Introduction and Contribution

Democracies face a host of ongoing challenges, including the rise of elected autocrats, income inequality, mainstreamed forms of xenophobic nationalism, and political apathy. All of these challenges pose threats to democratic participation — elected autocrats restrict it, inequality makes it easier for oligarchs to sway election outcomes, and xenophobia can discourage cultural outsiders from voting.

Apart from practical threats to democratic participation, an established intellectual tradition has viewed participation with deep skepticism. In this view, democracy is good simply because it ensures peaceful transfers of power and protects individual rights. Collective decisions, however, cannot be meaningfully viewed as representing the “will of the people” owing to manipulation, apathy, and the high costs of acquiring political knowledge. Is this “realist” vision the best one can hope for in democratic life?

In “Can deliberation have lasting effects?,” James Fishkin, Valentin Bolotnyy, Joshua Lerner, Alice Siu, and Norman Bradburn show how a three-day deliberative experiment in late 2019 had large and long-term effects on turnout and voting behavior. Those most likely to exhibit these civic behaviors nearly a year later had come to follow politics more closely and see their political opinions as valuable. At the same time, the experiment’s effects on participants’ policy views were significant only in the short term — most deliberators eventually reverted to their previously held policy positions.

That three days of deliberation had such lasting civic effects suggests that efforts to create more inclusive forms of democratic participation are both possible and scalable. Moreover, it suggests that academic skepticism about democratic participation is not an argument against citizens’ capacities for reasonable decision-making; rather, it is an argument against our imperfect contexts of participation. Deliberative experiments may offer hope for improving these contexts.

The Deliberative Experiment and Its Effects on Policy Views

In September 2019 — one week prior to the experiment — a treatment group (i.e., those who would deliberate) of 523 registered voters from around the US and a control group of 844 voters were surveyed on their political attitudes. Members of the treatment group then deliberated on five issue domains (the economy, environment, immigration, health care, foreign policy) in small groups and on 47 policy proposals (e.g., redistributing wealth in some way). 26 of these proposals were characterized by extreme partisan polarization, meaning significant numbers of those who identified as Democrats or Republicans held the most extreme views. After the deliberations ended, both the treatment and control groups were surveyed. Then, three subsequent surveys were conducted in late 2020.

Among the treatment group, deliberation produced significant, short-term depolarization on 20 of the 26 (polarized) policy proposals. In other words, the averages for participants who identified with each party moved closer together (though not necessarily toward the center). These changes were large, sometimes 40 percentage points, as in the case of Republicans abandoning extreme positions on immigration. Meanwhile, the control group’s policy positions changed hardly at all — pointing to the key role played by deliberation. Within the five issue areas, averages among deliberators shifted leftward on all but the economy.
 

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Figure 1. Policy-Based Score (PBS) Changes over Time
Note: Policy-based score (PBS) is constructed for each individual based on responses to 26 questions identified as the most polarizing. The upper chart shows the participant group, and the lower chart shows the control group. T1 is the survey wave prior to the deliberations, T2 is right after the deliberations, and T3 is 10 months after, in July 2020.
 

By late 2020, however, the treatment group’s policy positions mostly reverted to their pre-deliberation levels. The differences between these two points in time were still significant compared with the control group, yet relatively small in absolute terms. These policy reversions are perhaps unsurprising: deliberators returned to an environment of heightened polarization and aggressive campaigning during the 2020 election cycle. (To be sure, and from the standpoint of finding solutions to collective problems, policy reversion is not especially concerning — the aim of deliberation is to bring citizens together to reason and compromise, which the experiment accomplished.)

A Civic Awakening?

The lack of long-term policy effects suggests that three-day deliberations may be limited in their ability to create a more encompassing, participatory society. However, the treatment group demonstrated large and persistent changes in their intention to vote (i.e., turnout) and their candidate of choice. Among the control group, Joe Biden was favored over Donald Trump by about four percentage points — very close to Biden’s actual margin in the popular vote. Among the treatment group, however, Biden was favored by 28 percentage points. The gaps in turnout were similarly large. (Note that these are intentions, not reports of actual decisions. However, Tables 6 and 7 in the article show similar effects for recollected votes after the election.)
 

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Table 2. Voting Intention for Participant and Control Groups, Time 4
 

These civic outcomes are especially surprising because (a) voting behavior is thought to be stable and deeply rooted in one’s psychology and social context, and (b) experimental efforts to increase turnout have been most successful when undertaken shortly before elections, as opposed to one whole year prior. The effects were most pronounced among political moderates and those without college degrees — perhaps pointing to the educative effects of deliberation.
 

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Figure 5. Effects on Vote Intention Captured by Predictive Modeling, by Education
Note: Middle are those participants who have Policy-Based Scores between 3 and 5 (inclusive) at Time 1. Non-middle participants are all other participants. Positive prediction error shows that, on average, participants were more likely to vote for Biden than predicted by the model. Vote intention data are collected at Time 4, in October, 2020. Full calibrated model used to construct this figure can be found in the APSR Dataverse.
 

