Estimating Social Return on Investment in Global Mobility Programs
Including a downloadable SROI tool to try this at home
Social Return on Investment (SROI) is an important concept for labor and educational mobility programs. In these programs, the cost per individual directly served can be higher than what funders focused on in-country interventions expect, but the social benefits can also be orders of magnitude greater. SROI captures both sides of the equation in order to clearly estimate the social value of investing in mobility. We have seen a number of SROI models for labor mobility programs, but they can be complex and idiosyncratic. Below we introduce a simplified and generic SROI model in order to (1) illustrate how mobility programs produce such eye-popping SROI estimates, (2) show the major drivers of costs and returns of these programs, and (3) provide a downloadable tool that practitioners and funders can use to quickly estimate returns for a given mobility program.
What is SROI?
Social return on investment is a powerful impact measurement tool that quantifies the economic and social value created by programs relative to their costs. Unlike traditional financial ROI, SROI incorporates social and economic benefits that do not necessarily accrue to the investor—things like increased worker earnings, improved quality of life, and economic spillovers—into financial metrics that can be compared with the cost of the initial investment. By converting social impact into monetary terms, SROI can provide funders, policymakers, and practitioners with a clearer sense of a program’s value relative to alternatives.
Why Use Prospective SROI Analysis?
Most SROI analyses are retrospective—they look back at past programs and measure impact after the fact. However, prospective SROI, which models the expected social returns of a future program, can be equally valuable. Forward-looking modeling helps funders, policymakers, and program designers make informed decisions by estimating impact in advance, prioritizing high-return interventions, and refining program design before committing significant resources.
SROI analysis does have limitations. The results are susceptible to key assumptions, such as growth rates, discount rates, and time horizons, meaning small adjustments can lead to significantly different outcomes. Additionally, while SROI is a valuable tool for estimating impact, it is not a substitute for rigorous, long-term measurement of actual outcomes. Organizations like Malengo are rigorously tracking the long-term impacts of migration on both migrants and their origin communities, and their findings should inform future modeling efforts in this space. (They have developed a more complex SROI model for their own programs that also provides comparisons with cash transfers.) Another challenge is attribution—when multiple actors contribute to an outcome, determining how much credit any one investment or funder should receive is difficult. We take a partial attribution approach for this model, but there is no clear consensus on the best way to model the attribution of returns from future growth.
What’s a Typical SROI Multiple?
SROI ratios in social impact work vary widely, but any program with a return greater than 1:1 “breaks even” from a social return on investment perspective and pays for itself. Retrospective analysis of effective public health interventions found SROI ratios ranging from 1.1:1 to 65:1. A well-known global development innovation program yielded returns of 17:1, using a conservative methodology. Some interventions, particularly those with compounding effects, report even higher multiples. While methodologies, benchmarks, and measurement approaches vary, the ratio provides a sense of the potential of future investments or the performance of past investments.
Our Initial SROI Estimate
Our model suggests that certain global mobility programs may achieve an SROI multiple of 155:1. That is, for every $1 of philanthropic capital invested, $155 in social value is created in the form of increased earnings for workers. This figure derives from the design of mobility programs that start with donor-funded demonstration cohorts and transition to self-sustaining operations. The first demonstration cohort itself yields an SROI of 26:1, and then we partially attribute future impact from catalyzed growth. These returns vastly exceed typical social impact interventions and underscore the potential of mobility as an underutilized development tool.
Why are these numbers so high? Well, at some level, this is because workers moving from lower productivity to higher productivity areas represent a massive economic opportunity. Many of us know this from personal experience, whether from relocating to a big city for work or from family and ancestors who migrated in the past in search of greater opportunity. Arguably, this has always been the case throughout human history.
But several key factors make labor and educational mobility uniquely promising in terms of social returns:
Large Income Gains: One of the most compelling aspects of labor mobility is the sheer size of the income gains that workers experience. A nurse moving from the Philippines to Canada, or an electrician relocating from Kenya to Germany, can increase their wages 5-10x overnight. This change isn’t marginal—it’s transformational, immediately lifting families out of poverty and creating intergenerational benefits through remittances, savings, and investments in education and healthcare.
