproducibility of results, and aid their transferability across jurisdictions and categories of users. This recommended action is independent of others and is feasible in the short term. It requires soft cooperation, in the form of a structured exchange of good practices. Over time, the repository should become richer in terms of content, and therefore more useful.
R8. Step up cooperation and exchange of practices on the use of AI in government.
FCAI governments could set up, either as a stand-alone initiative or in the context of a broader framework for cooperation, a structured exchange on government uses of AI. The dialogue may involve AI applications to improve the functioning of public administration such as the administration of public benefits or health care; AI-enabled regulation and regulatory governance practices; or other decision-making and standards and procedures for AI procurement. This recommended action could be implemented in the short term, although collecting all experiences and setting the stage for further cooperation would require more time.
R9. Step up cooperation on accountability.
FCAI governments could profit from enhanced cooperation on accountability, whether through market oversight and enforcement, auditing requirements, or otherwise. This could combine with sectoral cooperation and possibly also with standards development for auditing AI systems.
R10. Assess the impact of AI on international data governance.
There is a need for a common understanding of how data governance rules affect AI R&D in areas such as health research and other scientific research, and whether they inhibit the exploration that is an essential part of both scientific discovery and machine learning. There is also need for a critical look at R&D methods to develop a deeper understanding of appropriate boundaries on use of personal data or other protected information. In turn, there is also a need to expand R&D and understanding in privacy-protecting technologies that can enable exploration and discovery while protecting personal information.
R11. Adopt a stepwise, inclusive approach to international AI standardization.
A stepwise approach to standards development is needed to allow time for technology development and experimentation and to gather the data and use cases to support robust standards. It also would ensure that discussions at the international level happen once technology has reached a certain level of maturity or where a regulatory environment is adopted. To support such an approach, it would be helpful to establish a comprehensive database of AI standards under development at national and international levels.
R12. Develop a coordinated approach to AI standards development that encourages Chinese participation consistent with an industry-led, research-driven approach.
There is currently a risk of disconnect between growing concern among governments and national security officials alarmed by Chinese engagement in the standards process on the one hand, and industry participants’ perceptions of the impact of Chinese participation in SDOs on the other. To encourage constructive involvement and discourage self-serving standards, FCAI participants (and likeminded countries) should encourage Chinese engagement in international standards setting while also agreeing on costs for actions that use SDOs strategically to slow down or stall standards making. This can be accomplished through trade and other measures but will require cooperation among FCAI participants to be effective.
R13. Expand trade rules for AI standards.
The rules governing use of international standards in the WTO TBT Agreement and free trade agreements are limited to goods only, whereas AI standards will apply mainly to services. New trade rules are needed that extend rules on international standards to services. As a starting point, such rules should be developed in the context of bilateral free trade agreements or plurilateral agreements, with the aim to make them multilateral in the WTO. Trade rules are also needed to support data free flow with trust and to reduce barriers and costs to AI infrastructure. Consideration also should be given to linking participation in the development of AI standards in bodies such as ISO/IEC, with broader trade policy goals and compliance with core WTO commitments.
R14. Increase funding for participation in SDOs.
Funding should be earmarked for academics and industry participation in SDOs, as well as for SDO meetings in FCAI countries and more broadly in less developed countries. Broadened participation is important to democratize the standards making process and strengthen the legitimacy and adoption of the resulting standards. Hosting meetings of standards bodies in diverse countries can broaden exposure to standards-setting processes around AI and critical technology.
R15. Develop common criteria and governance arrangements for international large-scale R&D projects.
Joint research and development applying to large-scale global problems such as climate change or disease prevention and treatment can have two valuable effects: It can bring additional resources to the solution of pressing global challenges, and the collaboration can help to find common ground in addressing differences in approaches to AI. FCAI will seek to incubate a concrete roadmap on such R&D for adoption by FCAI participants as well as other governments and international organizations. Using collaboration on R&D as a mechanism to work through matters that affect international cooperation on AI policy means that this recommendation should play out in the near term.
