So You Want to Reduce Poverty in the Developing World

Why microfinance failed, and what we can do better

In the 1990s, the development world was seized by an enthusiasm for microfinance. It held tremendous theoretical promise. People in the developing world often do actions which we do not expect to maximize profit. To explain this, we posit that they are unable to borrow as well as they would like. They face a credit constraint. The natural extension is that if we remove constraints on borrowing, they will be able to produce at the welfare maximizing level. All we need to do is offer them small loans, and they will work themselves up. Even better, the whole thing could be funded with a single donation, and run perpetually. The whole world could be set on a virtuous cycle of capital accumulation and growth, and all while making a profit.

This did not work out. The empirical evidence, always slight, was emphatically refuted by the randomized controlled trial revolution. There is definite utility to having lenders around, but the marginal impact of adding more was dominated by a cash transfer. This does not mean that credit constraints do not exist. They do, and the decisions of farmers and entrepreneurs in the developing world are substantially distorted away from what is optimal. Neither does this mean that there is no scope for international aid to take forms other than simple cash transfer. There is room for a balanced portfolio of charitable interventions.

This essay will survey the effects of microfinance, why the initial revolution failed, and what we can do about it, including how changes in contractual structure can improve welfare. We will then discuss whether international aid should seek to change people’s actions, and whether common interventions to share risk, internalize externalities, and induce investments are better than cash transfers.

The explosion of microfinance in the 1990s was in part due to technological innovations on the part of Mohammed Yunus and the Grameen Bank. He had founded the bank in 1976, lending money to the poorest of the poor in Bangladesh, with the motto that credit is a human right. With ineffective rule of law, lenders would normally find the cost of people running away with the money too high. The innovation was giving group loans to neighbors, almost all women, and having everyone be a guarantor of the loan. The neighbors would refuse to agree if they believed their neighbors to be a rotten apple, and they would be able to pressure each other into making a good-faith effort to pay it back. This basic model started spreading around the world – to Bolivia with the Banco-Sol, to Indonesia with the Bank Rakyat and the Bank Kredit Desa, and to India, where a patchwork of moneylenders developed in the 2000s.

International aid being not a hand out, but a hand up, was very popular with donors. In 1997, the Microcredit Summit, headlined by Hillary Clinton and other notables, started a drive to raise $20 billion and reach 100 million people by 2005. The peak of the movement’s influence was in 2006, when the Nobel Peace Prize was awarded to Mohammed Yunus and the Grameen bank. At that time, they had served 7 million people, and disbursed 6 billion dollars.

The Grameen Bank charged interest for their loans, and did have a high rate of repayment, but it still ran at a loss. It needed support from international donors for continued operation. So why support it? One theory is that the poor people are in a poverty trap. Suppose that people produce output by physical exertion. When one is famished, they are not capable of working as hard as they could, and produce just enough to survive. When they are well-nourished, then they can produce much more output. For this to be a poverty trap, there must be a region where adding a bit more food has only a small gain, less than the cost of buying more food, but with a big enough investment, you could get to the higher equilibrium of producing while well-nourished. Or for an alternative story, suppose that borrowing is impossible, there exist large and indivisible productive investments, but that if one accumulates savings they will face unbearable pressure to share from impecunious relatives. Borrowing allows you to shift to an asset which is more difficult for others to take. (I am going to defer a fuller discussion of this to an article next week, which will be linked here once that is complete).

To complete the story for the intervention of outsiders, we need some way for lending to be suboptimally low. This is where Stiglitz-Weiss (1981) (and in the same line of logic, Mankiw (1986)) come in. Suppose that borrowers possess private information about the riskiness of a project, or perhaps whether they intend to run away with the money. When the project fails and they are unable to pay back the loan, the lender is limited in how much they can get back. Raising the interest rate causes the people with safer investments to exit, forcing the interest rate to be even higher; in some cases, there will be no interest rate which is able to clear the market. The role of the donor is to eat the loss long enough to get people to jump out of the bad equilibria. If credit does exist, it will be rationed, and people will be prevented from buying as much as they wanted at the prices they want. This part definitely exists, and it is striking how much microfinance is viewed not as a reduction in the price of what you borrow, but of the amount which you can borrow.

The evidence base for microfinance being effective was always extremely thin. Morduch (1999), in an otherwise hopeful article on the development of microfinance, could not help but note the paucity of the available evidence that it was actually doing anything. The best evidence cited at the time for the Grameen Bank was Pitt and Khandker (1998), who used an eligibility requirement to infer the effect of the loans. However, they skip over the obvious approach in favor of considerably more complicated estimators. Simply running standard estimators, as Morduch and Roodman (2012) do, does not replicate the findings. Pitt and Khandker responded, but it’s almost beside the point – if you have $20 billion in aid money riding on it, you don’t want its utility to depend on involved arguments about what the correct estimator to use on a single dataset is. The comparison is to simply give people money – an action which we now know has substantial multipliers, and at the very least can’t harm anyone.

