The Economic Origins of Government: Allen, Bertazzini & Heldring (2023)

What this is. The paper’s core results, the natural experiment it exploits (river shifts in ancient southern Iraq), the identification strategy, and the main estimating equations: enough to know what it found and how, without reading all 39 pages. To replicate or extend it, read the full source at https://doi.org/10.1257/aer.20201919.

TL;DR

The paper uses river shifts in southern Mesopotamia around 2850BCE as a natural experiment to test two competing theories of government formation. When a river shifts away from a farming area, direct irrigation becomes impossible and public canals requiring multi-community coordination are needed. This creates demand for a coordinating government (cooperative theory), whereas an extraction-based theory (where government forms to collect surplus) predicts states would be more likely where the river stays. Constructing a new archeological panel dataset of 5x5 km grid cells across southern Iraq for the period 3900BCE-2700BCE, the paper runs a panel difference-in-differences design comparing grid cells that lost river access to control cells that did not. A river shifting away increased the probability of state formation by 14 percentage points, the probability of canal construction by 12 percentage points, the probability of tribute payment by 21 percentage points, and the number of administrative buildings by 0.44. The result is entirely driven by new state formation, not expansion of existing states. Text analysis of 5,885 surviving cuneiform tablets shows increased mentions of lineage-leader titles and tribute after the shift, consistent with the cooperative interpretation. The simple extractive predictions associated with Olson (1993) and Carneiro (1970) run in the opposite direction to the observed pattern.

Core results

Magnitudes and standard errors are as reported; clustered SE in parentheses, Conley (1999) SE in brackets. Locators point into the source PDF (pp.2507-2545).

#	Result	Locator	Magnitude
R1	River shift away increases probability of state formation by 14pp	Table 3, col 1, p.2530	+0.14 (SE=0.04) [0.03]; sample mean 0.06; treatment-period mean 0.24; pretrend p=0.23
R2	Effect driven entirely by new state formation, not expansion of existing states	Table 3, col 3-4, p.2530	New state: +0.11 (SE=0.04) [0.03]; existing state: +0.02 (SE=0.02) [0.01]
R3	River shift increases probability of canal construction by 12pp	Table 4, col 1, p.2534	+0.12 (SE=0.03) [0.02]; mean 0.28; pretrend p=0.81
R4	River shift increases probability of defensive wall in nearest city by 11pp	Table 4, col 2, p.2534	+0.11 (SE=0.04) [0.03]; mean 0.14; pretrend p=0.57
R5	River shift doubles the probability of tribute payment being recorded	Table 4, col 3, p.2534	+0.21 (SE=0.06) [0.10]; mean 0.19; pretrend p=0.20
R6	River shift increases administrative buildings in nearest city by 0.44	Table 4, col 4, p.2534-2535	+0.44 (SE=0.15) [0.17]; mean 0.70; pretrend p=0.69
R7	Effect concentrated in high population density areas before the shift	Table 5, Panel A, p.2536	High: +0.18 (SE=0.05) [0.03]; low: +0.03 (SE=0.02) [0.03]; Chow p=0.06
R8	Result holds across the full 7,000-year panel (5000BCE-1950CE, all 6 river shifts)	Table 6, col 1, p.2539	Canal: +0.11 (SE=0.02) [0.02]; mean 0.40
R9	Result persists when state presence is measured by nearest-city building presence only	Table 3, col 2, p.2530	+0.16 (SE=0.05) [0.04]; mean 0.12; pretrend p=0.26
R10	Lagged outcomes show no evidence that future treatment cells were already different	Table 2, cols. 1-6, p.2527	Settlements: lag 1 -0.15 (SE=0.13) [0.13], lag 2 +0.05 (0.08) [0.08]; city: -0.00 (0.00) [0.00], +0.00 (0.00) [0.00]; canal: +0.02 (0.03) [0.02], +0.03 (0.03) [0.04]; all insignificant
R11	River shifting closer has a consistently negative placebo association with state presence	Text p.2533, Results §V.A	Consistently negative coefficients; individual magnitudes are not reported in the main text
R12	State formation is concentrated where settlements align for canal construction	Table 5, Panel A, cols. 4-5, p.2536	Aligned: +0.22 (SE=0.06) [0.04]; misaligned: -0.01 (0.03) [0.02]; Chow p=0.00
R13	State formation is higher where irrigation has greater potential productivity returns	Table 5, Panel B, cols. 2-3, p.2536	High productivity difference: +0.16 (SE=0.05); low difference: -0.05 (0.02); Chow p=0.00
R14	State formation estimates are larger near slow-flowing rivers, which are easier to irrigate from	Table 5, Panel B, cols. 4-5, p.2536	Slow flow: +0.10 (SE=0.05); fast flow: +0.03 (0.06); Chow p=0.14
R15	Canal response persists in both first-state and subsequent-state periods	Table 6, cols. 2-3, p.2539	First two shifts: +0.15 (SE=0.03) [0.02]; four subsequent shifts: +0.10 (SE=0.02) [0.02]
R16	Tablet texts show more references to leaders and public goods after state formation	Figure 6, p.2541; text p.2540, §VII	Fraction mentioning gal (chief) increases about 40%; lugal (lineage head) is absent before and appears after; canal and tribute mentions rise, tribute is absent before; no exact percentage-point effects are reported
R17	Trade-pattern effects are not statistically distinguishable from zero	Text p.2532, Results §V.A (discussion of Results Appendix Table RA26)	Fail to reject no effect of river shifts on trade patterns; the main text does not report a coefficient