Proposed future topics for FCAI dialogues
– Scaling R&D cooperation on AI projects.– China and AI: what are the risks, opportunities, and ways forward?– Government use of AI: developing common approaches.– Regulatory cooperation and harmonization: issues and mechanisms.– A suitable international framework for data governance.– Standards development.– An AI trade agreement: partners, content, and strategy.
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Sub-Saharan Africa’s debt problem: Mapping the pandemic’s effect and the way forward
Background
The COVID-19 pandemic has, thus far, spared Africa from the high number of cases and deaths seen in other regions in the world (Figure 1). As of April 2021, sub-Saharan Africa accounted for just 3 percent of the world’s cases and 4 percent of its deaths. Some experts attribute the relatively low case counts in sub-Saharan Africa to the region’s extremely young population or, importantly, the swift and preemptive lockdowns that many countries implemented in March 2020. While these lockdowns have likely saved lives, they have also left significant scars on the fiscal position of sub-Saharan Africa and the market conditions it faces. Dwindling revenues following the fall in global trade met a wave of unemployment among a population that lacks widespread access to safety nets and health infrastructure.
Figure 1. Population, COVID cases, and COVID deaths, sub-Saharan Africa vs. world
Source: Our World in Data, 2021. Data taken on September 1, 2021.
In response, African governments have, by and large, borrowed to finance stimulus packages to support at-risk groups, struggling businesses, creative education solutions, and health-related infrastructure. International and regional financial institutions, such as the World Bank, International Monetary Fund (IMF), African Development Bank (AfDB), and European Union (EU) countries (both bilaterally and multilaterally) have responded through debt relief measures and restructurings. The fiscal and monetary responses of sub-Saharan Africa and various financial institutions will have important consequences for indebtedness, debt servicing capacity, and debt sustainability more broadly.
Chris Heitzig
Research Analyst – Africa Growth Initiative
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Aloysius Uche Ordu
Director – Africa Growth Initiative
Senior Fellow – Global Economy and Development
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Lemma Senbet
William E. Mayer Chair Professor of Finance – University of Maryland
Member, Distinguished Advisory Group – Africa Growth Initiative
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lsenbet
Debt was an increasing problem across all income groups of African countries prior to COVID-19, and the pandemic has only exacerbated the problem. In fact, African countries had been borrowing heavily in the global financial markets in recent years—a trend that has created both new opportunities and new challenges. Rising debt levels have corresponded with rising debt service cost, but countries have not necessarily improved their ability to finance such obligations. Indeed, failure to meet debt service obligations will have devastating impacts, including downgrading of credit ratings (and, hence, future higher costs), heightened pressure on foreign exchange reserves and domestic currency depreciation, and the real possibility of being rationed out of the market—and negative reputational consequences.
This paper utilizes new data to study the impact of the COVID-19 pandemic on debt sustainability and vulnerability in sub-Saharan Africa and sheds light on the channels through which these impacts have taken place. We find that debt levels have risen substantially in sub-Saharan Africa since the onset of the COVID-19 pandemic. We utilize IMF projections as a comparison to analyze the impacts on the pandemic on debt levels and how they covary with key determinants of growth and fiscal space.
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In particular, sub-Saharan Africa experienced a 4.5 percent increase in “pandemic debt”—the debt taken on above and beyond projections due to the COVID-19 crisis. HIPC countries in particular saw large increases in pandemic debt, with levels 8.5 percent higher than projected. Non-HIPC countries took on mostly planned debt and borrowed from both private and official (that is, bilateral or multilateral) credit markets alike. HIPC countries, on the other hand, were largely shut out of private credit markets and instead relied on official credit to fund increases in (largely unplanned) debt. We also find that the domestic bond market played a more important role in private borrowing than it has in recent years and that eurobond issuance was relatively scarce. Countries that rely on metal exports issued less pandemic debt than did those that rely on oil, thanks to the strong growth and relative stability of metal prices during the pandemic.
