Manipulation of information in times of crisis: evidence from Covid excess mortality

Layer 1: Overview

Karlinsky and Shayo ask which governments manipulate public information, in which direction, and by how much — questions that are normally intractable because the ground truth is unobservable. The Covid-19 pandemic supplies an unusual opportunity: all countries faced a broadly similar crisis simultaneously, and all-cause mortality — collected by national statistical offices as a routine bureaucratic function independently of Covid — provides a manipulation-resistant benchmark against which officially-reported Covid deaths can be evaluated.

The authors hand-collect all-cause mortality data for 134 countries and territories from national statistical offices, population registries, health ministries, and, in some cases, right-to-information requests facilitated by local journalists. Data span 2015–2021 at weekly, monthly, or annual frequency. Their sample covers 93 percent of countries with at least 75 percent Death Registration Completeness. They compute, for each country, a Misreporting Rate (MRR) defined as estimated Covid deaths minus officially reported Covid deaths, normalised by expected total deaths derived from pre-pandemic trends. Estimated Covid deaths equal excess mortality — itself estimated from a country-specific model with weekly/monthly fixed effects and an annual trend (R² = 0.997 in pre-pandemic prediction) — minus adjustments for excess deaths attributable to conflicts, natural disasters, and other identifiable non-Covid causes. Those adjustments are small: the mean total adjustment across the sample is 0.04 percent of expected deaths.

Six main findings emerge. First, between 45 and 55 percent of the 134 countries misreported Covid deaths. Second, the direction of manipulation is overwhelmingly one-sided: of 131 countries with sufficient data to estimate confidence intervals, 59 reported accurately, 62 significantly underreported, and only 10 overreported. The theoretical prediction that governments might exaggerate a crisis — to rally populations, legitimise repressive measures, or attract foreign aid — finds no empirical support. Third, the magnitude of underreporting is large: the sample reported 5.08 million Covid deaths in 2020–2021 while estimated actual Covid deaths were 12.47 million, nearly 2.5 times the official figure; the implied global MRR is 12.8 percent. Among the 62 underreporting countries, the average MRR is 14.5 percent of expected total deaths and the median is 12 percent. Individual-country MRRs range from above 37 percent (Bolivia, Nicaragua) downward, with Russia at 24 percent. Fourth, state capacity in counting and registering deaths explains some but far from most cross-country variation; the R² of the best capacity-only regression is 0.115. Chile and Russia have virtually identical Death Registration Completeness and Percent Well-Certified Death Registrations, yet Chile accurately reported while Russia’s MRR is 24 percent. Fifth, the extent of underreporting is strongly associated with constraints on governmental power. In individual regressions conditioning on capacity, each of three institutional constraint measures — Clean Elections, Executive Constraints, and Freedom of the Press — is associated with a 0.4–0.5 standard deviation lower MRR per one standard deviation stronger constraint. In a joint model including all 12 factors from four domains (macroeconomic incentives, culture, audience sophistication, institutions), institutional constraints are the strongest predictor (partial R² ≈ 0.11), followed by audience sophistication (partial R² ≈ 0.04–0.06). Macroeconomic incentives — tourism reliance, unemployment, foreign direct investment — are not jointly significant. Cultural factors (trust, individualism, religiosity) lose significance once other factors are controlled. The full model explains more than 50 percent of MRR variation. Sixth, countries with a communist legacy (defined as having had a communist or socialist regime for at least 10 years, covering 34 countries) show significantly higher misreporting even holding current institutional and cultural conditions constant. Countries that held elections during 2020–2021 also show significantly higher misreporting.

The results are robust to alternative expected-mortality models, alternative MRR normalisations, the inclusion of Bangladesh, China, and Indonesia (treated separately due to data quality concerns), year-by-year (2020 vs. 2021) splits, controls for age structure and GDP per capita, and alternative manipulation measures (underdispersion, Benford’s law deviations). The evidence that manipulation cannot be attributed to varying standards for false-positive attribution of cause of death is direct: four pre-pandemic measures of a country’s tendency to use unspecified cause-of-death categories are uncorrelated with MRR and individually account for less than 1 percent of its variation.

