Econometric Analysis of Microdata

The question, the setting and the relevant comparison

The central question is whether successful local terrorist attacks increase support for the Alternative für Deutschland (AfD), compared with attacks that fail. This is a narrower estimand than the effect of all political violence compared with no violence. Municipalities experiencing attacks may differ from untouched municipalities in population, migration, economic conditions or political history. A simple attacked-versus-unattacked comparison would therefore risk attributing those differences to terrorism.

The researchers instead condition on an attack having been attempted. They compare places in which an attack succeeded with places in which attempts failed. The proposed source of quasi-random variation is uncertainty in whether an attempted attack achieves its immediate physical objective. In the Global Terrorism Database, success refers to tangible execution, such as a device detonating, rather than whether the perpetrator ultimately achieves a political goal. Assassinations are excluded because their success classification is defined differently.

The institutional context also matters. The AfD entered Germany’s electoral landscape in 2013, and the study examines federal, European Parliament and state elections through 2021. These election types need not respond identically. Voters may attribute internal security and aspects of refugee settlement to different levels of government, and parties may emphasize these issues differently in their campaigns. Estimating separate election-type effects is therefore substantively motivated rather than merely a search for a significant subgroup.

From attacks to a municipality panel

The underlying database records 232 attacks in Germany between 2010 and 2020, mapped to 124 municipalities. Approximately 86 percent of attacks succeeded and 14 percent failed. Most were small and local, and roughly three quarters involved right-wing causes and attacks against foreigners. This does not mean that the share of treated municipalities equals 86 percent: repeated attacks and the municipality-level classification change the denominator.

A municipality is coded as successful if it experiences at least one successful attack. The comparison group consists of places with at least one failed attempt and no successful attack. In the baseline sample there are 124 municipality clusters but only 11 failed-only municipalities. The reference date is the first relevant successful or failed attack for its respective group. Municipalities receiving several attempts require particular care because later events can overlap with earlier post-treatment periods.

Election outcomes measure the AfD share of valid party votes. Where voters cast separate candidate and party ballots, the party ballot avoids conflating party support with the appeal of a particular candidate. The data include the 2013, 2017 and 2021 federal elections, the 2014 and 2019 European elections, and state elections from 2013–2021. Municipality covariates and restricted individual panel data support additional checks and mechanism analysis.

Success is not a label for the seriousness of all consequences. A failed attempt may still frighten residents or receive media attention. Consequently, even a valid comparison between successful and failed attacks initially identifies their difference. Interpreting it as the full effect of terrorism requires examining what failed attempts do relative to appropriate unexposed places.

Why balance and pre-trend evidence matter

The key assumption is that, conditional on being targeted, attack success is not systematically related to factors that would independently change voting. The study checks pre-attack income, employment, population, migration, asylum-seeker numbers, education, geography, social assistance and political characteristics. It also compares attack technologies and motivations. Successful attacks produce more casualties, which is a consequence of successful execution and does not by itself invalidate pre-treatment comparability.

A balance regression can be written as $X_{i,pre}=a+b{S}_i+v_i$, where $S_i$ indicates a successful attack and $X_{i,pre}$ is a characteristic measured before the event. A small and imprecisely estimated $b$ is reassuring, but failure to reject $b=0$ does not prove equality. Balance tests can miss relevant differences when samples are small, and they do not directly test unobserved features such as perpetrator organization, device quality or local police effectiveness.

The published study also examines event-study patterns using estimators that account for staggered treatment and heterogeneous effects. For the AfD outcome, the joint test of pre-period coefficients has a reported p-value of 0.15, and a linear pre-trend test has a p-value of 0.46. These values are not probabilities that the identification assumption is true. They indicate that the specified tests do not reject the relevant null hypotheses at conventional levels. The counterfactual trend after the attack remains unobserved.

Combining levels and trends is useful because equal baseline outcomes do not imply equal future changes, while different levels need not invalidate a difference-in-differences design. The broader lesson is to inspect the economic magnitude and uncertainty of potential imbalances, not merely whether a sequence of p-values exceeds 0.05.

The estimating equation and its interpretation

Let $Y_{iet}$ be the AfD vote share in municipality $i$, election type $e$ and year $t$. Let $S_i$ denote the successful-attack indicator and $Pos{t}_{iet}$ indicate that the election occurs after the reference attack. A transparent representation of the pooled model is:

The labels $F$, $E$ and $S$ denote federal, European and state elections. Municipality-by-election-type effects ${\lambda }_{ie}$ absorb persistent local voting patterns specific to each type, while year-by-election-type effects ${\lambda }_{te}$ absorb election-specific common shocks. The control terms contain the required lower-order interactions, with an appropriate normalization so that the three displayed coefficients are total election-specific effects. Including a redundant common success-by-post term alongside all three complete election-type interactions would create collinearity.

