Individuals and Organizations as Sources of State Effectiveness: Best, Hjort & Szakonyi (2023)

What this is. The core results, model, and method of this paper, condensed to know what it found and how, without reading all 47 pages. To replicate or extend, read the full source at doi.org/10.1257/aer.20191598 and the replication package at doi.org/10.3886/E184107V1.

TL;DR

The paper quantifies how much of the Russian government’s procurement performance is attributable to the individuals and organizations who run it. Using administrative data on 16 million off-the-shelf purchases (2011-2016), it estimates that individual bureaucrats and public-sector organizations together explain 39 percent of the variation in quality-adjusted prices paid. The identification strategy exploits the fact that many bureaucrats work with multiple organizations and vice versa, providing thousands of quasi-experiments from bureaucrat-organization switches. The paper is related to the evidence on active vs passive waste in public contracts by Bandiera, Prat and Valletti (2009), which it extends by separately identifying individual and organizational sources of inefficiency. Effective bureaucrats (those who pay lower prices) also achieve better spending quality (fewer delays, renegotiations, and cost overruns), so the multitasking problem is mild. In a second part, the paper studies bid preferences that give domestic suppliers a 15 percent price advantage. They increase domestic sourcing with little average effect on prices; participation falls somewhat in both samples, while spending quality improves slightly in the full sample and worsens slightly in pharmaceuticals. These averages mask large heterogeneity: prices fall substantially for ineffective bureaucrats and may turn positive for the most effective, consistent with participation costs being a key friction.

Core results

Coefficients and standard errors are as reported; significance wording follows the paper’s text.

#	Result	Locator	Magnitude
R1	Bureaucrats and organizations jointly explain 39 percent of the variation in quality-adjusted procurement prices	Table 2 col 6, p. 2143	Combined bur+org SD within connected sets = 0.489 log points; SD of log price | good, month = 1.280; ratio = 38.2% (covariance shrinkage method)
R2	Bureaucrats account for ~21 percent and organizations for ~26 percent of price variation separately	Table 2 col 6, p. 2143	Covariance-shrunk SD: bureaucrats = 0.263 log points, organizations = 0.338; bur-org correlation = 0.311 (positive assortative matching)
R3	Moving lowest-quartile bureaucrats to the 75th percentile would reduce procurement costs by 4.6 percent; moving both below Q25 to Q75 saves 13.9 percent	§IVC, pp. 2143-2144	4.6% savings from bureaucrats only (Q1 to Q75); 13.9% combined (~US$10 billion/year, ~0.7% of nonresource GDP)
R4	Price effectiveness and spending-quality effectiveness are positively correlated across bureaucrats and organizations	Figure 3, p. 2148	Correlation = 0.43 for bureaucrats; 0.48 for organizations; low-price buyers also deliver fewer delays and renegotiations
R5	Bid preferences increase domestic sourcing with little average price effect: the paper describes the probability of a domestic supplier winning as 14 percent higher	Table 5, p. 2155	Price effect: -0.004 (SE 0.010), not significant; domestic winner probability: +0.042 (SE 0.005), or 4.2 percentage points, in pharmaceuticals
R6	Bid preferences reduce prices by up to 12 percent for the least effective bureaucrats; effects are much smaller and may turn positive for the most effective	Table 6 cols 1 and 4, Figure 7, pp. 2156-2158	Coefficient on Bureaucrat FE x Preferenced x PolicyActive: -0.090 (SE 0.018) full sample; -0.466 (SE 0.090) pharmaceuticals
R7	Optimal bid penalty varies across bureaucracy types, from 23 percent for the most effective to 10 percent for the least effective subgroup	Figure 9, pp. 2162-2163	Equivalent penalty for same price outcome as the 15% rule achieves overall, across ten effectiveness deciles
R8	Bureaucrat switches support a causal interpretation: moving from a worst-quartile to best-quartile bureaucrat lowers prices, with no concerning pre-trend	Figure 1 and text, pp. 2137-2138	Price decrease = 18%; of 16 quartile trajectories, 10 have no rejectable pre-trend and 5 of the other 6 pre-trends run opposite the concern
R9	Main effects persist in the homogeneous pharmaceutical subsample	Table 3, p. 2145	Split-sample combined bureaucrat + organization SD = 0.183 versus log-price SD conditional on good and month = 0.430; covariance-shrinkage SD = 0.0846
R10	Procurers also explain a material share of spending-quality variation	Table 4, p. 2147	Covariance-shrinkage method assigns 24%; combined effect SD = 0.139 versus quality-index SD = 0.592
R11	Mechanism evidence points to supplier entry barriers and buyer experience; corruption measures explain little	Figures 4-5 and text, pp. 2150-2152	LASSO selects 30 of 85 bureaucrat predictors; 22 of 34 strongest price predictors recur among the 32 strongest quality predictors; regional corruption measures have weak predictive power
R12	Average bid-preference effects also reduce participation slightly, with spending-quality effects varying by sample	Table 5, p. 2155	Bidder-count effect = -0.041 (SE 0.011) all products and -0.024 (SE 0.020) pharmaceuticals; quality-index effect = -0.022 (SE 0.005) and +0.013 (SE 0.007), respectively
R13	Participation and domestic-sourcing effects vary with bureaucrat effectiveness; organization interactions differ by outcome and sample	Table 6, p. 2157	Bureaucrat interactions: bidder count +0.081 (SE 0.018) all products and +1.12 (SE 0.201) pharmaceuticals; quality -0.051 (SE 0.037) and +0.064 (SE 0.067); domestic-winner probability +0.211 (SE 0.037). Organization interactions: bidder count +0.005 (SE 0.013) and +0.450 (SE 0.193), domestic-winner probability -0.145 (SE 0.036)

