Can we drive development at scale? An interim update on economic growth work
Disclaimer: This is an interim report and the views expressed here do not represent “house views” of our employer Founders Pledge
Global health and development is still arguably the most popular EA cause. For example, payouts from the Global Health and Development EA Fund comprise 45 percent of the total amount of money granted from EA Funds. Almost all of this spending supports so-called “randomista”-type development: direct interventions that have strong experimental evidence of effectiveness. This allocation is justified by the claim that these interventions are the most cost-effective way to improve the lives of people in low- and middle-income countries (LMICs).
Earlier this year, John Halstead and Hauke Hillebrandt published an EA Forum post that argued this is likely mistaken. In “Growth and the case against randomista development” they write that past poverty alleviation has overwhelmingly been achieved by economic growth, not direct interventions. They argue that the magnitude of the gains of growth are so large that interventions which can increase growth rates are likely more cost-effective even if there is less evidence of their effectiveness or they have a low chance of success.
The post generated a lot of discussion and commenters raised several important potential objections. These included:
Growth work is not neglected and there are no good marginal funding opportunities
Economic growth does not make people much happier
Economic growth does not help the poorest of the poor
We are clueless about the causes of growth
Implementing better economic policies faces political economy challenges that EA funding cannot overcome
Over the past few months, we have spent between 100 and 150 hours looking deeper into these challenges to try to determine the likelihood of finding concrete, cost-effective funding opportunities to promote economic growth. We conducted a brief literature review, but given the breadth of the subject matter and the uncertainty of the research question we relied heavily on conversations with experts. After about 30 such interviews, we’ve decided to stop looking for concrete funding opportunities for now. So far we haven’t found any growth-focused policies or programs which experts agree would be highly-valuable to support. We think good opportunities probably exist, but identifying them will require thorough evaluations of potential funding opportunities. Since evaluating policy-focused interventions requires considerable investment from both us and representatives from the organisation under investigation, we’re deprioritizing this project for now. We’re posting this wrap-up to share what we’ve learned, get feedback on our current conclusions, and stimulate further discussion on this important topic.
Is growth work neglected?
A key question we looked into was whether or not research and advocacy into economic growth is relatively neglected compared to direct, randomista-style interventions. A complication here is that almost all development programs, including randomista programs, affect growth at some level. This makes it difficult to separate out funding for the policy-focused work we’re interested in. For example, at first glance the International Growth Centre seems like it would be a relevant funding opportunity with a large budget. After further investigation, though, our impression is that the IGC’s work is more in the randomista school.
If one were to simply add up all the money multilateral organisations like the World Bank, Official Development Assistance (ODA) agencies, and large NGOs spend on work they classify as “economic growth”, it would be a large amount—much more than $100 million per year. There are also several organisations like the UN Development Programme, whose budget is $6 billion per year, dedicated to supporting policymaking in LMICs. However, much less money is spent evaluating the effectiveness of this spending to find out what works well and is cost-effective.
The two most important funders in this space are the World Bank and the UK’s Department for International Development (DFID). The World Bank’s Development Economics Department (DEC) manages an annual budget of roughly $100M. The World Bank also makes $30 billion in loans and spends $200 million per year on “analytical and advisory products” to client countries in a typical year. DFID has a joint program with the Economic and Social Research Council to fund growth research. That program has a budget of £25 million ($31 million) per year and has so far “funded a portfolio of 43 grants across four research calls.” Together, these budgets make a conservative lower bound for total funding in this space.
While $130M per year is a lot of money, it’s negligible compared to the value of sparking a growth acceleration or avoiding a growth deceleration. Funding from multilaterals or ODA agencies may also neglect impactful funding opportunities due to political or institutional considerations. We found fewer private funders of growth research than we expected. There are few clear examples of philanthropists interested in this kind of work (the Ford Foundation being one exception). Jim Cust, a World Bank economist, suggested this is because growth interventions are much less “attributable” than direct interventions and that “much of the development industry is risk-averse.” This means interventions which have an impact, but where the influence of a given funder is difficult to see, are likely to be neglected.
