Lant Pritchett

Awhile back I was exchanging emails with a very savvy economist who works almost exclusively on the USA. I was arguing that “development” should be 80 percent devoted to “national development” and 80 percent of that to (inclusive) growth and only 20 percent to programs that mitigate the consequences of the lack of national development (an argument I have been making for some time, here and here).

My impression is that the fad is for “development” (among philanthropists and others) to be 20-80–mostly about programs targeted to specific households (e.g. anti-poverty programs) or specific outcomes (e.g. safe water) or both (e.g. safe water for poor households). Only in that allocation of development assistance/effort can RCTs even seem an important part of development in providing “evidence” for “what works” (and event that isn’t really true) as it is obvious that most RCTs are not useful for country level questions about national development or growth (here) (and here, with new data, in this new blog by Dany Bahar).

He in response, gave a quick and thoughtful response:

Taking your fractional breakdowns, could you fill in these numbers: Absent the randomistas it would have been X:(100-X) development: charity. With them it is 20:80. But the 80 is Z times more effective than the (100-X) would have been because we spend the charity better. So their net impact is…

Here is my emailed response (slightly edited and with more links added);

I am worried my quick and long answers might give you the impression I am not busy and have time weighing on my hands, but it is the refreshing opportunity to discuss these issues with an open minded economist’s economist that has me procrastinating other things to respond.

Four big sections.

First, but we have to get the causal order straight.  RCTs are the child of the shift to 20-80 where 80 is “programmatic charity work.”  The story of that shift is really the politics of Western support for development assistance.  That story is something like the two-fold impact of the end of the Cold War (which was a major motivator of “getting to Denmark” national development efforts to keep the Third World out of the Second by keeping it on the way to the First) and the decade(s) of addressing the debt crisis via structural adjustment (starting in 1982 with Mexico).  

There are two key turning points.  

One is the 1990 World Development Report on Poverty which adopts for measuring the total number of people poor the ‘dollar a day’ poverty line, which was, as I argued at the time, ridiculously low and would destroy efforts to pursue national development, but the line was adopted because of World Bank politics (the director was nervous that anything but the lowest possible poverty line would make the WB seeming like a special pleading advocacy group).  (This narrative is in this paper). 

Two was the 2000 Millennium Development Goals, which were a Jeff Sachs brainstorm that to protect foreign aid from being part of the “peace dividend” the West country voters needed more “accountability” of aid for concrete results so set up as the “goals” of development consisting of on a few goals, for topics very popular (with West voters), all set at a very low bar.

So, for instance, the goal for “education” was “every child complete primary school” (nothing about quality of learning, nothing about middle school, high school, nothing about job skills, nothing about higher education). The goals for health were not about the health system but about specific diseases (goal 4 was child mortality, which is broad but age specific), goal 5 was maternal health, goal six was HIV/AIDS, Malaria, and other diseases.

It was only with this new (in 2000) framing that “development goals” was not “India becomes the UK (or Denmark)” but “malaria is reduced” or “every kid completes primary school” or (Goal 1) “lower dollar a day poverty” and that foreign aid should be “accountable” for these goals that the “programmatic charity” view of development really took off.

And, only in that broader context could the “randomista revolution” happen as the RCT could be sold as both (i) an “accountability” device–prove your programs work to get funding and (ii) a learning device of “what works” to cost effectively meet these specific target outcomes.  

So, to first order, the shift from to 20-80 was not in the context “and with RCTs these can be more effective” as in 2000 there was still only a handful of RCTs (I helped Esther get the data from Indonesia for her job market paper while I was working with the WB in 1999, so she was still in grad school).  

Second, in the pre-randomista revolution era (1990s into 2000s) the question was the relative efficacy of “national development” efforts (mainly growth, but also “system” work in sectors and on governance) versus “programmatic” efforts. 

I was working at the time a lot on health and education so let me give just two examples.

Larry and I did a paper “Wealthier is Healthier” that used IV approaches to show the higher income to better child mortality outcomes was causal and pretty big.

Deon Filmer and I did a paper “The Impact of Public Spending on Health: Does Money Matter?”  (which has 1240 citations and so it is not obscure) in which we did two things.

a) We showed that the cross-national variation in (well measured) child mortality was almost completely explained by:  GDP per capita, the Gini (which is needed due to Jensen’s inequality even without any “direct” inequality effect), women’s stock of schooling, and a dummy for Muslim countries (due to excess female mortality in those countries), and regional dummies.  The Rsq of that regression is .95 (and since measurement error in child mortality is only the order of 2.5 to 5 percent of the variance that means pretty much all of it).  The important point here is that it isn’t just “growth is important” the result is that if these correlates are causally prior to health spending (or anything about the health system), which they plausibly are (and we could do IV and all that) then the challenge of finding the impact of exogenous health actions conditional on those factors is that there is almost nothing left to be explained.  I know Rsq is old fashioned (and was even at the time, because of its mis-use in model selection) but it actually means something concrete and useful. 

b)  We then made it clear we were not doubting that modern medicine worked, nor that there were not “cost effective” interventions in health (like vaccinations for childhood diseases or Oral Rehydration Therapy).  We were making the case there was a micro-macro paradox.  One should show evidence (at the time) that there were feasible interventions that had cost effectiveness of 100 or 1000 dollars per life saved.  But, if you inferred the “public dollars spent per life saved” from the cross-national data then (i) it was not robustly statistically different from zero and (ii) the point estimates implied millions of dollars per life saved.  (This was backed up later by micro studies of provider behavior showing publicly provided ambulatory care in India did not reach the level of “do no harm” (here and here)).

So the idea that shifting development assistance from “promoting growth” to “spending on health” was better, even for health, was actually not so well proven.  It just “seems” more concrete to non-economists.

(And, on the plane to Cambridge last night I read a 2025 report “Taking stock of development assistance for health in the 21st century” written by committed global health advocates (not skeptical economists) which finds very weak support in the aggregate data for an impact of development assistance on health outcomes).  

The other example is that Deon Filmer and I did a similar paper on education titled “What do education production functions really show?” in which we looked at micro evidence about the impact of different types of education and found that generally there were order of magnitude differences in “learning gain per dollar” across various types of expenditure and that, generally, things that entered directly into teacher utility (e.g. wages, class size) were orders of magnitude smaller at the margin than things that did not (e.g. learning materials, desks, quality of facilities).  This meant, again, that the question “What is the impact of additional spending on learning outcomes?”  has the answer “depends radically (up to three orders of magnitude) on what you spend it on and that is a political, not a technical question.”  Again, the “take money from the growth or national development side and devote it to the education side” is not an obvious plus, even judged on the effect on education (learning) alone.  

Third.  So the young Turks of randomization come into a world in which (a) “development” within the Western world of development assistance (actual developing countries never bought into this) had been “defined down” (to what I call “kinky development” as if just getting above some low-bar is the actual goal) and (b) in which the evidence that, at the margin, additional public spending (and hence most development assistance if money is fungible) on these specific goals (poverty, primary enrollment, malaria) is far, far, from efficient (and even debatable it is even efficacious).  (And with the added factor that Gates (and other NGOs a bit) are bigger players). 

(And, within the logic of the world of academic economics there was increasing skepticism of non-randomized ways to identify causal effects as the IV approach was under increasing attack for reasons good and bad).

In that context, there were two narratives of why and how RCTs would make a difference that had to have two elements.

One element was how the evidence would translate into action.  

a) the “accountability” story, which is that “independent impact evaluation” would be a tool for a central allocator of funding to evaluate the claims of sector advocates about efficacy and stop funding of interventions that were not effective (or cost effective) and reallocate that spending to interventions which had “rigorous evidence” of being effective.  Note that this story of impact of RCTs in development assistance is (and has to be) explicitly a political story about the people who are choosing to allocate expenses and in this story the “independent” (of the actual do-ers) evaluator works for the “policy maker” and takes money away from some people and gives it to other people (or, the policy maker is able to “force” the doers to do something else).

b) the other narrative is the “learning” story in which there are well meaning actors, with resources, who are fungible across approaches (e.g. not wedded to “micro-finance”), who are willing to follow the evidence to cost effective things.  This is more a story about (some) philanthropy than a realistic view of either governments (and their agencies) or the development assistance bureaucracies. 

