Persuasion through Slanted Language: Djourelova (2023)

What this is. The core results, identification design, and estimating equations from this paper on slanted language and media persuasion: enough to know what it found and how, without reading all 36 pages. To replicate or extend it, read the full source at the original.

TL;DR

The paper studies whether slanted language in news media affects readers’ policy views, using the Associated Press (AP) ban on the term “illegal immigrant” in April 2013 as a natural experiment. The ban exploits supply-side variation: the AP distributes a common feed to member outlets, and outlets differ in how heavily they rely on AP copy (their AP intensity). The paper has three parts: (i) the ban caused near-complete disappearance of “illegal immigrant” from AP dispatches, while leaving other dimensions of immigration coverage largely unchanged; (ii) the ban diffused into local media, with 1-standard-deviation higher AP intensity associated with a 3.5 percentage point decline in use of the term; and (iii) respondents in high-AP-intensity counties showed significantly lower support for restrictive immigration policies after the ban. The estimated effects are largest for moderates and in areas with fewer immigrants, though the ideology-group difference is not statistically significant (p = 0.224) and the immigrant-share difference is marginal (p = 0.054). The results are specific to immigration and do not generalize to other policy issues, consistent with a persuasion rather than a social-signaling mechanism.

Core results

Magnitudes are reported as in the source; standard errors are shown in parentheses where available. Locators point to the source PDF.

#	Result	Locator	Magnitude
R1	Ban diffused to local media: 1-SD higher AP intensity associated with 3.5 p.p. decline in use of “illegal immigrant” by media outlets	Table 2, col 1, p. 814	Coef. on PostBan x IHS(AP-intensity) = -1.613 (0.171); effect for 1-SD = -3.53 p.p. (17% of mean 20.28); robust across 7 specifications including outlet-specific trends and DMA x year-month FEs
R2	Diffusion driven by AP-sourced content, not original reporting; effect on original articles small and only marginally significant for print newspapers	Figure 5, Panel C, p. 815	AP-sourced: large, sharp post-ban decline; original articles: near zero; visual evidence complemented by online Appendix Table B2
R3	Left-leaning outlets show ~2x larger diffusion than right-leaning ones; significant in 3 of 4 ideology quartiles	Figure 6, Panel A, pp. 817-818	Most left-leaning (Q1): coefficient approximately twice as large as most right-leaning (Q4); declines significant for Q1-Q3; effect for Q4 near zero and insignificant
R4	Ban reduced support for border security by 0.8 p.p. per 1-SD AP intensity increase (intention-to-treat)	Table 4, col 1, p. 824	Reduced-form coef = -0.0051 (0.002); effect for 1-SD = -0.0083; mean dep. var. = 0.55; robust to county-specific trends, DMA x year FEs, and state x year FEs (cols 2-5)
R5	LATE (2SLS): 10 p.p. increase in “illegal immigrant” usage in local media associated with 4.5 p.p. more support for border security	Table 4, col 2, 2SLS panel, p. 824; text p. 826	2SLS coef = 0.0045 (0.002); first-stage F = 27.61; persuasion rate is 4.4% for the full sample and 1.9% for newspaper readers (equation 8, p. 830)
R6	Effects specific to immigration: null effect on index of 9 non-immigration CCES policy questions (falsification test)	Table 6, col 8, p. 828	Reduced-form coef = -0.0002 (0.001); 2SLS coef = 0.0002 (0.001); both statistically zero; covers abortion, gay marriage, healthcare, and economic policy questions
R7	Largest point estimate is for moderates; estimates are not statistically different from those for liberals or conservatives	Table 7, cols 4-6, p. 829	Moderates: -0.0071 (0.003); liberals: -0.0029 (0.003); conservatives: -0.0014 (0.002); p-value for equality of moderate vs extreme groups = 0.224
R8	Larger estimated effect in counties with fewer immigrants (low share of foreign-born); group difference is marginal	Table 7, cols 7-8, p. 829	Low-foreign-born (bottom quartile): -0.0087 (0.003); high-foreign-born (top quartile): -0.0001 (0.004); p-value for equality = 0.054
R9	The AP ban reduced use of the label in AP dispatches	Figure 2, pp. 808-809	Use fell from 50% before the ban to less than 5% after. AP-approved substitutes increased by 6 p.p., compared with a 47 p.p. decline in the banned term; dispatch volume using “immigrant” did not change
R10	The decline was only partly replaced by equivalent legal-status wording	Figure 3, Panel A, p. 810	Co-occurrence of “illegal” with “immigrant” fell from 0.66 to 0.21. “Illegally” increased by less than half that decline, and no other unigram compensated
R11	Other AP immigration coverage dimensions were largely unchanged	Figure 4, p. 811; text p. 819	Excluding the banned term and its substitutes, the AP slant index was flat. The sentiment shift was almost entirely due to removing “illegal”; no change in immigration coverage volume
R12	Reuters intensity predicts no comparable media-language change	Table 3, cols. 4 and 8, p. 817	Reuters placebo coefficients: 0.292 (0.311) for the full sample and 0.482 (0.416) for daily print newspapers; neither statistically significant
R13	Reader estimates differ in size, but not significantly	Table 7, cols. 1 and 3, p. 829	Nonreaders: -0.0009 (0.003); print readers: -0.0068 (0.003); equality p-value = 0.127
R14	Reuters intensity does not predict border-security views	Table 5, col. 4, p. 826	Reduced-form placebo coefficient = -0.0019 (0.0038), statistically insignificant
R15	The effect extends to other restrictive immigration policies	Table 6, cols. 2-7, p. 828	Reduced-form / 2SLS coefficients: employer sanctions -0.0062 (0.002) / 0.0051 (0.002); police questioning -0.0047 (0.001) / 0.0046 (0.002); prohibit services -0.0038 (0.002) / 0.0033 (0.002); legalization -0.0011 (0.001) / 0.0011 (0.001), not significant; all-policy index -0.0064 (0.002) / 0.0060 (0.002); consistent-question index -0.0059 (0.002) / 0.0056 (0.003)
R16	The announcement itself received little coverage	text p. 831	37 articles about the ban among approximately 1 million articles containing “immigrant”; article count correlation with AP intensity was close to zero
R17	Diffusion increases with baseline AP intensity	Figure 5, Panel B, p. 815; text p. 816	Relative to outlets with zero AP intensity, outlets in the top quartile of positive AP intensity reduced use of the term by 10 p.p.
R18	The estimated policy-view effects persist through 2020	Figure 7, p. 825; text p. 826	The dynamic estimates remain consistent with an effect through the 2020 survey wave; the text reports no single coefficient for this figure

