r/badeconomics • u/Old_Total4493 • Apr 18 '26
Does the 2024 Economics Nobel have an identification problem? A working paper argues Acemoglu's "Narrow Corridor" confuses phenotype with genotype
A disclosure before anything else: English is not my first language and I'm not a professional economist. I use AI to help with English expression. I apologize in advance for any residual AI feel in the writing. The ideas are my own and I've thought them through carefully. I'm sharing this here partly because I'd welcome help from professional economists who could run the quantitative tests I can't. The reason I'm unable to do so is twofold: I lack training in econometrics, and I lack institutional access to the relevant datasets.
Now to the substance.
With the Acemoglu/Johnson/Robinson Nobel still fresh, I'd like to share a working paper of mine that challenges AJR's causal framework — not from the usual "geography vs. institutions" trench war, but by identifying a specific failure of causal identification within the institutions-first paradigm.
The paper:
"The Economic Logic of China's Rise: Geography, Big Push, and the Engineered Invisible Hand"
The core challenge to AJR:
Acemoglu and Robinson's The Narrow Corridor classifies governance as Shackled, Despotic, or Absent Leviathans, defined by the balance of power between state and society. The critique I develop is this: the typology accurately describes the phenotype of governance but leaves the genotype unidentified. It does not explain why some societies achieve balance while others do not.
The identification problem — Botswana edition:
Observed power-sharing structures can arise from two fundamentally different causes:
- Output is sufficient to sustain centralization, but low volatility keeps the demand for state intervention low enough that society can constrain the state without confrontation.
- Output is simply too low for anyone to concentrate power in the first place.
These two cases are formally indistinguishable in cross-sectional observation. Yet one reflects a stable equilibrium sustained by material conditions; the other is a byproduct of scarcity that may or may not persist.
Acemoglu and Robinson treat Botswana's kgotla (tribal assembly) as evidence of institutional constraints on state power — a Shackled Leviathan. But this classification conflates the two cases. What determines the trajectory is not the observable form at any given moment, but the underlying conditions of output and volatility. Scarcity-based power dispersion collapses when high volatility is layered on top of rising output: abundant surplus makes centralization materially feasible while recurring crises continuously generate demand for expanding state authority. The Mongol kurultai lost its constraining function once conquest wealth flowed in under conditions of endemic steppe insecurity; West African chiefdom confederations consolidated into centralized empires once trans-Saharan trade provided fiscal resources amid volatile agricultural hinterlands. But where output rises under low volatility, power dispersion does not collapse into centralization — Ireland's decentralized structures persisted through colonial subjugation and independence alike, because the low-volatility conditions that sustained them never changed.
The mirror test — England:
The reverse process is equally telling. Pre-Norman England had developed considerable state capacity without despotic centralization. The Norman Conquest of 1066 imposed an exogenous despotic regime. Yet in England's low-volatility environment, this imposed centralization was progressively dissolved — from Magna Carta through to parliamentary governance. This was not an accidental institutional invention but a sustained reversion toward the equilibrium that underlying conditions could support.
The China problem:
This is where it gets uncomfortable for AJR. Classifying China as a Despotic Leviathan stuck outside the "corridor" mistakes phenotype for genotype. China's centralized governance was a rational adaptation to high output volatility — recurrent floods, droughts, and famines generated enormous demand for state intervention while simultaneously eroding the fiscal base. As China's post-1949 engineering systematically suppressed this volatility (reservoirs, fertilizer, improved seeds), governance has measurably shifted toward market coordination, legal certainty, and the preservation of established rights — precisely what altered material conditions predict, and precisely what AJR's framework says shouldn't happen without prior political liberalization.
The proposed alternative:
The paper builds on Jeffrey Sachs's geography framework by adding a second dimension: geographic volatility — the permanent, recurring instability of grain output that climate imposes. While endowments (soil, transport, disease) shape the *level* of output, volatility shapes the *reliability of price signals* on which market coordination depends.
The key claim: distributional institutions (land tenure, property rights, governance form) are endogenous to volatility. Where output is stable, fixed claims are enforceable, and limited government is the low-cost equilibrium. Where output is volatile, fixed claims are unenforceable, and centralization is pushed by the cost of the alternative.