The other element is that RCTs would produce better evidence for decision making, which kinds of depends on the mechanics of decision making above.

All of this, all the elements of how RCTs would lead to better outcomes, was pretty dubious to people actually working in development from the get go.  That is, for all of the hoopla about “evidence” there was never any evidence that evidence was the binding constraint to improved efficacy at scale.

Just two points and then time to answer your concrete question.  

One, after years and years of RCTs on education JPAL published a table of the “cost effectiveness” on learning of various interventions that looked exactly like the results of Filmer and Pritchett (1999) from 20 years earlier, showing that some interventions were orders and orders more efficacious than others (and even, to some extent the same pattern).  But instead of concluding, as Filmer and Pritchett did, this shows that the politics of how education spending is allocated matter and we need a positive model of why there is so much spending on inputs ineffective at the margin in order to make “recommendations” they (JPAL) say this evidence implies the recommendation “do the effective thing” as if that isn’t completely naive of a positive economics of the existing outcomes, to who, exactly, is that a “recommendation”?

Two, the idea that RCTs generate useful evidence for decision making in a development context hinges on the relative merits of “internal validity” (bias) and “external validity” (contextual variation).  As either the RCT has to be done context by context (which is hugely expensive) or one has to believe that evidence “rigorous” in one context is “good enough” for another.

On that, two points. (a) “external validity” claims are logically incoherent if there is large variation in observational estimates (a point I won’t try and articulate here) and (b) I have a couple of papers (including one that is recent and really good)  showing that the claim that “do RCTs, do a systematic review, take the mean of the RCT estimates as the point estimate for the LATE in your country” produces (much) higher Root Mean Square Error of prediction of the LATE across countries than does “Use the OLS estimate in your country.”   This is just a statement that, at least across four different phenomena for which we could generate enough estimates, the internal validity problems with OLS was (much) smaller than the variation across countries in the LATE, so using the mean LATE was worse than using the biased country specific.  And, in the paper, I make the point that we have, as economists, zero basis on which to have any prior about this.  That is, the LATE of an intervention is not like “electron mass” about which physics has a theory of external validity (“electrons have no hair”) or even the “boiling point of H20” which varies by pressure, but according to a formula.  In my paper I take the “learning impact of an exogenous move of a child from public to private school.”  We do not have a theory that this is constant across countries and no theory that the selection bias that contaminates OLS is constant across countries (and good evidence that both do vary across countries).  So there is no reason why one could or should start from any presumption that an aggregate of rigorous LATE estimates applied across countries (or contexts) is better than using context specific OLS on observational data.

Fourth, so, finally, back to your question.

1)  we know that the gains from growth accelerations are huge.  In Trillions Gained and Lost me and my co-authors estimate the timing and magnitude of growth episodes versus a counter-factual of BAU.  So, for instance, we find the total NPV of gain from the growth accelerations (there were two in our dating, one in the early 1990s and another in early 2000s) was on the order of 2 trillion dollars of income to Indians (so people with high marginal utility of money as the growth broad-based).  What could you have spent probabilistically on a lottery ticket of affecting the likelihood of these growth accelerations in order for this to have been a massively high benefit cost decision?  In a paper Method or Madness I discuss that the Ford Foundation spend, many, ten million dollars over a number of years to support a think tank (ICRIER in Delhi) that, according to many narrative accounts, helped with research and advocacy pave the way to make the reforms in India in the early 1990s, which plausibly accounted for the growth acceleration possible.  Suppose that only increased the probability of this outcome by 1 percent, well, 1 percent of 2 trillion is a big number.

Alternatively, the value of the China growth accelerations are even bigger.  And Edward Lim, the World Bank country director, tells a narrative of a set of very behind the scenes discussions with the Chinese leadership as Deng took power that helped along the decision to launch their (incremental, cross the river a step at a time) reforms.  I did the semi-facetious calculation that even if all of academic economics from Adam Smith to 1978 did nothing but create a body of evidence that persuaded the Chinese leadership to embark on their reforms it was all worth it.    

And the losses from the two decade long growth deceleration in Brazil were an NPV of 7.5 trillion.  Suppose we as development economists had managed to help the loss be half that large, that would be 3.75 trillion.  That probably pays for all of the IMF for its entire history as a hugely positive NPV investment.

So the old “80 percent on national development” was some mix of specific growth projects but research/knowledge dissemination/persuasion about things that work to accelerate growth.  Even if this is a highly uncertain endeavor (both on that it generates the right answers and that these are taken up) the upside is huge (and avoiding downsides is huge). 

2)  Against that, the shift from 80-20 to 20-80 on “spend more on programmatic interventions” as BAU efficacy is, at best a wash, at worst, worse because the average incremental gain of “spend more” on outcomes is plausibly quite near zero–not because it cannot work but because the systems that would make it work as a routine result of the way the system works are highly dysfunctional (and making systems functional is kind of what national development is all about).

3) The incremental improvement of RCTs on the spend within the 80 percent is small, I would say bounded between 0 percent and maybe 2 percent, meaning the average gain in outcomes per dollar spend is higher by 2 percent on average.  This is both because (i) they have never had a plausible positive (policy or political) model of how “better evidence” would get to “better spend” and (ii) they have not really discovered many generalizable “silver bullet” like interventions.  (And even in the rare instances when they claim this, like for “teaching at the right level,” the story (which I have been a part of) is much much more complex than “RCT evidence convinced people so they adopted it.”)

So, on net, the shift from 80-20 on national development, 80 of that on growth to 20-80 on “mitigation of the consequences of the lack of national development” plus RCTs is a huge loss.  Basically it is a shift from low cost, low probability lottery tickets with huge upsides to low probability lottery tickets on small upsides and adding RCTs on top of that is a high cost lottery ticket on a very small probability of impact with low upside anyway (e.g. the Science publication on the giant six country impact evaluation of the Graduation approach to poverty that finds, meh, about cash transfer impact or worse, the Niger experiment in Nature magazine with a “psychosocial” component of a cash transfer program finds tiny impacts at very low cost so high “cost effectiveness” because both are small).

And, to be clear, I don’t need a very high probability of being able to give good advice that causes a growth acceleration (or avoid a loss) in a country, just better advice because the ticket is big. (Nor am I anywhere here making an argument that depends on right wing views that “free enterprise” is always the right answer to promoting growth nor does this depend on any antipathy to taxation to fund effective social programs),

Yesterday (April 29, 2025) I was at a conference of scholars who work mainly on the USA (the first time in my life I have been to a two day event devoted exclusively the the USA). I met someone new and she asked “What is it you do?” and I said “I am a development economist” and she said “What is that?” and I said “I do the economics of poor countries like, say, India and Indonesia” and she said “So, like micro-finance?”

I did not know whether to laugh or cry. So I laughed. I said: “No, nothing like micro-finance, nothing at all. I am the type of development economist who worries whether the financial system in India (banks, stock markets, corporate bonds) can support the level and allocation of financial services (including the financing of private sector investment) that India’s economy needs to reach the level of worker productivity of the USA or France or Japan.”

But I was crying inside. How did we get here? How did the noble vision of development economics as the study of, and if possible, support to, the four-fold transformation of national development get reduced to the evaluation of discrete ‘interventions’ (which don’t even pass a smell test as important to national development)? How did development economics become understood in the global North as the field that generates evidence about how to do more effective charity work?

Sometimes when you get very lost the only way back is to trace your path back to the first wrong turn and start over from there. The root of the problem is that, stunningly, a large part of the development field acquiesced in a measure of development progress, poverty measured at low-bar poverty lines–that excludes the progress of most of the the world.

That was the result of three steps.

One, the claim that “development is about poverty reduction” became widely accepted. So far, so okay, but that statement generates consensus only by constructive ambiguity and its implications depend entirely on what is meant by “poverty.”

Two, it became commonplace to measure “poverty” by a class of measures (Foster, Greer, Thorbecke 1984) that have the property that above some threshold of income, called a “poverty line”, the incremental contribution of income to poverty reduction is exactly zero.