Overall (paper’s conclusion, p.2540-2541). Where rivers shifted away, communities formed new states, built canals and defensive walls, and paid tribute to their governments. These results are consistent with cooperative (demand-side) theories: states form to solve coordination failures in public good provision, not to extract. The first states resembled scaled-up versions of the lineage social structure that preceded them, with government coordinating between extended kinship groups (lineages) rather than ruling over individuals.

Theory / model

The paper has no formal mathematical model. Instead it derives two competing sets of testable hypotheses from the theoretical literature on state formation and maps them onto the sign of the main difference-in-differences coefficient.

Cooperative (demand-side) theory. Government is an organization with a comparative advantage in providing public goods (Baumol 1952; Samuelson 1954). Problems of externalities and coordination failure (Olson 1965) prevent private provision. When a river shifts away, arid-land farming can continue only via public irrigation canals spanning multiple communities. No individual community can credibly commit to build and maintain such canals alone. This coordination failure creates demand for a government. Members pay tribute in exchange for canal access and defense. The framework builds on Acemoglu and Robinson (2000), who model the bargaining between social groups that determines whether and what form of government emerges. Under this view, states form where the river shifted away (positive $\beta_0^{\text{treatment}}$ in equation 1).

Extractive (supply-side) theory. Government is an organization founded by an elite with coercive power to manage extraction (Carneiro 1970; Olson 1993). Under Olson (1993), “roving bandits” settle where the expected tax base is largest and find it worthwhile to provide minimal stability in exchange for an extractive monopoly. Where a river shifts away, the agricultural tax base collapses: land is no longer productive without irrigation. A predatory ruler would prefer to locate where the river remains. Under this view, states form where rivers stay, not where they shift (negative or zero $\beta_0^{\text{treatment}}$). Mayshar, Moav, and Pascali (2022) provide recent empirical support for a related extractive channel in which taxable surplus (storable crops) predicts state location. The circumscription theory of Carneiro (1970) also fits this supply-side cluster: states arise where it is hard to escape extraction.

Empirical mapping. The sign of the main coefficient distinguishes the two clusters. A positive and significant $\beta_0^{\text{treatment}}$ (R1 = +0.14) fits the cooperative prediction and runs against the simple extractive predictions, which imply an opposite or null effect. The placebo test (estimating the effect of a river shifting closer rather than away) yields negative estimates, an additional pattern consistent with the cooperative interpretation. Sánchez De La Sierra (2020) finds extractive state formation in the Congo using a comparable natural-experiment design; the two settings differ in the nature of the shock and the role of public goods versus looting.

Method

The paper applies a standard panel difference-in-differences estimator via OLS, using the first large river shift in history (around 2850BCE) as a quasi-random shock. It builds on difference-in-differences and panel-regression.

Treatment definition. A 5x5 km grid cell $c$ is defined as on a river in period $t$ if its centroid is within 5 km of the nearest river. Cell $c$ is treated in period $t = 0$ (Early Dynastic I, 2900BCE-2700BCE) if it was on the river in period $t - 1$ (the Jemdet Nasr period, 3100BCE-2900BCE) and is no longer on a river in period $t$. Treatment is time-invariant and applies to roughly 13 percent of the 1,374 grid cells in the study area (Table 1, p.2517). Rivers shift by 30-40 km on average, so treated cells lose water access entirely, not marginally.