Overall (paper’s conclusion). The AP ban on “illegal immigrant” propagated into local media language and shifted public opinion on immigration policy. The estimates are largest for moderates and in low-immigrant areas, consistent with a persuasion mechanism in which readers with weaker priors on the issue are more susceptible to media framing of immigration; the ideology-group estimates are not statistically different, and the immigrant-share difference is marginal. Effects do not transfer to other policies, which weighs against a general shift in political views.

Theory / model

The paper has no structural economic model. The empirical strategy rests on two design features and a tested set of hypotheses.

Hypotheses tested. The AP functions as a centralized editor of news language: its guidelines affect a shared input in the editorial production of thousands of member outlets. The ban provides an exogenous variation in that input, orthogonal to outlet-specific demand, for the following reasons (pp. 801-802): (i) the AP is a cooperative that balances the interests of politically diverse member outlets, so the decision to ban reflects organizational consistency policy rather than ideological pressure; (ii) the ban did not coincide with any change in immigration volume, slant (excluding the banned term), or sentiment in AP dispatches; (iii) Reuters, a competing news wire that did not change its guidelines, serves as a placebo.

The tested hypotheses are:

1. Diffusion: Outlets with higher pre-ban reliance on AP copy (AP intensity) will show larger post-ban declines in use of “illegal immigrant” (Sections I-II).
2. Persuasion: Exposure to the reduced use of the term through locally circulated newspapers will lower support for restrictive immigration policies (Section III).
3. Mechanism specificity: Effects should be specific to immigration (not other policies), and stronger for politically persuadable readers (moderates, low-prior areas), consistent with a persuasion rather than social-signaling mechanism (Section III.E).