Whoa, careful here. Already this second step implied these poverty measures were no longer compatible with standard economics of welfare measurement. Unlike the silly caricature that economists believed GDP per capita (an average) was itself a measure of wellbeing, most economists believed in distribution sensitive measures of wellbeing because improvements of the less well-off should count for more in assessing progress than the same progress of the better off. But the idea that gains to wellbeing above some threshold of income should count for exactly zero in an aggregate measure of wellbeing even though people themselves rank this income as valuable is explicitly paternalistic. This violates the (strong) Pareto principle (that if one person was better off and no person worse off then an overall ranking should increase), a widely accepted axiom of an adequate measure of aggregate wellbeing.

There was a debate with poverty measures about the tradeoff between a measure of wellbeing that satisfied the (strong) Pareto Principle for social welfare measures and what Amartya Sen proposed as the “focus axiom” (which was, properly speaking, never an “axiom”) that a poverty measure should be invariant to increases in income (or redistribution among) “the rich.”

This opened up a gap between mainstream economics and “poverty” economics. Mainstream economics was always perfectly open to social welfare functions that counted gains to poorer people as more important than gains to richer people but did not accept the paternalism of counting gains to some people at zero. Squire and van der Tak 1976 proposed that the World Bank’s cost-benefit analysis of projects should incorporate methods that gave more importance to the gains of poorer people, a suggestion which was not at all controversial or rejected in principle.

But “poverty economics” adopted poverty measures which were explicitly paternalistic. The practical severity of the differences between standard distribution sensitive money -metric measures of wellbeing (such as the Atkinson index) and poverty measures (such as the FGT class) depended on where one drew the poverty line. As Angus Deaton has said in the past: “I have no problem with poverty measures as long as the poverty line is infinity.” I would guess among most development economists would have little problem with a “focus axiom” that was invariant to the income of “the rich” if “the rich” meant those with income over 100 dollars a day. Or even perhaps if “the rich” were defined as those with consumption over 50 dollars a day. But this is not what happened.

The third step is where it all went wrong.

The World Bank’s annual flagship report, the World Development Report was titled Poverty. It was felt (and this is first person report oy my conversations with the people responsible at the time) that the report needed a headline number for “How many people in the world are poor?” The choice was made to report the number of poor in the world based on a poverty line of (roughly) dollar-a-day in income/consumption per person per day (adjusted for PPP). This poverty was chosen because it was the lowest plausible global poverty line. The only rationale for this particular, very low, poverty line was that it was the poverty line of the poorest countries in the world (Ravallion, Datt and van de Walle 1991)–with the claims that there was a lower asymptote in the relationship between national poverty lines and GDP per capita).

It was obvious (to me at the time, and I expressed these objections at the time) that using the dollar-a-day line makes the problems with FGT poverty measures as bad as they could possibly be.

With a low poverty line the discrepancy between a standard distribution sensitive measure of social welfare and the poverty measure becomes very stark. Any reasonable social welfare function is going to treat the gains of those just below dollar-a-day as very important–much more important than the gains to “the rich” at, say, 100 dollars a day. But any reasonable social welfare function is also is going to treat gains to those just above dollar-a-day as very important, and much more important than the gains to those at 100 dollars a day. And any reasonable social welfare function is going to treat gains to those just above the poverty line and those just below the poverty line about the same.

FGT poverty measures with a dollar-a-day poverty line are completely unreasonable measures of social welfare. The gains to those just above dollar-a-day are treated as worth zero in reducing poverty. This has two implications. One, the ratio of the poverty gains from income gains of those just below and just above the poverty line is infinitely large (a positive number divided by zero). Two, the gains to those just above dollar-a-day and those at 100 dollars a day are treated as exactly alike–both count for zero.

Think of the “focus axiom” that a poverty measure should be invariant to changes in the income of “the rich.” But a low-bar poverty line like dollar-a-day does not divide the distribution into “the poor” and “the rich,” it divides people into “the extremely poor” and the “not-extremely-poor” where the “non-extremely-poor” category includes both very poor people (those just above the dollar-a-day poverty line) and very rich people. So the focus axiom with a dollar-a-day poverty line implies the measure of poverty should be indifferent if we took some money from someone at P$1.25 pppd consumption and gave it to Bill Gates. As a proposal for how to measure human wellbeing in a country this is not just wrong, it is completely nuts.

The combination of (i) “development is about poverty reduction”, (ii) poverty measurement with poverty lines, and (iii) the adoption of a extremely low poverty line as the primary (or exclusive) poverty line has radical implications.

And yet that last step never had any justification sufficient to merit the radical implications. That is, one can accept dollar-a-day as one poverty line among many, a measure of one type of poverty, called “extreme poverty”, but there was never any reason given why the lowest plausible poverty line–and only proposed and rationalized as such–should be treated as “the” international poverty line or become the special or even most important measure of global poverty.

The dollar-a-day line was never widely accepted among countries in the Global South. After all, since the line was chosen because it was (roughly) the average national poverty line of the poorest countries this necessarily implies most countries had, for their own measurement of national poverty, a higher, sometimes much higher, poverty line.

Nevertheless, powerful political forces in the Global North favored this penurious standard and hence its use spread.

When the Millennium Development Goals (MDGs) were adopted in 2000, Goal 1 was “Eradicate extreme poverty and hunger” and the only target for poverty was Target 1A: Halve, between 1990 and 2015, the proportion of people living on less than $1.25 a day. (which was the inflation updated dollar-a-day standard). So in ten years progress on poverty measured at the dollar-a-day poverty line had gone from being plucked pretty much out of thin air and adopted because it was such a low threshold to become a central goal of development.

Fast forward another 11 years and in 2011 the book Poor Economics (Banerjee and Duflo 2011) is published and becomes a wildly popular and influential book. They proposed that many problems of poverty could be improved through a development economics that relied more on the use of rigorous evidence about the impact of specific, targeted, interventions, with priority given to the use of randomized controlled trials (RCTs). They argued that this “small” approach could nevertheless lead to big impact.

Agnes Labrousse (2020) in a brilliant (but widely ignored) essay explains the puzzling success of this book as due to its “rhetorical superiority.” She argues it is extraordinarily weak as scholarship but a brilliant exemplar of Aristotle’s three principles of ethos, pathos, and logos.

She also points out that the tell a narrative about poverty that ignores nearly all of development economics. “They leave out of the field, without specifying this—it is in this way that the framework is manipulative—fundamental questions of development economics.”

She provides a list of words never mentioned (or mentioned only once):

These off-camera elements are, in fact, out-of-discourse. The organization of production and business, innovation dynamics, meso-economic and territorial questions, local and international financial and commodity flows, macroeconomic dynamics and politics, the environment and inequalities are largely absent. As such, there are no instances in the body of the text for inequal* and unequal, Gini coefficient, income/wage disparity/ies, justice, ethics*, dependency, terms of trade, import, comparative advantage, commodity/ies, stabilization, specialization, international relation*, industrial revolution, capitalism, market economy, modernization, westernization, globalization, tariff*, reserves, foreign investment, capital flow*/flight*, brain drain, volatility, instability, speculation/tive, deregulation, Dutch disease, monetary policy, fiscal/budgetary policy, redistribution*, protection/ist*, lost decade, (Post)Washington consensus, IMF, structural adjustment, foreign debt, foreign investment, fair/free trade, regional development, value chain, production network, corporate governance/interests, innovation fund, technology gap, patent, license, intellectual property, agrarian reform, land grabbing, deforestation, commons/common pool, natural resources, climate change, greenhouse (gas), bio diversity, public good. Industrial policy appears only once, which is the same for domination, dynamics (familial), inequity (intrafamilial), trade (the idea of trade credit), remittance, diversification (of risks), pollution (“pollution inspectors”), externalities (“treatment externalities”), global warming, carbon emission, liberalization (“early years of Chinese liberalization”), privatization (“privatization voucher” for school fees) or recession. Energy is used only in the psychological sense (3 uses); the same is true for 5 out of 7 instances of depression. The results were similar for structure and macro (cf. Section 8.5). This is revelatory of the fundamental difficulty of RCTs in tackling historical dynamics (including microeconomic dynamics), and meso and macro questions. These issues are not amenable to RCTs.”.

So roll forward to 2019. Banerjee and Duflo and Kremer won the Nobel Prize. The scientific background statement says: “This year’s Prize in Economic Sciences rewards the experimental approach that has transformed development economics, a field that studies the causes of global poverty and how best to combat it. In just two decades, the pioneering work by this year’s Laureates has turned development economics ― the field that studies what causes global poverty and how best to combat it ― into a blossoming, largely experimental field.”