The paper’s contribution to the economics of information manipulation is methodological as well as empirical: it provides a comparable, country-level measure of governmental misinformation based on actual observable actions regarding a policy issue of central importance, covering a large and diverse cross-section of countries.

Layer 2: Deep Dive

What is the identification strategy and what are the main threats to it?

The strategy compares officially-reported Covid deaths (the variable that attracted political attention and over which governments had strong incentives and ability to intervene) with estimated Covid deaths derived from excess all-cause mortality (a statistic collected routinely by national bureaucracies under very different incentive structures, harder to manipulate, and less visible publicly during the pandemic). The identifying assumption is that all-cause mortality data are not themselves systematically manipulated in response to Covid. The authors defend this on four grounds: (1) all-cause mortality has long been collected independently of Covid; (2) ascertaining that someone died is far easier than attributing a cause of death; (3) Covid figures attracted vastly more public attention, making their manipulation more urgent; (4) when governments appear to have discovered the evidential value of excess mortality, their response has been to delay publication of all-cause data rather than to alter it (Belarus is cited as an example). The main remaining threat is that the adjustment for non-Covid excess deaths (conflicts, disasters, traffic accidents, suicides, homicides) is imperfect in countries with poor data on those causes. The authors note this caveat but show mean adjustments are tiny (0.04% of expected deaths) and the largest individual adjustments (Armenia 6.1%, Azerbaijan 3.2%) are driven by the Nagorno-Karabakh war and are handled explicitly.

How is excess mortality estimated, and how sensitive are the results to modelling choices?

Country-specific models are estimated using 2015–2019 all-cause mortality data, including country-specific weekly or monthly fixed effects and a country-specific annual trend to capture seasonality and long-run factors (population ageing, improvements in health care, etc.). The model achieves R² = 0.997 in predicting pre-pandemic mortality. The authors report in Supplementary Material B that alternative expected-mortality approaches from the literature yield very similar results, as do alternative normalisations of the MRR. Sensitivity to model choice is low because the discrepancies between excess and reported deaths in weak-institution countries are so large that they persist across methodological variants.

How do the authors distinguish intentional manipulation from limited state capacity?

They use two pre-pandemic, capacity-specific measures: (1) Death Registration Completeness (DRC) — the share of deaths captured by the vital registration system — and (2) Percent of Well-Certified Death Registrations (PWC) — the share with proper cause-of-death attribution. Both are computed before the pandemic so they are not contaminated by Covid-era behaviour. Regressions confirm that capacity predicts MRR negatively (R² up to 0.115), but the residual variation remains large. The clearest illustration is Chile vs. Russia: both have complete DRC and near-identical high PWC, yet Chile reports accurately and Russia has an MRR of 24 percent. All subsequent analysis of correlates conditions on these capacity measures.

How do the authors rule out the possibility that differences in false-positive aversion (rather than manipulation) explain MRR variation?

They construct four pre-pandemic measures from WHO Mortality Database ICD-10 cause-of-death data: (1) number of ICD codes reported; (2) share of specific-viral deaths among all viral deaths; (3) share of specific-infection deaths among all infection deaths; (4) share of specific-respiratory deaths among all respiratory deaths. A country more averse to false positives would report less specific causes. None of the four measures is significantly associated with MRR, and none accounts for more than 1 percent of its variation. This rules out differences in diagnostic/reporting standards as a driver of the observed discrepancies.

What is the direction of manipulation and what does this imply for theories of governmental information behaviour?

Of 131 countries with estimable confidence intervals, 62 significantly underreported and only 10 overreported. The four main theoretical channels for overreporting — rally-around-the-flag effects, legitimising repression, attracting foreign aid, and inducing flight-to-safety compliance — find no empirical support. The authors argue that the rally-around-the-flag mechanism requires an outgroup-related threat (Covid, unlike a foreign military attack, was not easily framed this way), that Covid mortality does not signal repressive capacity, and that international economic actors appear sufficiently sophisticated to be sceptical of inflated figures. The pattern is consistent instead with governments downplaying to project competence, reduce accountability, and justify inadequate responses.

What factors are most strongly associated with misreporting, and how are they ranked?