While we didn’t conduct a systematic search, we did lightly engage with a few potential funding opportunities to gauge what kind of work additional funding could support. We found more active organisations than we expected. An incomplete list of organisations we identified or spoke with includes:
We also expect that there are some “single-issue” think tanks or organisations working in low- and middle-income countries working in this space, but which we did not come across.
These organisations differ greatly in their work, focus, theory of change, and size. We haven’t evaluated the cost-effectiveness of any specific projects or programs these organisations run. We’re not suggesting that these are the most cost-effective opportunities in this space or that donations to any of them are sure to be more cost-effective than donations to GiveWell’s top charities. Nevertheless, we were struck by the number of potential funding opportunities we found.
Does growth make people happy?
Growth and Subjective Well-being
Income, and hence economic growth, isn’t good for its own sake. We care about it to the extent it brings about other ends that we care about intrinsically. A plausible candidate for something we care about intrinsically is well-being, so it could be important to investigate the extent to which economic growth improves well-being. There is a rich body of literature on subjective well-being, which typically involves asking people how happy they are or overall how satisfied they are with their lives.
Log GDP per capita correlates well with self-reported happiness and life satisfaction across countries and log income correlates well with happiness and life satisfaction across individuals, which suggests we should measure impact in logs, rather than dollars. Surprisingly, however, log GDP per capita doesn’t correlate much with positive and negative affect once we control for other factors such as social support and freedom to make life choices., This doesn’t necessarily imply that increasing GDP per capita wouldn’t improve positive and negative affect though – increased GDP per capita could improve affect via its effects on variables such as social support and freedom to make life choices.
In the World Happiness Report 2020, social support, freedom to make life choices, generosity and perceptions of corruption all had greater regression coefficients than log GDP per capita (in absolute value) in regressions of Cantril Ladder (roughly, life satisfaction), positive affect and negative affect. This suggests that these variables have a greater effect on subjective well-being than income. As a result, we should bear these variables in mind in evaluating and prioritising growth interventions.
The report also found that 32% of the difference in happiness between top 10 and bottom 10 happiest countries is due to log GDP per capita; next largest contributors are social support (27%) and healthy life expectancy (21%).
Life satisfaction and GDP per capita data from the previous World Happiness Report and the World Bank, gathered by Our World in Data, show a strong relationship between life satisfaction and GDP per capita:
It doesn’t follow automatically though that economic growth (i.e. increasing GDP per capita) improves life satisfaction: it could be the case that while life satisfaction tends to be higher in countries with higher GDP per capita, increasing GDP per capita does not cause life satisfaction to increase. The economist Richard Easterlin has famously argued that economic growth does not increase happiness. There now seems to be a consensus that economic growth increases happiness in the short-run but there is little consensus over the long-run effects of growth on happiness. For example, Stevenson and Wolfers (2008) argue that growth does have long-run effects on happiness but Easterlin (2016) maintains that growth only increases happiness in the short-run (less than 10 years). Charles Kenny (Center for Global Development) confirmed that the long-run effects of growth on happiness are unclear.
Rough cost-effectiveness analysis informed by the subjective well-being literature
Lant Pritchett and later Hillebrandt and Halstead have made very rough historical cost-effectiveness estimates of attempts to influence recent growth accelerations. These aren’t detailed, accurate estimates but are intended to make the case for the plausibility of the high cost-effectiveness of trying to accelerate economic growth, to stimulate further research.
Pritchett considers the impact of the Ford Foundation in funding the India Council on Research on International Economic Relations (ICRIER), which was possibly highly influential in India’s recent economic growth episodes. Hillebrandt and Halstead extend this example by considering similar, hypothetical attempts to influence Vietnam’s economic growth acceleration between 1989 and 2010 (the median growth episode identified in Pritchett et al. 2016). They compare this to direct interventions such as the Graduation Approach and funding Malaria Consortium. Both estimates measure the goodness of growth episodes in net present value, which fails to account for the diminishing returns to income of subjective well-being.