Overall (paper’s conclusion). State effectiveness is embedded in individuals and organizations: bureaucrats and their employers account for a large fraction of procurement price variation. Experience, supplier networks, and the ability to reduce auction entry barriers help explain bureaucratic effectiveness; regional corruption measures have weak predictive power, and the paper concludes that corruption is not the primary source of effectiveness differences. Because raising bureaucratic effectiveness directly is often difficult, a feasible alternative is to tailor policy design to the capacity of the implementing bureaucracy. The Russian evidence suggests that a “buy local” bid preference can perform better under less effective bureaucrats than under more effective ones.

Theory / model

Section III presents a stylized model of public procurement in which state effectiveness is modeled as costs imposed on potential suppliers wishing to participate. A pair consisting of a procurement bureaucrat and an end-user organization (jointly, the bureaucracy) purchases an off-the-shelf good through a descending open-outcry auction, approximated as a second-price sealed-bid auction (p. 2133, following Milgrom 2004).

There are two potential suppliers: a foreign firm $F$ with higher expected productivity (Pareto parameter $\delta_F$, where $\delta_F < \delta_L$) and a local firm $L$ (parameter $\delta_L$). Both face a common fulfillment cost component $\log(\bar{\theta}) = \mathbf{X}'\boldsymbol{\beta} + \alpha_\theta + \psi_\theta$, where $\mathbf{X}$ are observable item attributes and $\alpha_\theta$ and $\psi_\theta$ are bureaucrat and organization fulfillment-cost shifters. The bureaucracy also imposes participation cost $c_i$ on each supplier, determined by specification parameters $\alpha_c$ and $\psi_c$:

$$c_i = \frac{\bar{\theta}}{1 + \delta_i} - \frac{\bar{\theta}}{1 + \delta_L}\sqrt{1 - \alpha_c - \psi_c}$$