Despite taking on substantial pandemic debt, HIPC countries experienced less extreme drops in GDP compared to their non-HIPC counterparts, underscoring the need for HIPC countries to accelerate financial sector development and enhance public-sector financial management, including mitigating financial leakages, curbing illicit follows, and galvanizing domestic resource mobilization. Looking forward, this paper argues that both sub-Saharan Africa’s recovery and debt sustainability depend on two factors: the success of the African Continental Free Trade Agreement (AfCFTA) and obtaining the participation of private partners in debt restructuring. Economic recovery, in this regard, will affect the millions of informal workers that have lost their jobs at the hands of the pandemic as well as revenue levels that coincide to some degree with the workers’ eventual participation in the formal economy.
Key findings
Debt levels in 2020 were 4.5 percent higher in sub-Saharan Africa than projections. The increase was particularly acute in HIPC countries, whose debt had mirrored non-HIPC countries the decade prior.
Non-HIPC countries and especially upper-middle-income countries retained access to credit markets and used a mixture of private and official creditors to finance increases in debt (which were largely in line with projections).
HIPC countries were largely shut out of private debt markets and instead relied on unplanned borrowing from official creditors.
Domestic bond markets played a relatively more important role in private borrowing. Eurobond issuance dropped sharply.
Some resource-rich countries saw sharp increases in bond yields despite having comparatively low yields pre-pandemic.
Metal prices showed more stability and higher growth than oil prices during the pandemic. Consequently, top metal exporters took on less debt than top oil-exporting countries.
Many sectors, especially manufacturing, witnessed “formalization” of employment during the pandemic.
Policy recommendations
Obtain full participation of all creditors, including private ones, in debt restructuring
Accelerate financial sector development
Enhance public financial management and internal resource mobilization
Mitigate financial leakages and illicit flows
Harness and accelerate opportunities afforded by AfCFTA
Design incentive-compatible and state-contingent contracts
Revisit existing institutional mechanisms for debt resolution
This paper is organized as follows. Section 2 begins by taking brief stock of the region’s debt landscape prior to the advent of COVID-19, before illustrating how the debt burden has changed during the pandemic. It also reviews key reasons why indebtedness has risen, including stimulus packages, current account deficits, and borrowing costs. Section 3 examines key economic channels along which the pandemic shock unfolded. Section 4 considers the magnitude of revenue loss and the vulnerability of the informal workers during the pandemic. Section 5 discusses attempts to rectify the unexpected, unsustainable increases in debt (or “pandemic debt”) and explores important considerations of which effective policies must take account. Section 6 recommends a number of policies and the way forward.
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Sub-Saharan Africa’s debt problem: Mapping the pandemic’s effect and the way forward
Background
The COVID-19 pandemic has, thus far, spared Africa from the high number of cases and deaths seen in other regions in the world (Figure 1). As of April 2021, sub-Saharan Africa accounted for just 3 percent of the world’s cases and 4 percent of its deaths. Some experts attribute the relatively low case counts in sub-Saharan Africa to the region’s extremely young population or, importantly, the swift and preemptive lockdowns that many countries implemented in March 2020. While these lockdowns have likely saved lives, they have also left significant scars on the fiscal position of sub-Saharan Africa and the market conditions it faces. Dwindling revenues following the fall in global trade met a wave of unemployment among a population that lacks widespread access to safety nets and health infrastructure.
Figure 1. Population, COVID cases, and COVID deaths, sub-Saharan Africa vs. world
Source: Our World in Data, 2021. Data taken on September 1, 2021.
In response, African governments have, by and large, borrowed to finance stimulus packages to support at-risk groups, struggling businesses, creative education solutions, and health-related infrastructure. International and regional financial institutions, such as the World Bank, International Monetary Fund (IMF), African Development Bank (AfDB), and European Union (EU) countries (both bilaterally and multilaterally) have responded through debt relief measures and restructurings. The fiscal and monetary responses of sub-Saharan Africa and various financial institutions will have important consequences for indebtedness, debt servicing capacity, and debt sustainability more broadly.