Measured this way, growth accelerations in countries that are already rich will look more valuable than accelerations in poor countries. But if well-being is a linear function of log GDP per capita, then equally proportionate increases in income increase well-being equally, no matter the initial value of GDP per capita. Here, we build on aforementioned estimates by measuring impact in income doublings (or changes in log2 income). Measuring impact in this way paints a somewhat less optimistic picture of accelerating growth. The model is here with the main results displayed below.
If per capita income grows at rate over a year, then the number of income doublings per person due to the economic growth in that economy is . In the case of a growth acceleration, countries grow at an additional rate of growth g relative to counterfactual growth rate (i.e. they grow at rate rather than ), where is estimated by Pritchett et al. 2016 for various growth episodes. Then the number of income doublings per person due to economic growth acceleration is:
g′is typically small (on the order of a few percent), so we can fairly safely ignore it and approximate per capita income doublings as . (This is an overestimate by a usually negligible degree.) We then approximate total income doublings by multiplying the per capita income doublings by population size.
Pritchett outlines two scenarios, which Hillebrandt and Halstead adopt: one optimistic and one pessimistic, with respect to accelerating economic growth in India. In both cases, we suppose that the ICRIER received $36 million in funding shortly before it was created. In the optimistic case, this funding increased the probability of the ICRIER being created by 50 percentage points, and the ICRIER in turn increased the probability of India adopting growth accelerating policies by 10 percentage points. In the pessimistic case, these numbers are 10 percentage points and 1 percentage point, respectively.
Hillebrandt and Halstead find that in the pessimistic scenario, and considering Vietnam’s recent economic growth episode instead of India’s, trying to accelerate economic growth is roughly on a par with the Malaria Consortium in cost-effectiveness. In our model, this scenario favours the Malaria Consortium by a factor of about 20. Hillebrandt and Halstead’s optimistic case (with respect to accelerating economic growth) finds trying to accelerate India’s growth to be about 3,000x more cost-effective than the Graduation Approach. In our model, this falls to about 350x. This tentatively suggests that, historically, trying to accelerate growth was about an order of magnitude less cost-effective than previously estimated but that it could still be better than direct interventions such as the Graduation Approach and Malaria Consortium.
The usual caveats apply to these (very rough!) estimates. Don’t take them literally. Since our model is much more speculative and less sophisticated than GiveWell’s detailed cost-effectiveness analyses, we shouldn’t place as much weight on it. Even if the Ford Foundation’s funding of ICRIER was highly cost-effective, it doesn’t follow that there will necessarily be similarly cost-effective opportunities available today. We should also be wary of hindsight bias in choosing parameters of the model. Ex post, it might seem obvious that the Ford Foundation’s funding of ICRIER drastically increased its chances of being created and in turn that the ICRIER significantly increased the chances of India adopting growth-accelerating policies. But presumably, that sequence of events was less predictable ex ante.
Measuring impact in income doublings rather than net present value reduces the lead of growth interventions over randomista interventions but maybe not drastically. To determine whether there are growth interventions that are more cost-effective than randomista interventions, a more detailed analysis of specific growth interventions or case studies to come to more robust cost-effectiveness estimates would be helpful. We haven’t tried to establish here that increasing economic growth is highly cost-effective – this is intended more as a plausibility argument to encourage more in-depth research.
|Ethiopia graduation approach||low||medium||high|
|Additional income per $|
|Spending on graduation approach (current US$)|
|Additional GDP per capita|
|Ethiopia GDP per capita (current US$)|
|Change in log_2(GDP per capita)|
|Total income doublings|
|Cost-effectiveness (income doubling/$)|
|Growth episode||India 1993-2010|
|Approx total income doublings of growth episode|
|Probability that $36m leads to creation of think tank|
|Probability that think tank affects growth episode|
|Expected number of income doublings|
|Cost-effectiveness (income doubling/$)|
|Ratio over Ethiopia Graduation Approach|
|Ratio over Malaria Consortium|
|Growth episode||Vietnam 1989-2010|
|Approx total income doublings of growth episode|
|Probability that $36m leads to creation of think tank|
|Probability that think tank affects growth episode|
|Expected number of income doublings|
|Cost-effectiveness (income doubling/$)|
|Ratio over Ethiopia Graduation Approach|
|Ratio over Malaria Consortium|
Does growth reduce poverty and help the poorest of the poor?