Main estimating equation. The panel DiD model for the main study period is (equation 1, p.2525):

$$Y_{ct} = \sum_{k=-4}^{0} \beta_k^{\text{treatment}} \times \mathbf{1}\{\text{period}_k\} \times \text{treated}_c + \rho_c + \gamma_t + v_{ct} + \varepsilon_{ct} \tag{1}$$

where $Y_{ct}$ is the outcome for grid cell $c$ in period $t$; $\text{treated}_c$ equals 1 if cell $c$ loses river access at $k = 0$; $\mathbf{1}\{\text{period}_k\}$ are indicators for each period $k$ relative to treatment; $\beta_k^{\text{treatment}}$ are the period-relative-to-treatment interaction coefficients (normalized to zero at $k = -1$, the last pre-period); $\rho_c$ are unit (grid cell) fixed effects; $\gamma_t$ are period fixed effects; $v_{ct}$ is a vector of period fixed effects interacted with time-invariant covariates (survey area indicators, average rainfall, average temperature, pre-shift urban status); and $\varepsilon_{ct}$ is the error term. Standard errors are clustered at the grid cell level. Conley (1999) standard errors with a 484 km spatial cutoff are reported in parallel.

The coefficient of interest is $\beta_0^{\text{treatment}}$: the treatment effect in the Early Dynastic I period (the period of first state formation). Pre-period coefficients $\beta_{-2}^{\text{treatment}}, \ldots, \beta_{-4}^{\text{treatment}}$ serve as pretrend tests; all are indistinguishable from zero (pretrend p-values reported in Tables 3-4, pp.2530, 2534).

Extended panel estimator. For all six river shifts across the full 7,000-year panel (5000BCE-1950CE), a pooled DiD (equation 2, p.2538) is estimated:

$$Y_{ct} = \beta \cdot \text{treated}_{ct} + \rho_c + \gamma_t + v_{ct} + \varepsilon_{ct} \tag{2}$$

where $\text{treated}_{ct}$ is now time-varying (equals 1 when cell $c$ is treated in period $t$ for any of the six shifts), and $\beta$ captures the average effect across all shifts.

Empirical specifications

All regressions are OLS. Unit of observation: 5x5 km grid cell. Time series: archeological period (average 240 years in the main sample). Standard errors clustered at the grid cell level; Conley (1999) SE with a 484 km cutoff in brackets.

State formation (R1-R2, R9; Table 3, p.2530). Equation (1) with two outcome definitions: column 1 uses an indicator for whether a grid cell is part of a city state, defined using administrative buildings (palaces, temples, ziggurats) and reconstructed territorial borders; column 2 uses only building presence without borders. Columns 3 and 4 decompose into new state formation (an indicator equal to 1 if the nearest city gains state status for the first time) and expansion of an existing state. Samples are 4,631 observations in columns 1, 3, and 4, and 4,424 in column 2. All columns include grid cell and period fixed effects plus period interactions with survey area, rainfall, temperature, and prior urban status. Pretrend p-values are 0.23, 0.26, 0.24, and 0.84.

Public good provision and tribute (R3-R6, Table 4, p.2534). Four separate regressions using equation (1):

• Column 1: indicator for canal presence within 5 km of cell centroid, reconstructed from Chicago Oriental Institute excavation reports (Adams 1965, 1981; Adams and Nissen 1972).
• Column 2: indicator for defensive wall in the nearest city, from Bryce (2009) and Meyers (1997).
• Column 3: indicator for surviving cuneiform tablet in the nearest city (validated as a proxy for tribute payment and redistribution via keyword analysis of transliterated texts; Table RA29 in Results Appendix).
• Column 4: total count of palaces + temples + ziggurats in the nearest city (measure of state capacity and administrative infrastructure). The outcome-specific sample sizes are 4,320 for canals and 4,424 each for walls, tribute, and administrative buildings. All four include the fixed effects and covariate interactions described above.

Heterogeneous effects (R7, Table 5, p.2536). Equation (1) estimated separately in two subsamples split by the median of the spatial lag of pre-treatment settlement density (a proxy for returns to coordination). The Chow test for coefficient equality has p-value 0.06. The other Panel A split is by settlement alignment for canals: the estimates are +0.22 for aligned and -0.01 for misaligned cells (Chow p=0.00). Panel B splits by irrigated versus rainfed potential productivity difference, with +0.16 in the high-difference group and -0.05 in the low-difference group (Chow p=0.00), and by river speed, with +0.10 near slow-flowing rivers and +0.03 near fast-flowing rivers (Chow p=0.14). These regressions use the state-presence outcome and include grid-cell and period fixed effects plus period interactions with survey area, rainfall, temperature, and pre-period urban status. Samples are: density high/low, 2,323/2,308; settlement aligned/ misaligned, 2,365/2,266; high/low productivity difference, 2,319/2,311; slow/fast river flow, 2,775/1,856. Standard errors are clustered by grid cell; Conley standard errors are also reported for Panel A.

