Data-Driven Analysis

Explaining Conflict in Estonia Through Ethnic and Political Polarization

In my last blog post, I detailed my interest in explaining ethnic conflict in Estonia through a predictive political framework, provided context on Estonia’s repressed Russian minority, and illustrated the importance of researching East Central European affairs. The research paper I discussed in the last post was instrumental in developing my research project. This paper, written by Jeffry Kopstein and Jason Wittenberg (2010), examined the extent to which ethnically mixed communities in interwar Poland were more prone to pogroms than other communities that shared similar ethnic makeups.

According to the United States Holocaust Memorial Museum, pogroms were organized massacres that targeted specific groups of individuals, typically based on their religious and cultural background. Pogroms against Jewish people were physical manifestations of antisemitism, and occurred throughout East Central Europe and the Russian Empire in the nineteenth and twentieth centuries[1]. They were a significant barrier impeding integration of ethnic minorities in interwar Poland, and resulted in ethnic polarization and partisanship that prevented meaningful communication and interaction between different groups.

Kopstein and Wittenberg crafted their theory using Stathis Kalyvas’s specific definition of polarization, which Kalyvas operationalizes “as the sum of antagonisms between individuals belonging to a small number of groups that simultaneously display high internal homogeneity and high external heterogeneity[2]” . Using this concrete definition, Kopstein and Wittenberg argued that higher rates of polarization —  specifically, political polarization — in a community were positively correlated with that community’s eventual likeliness to suffer a pogrom.

In testing this hypothesis, Kopstein and Wittenberg found that political polarization along ethnic lines had statistically significant impact on predicting pogroms. However, they also found that voting patterns of ethnic minorities in interwar Poland were far more accurate in foreshadowing conflict than voting patterns of the community’s ethnic majority.

In many Polish communities at the time, Catholic Poles were the majority; Jews from Poland and elsewhere in East Central Europe constituted a sizable minority in these communities. During interwar Poland’s brief flirtation with democracy, several political parties emerged to suit the ideologies of the country’s diverse population. Two of these groups were the Bloc of National Minorities (“the Bloc”), which catered primarily to minority citizens, and the Polish National Democrats, who embodied the nationalist views of predominantly Catholic ethnic Poles.

Kopstein and Wittenberg tracked two voting statistics for each community in Poland. First, they determined the percentage of the ethnic majority, Catholic Poles, in the community that voted for the Polish National Democrats. Similarly, they calculated the portion of the community’s minority — in many case, Jewish Poles — that cast ballots in favor of the Bloc. They found that minority voting patterns were more correlated with pogrom outcomes than majority voting patterns were, indicating that political activity among ethnic minorities was more highly correlated to whether or not a pogrom took place than the political activity among Catholic Poles. Whether ethnic Poles voted and who they voted for mattered far less in determining whether a community would suffer a pogrom; the behaviors of ethnic and cultural minorities, including Jews, were far more predictive.

Even more troubling was Kopstein and Wittenberg’s discovery that pogroms were more prevalent in communities where ethnic minorities voted for the Bloc at higher rates. These findings suggest that violence against minority citizens may have been instigated by a perceived threat by ethnic Poles that their minority counterparts were organizing politically.

This research indicates that the ideologies, perspectives, and voting patterns of a community’s ethnic majority are not terribly effective in predicting the likelihood of conflict. Instead, Kopstein and Wittenberg’s paper provides compelling evidence that the voting patterns of minority individuals matter much more and communities with higher degrees of activist voting behavior among ethnic minorities may be the same communities that eventually suffer from violent backlash against those minorities.

I find this paper’s approach intriguing and sought to mimic its research design within my own research. Like interwar Poland, modern Estonia has an indigenous ethnic majority and a sizable ethnic minority — could ethnically-driven conflict between Estonians and Russians be better understood by examining the rate of minority political activity?