In joint regressions with all 12 factors from four domains, after conditioning on capacity: (1) Institutional constraints (Clean Elections, Executive Constraints, Freedom of the Press) have the highest partial R² (approximately 0.11 for Executive Constraints alone) and are jointly significant at p < 0.001; each standard deviation of stronger institutional constraint is associated with roughly 0.4–0.5 standard deviations lower MRR. (2) Audience Sophistication (tertiary education, HDI Education Index, internet access) is the second strongest domain (partial R² in the range of 0.04–0.06 per variable; jointly significant at p < 0.05). (3) Cultural factors (trust, individualism, religiosity) are individually significant in bivariate regressions but lose significance when institutional and other factors are controlled. (4) Macroeconomic incentives (tourism, unemployment, net FDI) are not jointly significant in any specification. Specification-curve analysis across all combinations of controls confirms that Executive Constraints is the single most robust predictor, retaining sign, magnitude, and significance across all models. The full model (Table 4, column 1) has R² exceeding 0.50.

What is the communist legacy finding and how is it interpreted?

Countries defined as having had a communist or socialist regime for at least 10 years (34 countries) show significantly higher MRRs even after conditioning on contemporary institutional constraints, audience sophistication, culture, and capacity. The coefficient is statistically significant at p < 0.05 or better in the main and most robustness specifications. The authors point to Harrison (2017) on the pervasiveness of information manipulation in communist states as a historical precedent, and interpret the finding as a persistent legacy operating through channels not fully captured by current measures. This suggests that historical exposure to a political culture of systematic information manipulation may have durable effects on bureaucratic behaviour or political norms that current V-Dem indices do not fully absorb.

What is the elections finding?

Countries holding national parliamentary or presidential elections during 2020–2021 (76 of 134 countries) show significantly higher misreporting, consistent with electoral incentive theories of information manipulation. This finding is robust to including controls for GDP per capita, population age structure, and other domains, and is stable across the 2020-only and 2021-only sub-samples.

What robustness checks are performed?

The authors conduct: (1) specification-curve analysis across all combinations of covariates; (2) a joint model with all 12 individual factors; (3) principal component analysis within each domain to recover common variation and reduce dependence on specific measurement choices; (4) alternative expected-mortality models (Supplementary Material B.1); (5) alternative MRR normalisations (Supplementary Material B.2); (6) separate year-by-year analysis for 2020 and 2021; (7) inclusion of Bangladesh, China, and Indonesia as robustness cases despite lower data reliability; (8) addition of GDP per capita to check whether the institution-misreporting link is proxying for development; (9) analysis using underdispersion (Kobak 2022) and Benford’s law deviations as alternative manipulation measures; (10) exploration of colonial legacy as an additional historical variable (no significant effect found). The primacy of institutional constraints is robust across all of these.

How do the authors treat China, Bangladesh, and Indonesia?

These three large countries are excluded from the main analysis because their all-cause mortality data come from surveys (Bangladesh, China) rather than vital registration systems, or are very incomplete (Indonesia), making excess mortality estimation unreliable. They are included in a robustness regression (Table 4, column 6) and results are described as qualitatively similar. The authors flag that China’s data may itself be informative as a potential indicator of data suppression.

How does this paper relate to and differ from prior work?

The paper is closest in spirit to Olken (2007), who uses the gap between reported and actual infrastructure spending to measure corruption, and Martinez (2022), who compares GDP growth to night-time-light-implied growth and finds autocracies overstate growth by more than a third. The authors extend this approach to a different domain (health/mortality) with broader country coverage. Prior Covid-specific work documented anomalies — underdispersion (Kobak 2022) and Benford’s law deviations (Kapoor et al. 2020; Kilani 2021) — and noted that autocratic regimes reported lower-than-expected deaths (Annaka 2021; Cassan and Van Steenvoort 2021), but these studies relied on regime type as the sole or primary explanatory variable and did not systematically rank competing factors. Neumayer and Plümper (2022) and Wigley (2024) used the authors’ own World Mortality Dataset to test data manipulation. This paper is distinctive in that it: (a) provides what the authors describe as the most systematic estimates to date of Covid mortality and misreporting; (b) examines a broad range of factors across four domains without a priori privileging any; (c) directly tests and rejects capacity and false-positive aversion as alternative explanations; and (d) identifies communist legacy and elections as additional significant correlates.

What are the policy implications and their scope conditions?