A common objection to focusing on economic growth and a possible issue with the above analysis, which only uses the per capita GDP growth data, is that growth may not benefit those living in extreme poverty. We take this concern seriously, and the large literature on “inclusive” or “equitable” growth suggests many economists and policymakers do so as well. However, we did not spend much time looking into this question. This is mainly because we have yet to see a strong counterargument to Pritchett’s claim that this isn’t a major concern due to the strong negative correlation between national median income and national poverty rate.
Do we know anything about what causes growth?
A fourth objection to growth research or advocacy is that it’s a waste of time because the causes of economic growth are unknown and unknowable. “The Empirics of Growth” by Kevin Grier gives a high-level overview of our knowledge about growth. He highlights the fragility of most findings that attempt to link specific policy changes to growth rates. Even the influence of broad measures like education levels or investment rates is controversial, while “the policy variables that seem most robustly related to growth are sound macroeconomic policies (mainly stable and reasonably low inflation), openness to trade, institutional quality (i.e., little government corruption), and financial development.”
Grier summarizes 2004 work by Hausman, Pritchett, and Rodrik:
Ricardo Hausmann, Lant Pritchett, and Dani Rodrik (2004) [...] studied eighty-three cases in which a country rapidly increased its growth rate and sustained the increase for at least eight years. Their most statistically significant results are that a financial liberalization raises the probability of a growth increase by around 7 percent, and that a political regime change toward autocracy (from democracy or less-strict autocracy) raises the probability of increased growth by almost 11 percent. Most growth increases (which they call “growth accelerations”) are unpredictable, however, and as they put it, “the vast majority of growth accelerations are unrelated to standard determinants such as political change and economic reform, and most instances of economic reform do not produce growth accelerations.”
It’s important to note that this finding relates to what proportion of reforms spark growth accelerations. Because growth accelerations are so valuable, such reforms may still have high expected value even if they usually fail to effect a change (so long as the downside risk is also low).
One of the major reasons it is difficult to know what causes growth is that all factors are endogenous—that is, most variables that plausibly affect growth rates tend to go up or down at the same time and there is almost always reverse causation for the variables that are correlated with growth. Cross-country growth regressions can identify these covariates but not causal relationships. As a result, economists have tended to model growth as exogenous—something that either does or does not happen randomly—and then use empirical data to see what goes along with growth.
Given the importance of growth, we find it surprising that so much growth research uses models which cannot help us identify its causes. Doug Gollin (University of Oxford, Structural Transformation and Economic Growth) told us that “It’s not that economists believe that growth happens exogenously; it’s just that for some questions, the sources of growth are less interesting than the consequences of growth. For these questions, exogenous growth models are sufficient. And these models are also nicely transparent. There is of course also an important literature on endogenous growth. In these models, the growth rate is determined within the model – but the sources of growth are essentially baked into the models, so the models don’t add a lot to our understanding.” This seems like clear evidence that there is potential improve these models, which help researchers describe the effects of growth in a country but don’t help inform policy prescriptions to stimulate growth.
Perhaps the most well-known attempt to articulate the conditions for growth was the Washington Consensus, a set of policies that the IMF, the World Bank, and the U.S. Treasury prescribed for debt-burdened developing economies throughout the 1980s and 1990s. These reforms were broadly oriented towards market-based approaches. They included things like fiscal policy discipline, tax reform, market exchange rates, trade liberalization, and privatization. Today the Washington Consensus has a pretty bad reputation. That said, recent work by Easterly, Grier and Grier, and Chari, Henry, and Reyes suggest that policy reforms in line with Washington Consensus prescriptions generally show positive effects. This suggests that we are not completely clueless. Some clearly bad policies such as extreme inflation, negative real interest rates, and trade suppression do tend to reduce growth.