This statement reveals the centrality of the complete embrace of low-bar poverty lines and how that re-defines what development economics is about.

If by “global poverty” one means the very wide variety of poverty measures that countries use, or the higher poverty lines used by the World Bank, or more ambitious measures of global poverty enshrined in the SDGs, or global poverty lines such that those not in “poverty” are “prosperous”, then it is true this is broadly what the field of development economics studies. But then it is not true that this field and these questions have been particularly helped by experiments.

Take Pakistan. At the World Bank dollar-a-day (now P$2.15) line the headcount poverty rate is 4.93 percent. But at P$6.85, a poverty line the World Bank also routinely reports–which is still a low poverty line relative to developed countries or what we argue in our paper are defensible upper-bound development poverty lines–the headcount poverty rate is 84.53 percent of the population. One can imagine that targeted programs informed by the results of RCTs might help reduce dollar a day poverty in Pakistan. But it is not at all plausible to think that the level of P$6.85 poverty line poverty does not require broad based improvements in the overall level of productivity of the economy. Programs for “the poor” are not relevant to 84.53 percent of the population.

When the statement says this “transformed development economics” one might think this means there was a field of development economics that addressed an array of questions and the advent of experimental methods improved that field. But that is not what happened. What happened was that there was a very, very narrow subset of questions within development economics which were, perhaps, in principle, amenable to experimental method and a large set of questions important to countries becoming productive and prosperous that these methods were not amenable to. What the randomista movement did was shift the attention within and among a group of academic researchers to the set of questions that were amenable to their method and just, as in Poor Economics ignore the other questions.

The point here is that the question of whether or not RCTs are important contribution to development economics hinges on the question of the poverty line. If one accepts that the only, or primary, or most important, meaning of “global poverty” is a measure of poverty that uses a very low poverty line then this debate can be entertained. But if one rejects that dollar-a-day represents the only, primary, or most important definition of global poverty then claims for the importance of RCTs to reducing global poverty are just obviously false.

Take Niger. There was a recent paper, published in the prestigious magazine Nature doing an RCT about adding various design elements to a cash transfer program, including a “psychosocial” component. Does a study like this contribute significantly to reducing global poverty? At the World Bank P$6.85 poverty line headcount poverty in Niger is 96.5 percent. So the answer is obviously no. Does this paper show there are interventions which can cost-effectively add very very small amounts to the incomes of very very poor people in Niger? Yes. Was that an important question in development economics? That is only even conceivably true if one takes the low-bar poverty lines as being much, much more central to development than development country governments or people themselves do (that is, no one in Niger thinks being just above a dollar-a-day is discontinuously important).

All of this is background to why I think my new paper with Martina Viarengo titled “Raise the Bar” (and my previous paper “dollar-a-day” poverty was a mileage marker, not the destination) are interesting and relevant. In our new paper we start from the fact that the poverty goal in the 2015 Sustainable Development Goals is radically different from the MDG of 2000. Instead of the narrow goal of “End extreme poverty and hunger” the SDG is “End poverty in all its forms everywhere.” This is an explicit rejection of the notion that “extreme poverty” (dollar-a-day poverty line) is the primary definition of global poverty.

This embrace of an array of poverty lines raises the question: “If dollar-a-day is the lowest plausible line for global poverty, what is the highest global poverty line for a development goal?” The paper analytically and empirical justifies a global upper-bound poverty line of P$21.5, ten times the dollar-a-day standard.

With “global poverty” defined as the fraction of people living below P$21.5 (and with more weight on improvements on those with lower incomes within that) I am then happy to define development economics as: a field that studies the causes of global poverty and how best to combat it. With that idea of global poverty we need a better development economics that can answer the important and pressing questions political leaders and policy makers in developing countries face, very few of which are amenable to improved techniques for project evaluation. And, to circle back to the opening anecdote, in that development economics, microfinance is, at most, a tiny concern.

This new paper, with Martina Viarengo, addresses a narrow technical question in order to get traction on some very large consequential (and currently hotly debated) questions about development and development assistance.

The technical question is pretty easily posed, with a little background. A global lower-bound poverty line of “dollar a day” (which is now P$2.15 per person per day in 2017 PPP) is widely accepted. It is also widely accepted that the dollar-a-day threshold is not “the” poverty line but only the lowest line (often called “extreme poverty” because it is an extremely low threshold) and that one can legitimately measure and report national and global poverty at a wide variety of poverty lines, the World Bank data platform routinely reports poverty at P$3.65 and P$6.85. In addition, many countries have their own national poverty lines, nearly all of which are higher than P$2.15, and the World Bank reports a country specific “societal poverty line.” The first Sustainable Development Goal is “End poverty in all its forms everywhere” which obviously allows for a wide array of measures of poverty. The question is: Is there a global upper-bound poverty line for development related poverty? Or, put another way, if development assistance is about helping developing countries improve their wellbeing and among those goals for wellbeing is the reduction of poverty, what is the highest poverty line, beyond which one would say, people above this threshold are prosperous, not poor?

Our paper argues, with an analytical framing and empirical estimates that a good candidate for a global-upper bound poverty line is P$21.5. This is a good candidate in part because focal points matter and this is exactly ten times higher than the lower-bound, dollar-a-day line for extreme poverty of P$2.15.

This is much higher than the current highest line used by the World Bank (P$6.85) but consistent with other proposed poverty or prosperity lines. The World Bank prosperity gap measure uses a threshold of P$25 per person per day (here, here). Max Roser has powerfully argued (here and here)–extending and improving on earlier augments of mine (here)–that the poverty lines of the richest countries should be the upper bound, on the pretty reasonable grounds that :the poor” should mean “the poor” no matter where they live. P$21.5 is around the societal poverty line (here, here) of the lower consumption per capita but fully “developed” countries, like Spain.

The larger point of proposing and defending as reasonable a high upper-bound for a global poverty line (10 times the low bound) is to change the discourse on what “development assistance”–from multi-lateral organizations, bilateral organizations, and development related philanthropy–is about.

The original vision as “development” as a distinctive field and endeavor came into existence in the 1950s was that, as the new post World War II world order was inexorably leading to decolonialization, these newly sovereign states would develop as countries to become “developed” by which was meant a four-fold transition to (i) high productivity, (ii) capable public sector organizations, (iii) a responsive polity, and (iv) social cohesion and equal treatment. Francis Fukuyama as called this “getting to Denmark.” It was envisioned that “development assistance” could accelerate the progress so that rather than “getting to Denmark” happening at the centuries long-pace of the actual Denmark, this transition to national development could happen in decades. This “national development” process was not seen as an end in itself, but believed that this four-fold transformation of national development and “getting to Denmark” would lead to higher levels of human flourishing, as in Denmark.

The entirety of Article I of the World Bank (International Bank for Reconstruction and Development) which lays out its purposes are worth reproducing:

The purposes of the Bank are:

(i) To assist in the reconstruction and development of territories of members by facilitating the investment of capital for productive purposes, including the restoration of economies destroyed or disrupted by war, the reconversion of productive facilities to peacetime needs and the encouragement of the development of productive facilities and resources in less developed countries.

(ii) To promote private foreign investment by means of guarantees or participations in loans and other investments made by private investors; and when private capital is not available on reasonable terms, to supplement private investment by providing, on suitable conditions, finance for productive purposes out of its own capital, funds raised by it and its other resources.

(iii) To promote the long-range balanced growth of international trade and the maintenance of equilibrium in balances of payments by encouraging international investment for the development of the productive resources of members, thereby assisting in raising productivity, the standard of living and conditions of labor in their territories.

(iv) To arrange the loans made or guaranteed by it in relation to international loans through other channels so that the more useful and urgent projects, large and small alike, will be dealt with first.

(v) To conduct its operations with due regard to the effect of international investment on business conditions in the territories of members and, in the immediate postwar years, to assist in bringing about a smooth transition from a wartime to a peacetime economy.

The Bank shall be guided in all its decisions by the purposes set forth above.

The world “productive” (or variants) occurs six times. The stated goal of “development of productive resources” is raising “the standard of living and the conditions of labor.”