Why did deliberation produce only short-term policy effects but long-term effects on voting behavior? The authors posit that deliberation caused an “awakening of civic capacities.” They reason that deliberation was a transformative experience in terms of stimulating political engagement and a sense of efficacy. And indeed, the treatment group was, in the long term, more likely than the control group to follow the 2020 election campaign, believe their political opinions mattered, and acquire general information about American politics. (The latter is measured in terms of knowing which party controlled the House and Senate.)
 

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Figure 9. Having “Political Opinions Worth Listening to”
Note: Policy-based score is constructed for each individual based on responses to 26 questions identified as the most polarizing. Responses to the question “How strongly would you disagree or agree with the following statement?”[I have opinions about politics that are worth listening to.] were collected at T1 (just before deliberations), T2 (just after), and T3 (10 months later, July 2020).
 

The authors close by discussing efforts to scale up civic engagement, such as the Stanford Online Deliberation Platform. In all, “Can deliberation have lasting effects?” provides a rigorous case for the value of deliberation in strengthening democratic participation.

*Brief prepared by Adam Fefer.

For over 25 years, Stanford’s Kimberly Babiarz has felt fiercely devoted to addressing the complex injustice of human trafficking, a crime she is quick to point out, “is everywhere.” 

Modern slavery, which includes sex and labor trafficking, forced labor, domestic servitude, debt bondage, and forced marriage, is “a big, systemic problem in need of big, systemic solutions,” she notes.

Those kinds of solutions typically require a diverse array of skillsets and experience – not to mention a great deal of dedication to the work, both of which Babiarz found when she and longtime collaborator Grant Miller, Henry J. Kaiser, Jr. Professor in the Department of Health Policy at Stanford School of Medicine, linked up with Jessie Brunner of Stanford’s Center for Human Rights and International Justice, Clinical Associate Professor of Pediatrics Vicki Ward, and Luis Assis, labor prosecutor and Chief Data Scientist at Brazil’s Federal Labor Prosecution Office.

The group coalesced around a shared sense of frustration at the lack of an evidence base undergirding decades of anti-trafficking interventions and the Stanford Human Trafficking Data Lab was born in 2019 with a mission to bring data and evidence to the fight against human trafficking.

“Few things give me more satisfaction than gathering folks with disparate backgrounds and skillsets towards achieving a common goal to advance human rights,” said Brunner. “Forming the Lab was a unique opportunity to leverage advances in data science and technological innovation to bear on one of the most grievous abuses of our time.”

Fairly quickly, the team identified a gap they could fill in and around the remote Amazon region in northeastern Brazil.

“The steady pace of deforestation and land conversion in the Amazon relies on the exploitation of people, including some children, who work strenuous 16-hour days, often without pay, burning trees at extremely high temperatures and converting them into charcoal,” explains Miller, the Lab’s Principal Investigator. “This all takes place at illegal work sites, where laborers live without adequate shelter or clean water, that are intentionally hidden from view in remote areas.”

Because these sites consist of an almost geometric line-up of dome-shaped kilns devoted to burning trees and an output of thick smoke, the researchers realized their footprints are uniquely visible from space. 

Using geospatial data and remote detection algorithms, the lab was able to pinpoint a clear way in which data science could be leveraged to help Brazilian prosecutors locate and investigate the stealth work sites where laborers are often exploited in conditions analogous to slavery. 

At a Stanford Impact Labs storytelling event in April, Babiarz vividly described the thrill of realizing that an algorithm could be put to work against this severe, yet frequently invisible, human rights issue plaguing Brazil. By building a data-driven tool to find and map illegal charcoal production sites, she described how her team helped law enforcement uncover nearly 200 previously unknown sites.

Most exciting of all, prosecutors were able to inspect 130 of those sites and carried out 10 times more task force raids than historically observed. Most importantly, the team observed a surge in the number of workers rescued from conditions of modern slavery, a more than 10-fold increase compared to typical years. The team is now rolling the tool out to every charcoal producing region in Brazil and exploring the tool’s applicability potential in other geographies and sectors. 

“We found a way to build a solution big enough to meet the scale of the problem in a corner of the world that really needs it,” Babiarz notes. "We also found a great deal of hope in being a part of this solution. By harnessing powerful technology in the context of a trusted partnership, we were able to deliver real impact to people in need of real help.”

The team's work in Brazil is far from over. 

With funding from Stanford Impact Labs, the Lab is expanding the impact of their work in Brazil’s charcoal sector, working with Brazil’s Federal Labor Prosecution Office to create a new data-driven technical tool to map exploitative supply chains. The tool will identify labor trafficking and illegal deforestation in distant tiers of the supply chain by integrating data from siloed publicly available administrative and legal records, making it easier for private sector steel manufacturers to address both trafficking and deforestation in their supply chains.

“The supply chain tracing tool is yet another example of the way our Lab is leveraging advances in data and social sciences to reach more trafficking victims and hold their exploiters accountable,” said Assis. “This has the potential to really change the game.”

This story was originally published by Stanford Impact Labs Director of Strategic Communications & Outreach Kate Green Tripp.