So by the later part of the 2000s, a movement to test microcredit with randomized controlled trials developed. A randomized controlled trial (an RCT) is one where the treatment – in this case, access to microfinance – has been randomized, and data is collected on the control group who was never offered the treatment. What randomization does for you is get rid of selection. The people who are likely to seek out loans may systematically differ from the population at large, and simply controlling for the things you can observe is unlikely to fix. For example, imagine that people who discover a profitable idea would pay for it out of savings without microfinance, but if loans are available will seek a loan. Thus, you would observe that people who go on to have higher incomes are more likely to obtain loans, without the loans having had any actual causal influence on the outcomes. With randomization, you would observe that there is no difference in investment or income between the groups, and thus correctly infer that the offer of a loan had no effect.

Things came to a head in a special issue of the AEJ:Applied in 2015, which featured six randomized controlled trials in Morocco, Bosnia-Herzegovina, Mexico, Mongolia, Ethiopia, and India. These trials were deliberately designed to be similar enough to aggregate together, and Rafe Meager (2019), conducting a meta-analysis on these and also Karlan and Zinman (2011) found that the average effects were simply nothing. The RCTs, even in very different contexts and with different methods, had similar null effects. The only light for microfinance was that Meager (2022), interested in if there were different impacts among subgroups, found that households with prior business experience were overrepresented in the tail of outcomes.

There are two ways to conduct an RCT on microfinance, randomizing at the individual level and randomizing at the village level. These have different tradeoffs. Randomizing at the village level means that you capture the interpersonal spillovers which individual randomization would miss. For instance, if people live in households, then it might not matter if one member of the family was not offered if somebody else got a loan and shared it. In a more negative sense, some people receiving money in a village and consuming more might increase the prices of goods, and reduce the consumption of others.

Randomizing at the village level captures the general equilibrium spillovers from person to person, but it is possible that the control group is contaminated. Lending is offered for profit. It stands to reason that profit-seeking firms will choose to locate themselves where the researchers are not competing. This would bias the effect toward zero, as untreated groups are secretly treated.

An immediate objection is that the RCTs are estimating something different from credit constraints, particularly the studies which are randomized at the individual level. The natural thing to point out is that lending is useful both for investment and mitigating risk, and what actually matters for the latter is the credible promise to be able to borrow. Of course take-up is extremely low for one time offers – not everyone has investment opportunities just waiting to go. What we need is for lenders to stand ready when an investment opportunity does come along. Unfortunately, we were able to test it, and it once again failed.

In 2001, the newly elected Thaksin Shinawatra government announced the Million Baht Initiative. One million baht, about $25,000, was transferred to each of the 77,000 villages in Thailand in order to start a village bank. These banks lent out most of the principal at rates matching prevailing interest rates, and the loans were not systematically looted through default. By all accounts, it was implemented in a fair and evenhanded manner.

The results were puzzling. We might have expected people to use the loans to finance investment, but there was no change in investment. Neither was there any change in the price of credit. What happened is that total consumption rose dollar for dollar with the amount available to the villagers to borrow, which is difficult to reconcile with the lack of change of the price of credit and the low default rate. It’s not even consistent with a cash transfer, because people would save a portion of the transfer and spend the interest.

Kaboski and Townsend (2011) try to fit the facts with a macro model. Each household has declining marginal utility of consumption in a given period, and so would like to consume the same amount in each period. Since they discount the future, though, they will not spend exactly the same amount, but consume more now at the cost of consumption later. Each household faces both permanent income and transitory income shocks in each period, drawn from an unknown distribution, and likewise are stochastically presented with the opportunity to invest into a randomly sized, lumpy investment. They can borrow up to a limit, denoted s, at an interest rate r, and if they are unable to pay back the loan default to a minimum level of consumption c. Lastly, there is measurement error in income, which we will estimate, and there is a return to investment which is plugged in from elsewhere (as there is not enough investment in the sample to tidily identify it).

The objective is to choose a set of parameters which will match the period before the intervention. If our model is correct, then that same set of parameters – properly modified to account for the change in the borrowing limit, and the general upward trend after the East Asian financial crisis of 1997 – should replicate the outcomes afterwards.

We find those parameters with the method of simulated moments, which is an extension of generalized method of moments. A brief aside on what those are. Generalized method of moments works by slowly converging to some set of parameters which match the outcomes in the data, or “moments”. You take a guess, solve, and see how far off you are. How far you are updates what you guess next, and eventually you converge to something which comes close enough to matching. The simulation aspect comes in for the inner loop, where you “solve”. The model which they sketch out does not have a tidy solution. Instead, they simulate what agents would do under the parameters with random shocks many times, and take the average. The results from the inner loop can be compared to the outcomes of interest, and you crawl around finding the values that fit.