Identification assumption. Support for restrictive immigration policies in counties with high versus low AP intensity would have followed parallel trends in the absence of the ban. The identifying variation is the county-level AP intensity of locally circulated newspapers interacted with the post-ban indicator, a shift-share design where PostBan is the aggregate shock and IHS(APintensity) is local exposure (p. 823).

Method

The analysis uses three estimators, all built on the DiD principle of comparing high-AP-intensity units to low-AP-intensity units before and after the ban.

Diffusion (Section II). Before the regressions, the paper uses Pearson’s chi-squared statistic to identify phrases predictive of pre- versus post-ban publication, equation (1), p. 809:

$$\chi^2_{pl} = \frac{\left(f_{pl,\text{before}}\tilde f_{pl,\text{after}} - \tilde f_{pl,\text{before}}f_{pl,\text{after}}\right)^2}{(f_{pl,\text{before}}+f_{pl,\text{after}})(f_{pl,\text{before}}+\tilde f_{pl,\text{before}})(f_{pl,\text{after}}+\tilde f_{pl,\text{after}})(\tilde f_{pl,\text{before}}+\tilde f_{pl,\text{after}})} \tag{1}$$

Here, $f_{pl,\text{before}}$ and $f_{pl,\text{after}}$ count occurrences of phrase $p$ of length $l$ before and after the ban, while tildes count all other phrases of that length. The outlet-level diffusion regression is the continuous-treatment difference-in-differences equation (2), p. 813:

$$\frac{\text{Illimm}}{\text{Imm}}_{mt} = \alpha_m + \beta_t + \rho \cdot \text{IHS}(\text{APintensity}_m) \times \text{PostBan}_t + \varepsilon_{mt} \tag{2}$$

where $\text{Illimm}/\text{Imm}_{mt}$ is the share of articles in outlet $m$ and month $t$ using “illegal immigrant” relative to “immigrant”; $\text{IHS}(\text{APintensity}_m)$ is the inverse hyperbolic sine of AP intensity (share of AP-sourced articles, measured in the 12 months before the ban); $\text{PostBan}_t$ is a dummy for April 2013 onward; $\alpha_m$ and $\beta_t$ are outlet and year-month fixed effects; standard errors are clustered at the outlet level. Observations are weighted by the number of articles using “immigrant.” The parameter $\rho$ estimates how much a 1-unit increase in IHS-transformed AP intensity shifts the share after the ban.

County-level AP intensity and outcome aggregation. Newspapers’ AP intensity is aggregated to the county level as the circulation-weighted average across newspapers serving county $c$ (equation 3, p. 820):

$$\text{APintensity}_c = \frac{\sum_m \left( \text{circ}_{mc} \times \text{APintensity}_m \right)}{\sum_m \text{circ}_{mc}} \tag{3}$$

The circulation-weighted share of “illegal immigrant” articles in county $c$ and year $y$ is equation (4), p. 820:

$$\text{Illimm/Imm}_{cy} = \frac{\sum_m \left(\text{circ}_{mcy} \times \text{Illimm/Imm}_{my}\right)}{\sum_m \text{circ}_{mcy}} \tag{4}$$

Reduced-form effect on views (Section III, equation 5). The intention-to-treat effect on immigration policy views $X_{icy}$ for respondent $i$ in county $c$ and survey year $y$ is estimated as:

$$X_{icy} = \alpha_c + \beta_y + \rho \cdot \text{IHS}(\text{APintensity}_c) \times \text{PostBan}_y + \varepsilon_{icy} \tag{5}$$

where $\alpha_c$ and $\beta_y$ are county and survey-year fixed effects; standard errors are clustered by county. Respondent characteristics (age, gender, education, race, immigration status, household income) are included as controls (p. 822).