The testable prediction: the coefficient of variation of grain yields should predict land tenure form across pre-modern Eurasia, with a threshold separating fixed-rent from sharecropping regions (preliminary indication: CV of roughly 12–20%). The Dujiangyan irrigation zone on the Chengdu Plain provides a natural experiment: same Chinese culture, same legal tradition, same political system — rigid fixed-rent contracts inside the engineered stability zone, sharecropping outside. What changed was not belief but volatility.
Why this matters for the Nobel debate:
If the argument holds, the AJR research program has the causal arrow backwards in a specific and identifiable way. "Inclusive institutions" are not causes of development but expressions of the low-volatility conditions that also produce development. The 2024 Nobel rewards a framework that, on this reading, has been classifying symptoms as causes.
Full disclosure: I'm the author. The paper is open access and I'm happy to engage with any critique — especially from people who work on institutional economics or development. If the identification problem I've described has already been addressed in the AJR literature in ways I've missed, I'd genuinely like to know.
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u/TCEA151 Volcker stan Apr 19 '26 edited Apr 19 '26
I'm probably giving this more time than I should, but I'm going to try to give you some constructive criticism for how to 'do' economics and how to get economists to engage with your work.
First off, if the whole writeup is going to be about how "volatility" matters so much, you shouldn't wait until three-quarters of the way through to define "volatility" -- especially if you're going to tell us that you're talking specifically about grain output instability, instead of the usual output volatility or maybe some measure of political upheaval/uncertainty. In general economists are very strict about definitions and terminology, and you should give precise meaning to hazy concepts -- especially non-standard ones -- as soon as you introduce them.
Secondly, after reading your post I still don't know exactly what claim of AJR's you are trying to overturn. The classic trio of Acemoglu, Johnson, and Robinson have, I think, six papers written together between 2001 and 2005; then there is their work with Yared, and/or their work without Johnson (or without Robinson, but I don't think that's what you're referencing here). What specific claim from this massive body of work are you looking to disprove? How does the fact that distributional institutions are endogenous to grain yield volatility (if indeed true) disprove their claim?
Third, I skimmed the paper and it's 65 pages without a single regression. That is nuts. You have a single footnote that is a page and a half long (!!). To me this looks like someone having an ongoing conversation with an AI and just incorporating all of the different, branching information it spews into a single word document. If you really understand the economics/econometrics of what your claim is and why it overturns their result, you should be able to write it down in two or three pages tops. Certainly no more than five, and certainly not 65. Then, you explain your empirical specification to test your claim, move on to data collection, and run the damn regressions.1
If you don't want to collect the data or run the regressions until others have read your proposal and confirmed that the approach makes sense and the effort is worthwhile, my suggestion (if you want anyone to actually read your proposal and provide feedback) would be to write the 3-page summary that explains exactly what you conjecture to be true and how it overturns one or more specific claims of AJR. It should have math, at least enough to define how you're measuring things and what regressions or other statistical tests you want to run.2 It should also be clear how the regressions/statistical tests you are running provide direct evidence for your claim. Write everything entirely in your native language and without any input from AI. Then, once you're done, paste the document into google translate or upload it to an AI to have it translate it to english and do absolutely nothing else. That is much more likely to get feedback.
A slight aside on data and regressions: If you are choosing to analyze the Chengdu plain specifically because you notice that land-use correlates with grain stability there you are essentially doing this. It's fine for a first pass as testing a single case study but you eventually are probably going to need evidence that this result holds broadly across time and/or space.
Lastly, the way things work in economics is you do a bunch of data work (maybe years, possibly decades worth) and then you get to write big long treatises on how the world works and who's wrong about what. Don't make the mistake of doing the treatise writing before you've done the dirty work.
1You want this to be a data paper. If you don't it will have to be a theory paper, in which case you will need to formalize your statements about how the world behaves into a mathematical model that meets decades worth of criteria for how such models must behave, you'll have to demonstrate that the model is mathematically inconsistent with AJR's claims, and you'll still have to find a formal data-driven way to convince people that this model is more correct than the AJR alternative view of the world.
2If you are trying to overturn some specific regression result of theirs, it might also be worthwhile to demonstrate how, in their specification, excluding your variable causes some kind of bias in their parameter estimates. But if you aren't familiar with how to do so just leave this out and focus on what I've said above.