Introduction and Contribution:

Gossip — sharing information, both positive and negative, about absent third parties — plays a major role in social life. Two friends spread a rumor about their neighbor’s infidelity, an employee’s hard work is praised at a manager's meeting, and so on. 

From an evolutionary standpoint, however, the origins and proliferation of gossip are somewhat puzzling. Not only is it costly — acquiring personal information, sharing it with others, perhaps risking ostracism for being a yenta — but the evolutionary benefits are not clear. Indeed, gossiping may be entertaining, but it seems like a stretch to think it could improve human adaptation, let alone survival.

In “Explaining the evolution of gossip,” Xinyue Pan, Vincent Hsiao, Dana S. Naub, and Michele J. Gelfand conduct a series of computer simulations to model the gossiping process and shed light on the benefits it generates for both society and gossipers. The authors posit two such benefits that help explain its evolution: First, gossip spreads information about others’ reputations, helping people identify those who will (not) cooperate with them. Second, gossip reduces selfishness, as those who otherwise would not cooperate will choose to do so with gossipers in order to manage their reputations. These are called the “reputation dissemination” and “selfishness deterrence” functions, respectively.

Readers come away with a sense of the power of agent-based modeling to solidify our intuitions about complex social processes. Indeed, gossip involves dynamics of communication, cooperation, geographical proximity, as well as both personal and collective gain. Models help simplify these dynamics. In addition, the authors illustrate how an ostensibly non-strategic activity has very important strategic dynamics and implications. 

The Simulation Setup:

Agents in the simulation develop a strategy based on two decisions: whether or not to gossip and how exactly to cooperate with other agents. The former is a binary choice, whereas the latter permits six choices. Of these six, three are especially important: The “exploitive” agent is one who only chooses to cooperate if they believe their partner cannot easily be taken advantage of in the game. In other words, exploiters condition their choice on the other agent’s reputation. Next, the “virtuous” agent cooperates if it believes the other agent will and otherwise defects. Finally, the “opportunist” agent will only cooperate with gossipers and never with non-gossipers.
 

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Figure 2b. Plot (B) illustrates an agent’s action as a function of their own strategy and their belief about the interaction partner’s strategy.

The other three cooperation strategies are as follows: “unconditional defectors” and “unconditional cooperators” are, as the names imply, insensitive to information about others’ reputations. Meanwhile, “reverse opportunists” only cooperate with non-gossipers and never with gossipers. Perhaps unsurprisingly, the authors will show that agents tend to discard these three strategies over time. If gossip is, in fact, beneficial from an evolutionary standpoint, then both cooperative and gossiping strategies must increase over time.

To model the gossiping process requires that agents respond to new information and update their strategies accordingly. This will help them maximize their success and avoid being exploited by others. Information can thus be obtained in two ways: either agents observe each other’s behavior during the game and use it to infer their strategy, or information is disseminated to them via gossip.

The simulation proceeds in three steps. First, agents play a “cooperation game,” whereby each player simultaneously decides whether to cooperate or defect. (The rules are as follows for two agents, A and B: A incurs a cost of 1 for cooperating while B’s benefit is 3, and vice versa. A thus gains by cooperating, but is tempted to defect to gain 3 while B pays a cost of 1, and vice-versa.) The second step involves the dissemination of gossip. Finally, strategies are updated based on the first and second steps. 

Simulation Results:

The authors show that over time, the vast majority of agents (90%) choose to gossip and to cooperate (78%). The three most common cooperation strategies are “exploitive” (57%), “opportunistic” (18%), and “virtuous.” By contrast, none of the other three cooperation strategies is chosen more than 5% of the time.
 

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Figure 3. The evolutionary trajectories of different strategies and behaviors. The lines are average trajectories from 30 simulation runs with all the six cooperation strategies under the default parameter choice. The shadows show the SEs of the average trajectories. Plot (A) illustrates the evolution of gossipers. Plot (B) illustrates the evolution of different cooperation strategies. Plot (C) illustrates the evolution of cooperation. 

At first glance, the high prevalence of cooperation and exploitive/opportunistic strategies may appear puzzling. However, the growth of gossip helps guard against exploitation and opportunism. In other words, it remains strategically rational to exploit when feasible, but doing so simply becomes less feasible over time. 