Scalability Through Financing Innovations: Many traditional social programs require sustained funding to grow. Smart labor mobility programs, by contrast, have the potential to become self-sustaining once a worker moves and starts earning. Programs can use income-share agreements (ISAs), loans, or social impact bonds to finance upfront costs like visa processing and language training, recouping those costs with a capped repayment from workers successfully placed into jobs. Over time, destination country employers and governments should also make increasingly large contributions of capital necessary for mobility as the economic benefits to destination industries become apparent and essential. All this makes it possible for mobility programs to eventually scale without relying on continuous donor funding.
Relatively Reliability of Returns to Labor: Unlike volatile asset markets or more speculative development projects, returns to labor are consistent and generally lower risk. Economic history tells us that workers moving from low-income to high-income countries generate sustained economic gains, benefiting both origin and destination economies. While automation and AI threaten some sectors, recent developments suggest that nursing, skilled trades, and care will remain relatively insulated, ensuring continued demand for workers across geographies.
Breaking Down Our Model
To estimate the returns of a typical global mobility program, we built an SROI model that compares the present value of future earnings with program costs. Our assumptions should not be interpreted as benchmarks, given the nascence of the field, but are illustrative estimates informed by programs we have reviewed. The model can also be downloaded and adjusted to suit a specific program. Here’s how it works:
Calculating Program Costs: We estimate rough program costs based on programs we have worked with in the past. Models vary dramatically, so we attempted to choose middle-ground assumptions for a direct labor program with language instruction. These costs encompass recruitment, training, certification, visa processing, relocation support, and program staff and overhead. We assume that some of these costs are borne by the employer.
Simulating a Counterfactual Scenario: To estimate the potential earnings of a worker had they stayed home, we reference average data from past programs and subtract this from the projected earnings in the mobility program scenario. There is debate over how to value salaries in destination countries relative to the counterfactual, given that the cost of living is higher but consumption value is also greater, and remittances are spent in the country of origin. In our assumptions, we discount the destination-side salary by 50%, which we believe is conservative.
Making Key Assumptions: As labor income is a future cash flow, we apply a standard discount rate of 5% to convert 20 years of future wages into present-day value. To account for program delivery risks, we incorporate a program risk premium of 4% to further discount future earnings in the program scenario, but not in the counterfactual. 4% is relatively high but reflects the many moving pieces in successfully executing a mobility program.
Estimating Worker Earnings Over Time: Using an annuity with growth formula, we estimate the present value (PV) of future earnings for workers who relocate, factoring in moderate real wage growth over time. We consider future wages a proxy for social returns, meaning that additional social benefits, such as increased tax revenues or the ripple effects of entrepreneurship or remittances, are not included in the model.
Building in Future Growth and Attribution for Future Returns: We project that yearly placements of workers will increase at a compound annual growth rate of 10%, discounting the PVs of future earnings for workers who are added in the future, and ending the program after 20 years. We estimate the additional grant funding needed until the program becomes self-sustaining – we assume 3x the initial grant, though we have seen startups projecting much less. For attribution, we use a partial attribution approach to determine what portion of the future earnings can be directly attributed to the initial catalytic grant.
How to Use This Model
This model provides a launching point for thinking through the impact of labor mobility programs. If you are familiar with a specific talent mobility corridor, adjust the inputs like wages, costs, and discount rates to see how the results change. Explore the model and think critically about the assumptions. This model is sensitive to a few different variables, notably growth rates, discount rates, and the time horizon. Extending the time horizon too far can inflate results unrealistically, so ground your assumptions in reasonable expectations.
With an SROI of 155:1 in the base case, the model shows that achieving outsized impact does not depend on one key assumption or feature of model design. We still see impressive (if “only” double-digit) returns to the donor dollar if we triple costs for a high-touch model, assume workers only move and earn for 5 years, or remove any catalytic effect or assumption of future financial sustainability.
If you are a funder focused on poverty, livelihoods, or economic development, think about how the returns produced by this model compare to your current portfolio. Consider whether global mobility (i.e., cross-border livelihoods) efforts might complement or supplement your existing investments.
Continue the Conversation
We believe that labor mobility is one of the most underfunded yet highest-impact solutions to global poverty and workforce shortages. However, to scale these programs effectively, we need better data, stronger collaboration, and more innovation.
Are you working on a labor mobility initiative? Interested in the details of this model? As always, we’d love to hear your thoughts. Reach out to us at jason@talentmobility.fund to connect and continue the conversation.
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