The word “poverty” is never mentioned as among the purposes of the IBRD (nor in the articles of IDA the concessional lending arm).

The World Bank’s 1990 World Development Report had a one word title: Poverty. This report needed a headline number: how many people in the world were poor? The decision, which was entirely political (I was in the larger unit of the World Bank producing the report and peripherally–as a very junior staff–involved in these discussions), was made to choose for this “headline” measure of poverty the lowest defensible poverty line (the poverty line of the poorest countries (here)) and hence report the smallest number of people poor.

This choice of reporting “global poverty” at the “dollar-a-day” consumption per person per day poverty line was thought to be innocuous, but set in a motion a very strange process of “downward mission creep.” Usually when people say “mission creep” they mean that an organization takes on a broader and more expansive agenda. But in this case, the spread of the combination of ideas that “the goal of development is poverty reduction” and “poverty is defined by a dollar-a-day poverty line” led to tendencies to downward mission creep among development organizations, in which rather than seeing development as a process of helping countries achieve high productivity and hence high standards of living and “conditions of labor” (as in the IBRD articles), development was seen as an effort to help “the poor” where “the poor” were defined on a penurious, lowest possible, poverty line.

This has set off an increasing fracture among people and organizations working on “development” between those who maintain the original vision of national development and those who want to frame the endeavor of development as a mitigation of the worst consequences of the lack of development. This latter approach looks to frame the promotion of development as the implementation of interventions (projects, programs) that are cost effective at reaching the poor, while essentially bracketing the question of what helps a country in “getting to Denmark.”

This approach, creeping “defining development down” or embrace of “kinky development” since 1990, has reached several reductio ad absurdum moments but which, having swallowed the dollar-a-day’ poverty pill, people do not see as ad absurdum.

For instance, in 2016 Bill Gates proposed that promoting chickens would be an effective investment for poverty in Africa. That statement was both perhaps sensible and completely absurd. If one was asking the question: “what is a discrete intervention that can be implemented as a cocooned project that would be cost-effective at brining about modest increases in the sustained income of selected households, targeted based on their low-income (even within African countries) and hence reduce some very low-bar measure of poverty (and for which the impact was visibly attributable to the funding)?” Then perhaps programs to promote higher incomes from chickens is a sensible claim (one would need much more detail on design, implementation, and evidence, to be sure, but perhaps sensible). But, at the same time, if one means poverty reduction in the way that “getting to Denmark” means poverty reduction–getting to Denmark’s level of poverty–then chicken projects as a any serious component of a poverty reduction are clearly absurd. One might think an incredibly intelligent person like Bill Gates, having reached the conclusion that “chickens are the path out of poverty for Africa” might stop and reflect: “How did I come to accept a definition of poverty such that this statement might be even remotely plausible?”

A second example is what the latest World Bank data now say about extreme poverty. In the latest (April 2025) data the poverty rate in Pakistan is 4.93 percent. More astoundingly, poverty in Nepal is .37 percent–at the dollar-a-day poverty line less than 1 in 100 people in Nepal are poor. So if “development is about eliminating dollar-a-day poverty” then Nepal is a developed country. At which point anyone sensible says “any indicator that says Nepal has no poverty is an absurd standard for poverty.” That is right. Child mortality is still 27 per thousand (a level Denmark reached 65 years ago). Child malnutrition is 18.3 percent. So somehow about about 18 percent of Nepali children are not poor but are malnourished. 49 percent of households are not using safely managed sanitation and yet are also not poor. It is not absurd to care about a standard for the “poorest of the poor” but to act as if that is “the” development goal is absurd.

We are not arguing to give up on poverty as a goal for development. The opposite in fact. In order for development assistance to be relevant to development it needs to use an array of standards for global poverty as a goal. Certainly extreme poverty measured as dollar-a-day is one of those standards, but not the only one, and if there is a low bound and an array their either is an upper-bound poverty line (or, if not, infinity). A sensible upper-bound poverty line as a development goal is 10 times higher, P$21.5, a threshold that far more accurately reflects the aspirations of people and countries for their own lives and futures.

My 2022 paper on national development showed that the fourfold transformation of high productivity (economic growth), state capability, a responsive government, and social cohesiveness (equal treatment of all citizens) was empirically necessary and empirically sufficient for high levels of human wellbeing.

Knowing that this four-fold transformation or “getting to Denmark” reliably leads to good outcomes for wellbeing does not, however, say how to make progress. Some people respond, “well, ‘national development’ would be great, but ‘we’ (whoever ‘we’ that is) don’t know how to do that, so let’s stick to what ‘we’ do know how to do, like implement projects or programs.” Fair point. Knowing that faster than light travel would allow you to make a fortune betting on sports and stocks doesn’t make it possible, or even make a research program on how to achieve faster than light travel interesting as our best available theories say it is impossible.

But economist’s “best available theories” do not say accelerated economic growth is impossible, and cannot, as growth accelerations happen frequently (Hausmann, Pritchett, Rodrik 2005, Pritchett, Sen, Kar and Raihan 2016, Gootjes, de Haan, Stamm, and Yu 2024) and the sustained rapid growth episodes have transformed countries–and the growth accelerations in the population giants of China and India, have transformed the world.

Moreover, the legitimate and deserved criticism of the growth regressions as a research method and of the practice of “one size fits all” structural adjustment recommendations and the Washington Consensus was acknowledged and (mostly) absorbed (e.g. World Bank 2005). This led to new paths in the research into the question of how to produce more relevant and reliable recommendations based on growth diagnostics (e.g. Hausmann, Rodrik, Velasco 2004 ), analytic narratives incorporating context and politics (Rodrik 2003, Pritchett, Sen and Werker 2017), new approaches to structural transformation (e.g. Hausmann and Hidalgo 2009), and more sophisticated analysis of episodes to examine the contingent connections between policy reform and growth episodes (e.g. Peruzzi and Terzi 2021 ).

And recent decades (until at least COVID) have actually been quite good for growth in the developing countries and there is evidence that after decades (centuries) of divergence, big time (Pritchett 1997) there has been convergence (poorer countries growing faster than richer countries (e.g. Patel, Sandefur, and Subramanian 2021, Kremer, Willis and You 2021).

Recent growth raises the question of the joint dynamics of the elements of national development. That is, while many countries are experiencing rapid growth, and hence are “getting to Denmark” in levels of GDP per capita, is this growth accompanied by progress in state capability, responsive governments, and greater social cohesion and equal treatment (rule of law)? An interesting case is Bangladesh. For some time now people have been celebrating the “Bangladesh Miracle” of sustained rapid economic growth (and improvements in other indicators as well). But this was accompanied by concerns about the “Bangladesh Paradox”–that economic growth was rapid but accompanied by stagnating (or deteriorating) conditions of governance and “institutions”–and Raihan, Bourguignon and Salam 2024 asked “Is the Bangladesh Paradox Sustainable?” The events of August 2024 revealed that the existing political regime could not be sustained.

The theme of the Asian Development Bank Institute’s annual conference for 2024 was “Can Asian Economies Forge a High-Income Future and Avoid Burn Out?” For that conference I wrote the attached paper: “Exits from the Four-lane Highway to National Development: What are the Risks to Sustained Economic Growth?” The paper is mainly descriptive (with lots of graphs) and examines the association between the recent growth rates of GDP per capita and the growth rates of a measure of State Capability. The main empirical results are that: (a) having low levels of state capability is not (much) of an obstacle to country’s beginning episodes of rapid growth, (b) economic growth and state capability do not appear to reliably grow together as many countries have been experiencing rapid growth for more than two decades with stagnating or deteriorating state capability, and (c) if one looks for a “high income future” based on extrapolating recent past performance one has to believe that countries will be able to achieve high income at levels of state capability far, far, lower than the state capability of the now high income countries. Whether that is really feasible and how this growing tension will be resolved are, to me, super-interesting questions without much current research (theory or empirics).

The two other things along this line of the joint dynamics of national development are not even papers yet, but speeches given in December 2024.