One surprising finding is that gossipers and opportunists actually need each other. Opportunists cooperate with gossipers to protect their reputations, and in doing so give gossipers a real survival advantage over non-gossipers. As co-author Michele J. Gelfand said in a recent interview, “Opportunistic agents kind of get a bad rap. They’re seen as kind of sneaky…But in fact, they’re actually helping a lot in the population.”

The final aspect of the simulation involves showing that both functions of gossip, (1) disseminating reputations and (2) deterring selfishness, are jointly necessary to explain its evolution. To do this, the authors begin by analyzing solely function (1). Yet, this provides no direct benefit to gossipers: they spend time and energy disseminating information that helps others, but without deterring opportunists, who are not necessarily more likely to cooperate with them. In this situation, gossiping strategies will not proliferate. This is why function (2) must be part of the explanation. Gossipers directly benefit when opportunistic agents must manage their reputations by cooperating with them. In this way, they can deter the prospect of defection, which non-gossipers still face.
 

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Figure 4. Results of Steps 1 and 2. Each condition is the average of 30 simulation runs. The value is calculated as the average value from the 4,000th to the 5,000th iterations of each simulation run. The error bars show the SEs. On the Left side of each plot are the results from Step 1; on the Right are the results from Step 2. Plot (A) shows that gossipers evolve only when both reputation dissemination and selfishness deterrence functions exist (i.e., with-gossip and with-rep-manage). Plots (B and C) show that the existence of gossipers (yellow) increases reputation accessibility and cooperation. Plot (D) shows that opportunists evolve only when both reputation dissemination and selfishness deterrence functions exist. Plots (E and F) show that the existence of gossipers increases the proportion of reputation-sensitive agents.

In all, “Explaining the evolution of gossip” is an insightful exercise in “abductive reasoning.” No historical account could ever hope to conclusively show how gossip actually developed. Instead, the simulation shows readers how gossip could possibly have developed in order to provide us with better information and encourage cooperation.

*Brief prepared by Adam Fefer.

Introduction and Contribution:

Wars have wide-ranging effects on the political attitudes and behaviors of citizens and elites. For example, European leaders were pressured to make democratic reforms and build large welfare states during World War II in order to stabilize their countries and encourage wartime sacrifice. After the 9/11 attacks and the initial Afghanistan invasion, George W. Bush’s approval ratings soared to 90%.

The Russo-Ukrainian War has been one of the defining wars of this century. Its consequences for the politics of both countries — such as Ukrainian national unity or the repression of Russian dissidents — are beginning to be understood. Yet it is less clear how the war has affected third-party states. In light of Russia’s imperial ambitions, as well as Ukraine’s need for international support to combat a regional hegemon, this is a pressing issue.

Leaders of third-party states have responded to the war in a range of ways, including imposing sanctions on Russia and mediating between the two countries. Wars also affect how rulers communicate with their citizens: they may be motivated to emphasize their shared connections with either warring party, their need for national self-reliance in a hostile international system, and so on.

In “Between wars and words,” Ana Paula Pellegrino, Benjamin R. Burnley, and Laia Balcells show that leaders of third-party states increased their nationalist rhetoric on X (formerly known as Twitter) after the invasion. The authors analyze over 10,000 tweets from the heads of 130 states both before and after the invasion, mapping these along a nationalist-cosmopolitan spectrum. The effects of Russia’s invasion on nationalist tweets were strongest among North Atlantic Treaty Organization (NATO) members and weaker among members of the pro-Russia Collective Security Treaty Organization (CSTO).

“Between wars and words” provides evidence that leaders react to wars in similar ways as do masses, whose sense of national identity tends to increase during periods of global uncertainty and conflict. As politicians increasingly use X to communicate, a more precise understanding of their tweets before and after major conflicts may help social scientists better understand their beliefs and threat perceptions. 

Methodology:

The authors collect tweets from the 14 days before and after the invasion using an unsupervised learning algorithm called GloVe. This process is diagrammed in Fig. 1 below. GloVe identifies relationships between words by analyzing how often they appear together, grouping together words with similar meanings. This enables the authors to code each tweet according to its topic and sentiment (e.g., positive vs. negative).
 