Suppliers independently decide whether to pay $c_i$ and learn their type $\theta_i$. In the Nash equilibrium (Proposition 1, p. 2134), entry probabilities satisfy $q_i = \sqrt{\kappa(1 - \alpha_c - \psi_c)}$ where $\kappa = \min\!\left\{\left[\frac{1 + \delta_F + \delta_L}{1 + \delta_L}\right]^2, \frac{1}{1 - \alpha_c - \psi_c}\right\}$. Expected log prices are (equation 1, p. 2134):

$$E[\log(p)] = \log(\bar{\theta}) - \frac{q_F q_L}{\delta_F + \delta_L} = \mathbf{X}'\boldsymbol{\beta} - \frac{\kappa}{\delta_F + \delta_L} + \bar{\alpha} + \bar{\psi}, \tag{1}$$

where $\bar{\alpha} = \alpha_\theta + \frac{\kappa}{\delta_F + \delta_L}\alpha_c$ and $\bar{\psi} = \psi_\theta + \frac{\kappa}{\delta_F + \delta_L}\psi_c$. Equation (1) delivers two predictions: (i) bureaucracies that impose higher fulfillment costs $(\alpha_\theta, \psi_\theta)$ pay higher prices for otherwise identical goods; (ii) bureaucracies that impose higher participation costs $(\alpha_c, \psi_c)$ pay higher prices and also attract fewer bidders. Both channels reduce price performance and are captured by the combined effects $\bar{\alpha}$ and $\bar{\psi}$.

Bid preferences with heterogeneous effectiveness (Section IIIB, Proposition 2, p. 2135). A 15 percent bid preference gives local bidder $L$ a price advantage: a foreign winner receives only $\gamma = 0.85$ times her bid as the contract price. The model predicts three regimes of bureaucratic effectiveness: (i) for very effective bureaucracies with low participation costs $(\alpha_c + \psi_c \leq \underline{c})$, both firms still enter, the preference primarily shifts bidding, and prices rise; (ii) for intermediate effectiveness $(\underline{c} < \alpha_c + \psi_c \leq \bar{c})$, the foreign firm drops out and the local winner receives the maximum price, raising prices further; (iii) for ineffective bureaucracies $(\bar{c} < \alpha_c + \psi_c)$ with the highest participation costs, the local firm’s increased willingness to enter outweighs the bidding effect, participation rises and prices fall. The model thus predicts that preferences compress cross-bureaucracy variation in performance: they help the least effective the most and harm the most effective.

Method

The paper extends the variance decomposition method pioneered by Abowd, Kramarz, and Margolis (1999) from the private sector to public procurement, treating bureaucrats as workers and organizations as firms. The reduced-form model for log unit price of purchase $i$ procured by organization $j$ through bureaucrat $b(i,j)$ is (equation 2, p. 2138):

$$p_i = \mathbf{X}_i\boldsymbol{\beta} + \tilde{\alpha}_{b(i,j)} + \tilde{\psi}_j + \varepsilon_i, \tag{2}$$

where $\mathbf{X}_i$ includes log quantity, good fixed effects, month fixed effects, and interactions of two-digit HS product categories with years, regions, and lot size. The parameters $\tilde{\alpha}_b$ and $\tilde{\psi}_j$ are overall effects including connected-set intercepts (equation 3, p. 2139):

$$p_i = \mathbf{X}_i\boldsymbol{\beta} + \alpha_{b(i,j)} + \psi_j + \gamma_{s(b,j)} + \varepsilon_i, \tag{3}$$

where $\gamma_{s(b,j)}$ is a connected-set fixed effect normalizing $\alpha$ and $\psi$ to mean zero within each connected set. Identification of bureaucrat and organization effects separately requires observation of bureaucrats working with multiple organizations and organizations working with multiple bureaucrats (the switchers). The data contain 616 connected sets with an average density of 5.2 organizations per bureaucrat and 4.8 bureaucrats per organization.