First, in line with Kopstein and Wittenburg, I categorized Estonia’s political parties into three groups. One group consisted of parties with largely mixed constituencies where, according to my prior research, ethnicity does not play a primary role in party identification. This cluster of parties was excluded from further analysis, as I am primarily interested in parties that explicitly reference ethnic affiliation in their electoral platforms or consist of disproportionate ethnic membership compared to Estonia’s population as a whole. The second group consisted of Estonian ethno-nationalist parties, the group with support from the ethnic majority. My third cluster was comprised of minority rights parties, which are primarily supported by ethnic Russians — this group most closely resembles the Bloc, which ethnic minorities in Poland supported.

While Kopstein and Wittenberg had extensive data on pogroms throughout Poland, I lacked sufficient data on the prevalence of similar hate crimes against ethnic Russians. How could I hope to mimic this research without having a reliable dependent variable to test voting patterns against?

I eventually decided that using countywide crime rates would be an adequate proxy for this missing dependent variable. Counties with higher rates of civil strife are very likely to be the same ones that suffer most from poor social cohesion. Communities with limited cohesion and unideal safety conditions make it easier for hateful or divisive rhetoric to fester, so using county-level crime rates is an accurate gauge of conflict frequency between different community groups.

Obtaining countywide crime rates was easily done through Estonia’s online statistical records — calculating vote percentages was also a simple process, and soon enough I had developed an Excel spreadsheet with requisite information to run a regression comparing minority voting patterns (denoted here as Percent_EthMinorityParties), majority voting patterns (Percent_EthMajorityParties) and my dependent variable, Crime_Rate. I used a broad crime rate statistic that merely encapsulated the number of recorded legal infractions and incidents per capita in each county in order to prevent incorporating any extraneous information into the calculations.

County Percent_EthMinorityParties Percent_EthMajorityParties Crime_Rate
Harju 30.5 7 0.028379
Hiiu 13.2 12.1 0.010604
Ida-Viru 59.2 3.3 0.042995
Jogeva 27.3 12.2 0.028144
Jarva 21.2 9.2 0.020957
Laane 17.2 15 0.021728
Laane-Viru 24.2 9.9 0.032809
Polva 26.6 8.9 0.029813
Parnu 23.6 19.7 0.029897
Rapla 16.5 10.8 0.022418
Saare 17.8 10.9 0.016464
Tartu 18.55 8.1 0.028155
Valga 26 10.2 0.029822
Viljandi 18.9 8.3 0.020294
Voru 20 11.6 0.027463

The regression output is below. Note that there are two regressions being completed, one for the 2011 Estonian parliamentary elections and another for the 2015 elections):

A screenshot of a computer

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Stata output for the 2011 parliamentary elections. Note that percent_russianparties — denoted as Per_EthMinorityParties on the previous table to signify its representation of minority voting patterns —  has a positive influence (and a statistically significant one at that) on crime rates. The positive slope of the coef. and a p-value below 0.05 suggests this result. It is also worth noting that the voting patterns of Estonia’s ethnic majority, encapsulated in percent_estonianparties, does not appear to have a statistically significant impact on crime rates.

Stata output for the 2015 parliamentary elections. Like the 2011 elections, voting patterns of Estonia’s ethnic minority — expressed as percent_russianparties in the Stata regression and as Per_EthMinorityParties in the county data spreadsheet —  appears to have a statistically significant impact on crime rates, whereas the voting patterns of Estonia’s ethnic majority do not.

Preliminary findings from these regression analyses support Kopstein and Wittenberg’s theory that the voting behaviors of ethnic minority citizens are better determinants of crimes against minorities than the civic patterns of a community’s ethnic majority. In my next blog post, I will include statistical analysis of Estonia’s recent 2019 parliamentary elections that occurred earlier this month, while also continuing to investigate how ethnic conflict can best be explained and predicted within this post-Soviet state.

[1]Holocaust Encyclopedia: Pogroms”, United States Holocaust Memorial Museum.

[2] Kalyvas, Stathis. (2006). The logic of violence in civil war. New York, NY: Cambridge University Press.