To evaluate the counterfactual, they change the borrowing constraint s. Because one million baht was given to each of the villages, regardless of size, the variation in the borrowing constraint is plausibly exogenous to what was going on. Consumption increased equal to the amount of credit that was available in each village, and investment was unchanged, although investment was rare enough in the data that failing to observe a significant increase was not puzzling.

Key to understanding the puzzling result is that ex ante identical households which face different shocks will behave very differently. Those households which are living hand-to-mouth, and face a negative temporary income shock, will use the credit to smooth consumption. Their consumption will naturally rise. The more interesting thing is the consumption of people who have positive income shocks. They do not borrow, yet still benefit from the change in the borrowing limit because they can reduce the stock of precautionary savings which they would have held. Meanwhile, people who are insolvent are actually worse off – raising the borrowing limit leaves them more in debt, and they have to pay the interest in the next period. Finally, people who face investment opportunities may borrow to finance it, with ambiguous effects on consumption. Because of the fixed size of the investments, families might reduce their consumption to add to the new borrowing limit.

I think it’s important to point out the role that the default level of consumption c is playing. If default was impossible, and you could always lose more money, then the expanded borrowing limit would always be strictly better. However, with the bound on consumption, the borrowing limit is essentially how much the lender can hold you to from period to period. A higher borrowing limit essentially reduces how much you can default on the debt.

We can then evaluate what would happen if, instead of a loan program, they had simply transferred a sum of money which raised utility by as much. Earnings are in the model – simply move it up, and simulate. They find that a cash transfer could have produced the same increase in utility for 70% of the cost. And yes, that is fully accounting for the credit offerings being permanent. With the risk of having to pay interest for a long time, they would prefer a smaller cash transfer.

I would not suggest that lending services are not effective. Breza and Kinnan (2021) study the government of Andhra Pradesh’s abrupt interruption of moneylending services in 2010 on consumption, wages, and employment. The government in Andhra Pradesh claimed that it was for consumer protection, but this was of course nonsense; it was more akin to medieval kings periodically expropriating the moneylenders. Breza and Kinnan are not studying the direct effect of no longer being able to access loans in Andhra Pradesh, which would be hopelessly contaminated with changes over time and have only one observation. Rather, they have data from 25 providers of microfinance across multiple districts. Their exposure to Andhra Pradesh, where borrowers were able to default at will, varied, and so the places that saw bigger losses had to reduce their lending in other districts. From this variation, we can get the effect on wages and consumption.

It was not good! The places which saw reductions in lending activity also saw reductions in wages, earnings, and consumption, with employment showing a null effect. Agricultural wages went down 4%, non-agricultural wages went down 8%, and household consumption went down 5%. These losses did not show up immediately – only in the next year, when the banks could not make the loans they had made the previous year in view of their financial losses. Screwing the moneylenders is good exactly one time, and then you pay for it forever. Also, at a larger scale, Bau and Matray (2023) show that India removing the constraints on access to foreign capital which it had until the early 2000s caused firms which had higher marginal revenue products of capital to greatly increase revenue and investment.

The evaluations thus far are the short-run impact. Unfortunately, we can’t expect for the long-run impacts to be better, and in fact we should expect them to be worse. Buera, Kaboski, and Shin (2021) trace out the incentives, but the basic finding can be found from considering what happens if people draw down their buffer stock savings. With lower savings rates, the real interest rate rises in the long run, and the village accumulates less capital. To be clear, total consumption is sure to go up, and as a corollary, income is actually redistributed to labor through higher wages at the expense of less capital. But the rise in immediate consumption is a sugar high from being able to use one’s savings now, and does not stick around.

Nor have we been able to find much evidence of the poverty traps which microfinance would solve. A poverty trap is not merely “things are bad”. It is very specifically the idea that if you give people a substantial enough amount of money, they will be able to start building up toward a higher equilibrium on their own. We just have not found it. The story of people restricted by calories, for instance? Outside of maybe active famine, it just doesn’t make sense – calories are so astonishingly cheap now. Kraay and McKenzie (2014) survey with a sympathetic eye, and can’t find anything. It goes to show just how starved we are for poverty traps that a paper showing one example in rural Bangladesh gets a trumpeting paper – nevermind that, as Karlan, Raswan and Udry (2026) pointed out a couple weeks ago, none of the other instances of this program showed the same effect, and the one that does it probably just correlated geography.

The RCTs, combined with the structural evaluations of the general equilibrium effects, marked the end of microfinance as a charity darling. If you cannot beat a simple cash transfer, then you have no argument for existing.

Some mysteries remain. How do we reconcile the ineffectiveness of microfinance in the RCTs with the rate of return appearing to be well over the prevailing market interest rate? Just because microfinance was ineffective does not mean that credit constraints do not exist, or that decisions are distorted due to risk. Perhaps we need not to discard the spirit, but change the letter.

The second part of this article will cover whether poverty traps exist, microfinance with different loan structures, insurance for farmers, the utility of public health interventions, subsidizing education, subsidizing migration, and summing up.