Local average treatment effect (2SLS, equations 6-7). To estimate the LATE for readers of newspapers that changed their language solely because of the AP input, the paper uses a shift-share IV strategy. The second stage is (p. 822):

$$X_{icy} = \mu_c + \nu_y + \gamma \cdot \widehat{\text{Illimm}/\text{Imm}}_{mcy} + \eta_{icy} \tag{6}$$

instrumented by the first stage:

$$\text{Illimm}/\text{Imm}_{cy} = \alpha_c + \rho_y + \gamma \cdot \text{IHS}(\text{APintensity}_c) \times \text{PostBan}_y + \varepsilon_{cy} \tag{7}$$

The excluded instrument is the interaction of county AP intensity with PostBan. The first stage at the county x year level replicates the diffusion result: 1-SD higher AP intensity is associated with an 8% reduction in “illegal immigrant” use after the ban (p. 824). The second stage includes county and survey-year fixed effects, respondent controls, and standard errors clustered by county; the first stage uses county-year data from the same newspaper-circulation-linked sample (pp. 822-824).

Persuasion rate. Following Gentzkow and Shapiro (2010) and DellaVigna and Gentzkow (2010), the paper converts magnitudes into a persuasion rate: the share of exposed readers who changed their survey response because of the treatment (equation 8, p. 830):

$$f = \frac{db}{de} \cdot \frac{1}{1 - b_0} \tag{8}$$

where $b$ is support for restricting immigration, $e$ is exposure to “illegal immigrant,” and $b_0$ is the share who would oppose restrictive policy absent the treatment. Applied to the full-sample ITT with average newspaper readership, this yields a persuasion rate of 4.4%; the estimate for the sample of newspaper readers is 1.9%. For comparison, DellaVigna and Kaplan (2007) estimate a 12% persuasion rate for access to Fox News, and Chiang and Knight (2011) estimate 2% for expected and 6.5% for surprising newspaper endorsements. The estimates here are in the lower range, consistent with the milder nature of a language-change treatment.

Empirical specifications

All regressions use CCES data for 192,635 respondents across survey waves 2009-2017 (main sample) with additional 2007 and 2018-2020 waves for long-run trends.

Dynamic diffusion specification (Figure 5, Panel A, p. 815). The paper describes and estimates the lead-lag version of equation (2), with the half-year before the ban omitted; written out from Section II.B because the paper does not number or display the full equation:

$$\text{Illimm/Imm}_{mt} = \alpha_m + \beta_t + \sum_{k \neq -1} \rho_k \, \text{IHS}(\text{APintensity}_m) \times \text{Semester}_{kt} + \varepsilon_{mt}$$

Outlet and year-month fixed effects, outlet-clustered standard errors, and article-count weights are as in equation (2); the full outlet-month panel covers 2,385 outlets and 139,523 observations (Table 2, p. 814).

Diffusion regressions (R1-R3, Table 2 and Figures 5-6). Equation (2), estimated at the outlet x month level. The full sample has 2,385 outlets and 139,523 outlet-month observations. The subsample of daily print newspapers has 853 outlets. Columns in Table 2 (p. 814) progressively add: state x year-month FEs (col 2), outlet-specific linear trends (col 3), DMA x year-month FEs (col 6), and linear trends combined with DMA FEs (col 7). The coefficient on PostBan x IHS(AP-intensity) ranges from -1.3 to -1.8 across all seven columns. The key channel test (Figure 5, Panel C) splits the dependent variable into AP-sourced versus original articles; the sharp post-ban decline appears only for AP-sourced content.

Intention-to-treat regressions on immigration views (R4, Table 4). Equation (5), estimated at the respondent level. Main outcome is support for increasing border security (mean = 0.55). Columns in Table 4 add county characteristics x year (col 2), state x year FEs (col 3), DMA x year FEs (col 4), and county-specific linear trends (col 5). The reduced-form coefficient is stable at -0.004 to -0.006, corresponding to 0.77-0.92 p.p. per 1-SD AP intensity increase. The 2SLS estimates in Table 4 (bottom panel) yield a coefficient of 0.0045-0.0071 on the share of “illegal immigrant” articles (LATE), with first-stage F-statistics ranging from 10.5 to 29.8.