Equations (2)-(3) are estimated on the 11,339,187-purchase analysis sample in 616 connected sets. The controls include log quantity, good and month fixed effects, and two-digit HS interactions with year, region, and lot size; equation (3) adds connected-set fixed effects. The variance-component standard errors reported for Table 2 are based on 100 bootstrap replications (Table 2 notes, p. 2143). The variance decomposition (equation 4, p. 2140) attributes total price variation to its sources:

$$\text{var}(p_i) = \text{var}(\alpha_{b(i,j)}) + \text{var}(\psi_j) + 2\,\text{cov}(\alpha_{b(i,j)}, \psi_j) + 2\,\text{cov}(\alpha_{b(i,j)} + \psi_j,\, \gamma_{s(b,j)} + \mathbf{X}_i\boldsymbol{\beta}) + \text{var}(\gamma_{s(b,j)} + \mathbf{X}_i\boldsymbol{\beta}) + \text{var}(\varepsilon_i). \tag{4}$$

The paper addresses two finite-sample estimation problems. First, limited mobility bias: because the bureaucrat-organization network has only 616 connected sets with bounded within-set mobility, the naive OLS estimates of $\hat{\alpha}_b$ and $\hat{\psi}_j$ contain large sampling errors that inflate the apparent variance of each component and create a spurious negative covariance. The paper applies two bias corrections: (a) a split-sample estimator (following Finkelstein, Gentzkow, and Williams 2016 and Silver 2016) that randomly splits the sample and estimates the two fixed effects on each half, then forms variance components as cross-half covariances; (b) a covariance-shrinkage estimator that forms minimum-MSE predictions of the full vector $(\hat{\alpha}_b, \hat{\psi}_j)$ by weighting via a matrix $\Lambda^*$ accounting for both own-variance shrinkage and cross-component covariance (analogous to the shrinkage in Chetty, Friedman, and Rockoff (2014) but generalized to two dimensions). The covariance-shrinkage method is the preferred specification because it yields a plausible positive bur-org correlation of 0.311, unlike the split-sample or OLS methods which give negative estimates (Table 2, column 6, p. 2143).

Effective bureaucrats also display distinguishable process behaviors (Sections IVD-IVE). A LASSO procedure selecting about 30 process predictors from 85 potential variables shows that effective bureaucrats set lower reservation prices, attract larger and more diverse supplier pools, specialize in narrower product ranges, and avoid fiscal-year-end spending rushes, all consistent with the participation-cost channel in the model.

Empirical specifications

Event study for causal identification (Section IVA, Figure 1, p. 2137). To support the causal interpretation of bureaucrat and organization effects, the paper exploits 65,000 events in which organizations switch the bureaucrat they work with. Prices change sharply and in the expected direction when a new bureaucrat takes over, with an 18 percent price decrease when switching from a worst-quartile to a best-quartile bureaucrat. There is no systematic pre-trend, and price changes are symmetric for switches in both directions, consistent with the identifying assumption that drift in effectiveness and switches are uncorrelated.

Average effect of bid preferences (equation 5, p. 2153; Table 5, p. 2155). The paper uses a generalized difference-in-differences design, exploiting year-to-year variation in which goods appear on the domestic-preference list:

$$y_{igt} = \mathbf{X}_{igt}\boldsymbol{\beta} + \mu_g + \lambda_t + \delta\,(\text{Preferenced}_{gt} \times \text{PolicyActive}_t) + \varepsilon_{igt}, \tag{5}$$

where $\text{Preferenced}_{gt}$ indicates that good $g$ is on the preference list in year $t$ and $\text{PolicyActive}_t$ indicates the policy is in effect. The controls include the Section IV item controls; the specification includes good and month fixed effects, with standard errors clustered by month and good. Because a minimum of one local and one foreign bidder must be present for preferences to apply, $\hat{\delta}$ is an intent-to-treat effect. Table 5 uses 16,348,331 all-product observations and 460,533 pharmaceutical observations (Table 1, columns 3 and 6). The event-study analog (equation 6, p. 2153) stacks all list-publication events in a window of -3 to +4 months and uses the same controls, fixed effects, clustered standard errors, and policy samples:

$$p_{igt} = \mathbf{X}_{igt}\boldsymbol{\beta} + \mu_g + \lambda_t + \sum_{s=-3}^{4}\delta_s\,\text{Preferenced}_{gt} \times \mathbf{1}\!\left\{t - \text{ListMonth}_t = s\right\} + \varepsilon_{igt} \tag{6}$$