Debt was an increasing problem across all income groups of African countries prior to COVID-19, and the pandemic has only exacerbated the problem. In fact, African countries had been borrowing heavily in the global financial markets in recent years—a trend that has created both new opportunities and new challenges. Rising debt levels have corresponded with rising debt service cost, but countries have not necessarily improved their ability to finance such obligations. Indeed, failure to meet debt service obligations will have devastating impacts, including downgrading of credit ratings (and, hence, future higher costs), heightened pressure on foreign exchange reserves and domestic currency depreciation, and the real possibility of being rationed out of the market—and negative reputational consequences.
This paper utilizes new data to study the impact of the COVID-19 pandemic on debt sustainability and vulnerability in sub-Saharan Africa and sheds light on the channels through which these impacts have taken place. We find that debt levels have risen substantially in sub-Saharan Africa since the onset of the COVID-19 pandemic. We utilize IMF projections as a comparison to analyze the impacts on the pandemic on debt levels and how they covary with key determinants of growth and fiscal space.
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In particular, sub-Saharan Africa experienced a 4.5 percent increase in “pandemic debt”—the debt taken on above and beyond projections due to the COVID-19 crisis. HIPC countries in particular saw large increases in pandemic debt, with levels 8.5 percent higher than projected. Non-HIPC countries took on mostly planned debt and borrowed from both private and official (that is, bilateral or multilateral) credit markets alike. HIPC countries, on the other hand, were largely shut out of private credit markets and instead relied on official credit to fund increases in (largely unplanned) debt. We also find that the domestic bond market played a more important role in private borrowing than it has in recent years and that eurobond issuance was relatively scarce. Countries that rely on metal exports issued less pandemic debt than did those that rely on oil, thanks to the strong growth and relative stability of metal prices during the pandemic.
Despite taking on substantial pandemic debt, HIPC countries experienced less extreme drops in GDP compared to their non-HIPC counterparts, underscoring the need for HIPC countries to accelerate financial sector development and enhance public-sector financial management, including mitigating financial leakages, curbing illicit follows, and galvanizing domestic resource mobilization. Looking forward, this paper argues that both sub-Saharan Africa’s recovery and debt sustainability depend on two factors: the success of the African Continental Free Trade Agreement (AfCFTA) and obtaining the participation of private partners in debt restructuring. Economic recovery, in this regard, will affect the millions of informal workers that have lost their jobs at the hands of the pandemic as well as revenue levels that coincide to some degree with the workers’ eventual participation in the formal economy.
Key findings
Debt levels in 2020 were 4.5 percent higher in sub-Saharan Africa than projections. The increase was particularly acute in HIPC countries, whose debt had mirrored non-HIPC countries the decade prior.
Non-HIPC countries and especially upper-middle-income countries retained access to credit markets and used a mixture of private and official creditors to finance increases in debt (which were largely in line with projections).
HIPC countries were largely shut out of private debt markets and instead relied on unplanned borrowing from official creditors.
Domestic bond markets played a relatively more important role in private borrowing. Eurobond issuance dropped sharply.
Some resource-rich countries saw sharp increases in bond yields despite having comparatively low yields pre-pandemic.
Metal prices showed more stability and higher growth than oil prices during the pandemic. Consequently, top metal exporters took on less debt than top oil-exporting countries.
Many sectors, especially manufacturing, witnessed “formalization” of employment during the pandemic.
Policy recommendations
Obtain full participation of all creditors, including private ones, in debt restructuring
Accelerate financial sector development
Enhance public financial management and internal resource mobilization
Mitigate financial leakages and illicit flows
Harness and accelerate opportunities afforded by AfCFTA
Design incentive-compatible and state-contingent contracts
Revisit existing institutional mechanisms for debt resolution
This paper is organized as follows. Section 2 begins by taking brief stock of the region’s debt landscape prior to the advent of COVID-19, before illustrating how the debt burden has changed during the pandemic. It also reviews key reasons why indebtedness has risen, including stimulus packages, current account deficits, and borrowing costs. Section 3 examines key economic channels along which the pandemic shock unfolded. Section 4 considers the magnitude of revenue loss and the vulnerability of the informal workers during the pandemic. Section 5 discusses attempts to rectify the unexpected, unsustainable increases in debt (or “pandemic debt”) and explores important considerations of which effective policies must take account. Section 6 recommends a number of policies and the way forward.