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To code the level of nationalism versus cosmopolitanism, the authors employ the Concept Mover’s Distance (CMD) approach. CMD measures how closely the words in each tweet align with sets of ‘anchor terms’ representing each concept, placing tweets along a continuum between the two. Briefly, nationalism refers to a set of beliefs about the reality and value of nations, the obligations that members of a nation have to one another, and the right of that nation to determine its political affairs, whether as its own state or within an existing state. By contrast, cosmopolitanism is the view that all humans, irrespective of their national memberships, ought to be seen as part of a single world (kosmos) community. Returning to CMD, nationalist tweets are those that align with anchor terms such as ‘pride, glory, patriot, forefathers, homeland,’ and ‘heritage.’ Cosmopolitan anchors include ‘cooperation, humanity, multilateral,’ and ‘universal.’ 

Importantly, nationalistic tweets need not bear an obvious relationship to (inter)national security — the authors hypothesize that Russia’s invasion merely increased nationalist rhetoric, not rhetoric of a specific kind. For example, 10 days after the invasion, Bolivian President Luis Alberto Arce Catacora tweeted, “In 2019, the glorious Alteño people once again showed us their courage and love for our country.” Conversely, 12 days prior to the invasion, Argentine President Alberto Fernández espoused several cosmopolitan sentiments when he tweeted: “With the logic of multilateralism, Argentina has discussed with Russia the possibility of deepening financial assistance and increasing bilateral investment and trade between the two countries.” 

Findings:

The authors show that Russia’s invasion did significantly increase nationalist rhetoric by third-party heads of state, whether or not one controls for topic and sentiment. As an example of this, consider Austrian Chancellor Karl Nehammer: Three days prior to the invasion, he tweeted, “Austria continues to rely on diplomacy and de-escalation to prevent war. The OSCE…is the appropriate framework.” On the day of the invasion, by contrast, Nehammer tweeted “I promise that I will do everything to protect the people who live in Austria.” Other international leaders, such as Joe Biden and Justin Trudeau, tweeted out more cosmopolitan statements about the importance of collectively pressuring Putin and respecting international law.
 

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Why might an international war prompt the leaders of third-party states to use more nationalist rhetoric? The authors provide a number of hypotheses. For one, wars lead to feelings of uncertainty, and nationalist rhetoric by elites can afford citizens a sense of safety. Third parties might also make nationalist statements to signal their support of (or opposition to) one of the belligerent parties; for example, “our proud nation will not stand idly by as Russia attacks Ukraine.”

Although the invasion increased nationalist discourse on average, these effects are driven primarily by the behavior of NATO members. This is likely because the alliance has historically taken a strong stand against Russian aggression. As mentioned above, CSTO members did not tweet in a more nationalistic way, nor did leaders of states with histories of territorial armed conflicts resembling the Russo-Ukrainian War.
 

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*Brief prepared by Adam Fefer.

Introduction and Contribution:

The ongoing Russo-Ukrainian War has been one of the most devastating conflicts of the 21st century. Since Russia’s 2022 invasion, Ukraine has experienced not only mass casualties but immense cultural destruction, as well as the forcible deportation and adoption of thousands of Ukrainian children to Russian families. Ending the war requires understanding its causes, particularly from the point of view of Vladimir Putin and other key Russian decision-makers. 

Some observers of Russian and global politics — as well as Putin and his allies — have claimed that the prospect of Ukraine joining NATO caused the war. The argument here is that as a superpower, Russia could not tolerate the security implications of a country on its border joining a rival alliance. Russia’s war, then, was a preventive one — less a choice than a strategic necessity. Any superpower in such a situation would do the same.

In “NATO Did Not Cause Putin’s Imperial War,” James Goldgeier and Brian D. Taylor convincingly challenge the NATO hypothesis, showing it to be more a piece of Kremlin propaganda than a plausible account of Putin’s decision-making process. Instead, the authors draw our attention to Putin’s most deeply held and longstanding beliefs: that Ukraine is not a legitimate nation state, that Ukrainians would not freely associate with the West and its alliances (unless they were being manipulated), and that dominating Ukraine is essential to Russia reclaiming its status as a global superpower, one that is constantly disrespected by the West. 

As many social scientists focus on improving the causal power of their statistical inferences, Goldgeier and Taylor helpfully focus our attention on the beliefs and reasons of political actors who cause political outcomes such as wars and revolutions. More importantly, the authors provide a starting point for thinking about ending the Russo-Ukrainian war, one focused not on the distraction that is NATO arguments but on Putin’s imperial ambitions.