Pre-trend coefficients $\hat{\delta}_s$ for $s = -3, -2, -1$ are indistinguishable from zero (Figure 6, p. 2154), consistent with parallel trends. The preferred estimate of $\hat{\delta}$ is -0.004 (SE 0.010) for log prices, not significantly different from zero (Table 5, column 1), while the probability that an auction is won by a domestic supplier rises by 4.2 percentage points in pharmaceuticals. This near-zero average price effect differs from the positive price effects of domestic bid preferences found by Marion (2007) in US highway procurement, where preferences reduced competition and raised prices by 3.8 percent; the difference is consistent with the lower baseline state capacity in the Russian context reducing the scope for anti-competitive effects.

Heterogeneous effects by bureaucratic effectiveness (equation 7, p. 2156; Table 6, p. 2157). To test the model’s prediction that the policy effect varies by state effectiveness, the paper interacts the DiD design with the covariance-shrunk bureaucrat and organization effects:

$$y_{igt} = \mathbf{X}_{igt}\boldsymbol{\beta} + \mu_g + \lambda_t + \theta_b\hat{\alpha}_b + \theta_j\hat{\psi}_j + \delta\,(\text{Preferenced}_{gt} \times \text{PolicyActive}_t)$$ $$+ \rho_b\,\text{Preferenced}_{gt}\hat{\alpha}_b + \rho_j\,\text{Preferenced}_{gt}\hat{\psi}_j + \eta_b\,\text{PolicyActive}_t\hat{\alpha}_b + \eta_j\,\text{PolicyActive}_t\hat{\psi}_j$$ $$+ \pi_b\,(\text{Preferenced}_{gt} \times \text{PolicyActive}_t)\hat{\alpha}_b + \pi_j\,(\text{Preferenced}_{gt} \times \text{PolicyActive}_t)\hat{\psi}_j + \varepsilon_{igt}, \tag{7}$$

where $\hat{\alpha}_b$ and $\hat{\psi}_j$ are the covariance-shrunk estimates from equation (3). Table 6 includes the Section IV controls, good and month fixed effects, year-by-product-by-size-by-region fixed effects, and connected-set fixed effects; standard errors are clustered by month and good. Its samples contain 16,348,331 all-product observations and 460,533 pharmaceutical observations. The key parameter is $\hat{\pi}_b$: a negative estimate means that bureaucrats with higher baseline prices (higher $\hat{\alpha}_b$, i.e., less effective) experience larger price declines under preferences. Table 6 reports $\hat{\pi}_b = -0.090$ (SE 0.018) in the full sample and $-0.466$ (SE 0.090) in the pharmaceuticals subsample, both statistically significant based on the reported standard errors. An analogous decile-level regression (equation 8, p. 2157) shows that estimated price effects become less negative across bureaucrat effectiveness deciles, from about -20 percent for the bottom decile to slightly positive for the top decile (Figure 7, p. 2158); the paper describes the evidence of price increases for the most effective bureaucrats as suggestive. Heterogeneity by organization effectiveness $\hat{\psi}_j$ is small and imprecisely estimated, consistent with the model’s prediction that it is participation costs (driven by bureaucrats) rather than fulfillment costs (shared by both) that generate the heterogeneous policy effects.

Remaining numbered main-text specifications (8)-(10)

Equation (8) estimates treatment effects separately by bureaucrat and organization effectiveness deciles. Table 6 reports the associated outcome regressions with good fixed effects, month fixed effects, year-by-product-by-size-by-region fixed effects, and connected-set fixed effects; standard errors are clustered by month and good. The samples contain 16,348,331 all-product observations and 460,533 pharmaceutical observations (equation 8 and Table 6 notes, pp. 2157-2158).

$$y_{igt} = \sum_{k=1}^{10}\left[D_{kj} + D_{kb}\left(\rho_k\text{Preferenced}_{gt} + \eta_k\text{PolicyActive}_t + \pi_k\text{Preferenced}_{gt}\times\text{PolicyActive}_t\right)\right] + X_{igt}\beta + \mu_g + \mu_t + \varepsilon_{igt}. \tag{8}$$