Nevertheless today it is generally recognized that policy advice should consider structural and political differences between countries. To this end, growth diagnostics, an approach introduced in the mid-2000s, has gained some footing among multilateral organisations and consultants. The growth diagnostics method assumes that countries face multiple different constraints on their growth rate, but that the constraints which are most influential or “binding” vary among countries. Researchers may be able to use growth diagnostic tools to identify “the most binding constraints on economic activity, and hence the set of policies that, once targeted on these constraints at anypoint in time, is likely to provide the biggest bang for the reform buck.”
While this sounds promising, our impression is that growth diagnostics has largely failed to take off due to a lack of academic incentives to use the method amid concerns about causal identification. The development economics field has prioritized research methods with stronger claims to causal identification: chiefly randomized controlled trials, but also quasi-experimental methods like instrumental variables estimation and regression discontinuity designs. Proposals which use these methods are more likely to be funded and papers which use them are more likely to be published. Development economists may also be wary of studying growth at the macro scale as this research hasn’t reliably generated useful findings in the past. Angus Deaton writes that in academia and policy circles, “Past development practice is seen as a succession of fads, with one supposed magic bullet replacing another—from planning to infrastructure to human capital to structural adjustment to health and social capital to the environment and back to infrastructure—a process that seems not to be guided by progressive learning.” However, we understand that the growth diagnostics approach has gained some traction with policy makers outside of academia.
Our overall impression is that making confident, country-specific recommendations to affect growth rates is very difficult. It is hard to determine confidently which specific constraints are binding in a given situation. Country policies may vary along many dimensions. And so many factors also affect growth rates that it’s difficult or impossible to detect the effect of a particular policy change. Yet it’s important to also consider Pritchett’s argument that funding policy research or advocacy probably looks quite good in expected value terms. Although “most instances of economic reform do not produce growth accelerations,” the average growth acceleration increases GDP per capita so much that projects which have only a small chance of positively influencing policy look good in back-of-the-envelope calculations.
Could we even get growth-promoting policies implemented?
Another argument that was raised against this work was that, even if we did know what policies to implement to increase growth rates, we shouldn’t or wouldn’t be able to influence policy change in low- and middle-income countries. We think there are important practical and moral questions to consider here. On the practical side, one approach we find compelling is the idea that researchers or advocates could do important preparatory studies so that they have evidence and recommendations ready to go when opportunities for change arise. For example, on the 80,000 Hours podcast Rachel Glennerster suggested that researchers are currently playing such a role in Ethiopia. The country “is going through a tremendous reform, and we really ought to be focusing attention, and helping Ethiopia in that transition. Tremendous potential, because they’re absolutely fundamentally changing policy there in ways that could be really beneficial to the poor. So jump on those opportunities, but you can’t really make them happen. It’s something that the developing country themselves has to decide to do, then help them as much as you can.”
While we feel we’ve made some progress on these questions, we recognize that we are still highly uncertain about what specific activities would be cost-effective to fund. Some of the major unresolved questions we have are:
What kind of research would additional funding support? What are the major unanswered questions in this space researchers would tackle?
How valuable is research at the current margin? The average value of research is high, but it’s unclear how valuable research in this area is at the margin or whether research priorities can be altered
What sort of policies are most likely to affect a country’s growth rate? Growth seems really important, but concrete examples of things to work on usually seem quite small and unlikely to stimulate the large-scale change that makes growth so valuable
Should we prioritize countries and tailor policies to their specific context, or should we prioritize policies and look for countries that are open to policy change? Some experts thought the best approach would be to identify priority countries with large populations or people in poverty. A focused approach would allow a deeper understanding of the local context and influential actors, which would help us better identify good funding opportunities.
What’s the “hit rate” for growth episodes? It’s easy to imagine that funding growth research in Vietnam would have had very high expected value if it made the country’s growth acceleration more likely. However, it’s equally easy to imagine that lots of money could be fruitlessly spent trying to stimulate growth in the DRC, or other countries where local politics mean that policies will never be changed or the state does not have capacity to enforce policy changes. What is the Vietnam of the 2020s? Can we identify ex ante where funding is most likely to be effective?