Data-Driven Analysis

Trends and Limits Regarding Research on Gender and International Relations


Research on gender and international relations has become more positivist since 2000.[1] That is, scholars in the field have become more likely to test hypotheses using rigorous procedures and empirical data rather than using their research to explore gendered international relations phenomena from a less standardized, more partial point of view. Still, the growing prevalence of positivism in gender-related IR research has received criticism. For example, J. Ann Tickner (2005) argues that while feminist IR scholars have not limited their work to a singular positivist or non-positivist research method; rather, they are united by a fundamental goal—questioning “androcentric or masculine biases in the way that knowledge has traditionally been constructed in all disciplines.”[2] While she does not outright reject all quantitative or positivist approaches to gender and IR, she posits that attempting to make generalizations using large data sets and conventional social science methods risks overlooking hidden gendered hierarchies and “the everyday lived experiences of women.”[3] Likewise, despite his support for positivist approaches to gender and IR, Reiter (2015) adds that nonpositivist research allows for the development of new theories and questions that can be tested and explored by positivist scholars.

This debate within the field merits scholarship. Politicians, the international community, and popular media outlets often oversimplify and essentialize women’s experiences, especially

those related to war and conflict. [4] Consequently, addressing this tension within the field and developing appropriate methodologies for studying topics such as female terrorists, the unique impact of intra- and interstate violence on women and girls, the diffusion of international norms such as gender mainstreaming, and the use of sexual and gender-based violence as weapons of war are all the more important. As such, this blog post will consider emerging trends and obstacles in recent research on United Nations Security Council Resolution 1325 (UNSCR 1325), Women, Peace and Security (2000), as well as the gendered consequences of post-conflict reconstruction and peacekeeping.

The Lack of Data on Gender, Post-Conflict Resolution, and Peacekeeping

One common complaint of those who focus on gender and peacekeeping is the lack of data related to their field. This data has both academic and practical uses: Not only does it allow researchers to test hypotheses and make generalizations using positivist methods, but it also improves peacekeeping strategies. By increasing the ratio of female to male peacekeepers in certain conflict-affected areas, peace may become more viable: Women are often seen as less threatening and are able to communicate with and search women for weapons in regions where conservative gender norms dominate.[5]

The general absence of data concerning the gender balance of peace talks and peacekeeping operations could arise from two causes. The first could simply be that UNSCR 1325 was not signed until 2000, and the pro-positivism wave in gender and IR theory did not arise until around the same time. As a result, international organizations and scholars have been collecting data on the topic for a relatively short period of time.

A second—and perhaps more likely—explanation for the absence of data on the subject is the masculine-oriented norms that drive post-conflict peace negotiations and the international community’s response to conflict. For instance, in her qualitative study of 10 Secretary-General Reports on peacekeeping operations in various war-torn countries, Puechguirbal (2010) finds that the UN provides little gender-disaggregated data on peacekeeping operations.[6] While some reports offered gender breakdowns of the ex-combatant population and new recruits to national police forces, these useful statistics were not provided for other categories of data, such as the number of internally displaced persons (IDP) and those receiving humanitarian assistance.[7]

Thus, Puechguirbal contests, the “strong masculine norm of reference” that has historically dominated peacekeeping operations continues to drive the absence of gender-disaggregated data.[8] Between 2000 and the publication of her article in 2010, the UN has adopted numerous resolutions addressing the importance of understanding the relationship between gender and conflict, two of which specifically call for increased data collection. The first, UNSCR 1325, “[notes] the need to consolidate data on the impact of armed conflict on women and girls (emphasis in original).”[9] The second, UNSCR 1889 (2009) “[requests] the Secretary-General to ensure that relevant United Nations bodies, in cooperation with Member States and civil society, collect data on, analyze and systematically assess particular needs of women and girls in post-conflict situations” and “[requests] the Secretary-General… to deliver… data on women’s participation in United Nations missions.”[10] Yet, Puechguirbal argues, decision-makers continue to deliberately disobey these calls for the collection of gender-disaggregated data in order to preserve the status quo. In other words, by refusing to evaluate the distinct effects of conflict or traditional structures and institutions on women, policymakers are able to avoid accountability for how their policies negatively impact their countries’ female populations.