Pitfalls of the NATO Explanation:

The authors begin by noting that NATO enlargement clearly played a role in the deterioration of relations between Russia and the West over the past 25 years. In part, this is because many Russian elites — owing to their imperialistic beliefs, more on this below — never accepted that former Soviet Republics were free to join the alliance. However, NATO enlargement was but one item in a long list of Russian grievances, some based in reality and others fictional. These include the 2003-04 Color Revolutions — mainly reflecting widespread domestic sentiment, not Western machinations — and alleged American support for the 2011-13 Russian protests in the aftermath of Putin’s rigged elections, which were similarly homegrown.

There is good evidence that Putin and his inner circle neither feared NATO aggression nor believed that Ukraine could realistically join the alliance. After George W. Bush’s failed bid for Ukrainian membership in 2008, no American president has seriously entertained or pushed for Ukraine’s admission. NATO took minimal action after Russia’s 2014 invasion of Ukraine, before which time Ukrainians themselves didn’t support joining the alliance (likely because they anticipated the negative consequences for Russia-Ukraine relations). NATO itself has worked against admitting Ukraine; indeed, much of its security assistance has been designed to make it possible for Ukraine to defend itself without formal admission. What’s more, no country bordering Russia joined NATO after 2004 until Finland did so in 2023.

When Putin decided on war in 2021, his invasion plan was based on the assumption that victory would be quick and easy, evidencing his lack of concern for NATO intervention. Further, he knew that NATO lacked the troops and would be extremely wary of confronting nuclear Russia. 

Putin’s Imperial Beliefs and Goals:

For several decades, Putin has expressed the belief that Ukraine is not a genuine nation-state and that Russia both gave away and was “robbed” of much of its territory. One of Putin’s key goals is arguably to rebuild Russian greatness via imperial conquest. The West is not merely intervening in Eastern European politics but, according to Putin, actively working to downgrade Russia to a second-class country and undermine its sovereignty. Putin views the war as key to reversing Russia’s declining status.

Because Putin and his inner circle view Ukraine to be a natural part of Russia, the possibility that Ukrainians would freely tie their fortunes to the West is inconceivable — Ukrainian elites must have been tricked, co-opted, or bribed. Some Russian propagandists have even described the war as one of “Russians killing Russians.”

Putin’s imperialism is not only confined to privately held beliefs. During COVID-19, he spent a great deal of time reading historical texts to prepare a 5000-word article on the alleged historic inseparability of Russia and Ukraine. What could such an undertaking have to do with NATO expansion?

Russia’s wartime conduct also provides strong evidence for the imperialism explanation. As mentioned above, Russia has gone to great lengths to destroy Ukrainian culture. It has rejected multiple peace deals that would have prevented Ukraine from joining NATO. Putin saw these as failing to address the conflict’s “root causes,” arguably a euphemism for Ukrainian sovereignty. Instead, Russian conditions for peace include making Russian an official language, disbanding “nationalist” political parties, and ensuring the influence of Moscow’s Orthodox Church. These conditions smack of Russian chauvinism.

Of course, elites’ imperial beliefs do not necessarily lead to war. And indeed, Putin initially sought to control Ukraine through political measures, such as election interference. However, the authors argue that when President Volodymyr Zelenskyy seized the assets of a key Putin ally, Putin realized his position was weakening. Russian security officials then assured Putin — likely out of fear — that overthrowing Ukraine’s government would be easy. This flawed decision-making process led to war. Readers will come away struck by how many lives have been lost while policy and scholarly debates remained focused on the NATO hypothesis.

*Brief prepared by Adam Fefer.

Overview and Contribution:

The rule of law (RoL) is an important component of democracy, key to protecting individual rights and ensuring that representatives follow the same rules as those being represented. As countries become more democratic, one would expect corresponding increases in the rule of law.

In “Fabricated Justice,” Beatriz Magaloni and Esteban Salmón show how these expectations must be seriously qualified. Beginning in 2008, Mexico gradually implemented RoL reforms. Thereafter, citizens witnessed some important gains in due process and individual rights, in particular, a dramatic decline in torture. However, these changes coincided with rising insecurity, violence, and popular demands for retribution against criminals. Owing to these pressures — as well as their own desire to work with fewer constraints — police and prosecutors found ways to circumvent the new reforms, particularly by planting evidence (drugs and weapons) on suspects, a serious RoL violation. 