Identification checks (R9-R11, R17; Tables 2-3 and text pp.2532-2533). Table 3 column 2 replaces the border-based state outcome with an indicator for an administrative building in the nearest city. Table 2 estimates equation (1) using lagged settlement counts, city presence, and canal presence as outcomes, with six lags/specifications across 4,320 or 4,660 cell-period observations; the estimates are small and insignificant. The placebo exercise instead treats a river moving closer as treatment and finds consistently negative coefficients for state formation (the main text does not tabulate their magnitudes). As a check on trade as an alternative explanation, the authors fail to reject no effect of river shifts on trade patterns (Results Appendix Table RA26, cited in the main text on p.2532). The main regressions use 5x5 km grid cells, grid-cell and period fixed effects, period interactions with survey-area indicators, rainfall, temperature, and prior urban status, with grid-cell-clustered and Conley standard errors. For outcomes at the nearest-city level, the authors also report a robustness version with two-way clustering by grid cell and nearest-city-by-period (Method §IV.A, pp.2525-2526).

Extended panel (R8 and R15, Table 6, p.2539). Equation (2) is estimated on 27,106 grid cell-period observations across all 31 archeological periods (5000BCE-1950CE). Columns 2-3 split the sample into the first two river shifts (through 2350BCE; 9,718 observations) and the four subsequent shifts (2350BCE-1950CE; 17,388 observations). Estimates are +0.15 (SE=0.03) in the first period and +0.10 (SE=0.02) in subsequent periods. All three specifications include grid-cell and period fixed effects, period interactions with survey-area indicators, rainfall, temperature, and prior urban status; standard errors are clustered by grid cell and Conley standard errors are reported in brackets.

Tablet text mechanism evidence (R16, Figure 6, p.2541; §VII, p.2540). The authors compare fractions of 5,885 transliterated tablets mentioning lugal (lineage head), gal (chief), canals, or tribute before and after the first shift. The fraction mentioning gal increases by about 40%; lugal is not observed before state formation and appears afterward; mentions of canals and tribute also rise, with tribute absent before. Figure 6 reports graphical comparisons, not regression coefficients or exact percentage-point changes.

Datasets used

Dataset	Role in paper	Wiki page
Chicago Oriental Institute archeological surveys (Adams 1965, 1981; Adams and Nissen 1972)	Settlement history, canal network reconstruction, and city locations; basis for the 5x5 km grid panel covering 5000BCE-1950CE	No page yet
Cuneiform Digital Library Initiative (CDLI)	5,885 transliterated cuneiform tablets from the main study period; text analysis of tribute, canal, and lineage-leader term mentions (Section VII, Figure 6)	No page yet
Administrative building and state border data (Heinrich 1982, 1984; Meyers 1997; Bryce 2009)	Identification of palaces, temples, and ziggurats; reconstruction of state territorial borders	No page yet
River shift and course reconstructions (Cole and Gasche 1998)	Geographic reconstruction of river courses before and after each of six shifts; defines the treatment variable	No page yet
FAO soil and agricultural potential data	Potential productivity of irrigated vs. rainfed barley cultivation; used for geographic heterogeneity subsamples (Table 5, Panel B, p.2536)	No page yet

Sample: southern Iraq, 1,374 grid cells of 5x5 km covering the area between Baghdad and Basra. Main study: 3900BCE-2700BCE, five archeological periods (~240 yrs each). Extended study: 5000BCE-1950CE, 31 periods. The replication dataset (including all constructed variables) is publicly available at https://doi.org/10.3886/184167EV1.

When to read the full paper

Use the original paper if you are: testing theories of state formation in other historical or contemporary settings, where the identification approach (demand-side shock to coordination costs) can be adapted (Section IV); studying public good provision and tribute across the full 7,000-year Iraqi panel, including under the Babylonian and Assyrian empires (Section VI and Table 6); examining the internal organization of the first states via cuneiform tablet text analysis (Section VII and Figure 6, p.2541); or extending the dataset to other archeological outcome variables using the replication data. The online Data Appendix (68 pages) describes dataset construction, coding procedures, and robustness exercises in full detail.

Attribution and rights

Source: peer-reviewed, American Economic Review 113(10), October 2023. Freely available on the AEA website; no CC licence detected in Crossref metadata; redistribution is extract-only. This distillation was extracted by an LLM on 2026-06-24 and is not human-verified or independently reproduced.

Allen, Robert C., Mattia C. Bertazzini, and Leander Heldring. “The Economic Origins of Government.” American Economic Review 113, no. 10 (October 2023): 2507-2545. DOI: 10.1257/aer.20201919.