Nathan Nunn suggests industrial policy benefits are near zero-sum. That is, some countries will develop (driven by, e.g., demand for certain exports), and a growth acceleration in one country comes at the expense of growth in another. To what extent is this true?
Why we’re not continuing at this time
We’re not continuing this project because we think identifying funding opportunities would be too costly for us and for the organisations under consideration. We do think good funding opportunities likely exist in this space. One concern some experts expressed is that growth research output is not highly elastic to funding because the supply of researchers is fixed in the short-term and there are other large funders active in the space. While the magnitude of growth accelerations means that some growth work is valuable, it does not follow automatically that marginal growth work is valuable. However, our sense is that if Founders Pledge members (or a different funder) were unusually willing to fund research with low attributability they could have a big impact.
We have also identified a range of think tanks that work on these issues and think it’s likely that there are effective think tanks working in developing countries who could use more funding.
It remains the case that the expected value of information of further research is high. We could see this work being much less or much more cost-effective than global health interventions, so reducing uncertainty would be valuable. However, we didn’t find much consensus among the experts we spoke to regarding the best ways to learn more about how to have impact in this space.
In addition to these questions about how valuable more work in this vein would be for funders, our past work evaluating policy advocacy organisations suggests that this project would be burdensome for the organisations under investigation. Establishing a causal link between policy advocacy and policy change is challenging. Evaluating policy advocacy organisations requires us to build a detailed understanding of the organisation’s outputs and the policy ecosystem in which they operate to judge their chance of success. Ideally, we would evaluate this by considering case studies of previous work carried out by the organisation to determine how strong their track record of influencing policy is. Making such judgements requires lots of information from the organisation and relevant experts and referees, which is time-consuming to produce. We’re wary of doing harm by burdening these organisations with a costly evaluation process.
Paying these costs can be worthwhile if we expect to move a lot of money to the top organisations as a result of the evaluation. However, at this time we are very uncertain about how much money we expect to move in this space. We’re uncertain about our members’ donation preferences and the timeline of their liquidity events. It seems very likely that we would directly move less than a few million dollars, and probably much less, to any recommendations in this space. Without stronger assurances of funding, it’s difficult to justify further research when the costs are so high and the expected benefits are fairly low.
Proposed next steps for future work
Future work could carry out in depth evaluations of promising funding opportunities and try to compare such funding opportunities to direct global health and development interventions. We think that evaluating specific funding opportunities would be more valuable than general research aiming to prioritise interventions within the space of economic growth or more broadly aiming to prioritise between economic growth and other areas within global health and development. This is because we’ve found these latter prioritisation problems to be intractable, with little expert consensus. Direct cost-effectiveness estimates would likely provide more useful information.
We are extremely grateful to the experts with whom we spoke, some of whom are cited here and some of whom preferred to remain anonymous, for taking the time to help us with this research.
 We got this impression from multiple experts. One might also look at a sample of projects they have funded. Currently, on the first page of the projects funded under their “State” research theme are randomista-type projects focused on providing information to jobseekers, incentivizing bureaucrats, and increasing female labour force participation (see here).
 This is the impression we got from talking to experts, but seems like a safe assumption (high confidence, 90%).
 DFID is now in the process of merging with the Foreign and Commonwealth Office. This may change their funding priorities, but we don’t expect it to change our conclusions here.
 Julian Jamison (University of Exeter) also raised this concern. There are “internal and external pressures—for example, to reward the allies or punish the enemies of powerful shareholders, or to “push money out the door” to achieve professional prestige and budget maximization goals—that undermine the credibility of [the World Bank’s] policy conditionality” (Knack et al., p. 4).
 Conversation with Jim Cust, 29 April 2020.
 “Social support is the national average of the binary responses (0=no,1=yes) to the Gallup World Poll (GWP) question, “If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?”
 “Freedom to make life choices is the national average of binary responses to the GWP question, “Are you satisfied or dissatisfied with your freedom to choose what you do with your life?”
“New data confirm that for countries worldwide long-term trends in happiness and real GDP per capita are not significantly positively related.” Easterlin, 2016.
 Conversation with Charles Kenny, 11 May 2020.