One was a speech to the 3rd annual Pathways to Development conference at LUMS in Lahore Pakistan, which as given remotely on December 17th. As the theme of the conference was “Governance and Inclusion” the title of my talk was “Governance and Inclusion: “End of History” or “Hell in a Handbasket?” This plays on the theme that in the aftermath of the end of the Soviet Union Francis Fukuyama argued this was the “end of history” (in a quasi-Hegelian meaning of “end” and “history) as the contest over alternative systems that dominated the 20th century was effectively over. This might suggest that “getting to Denmark” would then be the common outcome. But the triumph of (managed) capitalism, strong bureaucracies, and liberal democracy was a tad pre-mature. Lots of countries have had only one of those, or none. The video of the presentation is here and the power point is attached.

I gave a speech more focused on the “hell in a handbasket” theme, using data to look at the joint evolution of GDP per capita, state capability, and other measures of governance. This was at the XKDR Forum in Mumbai India on December 10, 2024. In this presentation I lay out the data on the joint evolution of GDP per capita and governance indicators (state capability and measures of “responsive state”) and then explore specific mechanisms that allow for rapid growth with deteriorating governance.

These three products (paper and two speeches with new empirical results) are just trying to: (a) lay out the questions about the joint dynamics of the elements of national development, (b) provide some descriptive facts with the data we have, and (c) tentatively positive some models (or elements of models) that allow for the range of what we have observed over recent decades, which is examples of very rapid growth but with weak, and deteriorating, governance (on the usual measures). I am just scratching at the surface of these deep questions so far.

As a contribution to a Growth Summit in Washington DC on October 16 and 17 2024 I wrote a synthesis of recent empirical work of mine articulating that economic growth (and more broadly national development) is central to achieving goals for improving human wellbeing. I show that across an array of potential goals: (i) reducing poverty, (ii) improving the ‘basics” of material well, and (iii) general indicators of social progress.

This draws on, but also improves on and substantively expands on, four previous works (some published, some just posted): (a) “Economic Growth in Five Figures“, (b) “Randomizing Development: Method or Madness?” (which contains empirical analysis of the connection between headcount poverty and median income/consumption), (c) a new(ish) still unpublished paper on basics of human material wellbeing and GDP per capita, and (d) a paper on “national development” and measures of “social progress” (both in scare quotes as the words refer to specific concepts and measures) “National Development Delivers.”

Two things are new, or at least much better articulated in this paper than in the previous works separately.

First, I make clearly the case for “inclusive enough” growth. That is, previous work on the relationship between economic growth, taken as the growth in aggregate GDP (or consumption) per capita, and other indicators of wellbeing is that this approach “ignored” inequality in income, either in the level or in “growth incidence.” This paper takes the approach of building concerns about inequality directly into how the distribution income and growth incidence affects measures of wellbeing. So, just as an example, if one is concerned about child mortality as an important indicator of wellbeing (as it is) and one worries that “growth” isn’t necessarily connected to improvements because growth of the average may be driven by growth of “the rich” (e.g. “growth incidence” is pro-rich) then the elasticity (responsiveness) of child mortality to the actual pattern of growth incidence can be calculated as the interaction of the growth incidence (which, say, decile, grew faster or slower) and the elasticity of child mortality to growth in income/consumption at the level of income of each decile. This integrates direct concerns with important normative indicators (and so acknowledges economic growth as a means to an end) with concerns about inequality using empirically estimable relationships between levels of income and indicators of wellbeing. This approach can therefore document that the incidence of growth matters (so that pro-poor growth incidence has bigger impact than pro-rich growth) but can also document by how much it is better so that one can measure the impact of wellbeing of growth that is, while “pro-rich” nevertheless “inclusive-enough” to have powerful impacts on non-economic measures of wellbeing and social progress.

Second, this paper also makes three points about the case “against” economic growth. The case is often made (although often this is just implicit) that economic growth isn’t necessary for improving wellbeing and, that is OK because there are other project or programmatic approaches than can achieve goals for, say, poverty reduction. One, in general this claim just isn’t true. That is, while there might be cost-effective programs for reducing poverty this doesn’t imply that these programs can be scaled by a country to achieve any given desired reduction in country level poverty. The paper shows that there are no cases in which countries achieve very low levels of poverty at very low levels of income and there is very little variation in poverty rates among countries with similar median consumption. Growth in median consumption is an empirically necessary condition for large progress in poverty reduction. Two, the debate about economic growth and programmatic approaches often poses a false dichotomy, arguing that proponents for sustained rapid economic growth are somehow necessarily opposed to effective action to, say, reduce poverty or improve health or improve education or expand publicly provided sanitation. This just isn’t true. Most proponents of taking the actions needed to accelerate economic growth are happy with a “growth plus effective public action” approach. Three, often implicit in the debate about accelerating economic growth versus, say, programmatic approaches or reducing inequality is an assumption that achieving accelerated economic growth is hard or impossible or exactly how to accelerate growth is unknown or even unknowable but that in the same country conditions reducing inequality or implementing effective health improving programs is easy (or at least easier). This is possible, but it cannot be generally true as many countries have managed to achieve sustained rapid poverty reducing and wellbeing improving economic growth while not managing to reduce inequality or implement scaled effective public action in many domains.

I have presented this paper at recent seminars at Georgetown University School of Foreign Service, Harvard Kennedy School, and London School of Economics. Attached is the version of the presentation of the paper I gave at the Dean’s Dialogue series at LSE on October 10, 2024.

This is the title of a new column by the lead economic columnist for the Financial Times Martin Wolf that reflects some of my papers in discussing a challenge faced by all rich industrial countries: ageing. Ageing populations are leading these economically advanced countries into ratios of the labor force to historically unprecedented low levels.

The attached recent paper of mine (which is still a working draft and, as such, has not yet been refereed or published) documents the implications of the standard UN Population Division demographic projections for 31 rich industrial countries. Using the UN World Population Projections “zero migration” scenario and the ILO data on labor force participation rates I show that in the absence of migration and with constant labor force participation rates (by age and sex) by 2050 most of those ageing, rich industrial countries will move into ratios of labor force to population over 65 that will create massive economic and fiscal challenges. For instance, this ratio in Spain falls to 1–one worker for every person over 65 and in Italy it falls to .88 and the Europe wide average falls to 1.34.

This is the sense in which “immigration” (which I use in quotes because I want to analytically distinguish between modes of labor mobility) is “essential.” I don’t think anyone believes that anything like Spain’s existing social contract for its elderly population can be sustained by one worker for every person over 65 (until as recently as 1980 this ratio was above 3 in every country).

The paper emphasizes that the very pressures that make “immigration” essential also make it “impossible” politically. If countries were to make up for the smaller native born labor force through standard “pathway to citizenship” immigration this would to ratios of foreign born to native born among citizens about three times as high as that of the USA at its peak in the “open borders” of the early 20th century. It is hard to look at the recent elections in for the European Parliament or the ongoing 2024 Presidential election in the USA can conclude there is a political climate conducive to ratios of immigrants (who, of demographic necessity will have to come from the Global South in the aggregate) many-fold higher than the current levels.

I argue that a potentially solution to the tension that “immigration” is both essential and impossible is to shift to acknowledging that the question: “Who is allowed to legally reside and work in our country?” has not just two but three possible answers. One answer is “those who we (current citizens and voters) see as the future of us–those admitted on a direct (if perhaps lengthy and contingent) pathway to citizenship.” Another current answer is “those we allow as movers of distress (refugees, asylum seekers).” A third answer, which is already present in at least some form in nearly all countries is “Those who are allowed to reside and work in our country on a contractually time-limited basis.” The paper (together with an earlier companion paper focused on the politics) argues that a massive expansion of rotational labor mobility is a politically possible and administratively pragmatic.

And, and the underlying motivation of my engagement as a development economist, is that the calculations in the paper suggest that the annual gains to workers (and by extension their families and loved ones) in the Global South, workers who would otherwise lack attractive employment options, of large scale rotational labor mobility are in the trillions of dollars annually. The potential gains to development objectives from labor mobility are orders of magnitude bigger than the entire aid industry.

I was invited to give a keynote speech as part of a 10 speaker series as part of the celebration of the 75th year of CEPAL. I was invited with a specific title, which I pretty much stuck to, and which is the title of the attached longish essay that was the substance behind the speech (and which will by published in the CEPAL review along with the other essay).

The video of the speech is in this link, one main aspect of is a long and flattering introduction by the Executive Secretary of CEPAL, José Manuel Salazar-Xirinachs, which, naturally, I would encourage everyone to watch.