Two other pertinent resolutions, Resolutions 1820 (2008) and 1888 (2009), were passed between 2000 and 2010. While they do not explicitly request any sort of data collection, they urge UN member states to recognize and address the use of sexual violence during wartime and its destabilizing effects on international peace and security. In fact, UNSCR 1820 calls for protection of women and girls from sexual violence “in and around UN managed refugee and internally displaced persons camps.”[11] However, as Puechguirbal demonstrates, UN peacekeeping reports often do not report the gender breakdown of IDPs, likely making it difficult for peacekeepers and aid workers to create and implement strategies to fulfill this goal. Together, these resolutions indicate that the international community has recognized the importance of empirical data collection in mainstreaming gender into peacekeeping. However, provisions for data collection are not followed by peacekeeping forces nor consistently included in all relevant resolutions.

The Need for Mixed Methods Approaches

Tickner (2005) argues that IR feminists prefer to study “individuals and the hierarchical social relations in which their lives are situated” in order to uncover details that would have been hidden by more traditional social science methodologies.[12] Research on gender and post-conflict resolution and peacekeeping often combines this innovative approach with other positivist and quantitative methods, applying a feminist point of view not only to the study of individuals but also to peace agreements and processes.[13]

One illustrative example of a mixed-methods approach is Ellerby’s (2013) study, “(En)gendered Security? The Complexities of Women’s Inclusion in Peace Processes,” where she evaluates 48 peace processes from 1990 to 2010 on the extent to which they adhere to UNSCR 1325.[14] She finds that while the number of peace processes that include provisions related to women has increased since the early 1990s, only five displayed “high levels of (en)gendered security.”[15] After completing her broader content analysis of the entire dataset, she focuses on comparing two similar Sudanese peace processes–the Comprehensive Peace Agreement (CPA) and the Darfur Peace Agreement (DPA). The first, she argues, has a low level of (en)gendered security because it lacked a “women’s agenda,” “political space” for women at negotiations, and a “gender-conscious process,” or the notion that negotiators viewed gender equality as a means to bolster their own goals.[16] In contrast, the DPA included far more provisions for women since female negotiators had observed the mistakes made during the CPA process. These women, supported by the African Union and UNIFEM, were able to both lobby male mediators and enter the negotiation processes themselves as mediators. Thus, by combining a content analysis with a comparative case study, Ellerby is able to highlight both general trends concerning the mainstreaming of gender into peacekeeping and post-conflict reconciliation as well as the causal mechanisms that explain why some peace processes are more gender-inclusive than others.

A comparable example of the utility of combining qualitative and quantitative methods appears in Anderson and Swiss’ (2014) article, “Peace Accords and the Adoption of Electoral Quotas for Women in the Developing World, 1990-2006.” While their methodology is largely quantitative, they include a discussion of two case studies—Burundi’s and Guatemala’s peace processes—to demonstrate how the mobilization of women’s groups during peace processes can leave lasting impacts on post-conflict governments. In the Burundian example, the authors note that although female negotiators at the formal peace talks called for a 30% gender electoral quota, only a 20% quota was included in the final peace agreement.[17] However, due to continued lobbying by women’s groups, 30% of ministerial positions and seats in the National Assembly were reserved for women in the 2004 interim constitution.

The Guatemalan example demonstrates a similar trend, as women participated in both formal and informal peace negotiations between 1990 and 1996 and pushed for gender equality provisions in the peace accords. Consequently, not only were “extensive provisions” included in the final peace agreements, but several parties later voluntarily adopted gender quotas as a result of activism by women’s groups.[18] As in Ellerby’s (2013) article, Anderson and Swiss’ inclusion of qualitative case studies allows for greater focus on women’s individual experiences within in patriarchal structures—in the Burundi case, women were initially barred from peace talks and had to protest for negotiating positions, and in the Guatemalan case, only two women were allowed into formal peace talks. At the same time, the qualitative aspects of the methodology bolster the quantitative portions of the article by illustrating the causal link between the level of activism by women’s groups and the likelihood that a post-conflict government will adopt a gender quota.