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Renforcer les capacités en lecture et calcul des enfants en Côte d’Ivoire
Avant même que la COVID-19 ait laissé 1,6 milliard d’élèves non scolarisés au début de 2020, des millions d’enfants et de jeunes dans le monde n’avaient pas accès à l’éducation de qualité dont ils avaient besoin pour mener une vie saine, sure et productive. Pire encore, les enfants les plus pauvres et les plus marginalisés continuent d’être les plus touchés par cette crise de l’apprentissage, perdant ainsi leur droit à l’éducation. Cette situation est lourde de conséquences pour les générations à venir, notamment en termes de pauvreté, d’inégalités, de changement climatique et de santé publique.
Il est urgent d’agir pour élargir rapidement et durablement l’accès à des possibilités d’apprentissage de qualité pour tous les enfants. Bien entendu, la question est de savoir “comment?” S’il existe de nombreuses innovations qui améliorent l’apprentissage des enfants, la grande majorité ne touche qu’une petite fraction des enfants qui en ont besoin. Par conséquent, il existe une demande croissante pour plus de probations et de conseils sur la manière d’identifier, d’adapter et d’étendre des politiques et des pratiques rentables qui aboutiraient à l’apprentissage de millions d’enfants supplémentaires.
Laboratoire de Mise à l’Echelle en Temps Réel de la Côte d’Ivoire : Accompagner les efforts pour générer des changements durables et significatifs dans l’apprentissage fondamental des enfants
En réponse, le Centre pour l’Education Universelle (CEU) de Brookings a étudié les efforts visant à mettre à l’échelle et à soutenir les initiatives fondées sur des preuves et conduisant à des améliorations à grande échelle dans l’apprentissage des enfants. Le CEU a mis en oeuvre une série de Laboratoires de Mise à l’Echelle en Temps Réel (RTSL), en partenariat avec des institutions locales dans plusieurs pays, afin de produire des preuves et de fournir des recommandations pratiques autour du processus de mise à l’échelle dans l’éducation mondiale – encourageant un lien plus fort entre la recherche et la pratique. Ce rapport porte sur l’un des laboratoires de mise à l’échelle en Côte d’Ivoire – lancé en 2019 en collaboration avec le programme Transformer l’Education dans les Communautés du Cacao (TRECC) et le Ministère de l’Education Nationale et de l’Alphabétisation (MENA).
Il est articulé autour du processus de mise en oeuvre, d’adaptation et de mise à l’échelle du Programme d’Enseignement Ciblé (PEC), dirigé par le gouvernement, à travers une approche de rattrapage scolaire visant à améliorer la lecture et le calcul en début de scolarité et adapté de l’approche Teaching at the Right Level (TaRL). Bien que le laboratoire se soit concentré sur l’expérience du PEC à ce jour, ce programme sert d’étude de cas pour des questions plus larges sur la manière dont une initiative basée sur des preuves peut progresser vers une échelle nationale durable, avec des leçons qui sont transférables au-delà du PEC et de la Côte d’Ivoire.
La première section du rapport fournit un bref historique du cas, y compris une vue d’ensemble du Laboratoire de Mise à l’Echelle en Temps Réel et de l’écosystème de l’éducation en Côte d’Ivoire, ainsi qu’une brève description des principaux acteurs et initiatives engagés dans le PEC. La deuxième section détaille le parcours de la mise en oeuvre, de l’adaptation et de l’expansion du PEC en Côte d’Ivoire à ce jour, en explorant les facteurs critiques, les opportunités et les défis liés à sa conception, sa mise en oeuvre, son financement et son environnement favorable. La troisième section propose des leçons et des recommandations ciblées organisées autour de quatre thèmes clés qui sont apparus comme essentiels pour renforcer l’expansion continue du PEC, ainsi que pour informer les futurs efforts de mise à l’échelle de l’éducation en Côte d’Ivoire et au-delà.