The paper covered some old ground I have gone over many times (the fact that schooling has expanded massively but the quality of learning in schools varies massively across countries), goes over some old ground I have not revisited in a while (the “where has all the education gone” and “does learning to add up add up” papers about the lack of correlation of “schooling capital” growth and GDP per capita (or per worker).

It adds two new things.

One, drawing mainly on new cross-national data estimating a cardinal measure of learning of a typical enrolled youth, both in the World Bank’s Harmonized Test Scores and the new estimates of Gust, Hanushek and Woessmann 2023, I tried something that almost never works, which is to run a simple OLS regression on GDP per capita with schooling, average learning, and an interaction term. The interactive specification always made much more sense to me than a “horse race” of including schooling and learning separately was it seems that adding a worker with a given level of S should contribute more if they have more learning and conversely a higher level of learning should contribute more the more youth are getting it. Anyway, I was kind of surprised that simple regression gave sensible results (and robust across the two measures of learning, which is not so surprising given they are highly correlated).

The other new thing is some speculations on my part of both how the PISA-like learning estimates can be so low and why that probably matters for the contribution of schooled youth to output. The reason is that while these scores are a single number (e.g. Chile’s score on mathematics in PISA is X), this is a single number with (at least) two dimensions, like area is length times height. The two conceptual dimensions of an assessment score on a domain are “coverage” (e.g. across different sub-domains of mathematics) and “depth” (the extent to which the assessed individual have more than just a rote or purely procedural understanding). This in part helps understand “how can kids have attended 9 years of schooling and yet score so low?” if the assessment probes for depth of understanding whereas the child’s learning has just pushed through coverage and emphasized a merely rote, memorized, or procedural understanding.

The second aspect is that if schooling just emphasized a rote/memorized/procedural understanding this implies that when youth, once out of school, encounter a problem for which a conceptual understanding would help them produce better answers and solutions and judgments about concrete problems their schooling hasn’t really equipped them for this.

I want to make this last point because, as an economist who was worked on issues of education and development for a long time now I find that when economists say things like “a countries economic growth/level of productivity is associated with its scores on a PISA-like assessment of mathematics” we can be heard (caricatured) as saying three things I am not saying.

One, that “education” is, or has to be, instrumentally justified by its impact on economic measures only. No, of course not, education has lots of justifications and lots of (potential) positive impacts on human lives.

Two, that economists in pointing out a connection of economic contribution to a score on assessment therefore are “recommending” and certain type of approach to the process of teaching and learning because we economists are focused on test scores. Again, no, of course not. In particular, we economists are often accused of wanting “drill and kill” or “back to basics” approaches to education, but this is definitely not what I am emphasizing. I am emphasizing teaching and learning practices in schools that lead to the depth of conceptual understanding necessary to produce valued and use competencies in adults.

Three, that economists in pointing out a connection between, say, mathematics scores and economic output must think that lots of jobs require workers to use, say, algebra or trigonometry or calculus (and point out that is ridiculous). But again, no, of course not. Yes understanding mathematics per se is important for lots of careers and professions. But the main point is that lots of people have to reason their way to correct judgments when faced with new and non-routine situations and that this kind of in-context application of skills is a hugely important capability for youth to acquire. But this emphasizes more “depth” of understanding and ability to apply knowledge and skills than any memorized formula or factual information.

The “Washington Consensus” started from John Williamson’s 1989 attempt to briefly summarize some things that were driving the development agenda and then became, even in the pre-“meme” or “viral” era a two word viral meme that has elicited strong reactions ever since. With more bravery than I have, LSE professors Andres Velasco, Tim Besley, and Ricky Burdett are attempting to construct a new, better, more sophisticated statement of where thinking about improving the human condition is, calling this a “London Consensus“.

As I spent eight years as the head of a large research project, Research on Improving Systems of Education (RISE) they asked me to do a chapter on where was the “consensus” about basic education in the developing world relative to the Washington Consensus. So, in the usual way of global discourse, boil down an enormous topic (or even the over 500 works produced by RISE) into 10,000 words. While I wrote lots of things for RISE (papers, notes (e.g. on “evidence” and the challenges of internal and external validity), presentations, synthesis reviews on key topics (e.g. framework for system accountability, teachers, politics of learning)), even including co-authoring a RISE “policy brochure” that tried to boil down the action implications of RISE into acceptably brief nuggets, this is my first post-RISE writing, where I am just me, Lant Pritchett, not representing anyone or anything.

The overall direct of the shift in consensus is clear, but what follows is more contested.

Williamson’s summary of the Washington Consensus was (as advertised) just the consensus of the day, both among economists and educationists (e.g. the Jomtien Declaration on Education for All)–roughly that “governments should spend money to expand access to schooling to reach universal completion of primary (and basic, maybe secondary) schooling in order to expand human capital.” This consensus is not longer particularly relevant in three regards.

One, it is a victim of its own success as, since so many countries have expanded schooling so much the remaining incremental gain possible from expansion of access/enrollment/attainment alone for building human capital is pretty limited.

Two, if we take one aspect of “human capital” to be the school acquired cognitive skills that school curriculums aim to teach and which assessments of learning on reading, mathematics, science (and other topics) measure then the “learning crisis” is now front and center. Arithmetically the stock of cognitive skills a youth has at age 15 (or 17 or 19 or 23)–but 15 is the PISA assessment age–is just the number of years times the (net) gain in the stock per year and the net gain in stock per year from school is the number of years enrolled in school times the (net) gain per year of schooling. The “learning crisis” is the realization that in many countries the “learning profile” (the description of stock of cognitive skills by year of schooling) is show shallow (so little learning per year of schooling) that even if youth are enrolled at age 5 and stay in school until age 15 they are still far from what are widely regarded as the skills need to cope successfully as adults in the world today (and, even less so, of the future they will live in).

{This is a topic I have been writing on for a long time (e.g. 2006 (with Deon Filmer and Amer Hasan), 2013) with more and better evidence being generated on stocks of learning over time (among the many, there are four key recent additions to the body of knowledge about learning outcomes are the PISA-D (analyzed in Pritchett and Viarengo 2023), ASER’s work on assessing youth skills in rural India in “Beyond Basics” (2023), the use of DHS data to create information on trends on learning over long periods in Le Nestour, Moscoviz, and Sandefur (2022), and the efforts to amalgamate the existing assessments into a single measure for (nearly) all countries by the World Bank in the Human Capital Index (N. Angrist et al 2021) and separately by Gust, Hanushek and Woessmann (2022 ).}

Three, the idea that government “spend” was a sufficient statistic for learning or creating human capital or that “spend more” is, in and of itself, useful policy advice has been completely discredited through evidence across countries, across regions, across sectors, and over time. McKinsey’s latest report (2024) on improving education “Spark and Sustain” (2024) opens with Exhibit 1 that shows that there are countries with public expenditures per student between $2,000-$4,000 (Vietnam) and over $14,000 per student (USA) achieving roughly the same results and among countries spending between $2,000 and $4,000 the learning outcomes range from “below poor” (Dominican Republic, South Africa), to “Poor” (Brazil) to “Fair” (China) to “Good” (Turkey, Vietnam).

If one concedes that “access” has been successful but one needs “every child in school and learning” and that “spend” alone may be necessary but is far from sufficient for improving learning outcomes, where is a new consensus?

The first element is that while it is an obvious truism that for learning outcomes to improve there must be “better” spending on teaching and learning practices (and materials) that are “evidence based” and “cost effective” this is insufficient as a guide to action. This “proximate determinant” of learning approach that “recommends” doing this or that particular action (e.g. hire teachers this way, use textbooks of this type, generate and use this kind of information on learning) has to acknowledge that the existing status quo and its proximate determinant outputs and outcomes are the result of the way the system of education now operates. To scale better teaching and learning practices and processes one needs those efforts to be embedded in a system that allows, facilitates, and rewards that. So the first element of consensus is that if we want large, sustained, improvements over time in learning outcomes one needs to change the system so that it is geared to, and coherent for, achieving that goal.

Beyond that, what exactly are the ways to change education systems such they endogenously produce the dynamics of accelerated learning, I recommend five broad “principles” for action. But I will be the first to admit these are broad principles and how to achieve the granular instantiation of those principles in practices in any given country (or regional) context is far from known (and hence any “consensus” on particulars is pre-mature).