Equation (9) replaces the estimated bureaucrat and organization effects with selected purchase, buyer, auction, supplier, and region characteristics $Z_{igt}$. The authors use LASSO selection followed by an elastic-net sensitivity procedure; the outcomes and fixed effects follow equation (7), standard errors are clustered by month and good, and the all-product policy sample is used (equation 9, pp. 2159-2160; Figure 8 notes, p. 2161).

$$y_{igt} = X_{igt}\beta + \mu_g + \lambda_t + Z_{igt}\theta + \text{Preferenced}_{gt}\times Z_{igt}\gamma + \text{PolicyActive}_t\times Z_{igt}\eta + \delta\text{Preferenced}_{gt}\times\text{PolicyActive}_t + \text{Preferenced}_{gt}\times\text{PolicyActive}_t\times Z_{igt}\pi + \varepsilon_{igt}. \tag{9}$$

Equation (10) maps each subgroup’s estimated policy effect to the bid penalty that would match the full-sample average effect. It uses the decile treatment effects from equation (8) and assumes log prices are locally linear in the preference rate. The authors describe this as a back-of-the-envelope policy calculation, not a regression; see Section VD, pp. 2162-2163.

$$d\log(p_g) = \overline{TE} - TE_g = (1 - \gamma_g^* - 0.15)\frac{TE_g}{0.15} \Longleftrightarrow 1 - \gamma_g^* = \frac{0.15\overline{TE}}{TE_g}. \tag{10}$$

Datasets used

Dataset	Role in paper	Wiki page
Russian federal procurement register (zakupki.gov.ru / EIS)	16 million auction requests, protocols, and contracts for off-the-shelf goods, 2011-2016; main data for all price and spending-quality outcomes	No page yet
LVEMD pharmaceutical list (MinZrav 2016)	Barcode-level classification of drugs for the pharmaceuticals subsample; matches procurement items to active ingredient and manufacturer	No page yet
ClearSpending.ru anomalies database	Six spending-quality proxies (delays, cost overruns, contract renegotiations, end-user complaints, cancellations, quality bans) from a civil-society monitoring platform	No page yet
Rosstat firm databases	Financial characteristics of winning suppliers (balance sheets, employment) for the process-measurement analysis	No page yet
Ruslana (Bureau Van Dijk)	Additional firm financials and company identifiers for matching procurement winners to financial data	No page yet

Sample period: January 1, 2011 through December 31, 2016. All prices converted at an exchange rate of 43 rubles per US dollar. The analysis sample (connected sets) contains 11,339,187 observations. The pharmaceuticals subsample contains 181,963 observations.

When to read the full paper

Use the original for:

• Replication: the online appendices (Appendices A-H) contain the text classification algorithm, full variance-decomposition derivations, event-study robustness checks, all organization-side correlates, and sensitivity to alternative shrinkage procedures.
• AKM extension: Section IVB and Appendix C give the formal derivation of the covariance-shrinkage estimator and proofs that it yields lower-bound estimates of the true bureaucrat and organization variances.
• Policy design: Section VD and Figure 9 work out how to calibrate a bid-preference rate to a bureaucracy’s effectiveness distribution, the starting point for any government adapting the framework to its own context.
• Corruption robustness: Section IIC and online Appendix D show why the quality-adjusted price measure is informative even in the presence of corruption, and provide direct tests that corruption is unlikely to explain the main findings.

Attribution and rights

Source: peer-reviewed, American Economic Review 113(8), August 2023, pp. 2121-2167. DOI: 10.1257/aer.20191598. Replication data: doi.org/10.3886/E184107V1.

This distillation was extracted by an LLM on 2026-06-25 and is not human-verified or independently reproduced. No open license was found via Crossref; the paper is paywalled. This page contains extracted results and equations only (extract-only redistribution).

Best, Michael Carlos, Jonas Hjort, and David Szakonyi. “Individuals and Organizations as Sources of State Effectiveness.” American Economic Review 113, no. 8 (August 2023): 2121-2167. DOI: 10.1257/aer.20191598.