Le parcours de passage à l’échelle du PEC: Une confluence de facteurs avantageux
A bien des égards, le PEC représente le « scénario idéal » pour le passage à l’échelle et la pérennisation d’une initiative au sein d’un système éducatif formel. Le PEC a bénéficié d’une confluence de facteurs en sa faveur – dont certains ont été orchestrés de manière stratégique et systématique, et d’autres ont émergé de manière fortuite. L’approche de TRECC axée sur les problèmes a favorisé le développement de l’adhésion du gouvernement au PEC dès le début et a contribué à sa forte appropriation par le gouvernement. La simplicité de l’approche, le fait qu’elle rejoint les príncipes théoriques que les enseignants apprennent au cours de leur formation initiale, son pilotage par la prestation directe du gouvernement, ses résultats convaincants et sa trajectoire précise pour la mise à l’échelle dans le système éducatif ont également favorisé l’engagement du gouvernement et facilité l’expansion du PEC.
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Les partenariats noués dans le cadre du modèle TRECC ont été d’autres facteurs importants pour susciter le soutien au PEC, notamment la possibilité d’expérimenter différentes solutions potentielles avant d’en retenir une, le rôle d’une tierce partie neutre évaluant les résultats des projets pilotes, le soutien technique d’organisations ayant initialement développé et étudié l’approche TaRL, et l’existence d’un laboratoire de mise à l’échelle réunissant diverses parties prenantes pour la réflexion et l’apprentissage par les pairs. Le PEC a également réussi à obtenir un soutien de haut niveau au sein du MENA, avec des personnes influentes qui le défendent. Ce soutien essentiel a été maintenu malgré les changements politiques et les changements dans l’environnement éducatif plus large, incluant une pandémie mondiale. Enfin, la disponibilité d’un financement pour le PEC au-delà de la phase pilote initiale – y compris un financement pour une adaptation et une expansion supplémentaires et un accès potentiel à un financement quinquennal par la création d’un fonds commun public-privé – a été essentielle pour que le PEC dépasse le stade de projet éphémère et devienne une approche que le gouvernement a l’intention d’étendre au sein du système.
Néanmoins, malgré les nombreux facteurs en sa faveur, l’expansion et le maintien du PEC en Côte d’Ivoire ne sont pas garantis et des défis critiques demeurent, notamment la capacité limitée du gouvernement à incorporer et à pourvoir le modèle dans les systèmes existants avec qualité, la persistance d’une mentalité de projet chez certains acteurs clés impliqués, et une attention insuffisante à l’engagement des parties prenantes de l’éducation au niveau local (y compris les enseignants et les communautés). D’autres contraintes potentielles à l’expansion future et au maintien du PEC incluent des retards dans le lancement du nouveau fonds commun et des difficultés à identifier et à garantir un financement national durable.
Les leçons à tirer pour renforcer l’expansion du PEC et informer les futurs efforts de mise à l’échelle
En accompagnant le parcours de mise à l’échelle de PEC, des leçons ont été tirées du cas centré autour de quatre thèmes clés qui ont été déterminants pour le succès de la mise à l’échelle de PEC à ce jour, et qui continueront à jouer un rôle essentiel dans les efforts futurs. Ces thèmes sont: 1) l’institutionnalisation comme voie vers une mise à l’échelle durable; 2) les partenariats et les champions; 3) les coûts et le financement; et 4) l’adaptation et l’apprentissage continu. Chacun de ces thèmes offre des leçons tirées du cas du PEC et des recommandations ciblées, non seulement pour soutenir les progrès en cours afin d’étendre et d’approfondir l’impact du PEC, mais aussi pour informer les efforts de mise à l’échelle d’autres initiatives d’éducation basées sur des preuves. Un bref aperçu de chacune des leçons est présenté ci-dessous, avec des recommandations ciblées pour les responsables de la mise en oeuvre, les décideurs politiques, les bailleurs de fonds et les chercheurs, lesquelles sont plus amplement détaillées dans le rapport complet.
1. L’institutionnalisation comme voie à la mise à l’échelle dans l’éducation
Assurer sans relâche dès le départ une concentration sur qui va livrer à grande échelle : Le pilotage d’une initiative avec le gouvernement demande plus de temps et de capacités au départ, mais il favorise également l’adhésion, détermine ce qui est faisable et démontre le potentiel de fonctionnement d’une solution dans le système.