Attached is the text where it stands now.

Attached is a first draft of a paper that is pair with another paper in progress.

This paper articulates why the combination of (a) “poverty reduction is the objective of development” and (b) the poverty line used to define and measure global poverty is (or any update of) the WDR 1990 ‘dollar a day’ poverty line is no longer a viable stance for a development organization or actor.

The basic point is that ‘dollar a day’ was always an unreasonably penurious poverty line and now, with the continued economic growth (even where not particularly rapid) nearly all developing countries have reached a point where there are very few ‘dollar a day’ poor. For instance, in the latest World Bank data Pakistan is reported as having only 4.9 percent of its population in the ‘dollar a day’ (which is now P$2.15) or “extreme” poverty. This implies that development actions that produced broad-based benefits in Pakistan would not benefit the “global poor” as only 1 in 20 Pakistanis is “poor.” This is just ridiculous (or perhaps beyond ridiculous, I am not quite sure).

The companion paper, which is underway and co-authored with Martina Viarengo examines the question “what is a plausible global upper bound poverty line?” Whereas ‘dollar a day’ is the answer to the question ‘what is the lowest a global poverty line could be?’, if one is going to use a range of poverty lines (as we argue that one must to have poverty reduction and development be synonymous) then one needs a range, a lower bound (‘dollar a day’) and an upper bound. This paper should be finished soon (in the academic sense of ‘soon’).

There is a very nice blog from June 2023 on the interactions between climate change and poverty on the World Bank Open Data site (based on also very nice recent research papers by the authors (working paper and published version). However, their opening paragraph contains a statement that is, kind of, on the face of it, correct, but, at the same time, can create confusion.

“Eradicating extreme poverty and stopping global warming can only be tackled together: reducing poverty without considering carbon emissions is a self-defeating strategy, as climate change impacts will threaten hardly won development gains. But despite rapid progress to decarbonize the world economy, reducing poverty by increasing people’s consumption today requires increasing carbon emissions, since economic systems in most developing countries still rely on fossil fuel energy. And the eradication of extreme poverty is so urgent that it cannot be delayed, especially to fix a problem that has largely been created by the richest among us.”

The potential confusion here is that this paragraph can be read as implying that for a given country addressing their own poverty that the problems of poverty and carbon emissions must be tackled together. In a direct causal sense, this is just obviously false. That is, take a country with currently high levels of extreme poverty, say, Ethiopia at 27 percent or Malawi at 70 percent. If one is concerned about their levels of extreme poverty to 2050 then it should be clear that, to first order, nothing about Malawi’s own carbon emissions between now and 2050 will have anything to do with the consequences of climate change for Malawi and its poverty. This is because Malawi’s own incremental emissions between now and 2050 under any growth scenario for Malawi are so small relative to the global total stock of carbon in the atmosphere in 2050 that the consequences for Malawi (or any other country) are vanishingly small. This is for three reasons, which are three deep features that make the challenge of climate change so very challenging.

One, carbon stays in the atmosphere a very, very long time and hence much of the relevant stock in 2050 has already been determined by emissions that have already happened. (This feature is in sharp contrast to some, but not all, other environmental challenges where the natural processes can remove pollution such that if the flow goes down the stock can go down.)

Two, Malawi by being both very poor and very small in population has very small total emissions and hence incremental emissions under various feasible growth paths are also very small. So, according to the figures reported in Our World in Data the emissions in 2021 in Malawi were 23.85 million tonnes whereas in the USA they were 5.93 billion tonnes. This means even if Malawi’s emissions were to grow by 50 percent by 2050 this would be the equivalent of a .2 percent (one fifth of one percent) reduction in US emissions. And China’s 2021 emissions are 13.71 billion tonnes.

Three, climate change in a global phenomena and depends on the total global stock and hence all countries are affected by the totals, irrespective of their own emissions. So nothing Malawi does about carbon emissions will have any first order effect on Malawi’s damages from climate change compared to about anything China or the US or Europe or India do.

I think there are three important reasons to make this clear.

First, this aspect of the “sustainability” of economic growth is very different from considerations where the feedback loops are at the country level and hence the default concern was that a given country was using up its own stock of natural resources (e.g. groundwater, forests, fertile soil, fisheries, oil, minerals, etc.) or creating its own environmental damages (e.g. air pollution, water pollution) that these would put a check on future economic production or wellbeing. But these feedbacks loops were national (or perhaps regional) and hence the question was whether the processes of economic growth and wellbeing gains could be sustained or were ignoring the “true” total stock of productive assets of the country, including their natural resources. In this respect climate change is very different as Malawi’s growth/wellbeing prospects may well suffer and become “unsustainable” due to climate change, but this will be because of the past and future actions of others, not (to first order) because of anything Malawi does about its own emissions. (And “Malawi” is just a place holder for “small to medium sized developing country with high extreme poverty). So, from the point of view of a given country there may be much more pressing environmental sustainability issues actually under its more direct policy control than carbon emissions. And, from a country view the actions to mitigate the negative consequences of climate change on their wellbeing might also be much, much, more important than controlling their emissions.

Second, there are enormous legitimate political pressures to dissemble about this. That is, controlling climate change requires many countries to cooperate in lowering their emissions relative to their BAU baseline. But these reductions are a “global public good” and every country understands that their efforts alone are ineffective. So many countries may reasonably say: “I will only cooperate to reduce emissions if all other countries cooperate.” This creates a pressure to get all countries to commit to action on their own emissions. This in turn creates pressures to tell countries: “The consequences for your country of climate change are going to be very bad so you should, as a country, commit to actions to reduce your carbon emissions, at the very least for any given amount of economic growth (e.g. carbon intensity) even if not in absolute terms.” But, while that statement is rhetorical powerful, everyone, not least developing country political leaders and policy makers, can see that it is logically flawed and false, or, at the very least, leaves out the key feedback mechanism working through global politics: “…if you fail to commit to climate actions this may cause other countries, whose emissions in total will really make a difference to climate change, to not take action and these emissions of other countries could make climate change consequences worse in your country.” Improving Malawi’s carbon emissions will only benefit climate conditions in Malawi through an indirect channel of political transmission.

Third, this is especially important for the World Bank, which is seeking to make the “global public good” of climate change more important in its agenda. But this is going to necessitate a tricky change for the World Bank. Previously, if the World Bank (via IDA) was doing projects in Malawi it was usually the case that the costs and benefits of the project were justified exclusively by the costs and benefits to Malawi as, in the end, the World Bank is a Bank and it makes a loan (even if, on IDA terms this has a huge grant element relative to market costs) to Malawi. But if the World Bank is making a loan to Malawi that affects carbon emissions one has to be clear whether this is justified as actions by Malawi based on the costs and benefits just for Malawians or whether part of the justification is that Malawi is making a contribution to the “global public good.” And if the answer is that the actions are contributing to the “global public good” with resources borrowed from a global development agency like the World Bank, what is the appropriate allocation of those incremental costs between the future citizens of Malawi (who have to repay the loan) and the rest of the global population which enjoys the benefits of Malawi’s actions. The tensions here are obvious and the stakes are high and lack of clarity here can undermine in the long-run the World Bank’s legitimacy.

Interestingly, after this introduction claiming “reducing poverty without considering carbon emissions is a self-defeating strategy” the blog, based on real research and actual numbers, makes exactly the opposite point. The incremental carbon emissions to reduce extreme poverty are inconsequentially small. By their calculations the incremental carbon emissions to “eliminate” (drive to 3 percent or less in every country) extreme poverty at historical relationships of GDP growth to poverty and GDP growth to emissions was only 4.9 percent of 2019 levels–which is roughly the amount global emissions have been increasing every three years since 2020. So, however negative the consequences of climate change are in 2050 it almost certainly will not be because countries grew at a pace needed to reduce extreme poverty. Rather what will determine climate change (including on countries with extreme poverty) is whether the countries responsible for nearly all historical and current emissions–and who do not have any appreciable extent of “extreme poverty”–did or did not reduce their emissions. T

This is related to a presentation about “sustained” versus “sustainable” development for a group in Pakistan in 2022, in which I make this point about climate change without the benefit of the more accurate and concrete numbers this recent research provides.