 “There is a narrative in which Ford Foundation, a global philanthropy provides some millions (or tens of millions) of dollars of funding that play some role in creating a think tank that itself then plays some role in providing the conditions in which good policy choices are made that then results in the creation of 3.6 trillion in additional output of Indians.” Pritchett 2018, p. 21.
 If per capita income is y, average life satisfaction is ls0 = a + b log y, and income increases by a factor of p, then life satisfaction increases by ls1 – ls0 = (a + b log py) – (a + b log y) = b log py/y = b log p, which is independent of y.
 Note that Layard et al. 2008 suggests happiness and life satisfaction diminish somewhat faster than logarithmically. Working with logs makes the maths more straightforward, but further research to test the sensitivity of the analysis to this assumption could be valuable.
 If per capita income is y, then the number of income doublings is log2 (1+g)y – log2 y = log2 (1+g)y/y = log2 (1+g).
 “Optimistically, suppose this gift increased by 50 percent the chance ICRIER was created and became an effective think tank (perhaps other funding could have come along, perhaps not) and suppose the existence and actions of this think tank increased by 10 percent the odds 22 India adopted growth accelerating policies (my read of the situation is that it was higher).” Pritchett 2018, pp. 21-22.
 Personal communication from Doug Gollin, 8 October 2020.
 “While the lessons drawn by proponents and skeptics differ, it is fair to say that nobody really believes in the Washington Consensus anymore. The question now is not whether the Washington Consensus is dead or alive; it is what will replace it” (Rodrik 2006, p. 974).
 “(1) policy outcomes worldwide have improved a lot since the 1990s, (2) improvements in policy outcomes and improvements in growth across countries are correlated with each other (3) growth has been good after reform in Africa and Latin America, in contrast to the “lost decades” of the 80s and 90s” (Easterly 2019, p. 1).
 “New macro development takes structural differences between economies more seriously.” Conversation with Doug Gollin (University of Oxford, Structural Transformation and Economic Growth), 12 May 2020.
 “Policies that work wonders in some places may have weak, unintended, or negative effects in others. We argue in this paper that this calls for an approach to reform that is much more contingent on the economic environment, but one that also avoids an “anything goes” attitude of nihilism” (Hausmann, Rodrik, and Velasco 2005, p. 1).
 This impression is the result of our expert consultations, and is weakly held (60% confidence). There is some evidence that growth diagnostics are applied by some academics; for example, Harvard’s Growth Lab has produced 24 growth diagnostic studies. However, our research did not turn up a review of growth diagnostics that evaluated its success, and no experts were aware of any especially influential growth diagnostic studies.
 Conversation with Ranil Dissanayake (Center for Global Development), 12 May 2020.
 Even after adjusting for the fact that income gains are more valuable for people whose incomes start out low, our rough calculations suggest that if a $5M investment in policy advocacy has just a 0.5%, or 1 in 200, chance of sparking a growth acceleration like Vietnam’s (the median acceleration in Haussman, Pritchett, and Rodrik’s sample), it would be more cost-effective than donating to GiveDirectly.
 Julian Jamison (University of Exeter) raised this concern, conversation 22 May 2020.
 “Inducing a scientist to change their direction by a small amount–to work on marginallydifferent topics–requires a substantial amount of funding in expectation. The switching costs of science are large” (Myers 2019, abstract)
 Conversation with Jim Cust (World Bank), 29 May 2020.
 “Although each country can gain through this strategy, this is not true for developing countries as a whole. That is, every country can’t be a South Korean success story.This is because much of the gains from industrial policy come at the expense of other countries.Much of the benefits of this strategy are zero-sum. This raises the question of whether this is a successful strategy for economic development. The answer appears to be a clear no, at least not for most countries. However, the fact that these benefits are zero-sum provides insight into what can be done to help developing countries” (Nunn 2019, p. 7).
 Conversations with Charles Kenny, 11 May 2020, and Kevin Bryan (University of Toronto), 22 May 2020.
 Ranil Dissanayake (Center for Global Development) suggested that this is the case. Conversation with Ranil Dissanayake, 12 May 2020.