Qualitative research on gender, post-conflict resolution, and peacekeeping, both on its own and combined with quantitative analyses, is highly useful for understanding women’s experiences during post-conflict peace negotiations. Still, drawbacks to qualitative work exist. For instance, since qualitative methods are more difficult to replicate, it would be helpful for qualitative researchers in the feminist IR field to clearly explain their methodologies. Gumru and Fritz’s content analysis of eleven countries’ national action plans (NAPs) on gender exemplify this predicament. In their article, they identify 20 criteria for assessing and comparing NAPs adopted in response to UNSCR 1325, but do not specify how they created these criteria. Given that feminist IR theorists tend to use “methods not typical of IR,” it would be helpful for them to explain their approaches so that others in the general IR field could better understand them and perhaps even incorporate them into their own work.[19]

Ultimately, qualitative work provides starting points for positivist research and is useful in elucidating the relationship between independent and dependent variables. It also provides an alternative to data-driven, positivist work, which may be constrained by the general lack of data on the subject. Finally, it highlights women’s active roles as agents for peace, undermining gender-essentialist stereotypes of women as victims and mothers.



[1] Dan Reiter. “The Positivist Study of Gender and International Relations,” Journal of Conflict Resolution 59, no. 7 (2015): 1301-1326. doi: 10.1177/0022002714560351

[2] J. Ann Tickner, “What is Your Research Program? Some Feminist Answers to IR’s Methodological Questions,” International Studies Quarterly 49 (2004), 3.

[3] Ibid., 18.

[4] Laura J. Shepherd, Gender, violence and security: Discourse as practice (London: Zed Books Ltd., 2008); In my blog entitled, “Women, Peace, Security, and a Research Question” (August, 3, 2016), I discussed a CNN report arguing that ISIS was enticing women to join its ranks with images of kittens, Nutella, and emojis on social media sites.

[5] Sahana Dharmapuri, “Just Add Women and Stir?” Parameters (Spring 2011): 56-70.

[6] She studies reports from UN missions in the Democratic Republic of Congo, Haiti, Liberia, Timor-Leste, Darfur, Sudan, Nepal, Chad, Côte D’Ivoire, and Kosovo.

[7] Nadine Puerchguirbal, “Discourses on Gender, Patriarchy and Resolution 1325: A Textual Analysis of UN Documents,” International Peacekeeping 17, no. 2 (2010), 173.

[8] Ibid., 174.

[9] UNSCR 1325, 2.

[10] UNSCR 1889, 3, 5.


[11] UNSCR 1820, 4.

[12] Tickner, “What is Your Research Program?” 7.

[13] Christine Bell and Catherine O’Rourke, “Peace Agreements or Pieces of Paper? The Impact of UNSC Resolution 1325 on Peace Processes and Their Agreements,” International and Comparative Law Quarterly no. 59(2010): 941-980; Kara Ellerby, “(En)gendered Security? The Complexities of Women’s Inclusion in Peace Processes,” International Interactions no. 39(2013): 435–460; Lori Perkovich, “Empowering Women or Hollow Words? Gender References in Peace Agreements,” Journal of Political Inquiry at New York University, Spring 2015:111-123; Miriam J. Anderson and Liam Swiss, “Peace Accords and the Adoption of Electoral Quotas for Women in the Developing World, 1990–2006,” Politics & Gender 10(2014): 33-61; R. Charli Carpenter, “‘Women, Children and Other Vulnerable Groups’”: Gender, Strategic Frames and the Protection of Civilians as a Transnational Issue,” International Studies Quarterly 49, no. 2(2005): 295-334.

[14] For an explanation of the criteria Ellerby (2013) uses to assess her dataset, see page 443 of her article.

[15] Ellerby, “(En)gendered Security?” 436.

[16] Ibid., 453

[17] Arusha Peace and Reconciliation Agreement Agreement for Burundi 2000, Protocol II, Chapter II, Article 20.8

[18] Anderon and Swiss, “Peace Accords and the Adoption of Electoral Quotas,” 56.

[19] Tickner, “What is Your Research Program?” 7

Data-Driven Analysis

“Technology is Neither Good, Nor Bad; Nor is it Neutral:” The Case of Algorithmic Biasing

This post is one of two in a series about algorithmic biasing.