However laudable its reforms, Mexican authorities failed to equip justice system officials with the tools and capacities to properly fight crime. Facing similar social and professional pressures as they had prior to the reforms, fabricated evidence struck them as a reasonable adaptation to new procedures. 

Marshalling an impressive array of quantitative and qualitative data, Magaloni and Salmón show how these legal changes can be said to have led to changes in police tactics and in the categories of arrests made. Interviews with police and prosecutors make clear just how much RoL reforms have left justice system officials feeling impotent and compelled to “fabricate justice.”

Mexico’s (Staggered) Legal Changes:

Prior to 2008, Mexico’s legal system was an “inquisitorial” one inherited from Spanish colonial rule. This meant that judges largely based their rulings on an often-secretive case file assembled by police and prosecutors. Case files contained confessions frequently obtained by torture, which Mexico’s Supreme Court upheld on multiple occasions. After 2008, however, Mexico adopted an “adversarial” system with greater procedural oversight of detention and the early stages of investigation (when torture was more likely), stricter standards on the use of force and collection of evidence, and so on.

Importantly, Mexico’s RoL constitutional amendment set an 8-year period to fully implement the reforms. This led to a high degree of variation in when individual states adopted the reforms, as well as whether they adopted all of the reforms at once or in a piecemeal fashion. From a statistical point of view, this created a “quasi-experimental” scenario in which outcomes (e.g., whether prisoners reported being tortured) in “treated” states or municipalities (i.e., those that reformed) could be compared with “control” units that had not yet reformed. This helps ensure that other differences between states and municipalities (e.g., levels of economic development or state capacity) do not bias the results.

Quantitative and Qualitative Findings:

Magaloni and Salmón first draw on a 2021 survey of 60,000 prisoners conducted by Mexico’s National Institute of Statistics and Geography. The authors document (1) a substantial decline in reports of torture after 2014 (when many states and municipalities implemented the RoL reforms), (2) a rise in drug and weapons convictions by 2016 (likely the product of evidence fabrication), and (3) a decline in homicide convictions (because [a] homicide confessions could no longer be elicited through torture and [b] corpses are difficult to fabricate). These findings are largely borne out when the authors conduct their “difference in differences” analysis using the aforementioned geographical and temporal variation. As the authors show, declines in torture are likely driven by greater judicial oversight of cases, a key goal of the 2008 reforms.
 

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Fig. 1. Torture and objects (drugs and weapons).

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Fig. 4. Event study plots with imputation estimator: torture, objects, judicial oversight, and drug trafficking.
 

To show that these quantitative findings have some basis in the beliefs of criminal justice actors, the authors conducted extensive fieldwork across Mexico. This included interviewing over 100 police officers and prosecutors, observing the activities of investigative agencies for 18 months, and following dozens of cases from arrest to hearing. This generated some remarkably honest reflections about how arrests are systematically based on false accusations and the planting of evidence on suspects. 

Interviews with police reveal a widespread belief that the RoL reforms profoundly disrupted their work. To be sure, some of these “disruptions” simply concern how police can no longer torture suspects. For example, “With arrests, we used to investigate, we could pressure them, get information. Now we are just transporters. We catch them and deliver them. That’s all” (p.10, italics added). 

Another important aspect of these changes concerns just how much time it takes to complete arrest paperwork to meet new legal requirements. This highlights officers’ limited capacity to perform since the reforms were implemented. Many reported simply not making arrests, while others bluntly admitted:

Before, we pressured the person. Now we pressure the paperwork…chain of custody has to be perfect. If it’s not, the judge will throw it out. So…[w]e fix it. Sometimes that means planting what’s missing, sometimes writing what didn’t happen (p.10). 

Meanwhile, some prosecutors expressed nostalgia for the days when their authority was less constrained and, for example, they could raid homes without warrants. Prosecutors spoke openly about the strains on police capacity and the corresponding need for fabricated evidence: “If the police officers really investigated properly, they could get the criminals for what they actually did. They’ve just been instructed to take them out of circulation no matter what” (p.12). 