Other immigration policy outcomes (R6, Table 6). Equation (5) replicated for all five immigration policy questions available in pre- and post-ban waves, and for two aggregate indexes. Four of five individual questions show significant effects (border security, employer sanctions, police questioning, prohibiting services); legalization support shows no significant effect. The falsification test replicates the specification with a nine-item non-immigration policy index (column 8): coefficient -0.0002 (0.001) in the reduced form, 0.0002 (0.001) in 2SLS, both indistinguishable from zero.

Heterogeneity regressions (R7-R8, Table 7). Equation (5) estimated within subsamples split by self-reported newspaper readership (did not read, read any, read print), respondent ideology quartile (liberal/moderate/conservative), and county foreign-born population quartile. All specifications include respondent controls, baseline county characteristics x year, and county and survey-year FEs.

Reuters placebo. Tables 3 and 5 replace AP intensity with Reuters intensity (share of Reuters-credited articles per 10,000). Reuters did not change its guidelines on “illegal immigrant.” The PostBan x IHS(Reuters-intensity) coefficient is 0.292 (0.311) in the diffusion regression (Table 3, col 4) and -0.0019 (0.0038) in the views regression (Table 5, col 4), both statistically zero, supporting the identification assumption.

Datasets used

Dataset	Role in paper	Wiki page
Factiva (Dow Jones)	AP dispatches, June 2009-June 2017: ~28,000 dispatches using “immigrant” (8,000 using “illegal immigrant”); date, headline, full text	No page yet
NewsLibrary (NewsBank)	Media content: ~2,566 print and online outlets, July 2009-July 2017; article-level keyword search for “immigrant” and “illegal immigrant”	Historical newspaper archives (licensed)
ProQuest (Newsstream)	125 major newspapers missing from NewsLibrary, 2009-2017; complements NewsLibrary for diffusion analysis	Historical newspaper archives (licensed)
CCES (Cooperative Congressional Election Study)	Immigration policy views (5 questions) and other policy preferences across pre- and post-ban survey waves, 2006-2020; ~50,000-60,000 respondents per election-year wave	No page yet
Alliance for Audited Media (AAM)	Circulation by newspaper and ZIP code (Fall 2012 GEO/Circ report); used to aggregate AP intensity and outcomes to county level	No page yet
Gentzkow-Shapiro ideology index	Newspaper ideological lean for ~370 newspapers (based on congressional speech phrase scores, 2005 data); used for heterogeneity by outlet ideology	No page yet
American Community Survey	County-level demographics (share college educated, share foreign-born, log income per capita), 2012 five-year estimates	ACS
Editor and Publisher yearbook	Universe of daily print newspapers; baseline circulation for low-circulation outlets not in AAM	No page yet

Sample: AP dispatches June 2009 to June 2017. Media content July 2009 to July 2017. CCES survey waves 2006-2020 (election years 2008-2016 as main sample; 2018 and 2020 for long-run trends). County-level analysis covers ~2,300 counties matched to ~800 daily newspapers.

When to read the full paper

Use the original if you are: designing a research project on media language and political attitudes; replicating the diffusion or persuasion estimates (the Internet Appendix contains the plagiarism-detection algorithm, AP-intensity distribution, and additional robustness tables); studying the AP as a supply-side source of variation in media content; or comparing persuasion rates across media interventions (Tables 4-5 contain all the inputs for equation 8). The locators above point directly to the key tables and figures.

Attribution and rights

Source: peer-reviewed, American Economic Review 113(3). This distillation was extracted by an LLM on 2026-10-04 and verified against the source PDF on 2026-10-04; it has not been independently reproduced. No Creative Commons licence is asserted; the PDF is freely readable at aeaweb.org under AEA copyright. Redistribution of this distillation is extract-only.

Djourelova, Milena. “Persuasion through Slanted Language: Evidence from the Media Coverage of Immigration.” American Economic Review 113, no. 3 (March 2023): 800-835. DOI: 10.1257/aer.20211537.