In a simple two-period interpretation, each coefficient compares the before–after change in successful-attack places with the corresponding change in failed-attack places for that election type. The actual panel pools more events and years, so identification and weighting require the full model. The disturbance ${\epsilon }_{iet}$ is the regression error; it is not itself a standard error. Standard errors are estimated from the data and clustered by municipality to allow outcomes within the same place to be correlated.

Reading the election results without confusing units

The baseline has 734 observations and 124 municipality clusters. Its coefficients are reported on a zero-to-one vote-share scale. Multiplying both coefficients and standard errors by 100 converts them to percentage points. The table applies that conversion to the published baseline estimates.

The intervals use the normal approximation, $\hat{\beta }\pm 1.96SE\left(\hat{\beta }\right)$, and are included to make uncertainty visible. They do not replace the paper’s small-cluster robustness analysis. The state-election estimate is about 4.5 percentage points. Relative to the rounded sample mean of 17 percent, $100\left(0.0446/0.17\right)\approx 26.2\%$. This is a relative increase in vote share, not a claim that 26 percent of individuals changed party or that support rose by 26 percentage points.

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The federal and European estimates are not statistically distinguishable from zero in this baseline. Their uncertainty intervals include effects of both signs, so the evidence does not establish exact zero effects. Likewise, a significant estimate for state elections and an insignificant estimate for another type do not, on their own, constitute a formal test that the two effects differ. That comparison requires the standard error of their difference, including covariance.

The state-election coefficient remains positive across additional specifications: 4.16 points with East-by-year controls, 4.93 when omitting Berlin, 5.87 when removing municipalities with multiple attacks, and 3.34 when including the full set of baseline covariates interacted with year. These are alternative specifications of one substantive question; they should not be averaged as though they were independent studies.

Stress-testing identification and inference

The small failed-only comparison group makes inference a central issue. Hundreds of election observations do not supply hundreds of independent control municipalities. The paper therefore uses wild cluster bootstrap inference, examines the effect of omitting each municipality in turn, and applies an alternative estimator designed for staggered treatment. These checks address uncertainty, influential places and potentially problematic weighting under heterogeneous effects.

A rolling-window analysis includes additional attacks by comparing elections around each municipality–attack episode. This recovers information lost by considering only the first relevant event, but creates another difficulty: an election after one attack can be before a later attack. Overlapping windows make the estimand and dependence structure less simple. The study accordingly treats this as a robustness exercise rather than pretending that every window is an unrelated experiment.

To investigate failed attacks, the researchers construct an alternative comparison group of observably similar municipalities without attacks using propensity-score matching. Successful attacks compared with these matched places produce a state-election estimate of 2.99 percentage points, with a standard error of 1.32. Failed attacks compared with matched places produce −1.69 points, with a standard error of 1.98. The latter estimate is inconclusive. The exercise supports a stronger role for successful attacks but does not establish that failed attacks have precisely zero consequences.

Matching changes the identifying requirement: it relies on the selected observables adequately addressing relevant differences between targeted and untargeted places. It does not manufacture random assignment. Geographic spillovers raise an additional issue because nearby municipalities may respond even without being attacked. The study finds effects in surrounding areas, often about half the baseline magnitude at distances between 25 and 75 kilometres, diminishing by around 80 kilometres. Such responses are informative outcomes in their own right and also warn against assuming that every neighboring municipality is an unaffected control.

Turnout, vote shares and the ecological inference problem

A party can gain votes through newly mobilized voters, switches from other parties, or both. The distinction depends on denominators. Turnout is votes cast divided by eligible voters; a party’s vote share is its votes divided by all valid votes cast. A party-specific participation measure instead divides that party’s votes by eligible voters. These quantities cannot be used interchangeably.

The published estimate for overall turnout in state elections is 0.1469, or 14.69 percentage points. The corresponding AfD-votes-per-eligible-voter estimate is 0.0537, or 5.37 points. Their ratio is approximately 36.6 percent. Describing this as the share of newly mobilized voters choosing the AfD would require assuming no switching between parties. The data show evidence of switching, so the ratio is not an identified individual-level allocation of new voters. It also does not imply that the AfD captured a majority of the turnout increase.