Finally, the authors show that evidence fabrication is consistent with the strong desire for retribution held by ordinary Mexicans. There is a widespread perception that the new criminal justice system is too lenient, a source of impunity for criminals. Accordingly, cases that prosecutors deem especially likely to anger the public are classified as “relevant,” compelling prosecutors to resolve them at all costs, especially by encouraging officers to plant evidence. Prosecutors who don’t accept these cases may be demoted or fired. In sum, Magaloni and Salmón deepen our understanding of just how difficult it is to democratize in places where criminal justice systems are poorly resourced and where citizens demand a specific kind of retributive justice that often sidesteps individual rights.

*Brief prepared by Adam Fefer.
 

Introduction and Contribution:

Most people move for economic or personal reasons, such as attending college, starting a new job, or being closer to family. Accordingly, residential moves are often beneficial for movers: they can improve life satisfaction, offer movers new economic opportunities, and increase long-term earnings. However, what is often little acknowledged is that moving can generate significant political costs: movers may have to learn about the political issues salient in their new place of residence while simultaneously facing the daunting task of settling into a new place of residence. It is because of these challenges that moving can change the extent to which someone engages in politics.

In “The local costs of moving,” Hans Lueders proposes that to understand how moving affects political engagement, we need to distinguish between national and local engagement. Studying political engagement among movers in Germany, he finds that German movers remain similarly engaged in national politics but become considerably less engaged in local politics. Lueders argues that national engagement is unlikely to change much after a move because the political context remains the same. Intuitively, the same political candidates run for national office no matter where one lives, and the country’s most pressing political issues remain the same as well. By contrast, the political context changes significantly when it comes to local engagement: living in a new place means that movers have less political knowledge (e.g., of local political candidates or salient issues), limited social networks to facilitate local engagement, and a weaker sense of civic duty to engage. Lueders finds no evidence that movers adopt new norms or political ideas — mainly because Germans tend to move to places that are socially and politically similar.

Lueders draws our attention to how moving — and the disengagement it generates — can undermine local democratic accountability. Indeed, when movers cannot communicate their preferences to local leaders, what follows is “representational inequality” between movers and “stayers.” That domestic migration can add or remove 5-10% of a county’s population over a decade, thus has serious consequences for the quality of democracy.

Importantly, Lueders broadens the geographic scope of research on political engagement. Social science research on moving has been heavily informed by data from the US, where “strict voter registration requirements…have been described as more costly than the act of voting itself.” Indeed, the US’s unique — and uniquely burdensome — voting regime has been shown to weaken both local and national engagement for movers. Lueders’s research suggests that these findings do not travel beyond the US. In Germany and much of the Western democratic world, movers are legally required to register their new address with local authorities, and are then automatically added to the electoral rolls. This removes a key barrier to political engagement, at least at the national level. American readers may rightfully ask which interests are advanced or undermined by the current status quo.

Engagement Before and After Moving:

Lueders introduces two competing accounts of how moving affects political engagement. On the first account, moving imposes serious epistemological and social costs: movers must learn new information about politics, form new social networks, and come to see themselves as members of a new community. Not only does all of this take time, but movers usually prioritize more urgent personal matters — e.g., finding housing or childcare — such that politics takes a back seat. 

Weakened social ties mean that movers interact less often with people who could inform them about local issues, candidates, or initiatives — which are hard enough for longtime residents to grasp. Members of social networks also enforce norms of participation on each other; movers who lack social ties will thus be more content to abstain from voting or volunteering for campaigns. It could be inferred from this account that the further away one moves, the less engaged one will be with one's new home: candidates and issues seem even more novel, while social networks become even more fractured.

A second account highlights how the context of a new place can change engagement, as movers are exposed to new political ideas or norms around participation. This may be because movers are persuaded to approach politics differently, or for more instrumental reasons (e.g., if one’s preferred party already wins by large margins in the new place, engagement will seem less pressing). The contextual account assumes that moving entails a big change in one’s political environment.

Methods and Findings:

Lueders uses German household panel data collected between 1984 and 2020, which totals over 500,000 “respondent-year observations.” By comparing how engagement varies over time between movers and stayers, he can home in on the changes in engagement caused by moving itself, accounting for any baseline differences caused by the kinds of people who choose to move or stay. National engagement is measured by self-reported levels of national political interest, whether respondents voted in the last national election, and whether they plan to vote in the upcoming one. Local engagement is measured by self-reported attachments to one’s place of residence, how frequently they participate in local political and citizen initiatives, and their frequency of volunteering in local associations and organizations. 