In his 1985 address as president of the Society for the History of Technology (SHOT), Melvin Kranzberg outlined “Kranzberg’s Laws,” a series of six truisms about the role of technology in society. Kranzberg’s First Law states: “Technology is neither good nor bad; nor is it neutral.”[1] The first clause of this proclamation challenges the tendency towards technological determinism, and Kranzberg rejected his colleagues’ reductionist assumption that technology determines cultural values and societal outcomes. The second clause acknowledges that technology can propagate disparate outcomes. When combined, the two clauses suggest that machines and programs are simply as impartial as the humans who create them.

Now, 30 years after Kranzberg’s address, technology commentators and social theorists are rediscovering Kranzberg’s First Law, particularly as it relates to the problem of algorithmic bias (a phenomenon that occurs when an algorithm reflects the biases of its creator). The Urban Institute recently released a report accusing the augmented-reality game Pokémon Go of inadvertent “Pokéstop redlining.”[2] Because the algorithm that the game uses to locate Pokéstops draws from a crowdsourced database of historical markers that are contributed disproportionately by young, white males, Pokéstops are overwhelmingly concentrated in majority-white neighborhoods.

Ingress Portals

Pokémon Go players living in majority-black neighborhoods took to Twitter to highlight this disparity:

Pokemon Go 1
Pokemon Go 2

Pokémon’s Go’s virtual place-making algorithm is just one example of purported algorithmic bias. For example, criminal justice scholars have begun to evaluate risk assessment scores, which algorithmically forecast defendants’ probability of repeat offense with increasing scrutiny. While states now allow judges to factor risk assessment scores into their sentencing decisions, there is a debate about whether or not the algorithms that generate these scores decrease or increase racial disparities in sentencing, compared to judges’ subjective decision-making. Arecent ProPublica report suggested that black defendants were 77 percent more likely to be assigned higher risk scores than white defendants by the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) algorithm, one of the most widely used risk assessment algorithms on the market, even after controlling for prior crimes, future recidivism, age, and gender.[3]follow-up Brookings commentary, however, rejects ProPublica’s conclusions on methodological grounds, indicating the necessity for further discussion.[4]

Proponents of algorithmic risk assessments argue that they serve as a useful tool to combat the implicit biases that factor into judges’ subjective decision-making. But that claim hold true only if the algorithms underlying risk assessment scores are not racially biased. If these algorithms are indeed biased against black defendants, the use of risk assessment scores in sentencing decisions could amplify racial disparities in sentencing black and white defendants.


[1] Melvin Kranzberg, “Technology and History: Kranzberg’s Laws,” Technology and Culture 27.3 (1986): 547.

[2] Shiva Kooragayala and Tanaya Srini,” Pokémon GO is changing how cities use public space, but could it be more inclusive?,”, August 2, 2016,

[3] Jeff Larson et al., “How We Analyzed the COMPAS Recidivism Algorithm,”, May 23, 2006,

[4] Jennifer L. Doleac and Megan Stevenson, “Are Criminal Risk Assessment Scores Racist?,”, August 22, 2016,  

Data-Driven Analysis

The Mysterious Case of the Missing Evangelicals

Evangelical voters are a monolithic group, right? Certainly, the word “evangelical” conjures up a very specific image in our mind – perhaps a WASP who regularly attends church and strongly objects to homosexuality and abortion. Ever since the 1980s, when Ronald Reagan positioned himself as a champion of Christianity, faith-based voters have dominated the GOP. Around this same time, groups such as Jerry Falwell’s Moral Majority arose, mobilizing millions of evangelicals into political action. More recently, it seemed that religious conservatives had once again found their voice in the Tea Party. In 2010, the Tea Partiers administered a stunning defeat to the Obama administration during the midterm elections, wiping out the majority in the House of Representatives that Democrats had maintained for half a century. Obama himself referred to the election as a “shellacking.” Although these religious conservatives were unable to unseat Obama in 2012, they turned out in sufficient numbers in 2014 to take the Senate as well as the House. Meanwhile, rising stars like Ted Cruz had made their mark, pushing the national party’s rhetoric further and further to the right.[1] The future looked bright for religious Republicans.