Most major parties receive some additional votes per eligible voter, but the distribution is unequal. The AfD gains a large enough share of the additional support to raise its vote share. The SPD also records a smaller positive state-election vote-share effect, while other major parties show no comparable clear increase. Aggregate results alone cannot reveal precisely which individuals moved from abstention or another party to the AfD. This is the ecological inference problem: changes in group totals do not uniquely identify changes in individual behavior.

Using individual panel data to investigate mechanisms

The German Socio-Economic Panel follows the same people over time and allows the attack data to be linked to their municipality of residence. For an individual $p$, the study estimates:

Person fixed effects ${\delta }_p$ remove stable differences between respondents, while year effects ${\gamma }_t$ absorb common changes. The outcome is an indicator for an attitude or preference, and standard errors remain clustered by municipality because treatment occurs at that level. The regression therefore combines individual-level outcome variation with a group-level treatment design. The following selected estimates are percentage-point changes; the last column gives the number of individuals used for each outcome.

The results fit changes in political preferences and participation, not simply changes in the composition of the electorate. However, a stated party preference is not an observed secret ballot, and the different outcomes have different samples and observation frequencies. The small terrorism-concern estimate is especially imprecise. An already high baseline level of concern, around 84 percent, leaves less room for a further increase and may help explain the result without proving a particular psychological mechanism.

Further analysis distinguishes prior partisan attachment from prior political activity. Preference for the AfD increases among some respondents previously attached to the CDU, the Left party and ultra-right parties, as well as among less committed respondents. Previously inactive people also become more likely to prefer the AfD. These findings support both preference switching and political activation; they do not yield a complete, exact accounting of the aggregate vote-share effect. Effects also vary with educational attainment: the shift toward AfD preference is stronger among respondents without a higher-education degree, while the SPD response differs. A genuine heterogeneity claim requires testing the difference between group effects, not simply counting which group has a significant coefficient.

Attack motivation, political messages and media salience

The effects are concentrated in right-wing attacks and attacks directed against foreigners. The published point estimates are about 5.6 percentage points for right-wing attacks and 6.1 for attacks targeting foreigners, compared with smaller and less precise estimates for the alternative categories. These categories can overlap, so they are not additive components of the overall effect. The pattern also does not show that the AfD organized or directed the attacks: electoral benefit and responsibility for violence are different claims.

Political manifestos provide a complementary perspective on how events are framed. The researchers count selected word stems per 10,000 words, subtract a common 2009 CDU manifesto benchmark, and relate those language differences to prior successful attacks at state level. The AfD uses more language connected with crime, naturalization and integration in more exposed states, while responses by other parties differ. The normalization avoids mechanically assigning more occurrences to longer documents, although word counts remain imperfect measures of meaning, emphasis or persuasion.

This analysis has a weaker causal design than the municipality comparison. Aggregating to 16 states reduces independent variation and loses the sharp successful-versus-failed comparison. It therefore supplies evidence consistent with a messaging channel, not a separately randomized estimate of the persuasive effect of those words. Similarly, the media analysis finds more intensive local and regional coverage of successful attacks, with different tone and vocabulary. Highly covered attacks have larger electoral associations, including federal-election effects, but coverage itself is an endogenous response to events. Selection into extensive coverage prevents interpreting that contrast as an experiment that randomly assigns news exposure.

What the case teaches about causal evidence

This application brings several core principles together. A credible comparison is chosen around a plausible source of variation rather than around convenience. The estimand is stated as successful versus failed attacks, with separate evidence needed for attacks versus no attacks. Fixed effects absorb specified forms of heterogeneity, while balance, timing and sensitivity exercises probe remaining threats. Clustered inference respects the level of treatment. Mechanism evidence combines aggregate election outcomes with repeated observations of individuals and political communications, without pretending that these separate analyses fully identify one mediation chain.

The result is historically and institutionally specific: local attacks in Germany over the study period, a particular set of targeted municipalities, and a particular party and electoral structure. It does not establish that every terrorist event in every country benefits the political right. The most useful reading of the study is therefore both substantive and methodological: political violence may change participation and preferences, while the strength of each causal claim depends on its comparison group, identifying assumptions, uncertainty and measurement.

Research source: Navid Sabet, Marius Liebald and Guido Friebel (2025), Terrorism and Voting: The Rise of Right-Wing Populism in Germany, American Economic Journal: Economic Policy, 17(3), 407–440, DOI: 10.1257/pol.20230615. Baseline election results use Table 2; matched-comparison results use Table 3; individual responses use Table 4; partisan and activity analysis uses Table 5. Intervals and percentage-point conversions in this chapter are calculated from the reported estimates and standard errors.