Se concentrer sur la scalabilitéa d’une innovation dans le contexte local : S’il est tentant de rechercher des innovations qui bouleversent considérablement les méthodes de travail existantes ou qui testent des technologies de pointe, il est essentiel de se concentrer sur l’aspect pratique de la mise à l’échelle d’une innovation dans un contexte particulier, notamment sur la meilleure façon de l’intégrer durablement et équitablement dans les systèmes existants. Souvent, les innovations ayant le plus grand potentiel d’impact à grande échelle sont celles qui sont les plus faisables à supporter par le système.
Créer des structures de coordination dotées de capacités suffisantes et d’un mandat gouvernemental fort : Le passage à l’échelle par l’institutionnalisation nécessite une structure de coordination dotée d’un mandat de haut niveau pour prendre des décisions, harmoniser les efforts et s’assurer de l’avancement du travail de mise à l’échelle – en particulier lorsque l’institutionnalisation progresse au-delà de la description de poste
d’un individu ou d’un département.
Maintenir un pied sur l’accélérateur, et l’autre sur les freins : Même avec l’adhésion substantielle du gouvernement à la mise à l’échelle, il est important que toutes les parties prenantes comprennent la nécessité d’une approche à plus long terme et progressive de la mise à l’échelle, en mettant l’accent sur les questions de qualité et d’équité, et en équilibrant les compromis inévitables au cours du processus de la mise à l’échelle.
2. Partenariats et collaboration pour la mise à l’échelle dans l’éducation
Catalyser l’action collective, et repérer le point de rendement décroissant: L’engagement du gouvernement dans le processus de mise à l’échelle peut être essentiel pour étendre et soutenir une initiative d’éducation, mais une action collective est néanmoins nécessaire pour apporter des perspectives, des ressources, des expertises et des rôles différents. En même temps, une attention suffisante doit être accordée à la clarification de la motivation et des incitations de chaque partenaire, de la valeur ajoutée, de la vision de la mise à l’échelle et du succès, et de la tolérance au risque.
Soutenir les intermédiaires pour favoriser les partenariats et aligner les intérêts: Les organisations intermédiaires ou tierces – y compris les bailleurs de fonds – peuvent jouer un rôle essentiel de passerelle pour aligner des incitations disparates, développer des approches innovantes pour tirer parti des forces et perspectives uniques de chaque acteur, et rassembler les parties prenantes pour défendre un objectif commun.
Cultiver une ligue de champions de la mise à l’échelle: La création des conditions nécessaires à la diffusion de solutions efficaces requiert des champions de la mise à l’échelle à tous les niveaux au sein et en dehors du gouvernement, des salles de classe et des communautés, ainsi que la création délibérée d’un espace pour travailler ensemble différemment – appelé à perturber les modèles existants de collaboration et de prise de
décision. Le recours à une approche d’apprentissage collaborative, telle que le Laboratoire de Mise à l’Echelle en Temps Réel, est un moyen permettant de “rassembler les éléments du système dans la réunion” et à instaurer une nouvelle façon de travailler.
Soutenir un changement d’état d’esprit et de comportement pour la mise à l’échelle: L’identification et la mise en place d’un cadre de leaders et d’agents du changement pour la mise à l’échelle ne se limite pas à obtenir le soutien des parties prenantes pour la mise à l’échelle d’une initiative particulière: elles requièrent une sensibilisation aux principes clés de la mise à l’échelle, l’encouragement de l’application de ces principes par des actions concrètes et un changement de comportement, ainsi que le renforcement des compétences et des aptitudes nécessaires à la mise à l’échelle de l’impact.
3. Coûts et financement de la mise à l’échelle
Faire la lumière sur le financement public à long terme : Pour beaucoup d’innovateurs et de praticiens, les processus budgétaires et les filières du gouvernement restent opaques, et davantage de clarté est indispensable sur la méthode d’alignement ou d’intégration dans ces processus pour mobiliser des ressources à long terme pour une échelle durable.
