1. Why We Focus on Living Wage

We intentionally focus on making grants that improve incomes for individuals earning less than a living wage in their local context. Our ROI models are agnostic to the income levels of those we support, and a living wage strategy ensures we support those that can benefit most from, and sustain longer, those income gains. This means that for each grant we estimate the percentage of program participants that are earning below a living wage, as well as the percentage that would be earning above a living wage if the grant’s full impact is realized.

We explicitly care that individuals surpass the living wage threshold because we believe that earning above that level leads to better durability of income gains. Research has shown that earning above a living wage is correlated with higher financial stability; it has also been well documented that poverty traps persist without a ‘big push’ intervention that creates sustainable incomes. There are also additional benefits for children of living-wage earners, such as increased educational attainment and better health, which leads to higher lifetime earnings in the next generation, as well.

2. Living Wage Threshold Definition and Calculation

We use the Living Wage Coalition dataset for calculating living wage thresholds outside of the United States. For programs In the United States, we use MIT’s Living Wage Calculator. This allows us to find the specific wage threshold that applies to a program’s geographic location such as a metropolitan area, city, or state. We typically use the living wage threshold associated with two working adults and one child, as that is the most common living situation in the United States; however, we can tailor the threshold based on specific scenarios as needed for our US grants.

We conduct living wage estimates for all grantees - see our detailed Handbook process here. We do this by estimating the percentage of participants likely to earn below a living wage in the grantee’s focus population; we then estimate the percentage of participants likely to earn above a living wage due to the grant (see Methodology Appendix below). After a grant is complete, we run a similar analysis based on the actual results.

3. June 2024 Grantee Living Wage Estimates and Insights

We are on track to impact more than 3 million people: Including the EQOS systems change grant, we estimate our grants will increase incomes for more than 3 million people across our 39 grants (893,000 excluding the EQOS grant).

The majority of those impacted would earn less than living wages: Of the people our grants are impacting, we estimate that 82% (2.49 mil) would be earning below living wages in the counterfactual scenario (if they did not receive services from our grantee partners).

Our grants are currently helping to move just a fraction of clients to above a living wage: Including the EQOS systems change grant, of the people our grants are impacting, we estimate that 7% (220,000) people will have income increases that move them from below to above the living wage line. By this metric, there is likely more we could do to target interventions that increase incomes above the living wage threshold.

https://lh7-us.googleusercontent.com/docsz/AD_4nXcAanRkAPH37la3xswmcJZnB75_-SFXhuJq4tP3SHhYKyGAfSfwY7ixFv5hV2CFpsye74Z9y6hY5gA8YV7na8XwonAUHSWmsU4SqJNsIy9TAqxz1xIJHZ4k2JeSkfHVmZR5n36kWUlrBrl3cXpCZSVZcuh4?key=sgWhG8Zo9-aY0_9FuuahSg

https://lh7-us.googleusercontent.com/docsz/AD_4nXdoERNiGsRM7VY8tD9T3tjMBJ9-sjXp70aUEz3M-sylE81CacqNTlLmWdQtctfxI5XxT_YBebkjpklMVtgvRkGLdzvJdjcLRcTA_IJKM4ZMnT97balY5JNN_q8sXJ4aOno7eteHqZppmPwKFeWlI1Sn5W8?key=sgWhG8Zo9-aY0_9FuuahSg

When we look at all individuals served, pre- and post-intervention, we can derive a number of interesting insights:

1. **Some grantees work with a majority of individuals earning above a living wage—**13% (5) of our grantees are focused on impacting a majority of individuals already earning above a living wage. These grants were approved because they worked with college-degree populations with strong technology skills but were stuck in low-wage work due to a poor labor market. These organizations enable those graduates to find higher-paying work (Kibo School, 10 Academy, CodePath).
2. Many grantees are only increasing incomes by a small percentage - 36% (14) of our grantees are increasing annual incomes for their clients by 15% or less. This raises the question of whether we should prioritize investments in transformative income change solutions (>50% annual income changes).
3. Our Colombia and U.S. Emerging Talent Demand strategy areas are performing best in moving low-income individuals to above, or close to, the living wage threshold
   1. Colombia’s grantees are impacting communities that are earning 43% of the living wage threshold pre-intervention; post-intervention, those communities are earning 94% of the living wage threshold - almost crossing the line.
   2. The Emerging Talent Demand grantees are impacting communities that are earning 79% of the living wage threshold pre-intervention; post-intervention, those communities are earning 129% of the living wage threshold - crossing the living wage threshold line.
   3. In contrast, the AI for Economic Opportunity grantees are only increasing incomes by about 30% on average—not enough to move populations above the living wage threshold—and our Kenya grantees are working with populations that, on average, are already earning annual incomes that are 198% of the living wage threshold.

https://lh7-us.googleusercontent.com/docsz/AD_4nXf2IoHmy3bMy1evRGoDS_2Rb-WLh29iUESobE-3LIvBYPDMYJqiQJE59YdzaeWx1HAfWjaN3tcW65FQmXw23_6PTnAks298ECb5eJ2oCvkulOJJ2Lx2Tdg7bTE6GQCLOFn5VRiHNzLj5nyRv0IHm4WKC2u7?key=sgWhG8Zo9-aY0_9FuuahSg

4. Questions Under Consideration

Should we give more weight to seeking out grants that bring people above a living wage?

Should we avoid making grants to those serving people who are already above a living wage - even if they achieve over 100X returns?

Should we deprioritize grants that result in people still earning below a living wage, under the hypothesis that those gains will be difficult to sustain?

5. Methodology Appendix

We model the distribution of incomes that grantee participants are likely to earn before and after participating in the grantee’s interventions.

In the image below:

The top graph is an example of the distribution of incomes before an intervention (counterfactual)

The bottom graph is an example of the distribution of incomes after the intervention

The x-axis shows annual incomes, and the y-axis is the density distribution of program participants

The solid blue line shows the living wage threshold

The dotted red line shows the median wages of the population

This example illustrates how a grantee’s programs move participants above the living wage threshold.