The Impact Chain

Why growth alone is not enough

A short note on why a framework like this is needed at all.

There is a long-running and important debate in development economics about how much weight to put on overall economic growth versus deliberate distributional and structural interventions. The framework does not take a side. But it sits squarely in the part of the debate where the two views meet.

Lant Pritchett, in his influential essays for the Center for Global Development and his Smart Development agenda, has argued for years that broad-based growth in per-capita income is by far the most reliable driver of poverty reduction historically — that, in his phrasing, you cannot help the poor by not having growth. The data behind this claim is hard to ignore. China's per-capita income roughly tripled between 1990 and 2010 and pulled approximately 750 million people out of extreme poverty over the same window. Vietnam's per-capita income grew at about 6% annually from 1990 to 2020; its extreme poverty rate fell from 53% to below 5%. These are real, sustained, growth-led poverty reductions of a kind no targeted intervention has matched at scale.

Yet growth is necessary, not sufficient. Two pieces of evidence from India make the case. First, India's GDP grew at an average of approximately 6–7% per year between 2000 and 2024. Over the same period, the Azim Premji University State of Working India reports document that the employment elasticity of growth has hovered close to zero — formal employment in India barely moved despite two decades of high growth. The female labour force participation rate fell from about 30% in 2005 to roughly 20% by 2018, partially recovering only in recent years.

Second, the World Inequality Lab's India work — Chancel, Piketty, and others — documents that India's bottom 50% share of national income fell from approximately 22% in 1991 to approximately 13% in 2022. Over the same period, the top 10% share rose from approximately 35% to approximately 57%. India's growth, in other words, has been heavily concentrated at the top. Banerjee and Duflo, in Poor Economics and Good Economics for Hard Times, have argued for two decades that this is the rule rather than the exception: aggregate growth does not guarantee that gains reach the bottom of the distribution, or that they translate into the durable household-level outcomes — education, health, resilience, asset-building — that constitute mobility out of poverty.

Growth is the necessary precondition — countries that do not grow cannot sustainably reduce poverty. But growth alone, as the Indian record shows, does not guarantee that the income gains reach the bottom, or translate into mobility out of poverty.

This is where deliberate intensification along well-targeted chains matters. The Impact Chain framework is not an argument against growth-led development. It is an argument that, alongside growth, decision-makers deploying public or philanthropic capital need to know which kinds of intensifying investments produce which kinds of returns, for whom, over what time horizon. Without that visibility, the case for distributive and structural investment continues to be made on moral terms because the economic terms are too hard to assemble. The framework is an attempt to assemble them.

Why the question matters

Three structural reasons the full chain has gone uncounted, even when every step has been studied somewhere.

Evaluations end where programmes end. A three-year programme is followed for three years. Sometimes four. Rarely ten. Many of the most consequential effects only become visible across the longer window. The income gain at year two becomes a school enrolment decision at year four becomes a different labour market outcome at year fifteen. The chain is rarely traced through.

Evaluations measure one outcome at a time. The income study does not track education effects. The education study does not track health. Each dimension is studied on its own. The pieces are real; the assembled picture is missing.

Attribution gets harder as the work gets more leveraged. The most consequential catalytic work does not deliver services to people; it changes how systems deliver services to people. A foundation that funds the development of a cashflow-based loan product, then funds the technical assistance that gets it adopted by a state bank, has reached every borrower of that bank for the next decade — but the attribution chain is now diffuse, and no standard evaluation method is designed to trace it.

The instrument that closes this gap is a synthesis layer above evaluation. It takes the well-evidenced findings of multiple research communities and assembles them into a single chain of consequence with the attribution made visible.

Why catalytic capital, and how its returns compare

Different categories of capital have different mandates, different constraints, and different appetites for the kinds of returns the chain produces. Government capital operates under tight scrutiny on each rupee; a pilot whose outcome might be "we learnt this approach does not work" is hard to defend even though the learning is valuable. Bank capital has to clear a cost-of-capital hurdle and maintain prudential ratios; some banks fund evidence generation and product-design work at the margins, but a bank's commercial portfolio cannot sustain investments whose returns are not appropriable. This is a structural constraint, not a moral failing. Commercial impact capital still needs returns to be appropriable through equity or a structured instrument; the MIS upgrade, the policy convening, the playbook that lets one state government replicate what another piloted do not have a revenue model that returns to an investor.

Catalytic philanthropic capital sits outside these constraints. It can absorb losses without consequence to its underlying balance sheet, fund public goods without needing to recover the value of what it produces, and prove something works at a scale and patience nobody else has the brief to test. That structural position gives the catalytic dollar its leverage.

An important caveat before any comparison

This is where we have to be careful. The framework produces a headline return of approximately $113 of catalytic-attributable household income per $1 of catalytic philanthropy over five years. That number is real, but it is structurally different from the return numbers usually quoted for other categories of capital, and comparing them naively is misleading.

Catalytic philanthropy works indirectly. Each catalytic dollar in the India calibration enables approximately $57 of cohort-aligned public-platform spend to operate more effectively. The $113 per $1 includes that platform leverage. The catalytic dollar is the trigger; the platform is the engine.

Most other categories of capital work directly. A cash transfer goes directly to a household. An FDI dollar buys productive capacity directly. A vaccine programme delivers doses directly. The per-dollar returns for these categories are typically computed without riding on a much larger pool of public capital.

To make the comparison honest, the framework offers two views below: the per-catalytic-dollar return (what foundations and donors typically want to see) and the per-total-dollar-deployed return, where the total includes the catalytic outlay plus the cohort-aligned public-platform spend the catalytic enables.

How the returns compare

For order-of-magnitude anchoring, not a ranking. Read the per-total-$ column especially carefully — that is the honest apples-to-apples view.

Two notes on this table. First, when the catalytic philanthropy return is normalised to TOTAL capital deployed (catalytic + the public-platform spend it works through), the per-dollar return drops to a range comparable with cash transfers and the lower end of public-health investment. The framework is not claiming that catalytic philanthropy produces a magnitude of return no other capital can match. It is claiming that the catalytic LAYER produces a high return per catalytic dollar precisely because it enables a much larger pool of capital to work more effectively. Second, the closest structural analogue to catalytic philanthropy in the table is CGIAR agricultural R&D — both are indirect capital that leverages subsequent direct deployment. The per-catalytic-dollar return in both cases is high because both are upstream leverage rather than terminal delivery.

Comparing the catalytic dollar's return to a cash transfer's return as if they were the same kind of thing is the wrong frame. The right comparison is to other indirect, system-leveraging investments — R&D, public-platform improvement, evidence generation.