Lueders’ findings are consistent with the first account, in which local engagement declines due to lower-quality information and weaker social ties. He finds no evidence that Germans’ levels of national engagement change, regardless of the distance of one’s move.

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Figure 2. Changes in engagement around moves of varying distances. This figure explores whether movers’ engagement in national (top panel) and local engagement (bottom panel) changes around moves of varying distance. Each coefficient reflects the estimated change in engagement among movers compared to the baseline (all stayers plus movers six or more years before a move). Vertical bars are 95% confidence intervals. Data from the German Socio-Economic Panel (Goebel et al., 2019).
 

By contrast, the V-shaped patterns in the lower panel show that local engagement changes significantly, declining in the lead-up (around five years) before a move, reaching its lowest point in the year of a move, and then slowly returning to pre-move levels in subsequent years, but without fully recovering. Importantly, engagement declines with distance, as the most local moves (i.e., within the same town or county) leave engagement largely unchanged.
 

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Figure 1. Changes in engagement before and after a move. This figure explores whether movers’ engagement in national (top panel) and local engagement (bottom panel) changes around a move. Each coefficient reflects the extent to which movers depart from the overall trend in engagement in a particular year before or after their move. Vertical bars are 95% confidence intervals. Data from the German Socio-Economic Panel (Goebel et al., 2019).
 

Against the “contextual” account, Lueders finds that the majority of German moves occur over short distances, which makes it unlikely that contexts differ dramatically. And indeed, most of the places to which Germans move are sociopolitically similar (to where they left) in terms of levels of turnout and federal election outcomes.
 

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Figure 3. Most moves occur over short distances. This figure uses data on all cross-county moves in Germany in 2015 to compute various metrics of the distance of such moves. Left panel: distribution of the distance between origin and destination counties. Center panel: share of all moves from a particular county that go to neighboring counties. Right panel: share of all moves from a particular county that lead movers to other counties in the same state. Own calculations using data from FDZ der Statistischen Amter des Bundes und der Lander (2019). The vertical dashed line indicates the median move (left) or median county (center and right).

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Figure 4. Movers tend to move between politically similar environments. This figure reports the distribution of the change in environments that movers experience upon a move (dark blue). This distribution is contrasted with the distribution of change in environments one would expect when simply considering population totals between county pairs (grey). The vertical lines indicate the medians for the actual (dashed line) and benchmark (dotted line) distributions, respectively.
 

Ultimately, “The local costs of moving” underscores how highly individual life events can undermine the quality of collective governance.

*Brief prepared by Adam Fefer

Introduction and Contribution:

Addressing the climate crisis will require concerted action from political parties. Western countries arguably bear a greater responsibility to act given their greater levels of wealth. Some of this wealth has been accumulated at the expense of the countries most affected by climate change. Yet Western political parties vary widely in terms of their positions on environmental protection, particularly across Europe. Some parties conceive of climate action as a moral imperative, as a costly endeavor in which the government should not be involved, or even as a conspiracy to undermine national sovereignty.

Environmental party platforms would intuitively seem to align with familiar political “cleavages” — parties that support economic redistribution tend to favor climate action, while those resistant to social change tend to oppose it. But Europe is a continent with distinct regional and historical legacies. Indeed, some countries left the Communist bloc less than 35 years ago. All of this complicates simple inferences about party platforms and requires more thorough efforts to validate our intuitions. 

In “How green is my party?,” Anna Grzymala-Busse, Piotr Jabkowski, and Mariusz Baranowski assess the determinants of environmental platforms across 280 European parties in 38 countries. The authors find a significant relationship between support for climate action and three cleavages: the economy, cultural values, and populism. As one might expect, parties with right-wing economic positions and conservative cultural positions are less likely to support environmental protection. More surprisingly, both right- and left-wing populist parties are less likely to support climate action. However, these general associations vary considerably across regions, especially in Central and Eastern Europe (CEE).

The authors show that regional differences, especially between CEE and Northwestern or Southern Europe, persist and map onto climate politics. At the same time, and as populist parties in CEE such as Hungary’s Fidesz and Poland’s Law and Justice (PiS) gain power and promote climate skepticism, the reader gains a sense of why these parties depart from traditional understandings of European politics and ideology.