Three implications are highlighted. First, unconstrained regimes appear to manipulate not only economic statistics but also health information during the most salient public policy event of the era; travel restrictions and multilateral actions during the pandemic relied on reported Covid figures, so manipulation had direct international externalities. This raises broader questions about the credibility of official data from such governments across domains — foreign aid targeting, climate action, vaccination campaigns. Second, the MRR provides a comparable cross-country measure of institutional quality grounded in actual governmental behaviour, potentially useful as an input to studies of institutions, conflict, electoral outcomes, and economic performance. Third, some countries that score respectably on conventional executive constraint indices — Albania, El Salvador, India, Serbia — show high MRRs, suggesting these rates may be leading indicators of democratic erosion not yet captured by standard measures. The scope condition the authors flag is external validity: if pandemic mortality is an extreme case with unique incentive structures (tourism, investment, aid eligibility), then findings about determinants of manipulation may not generalise beyond crisis settings. The authors argue against this interpretation on the grounds that macroeconomic factors — which would be pandemic-specific — are not significant, while institutional constraints — which reflect general governmental behaviour — are.

What limitations do the authors acknowledge?

First, the analysis is explicitly descriptive rather than causal; factors are correlates, not proven determinants. Second, the MRR may understate true manipulation if all-cause mortality data are themselves selectively withheld or manipulated; the authors argue this is probably modest but acknowledge it cannot be fully ruled out. Third, important large countries — Pakistan, Nigeria, Ethiopia, Venezuela — cannot be scored because sufficient all-cause mortality data are not publicly available; the authors note this absence may itself be informative but cannot be quantified. Fourth, data on other causes of excess deaths (traffic accidents, suicides, homicides) are patchy in many countries, though the scale of these adjustments is very small. Fifth, some capacity controls (PWC) use data from as early as 2003, introducing measurement error. The paper does not claim to fully separate the channels through which institutions reduce manipulation (electoral accountability, press scrutiny, judicial oversight, professional agency independence), treating them as joint constraints rather than separately identified mechanisms.

Key Concepts

Misreporting Rate (MRR): The paper’s central measure, defined as (estimated Covid deaths minus officially reported Covid deaths) divided by expected total deaths for the country in the same period based on pre-pandemic trends. A positive MRR indicates underreporting; a negative MRR indicates overreporting. Normalising by expected total deaths rather than by reported Covid deaths accounts for differences in population size, age structure, and baseline mortality across countries.

Excess mortality: The number of deaths above and beyond what would have been expected in the absence of the pandemic, estimated from country-specific models with weekly or monthly fixed effects and an annual trend fitted to 2015–2019 data. Used as the primary building block for estimated Covid deaths after subtracting excess deaths due to identified non-Covid causes (conflict, natural disasters, traffic accidents, homicides, suicides).

Death Registration Completeness (DRC): In this paper’s usage, the share of all deaths in a country captured by its vital registration system each year, measured using pre-pandemic data. Treated as the most basic indicator of a country’s capacity to count deaths. Used as a control to separate capacity constraints from intentional manipulation.

Percent of Well-Certified Death Registrations (PWC): The share of death certificates in a country that carry a properly specified cause of death, measured using pre-pandemic data. Used alongside DRC as a second capacity control capturing not just whether deaths are registered but whether causes are correctly attributed.

Informational Autocrat: Following Guriev and Treisman (2022), the paper uses this concept to describe executives in countries where formal and informal checks and balances are weak, who systematically manipulate public information to project competence and reduce accountability. The paper’s empirical results are interpreted as evidence that such executives behave as informational autocrats not only in economic statistics but also in health data.

False-positive aversion: The tendency of some countries to apply a higher evidentiary bar before attributing a death to a specific cause — such as Covid — rather than leaving the cause unspecified, independently of capacity or intention to deceive. The paper operationalises this using pre-pandemic ICD-10 data on specificity of reported causes of death and shows it is uncorrelated with MRR, ruling it out as a driver of observed discrepancies.

Communist legacy: The paper’s binary indicator for countries that had a communist or socialist regime for at least 10 consecutive years (34 countries). The variable captures historical exposure to a political culture of systematic information manipulation and is found to be a significant positive predictor of MRR even after conditioning on current institutional constraints, consistent with persistent norms or bureaucratic practices.