Augmenter le soutien pour faire des projections de coûts solides à l’échelle: Il existe un besoin important de renforcer l’expertise et les capacités locales pour collecter, analyser et utiliser les données sur les coûts afin d’informer les projections à l’échelle. Des incitations sont nécessaires pour soutenir la collecte, l’analyse et le partage de ces données, et pour encourager une plus grande transparence et des opportunités d’apprentissage.
Tirer parti du potentiel du financement commun pour franchir la “vallée de la mort”: La collaboration des donateurs et le financement groupé peuvent fournir un financement relais important pour la mise à l’échelle, en aidant les initiatives à effectuer la difficile transition du stade pilote à la mise en oeuvre à grande échelle, mais il faut en apprendre davantage sur les avantages et les défis de ces mécanismes.
4. Adaptation et apprentissage collaboratif dans le processus de mise à l’échelle
Intégrer un processus d’apprentissage continu dans les systèmes gouvernementaux: L’intégration d’une approche d’apprentissage continu, telle que le Laboratoire de Mise à l’Echelle en Temps Réel, dans les systèmes gouvernementaux présente des avantages tangibles pour soutenir la mise en oeuvre, l’adaptation et la mise à l’échelle, avec des circuits de retours d’informations rapides et des possibilités de réflexion et de corrections de trajectoire. Le leadership gouvernemental d’un processus de type laboratoire peut conférer l’autorité nécessaire pour développer, tester et affiner une stratégie de mise à l’échelle avec les décideurs concernés.
Renforcer la capacité d’adaptation pour répondre à des environnements évoluant rapidement: Trop souvent, les adaptations testées dans le cadre du processus de mise à l’échelle ne sont pas systématiquement planifiées ou bien documentées, et l’apprentissage est perdu ; des approches plus systématiques pour planifier et tirer des enseignements des changements anticipés et spontanés sont nécessaires.
Investir du temps et des ressources dans l’apprentissage et l’échange entre pairs: De nombreuses initiatives en cours de mise à l’échelle travaillent de manière isolée et, malgré les différences contextuelles, peuvent bénéficier d’une plus grande collaboration pour partager leurs expériences, réfléchir aux défis et opportunités communs et résoudre les problèmes collectivement. L’apprentissage par les pairs doit aller au-delà d’occasions ponctuelles et doit être soutenu en tant qu’un aspect intrinsèque du travail qui bénéficie de suffisamment de temps, de capacités et de ressources.
Bien qu’elle n’en soit qu’à ses premiers chapitres, l’histoire du PEC est instructive à bien des égards. Plus que tout, le récit du PEC a mis en lumière les efforts inlassables et inspirants de tant de parties prenantes de l’éducation en Côte d’Ivoire qui s’efforcent d’améliorer les résultats de l’apprentissage pour les enfants, en particulier les plus vulnérables.
Et pourtant, le cas du PEC souligne également, que même avec ce scénario de scalabilité et d’opportunité « quasi idéal », l’augmentation de l’impact sur l’éducation reste une entreprise difficile et à long terme qui ne peut être considérée comme acquise. On peut dire que le PEC entre maintenant dans son chapitre le plus difficile, à savoir la phase intermédiaire delicate de la mise à l’échelle, car il dépasse le stade d’un projet pilote à petite échelle pour s’intégrer davantage dans les opérations gouvernementales et atteindre un nombre beaucoup plus important d’enfants.
Cette phase nécessitera une adaptation et une expérimentation continues, la collecte de données et leur utilisation dans des cycles d’apprentissage rapides afin de s’assurer que l’efficacité du PEC est maintenue à mesure qu’il se développe. Indépendamment de ce que l’avenir nous réserve, les efforts du gouvernement ivoirien pour mettre à l’échelle et soutenir le PEC – en partenariat avec divers acteurs – continueront à fournir de riches enseignements sur la mise à l’échelle et le changement systémique pour la Côte d’Ivoire et de nombreux pays dans le monde.
Télécharger le rapport complet ou les conclusions sommaires.
Credit photo: TaRL Africa