Now fast forward to the 2016 election. Enter Donald Trump. The current Republican frontrunner is neither the classic “establishment” frontrunner (who emphasizes tax cuts and small government) nor a prototypical Religious Right candidate (who focuses on social issues such as abortion, homosexuality, etc.) Instead, he is a man with a strange blend of nationalism and swagger, who definitely does not hew to the conservative orthodoxy. A former pro-choicer, Trump was a registered Democrat who in previous years actually supported Hillary Clinton.[2] Ordinarily, this sort of background would disqualify anybody from winning evangelical support. But of course, this is no ordinary election, and Trump is no ordinary candidate. Let’s take a look at the data to see what’s going on.

Candidate Poll

These are the results of a Pew Survey from this January. A few results jump out at us. Firstly, Trump is far and away the most likely to be seen as “Not at all” religious, which is frankly mind-boggling when you remember he is the GOP frontrunner. Now look at the two people above him – Bernie Sanders and Hillary Clinton. That’s right, the two Democratic candidates are actually considered to be more religious than the likely Republican nominee.[3]

Given this information, how do evangelicals actually feel about Trump? Well for starters, talking about “evangelicals” as one large group isn’t particularly helpful. Perhaps differentiating among levels of religiosity would be a good way to sort evangelicals into groups. Frequency of churchgoing is a decent stand-in for religious intensity, and the data show some fascinating results.[4]

Evangelcial Support

Ah-hah!” you exclaim. “Just look at the gap between Trump’s supporters and the Cruz/Carson people!” And indeed, there is a rather stunning gulf between the church attendance of Trump supporters and Tea Partiers. Among evangelicals who either never or seldom attend church, Trump’s support is over 50%. This plunges to about 35% support among those who fill the pews every Sunday. Clearly, even though all of these people self-identify as “evangelicals,” religious intensity is not particularly evident in Trump’s demographic. The larger point here is that it is misleading to think of evangelicals as a single voting bloc. Rather it is more useful to subdivide them in terms of religiosity, and then measure their voting preferences

In addition to religious intensity, infrequent churchgoers are also motivated by different issues – issues that aren’t associated with the classic “Christian Right” stereotype. Consider this data:

Evangelical Issues

Among infrequent attenders, “Moral and Cultural Issues” (a.k.a., the bastion of the Christian Right) are not particularly important. Indeed, their focus is on economic growth and jobs, far more so than their highly religious counterparts.

I believe that Donald Trump has shattered the paradigm of the “evangelical monolith.” Once upon a time, evangelicals were largely perceived to be a unitary voting bloc, but Trump has revealed the cracks in the façade. Trump’s message of economic growth and populism has captured the less religious wing of evangelical voters, while the more highly religious flock to Cruz’s message of social conservatism and open display of Christianity. As Marco Rubio might say, “We have to dispel this notion that all evangelicals vote uniformly.”


[1] Salam, Reihan. “Ted Cruz Is Stuck in the 1980s.” Slate Magazine.

[2] Gass, Nick. “Trump has spent years courting Hillary and other Dems.” Politico.

[3] Remember, we’re talking about the party of Mike Huckabee, who once wrote a book called God, Guns, Grits, and Gravy. You can’t make this stuff up.

[4] Perl, P. and Olson, D. V.A. (2000), Religious Market Share and Intensity of Church Involvement in Five Denominations. Journal for the Scientific Study of Religion, 39: 12–31. doi: 10.1111/0021-8294.00002



“Faith and the 2016 Campaign.” Pew Research Center. Pew: 27 Jan, 2016. Web. H

Gass, Nick. “Trump has spent years courting Hillary and other Dems.” Politico. Politico: 16 June 2016. Web.

Layman, Geoffrey. “Where is Trump’s evangelical base? Not in church.” Washington Post. Monkey Cage: 29 March 2016. Web.

Perl, P. and Olson, D. V.A. (2000), Religious Market Share and Intensity of Church Involvement in Five Denominations. Journal for the Scientific Study of Religion, 39: 12–31. D Doi: 10.1111/0021-8294.00002

Salam, Reihan. “Ted Cruz Is Stuck in the 1980s.” Slate Magazine. Slate: 4 March 2016. Web.

Zylstra, Sarah Eekhoff. “As Falwell Favors Trump, Pew Says Most Americans Still Want a R Religious President.” Christianity Today. Gleanings: 27 Jan, 2016. Web.