More generally, populist parties and movements have garnered huge followings in places as diverse as India, the United States, and Brazil. “How green is my party?” deepens our understanding of why these movements can be so unwilling to budge on addressing climate change. 

Data and Findings:

The authors use data from a 2019 questionnaire of nearly 1900 European party and election experts. The three cleavages mentioned above are measured on an 11-point scale, with higher values indicating more right-wing, conservative, and populist platforms. Countries are grouped into three regions: Northwest, South, and CEE. The South Caucasus and the closed autocracies of Russia and Belarus are excluded.
 

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Figure 1. European democratic countries covered by the Global Party Survey, 2019.
 

Two of the most interesting contributions in “How green is my party?” are to show that (a) populism is significant in shaping opposition to climate action and (b) CEE remains a distinctive region in respect of its climate politics. Why might this be the case? 

Regarding populism, one would expect left-wing populists — who denounce a wealthy elite as standing against “the people” — to support climate action. Indeed, this elite can be easily constructed as destroying the environment in order to accumulate wealth. However, the authors note that left populists tend to deemphasize environmental issues or reframe them as purely economic. For example, such parties have been skeptical of policies such as tax breaks for electric vehicle production, on the grounds that they primarily benefit wealthy corporations. 

More generally, populists tend to view appeals to scientific consensus with skepticism, as “technocratic” schemes to undermine the people. This likely explains why the authors find a strong association between populism and opposition to environmental action across all three European regions (Southern, Northwest, and CEE). Interestingly, right populists are not found to be particularly likely to oppose environmental protection. Left and right populists are also more likely to oppose climate action than social conservatives.
 

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Figure 3. Moderating effect of populism on the impact of party position on social conservatism-liberalism on the position on the environmental protection scale.
 

CEE is distinctive in part because right-wing populist parties have thrived there. Many of these parties view climate action as a foreign, leftist conspiracy. This has fueled skepticism and opposition to green agendas. Meanwhile, CEE’s reliance on fossil fuels has led parties to view climate action as a threat to economic growth — and thus as political suicide. For these reasons and because CEE states are relatively new, the region also lacks strong environmental civil society organizations or green parties. On the 11-point scale, median opposition to environmental action in CEE is about one point higher than in Southern Europe and nearly three points higher than in Northwestern Europe.
 

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Figure 2. Parties’ positions on environmental issues by region of Europe.
 

A third notable finding is that the intuitive link between economic and cultural values is weaker in CEE. In other words, opposition to environmental protection is only associated with the economic right in South and Northwest Europe. This is because populist parties in CEE tend to support both economic redistribution and conservative cultural values. Redistribution is framed as a means of protecting people from perceived threats to their way of life, such as immigration or social liberalism. By contrast, CEE social liberals tend to support the free market, a position owing to their negative experiences with communist central planning.

“How green is my party?” both accounts for the high degree of variation across European party platforms and identifies patterns and regional clusters to help readers sift through climate politics across the continent. It remains to be seen whether supranational institutions such as the EU can offset weak climate action by some European ruling parties.

*Brief prepared by Adam Fefer.

Introduction and Argument:

Many authoritarian countries hold elections where the incumbent might lose. The odds tend to be quite narrow, however, owing to the autocrat’s asymmetric control over economic resources, security forces, media, and so on. An important practical and theoretical question, then, is how the opposition can beat these narrow odds. 

Some scholars have argued that oppositions can defeat authoritarian incumbents by building broad, multiparty coalitions. Doing so should not only decrease the autocrat’s vote share but should also deter him from deploying state repression against the opposition’s supporters. Indeed, security forces will struggle or be hesitant to shoot at such large numbers of people, and doing so will likely attract international condemnation. All of this sounds intuitively plausible. 

In “When you come at the king,” Oren Samet shows how arguments for building big coalitions overlook a crucial possibility: If the opposition unites and performs well but still fails to defeat the autocrat, he may be “spooked” and react by doubling down on repression. This is because elections provide the government with information about its own and the opposition’s popularity. Too much opposition success further decreases the autocrat’s willingness to tolerate popular elections and freedoms. Therefore, the same strategy enabling oppositions to achieve a “stunning election” can — if the coalition fails to take power — lead to a “nearly stunning” election that further entrenches authoritarianism. Hence the paper’s title, a quote from The Wire’s Omar Little: when “you come at the king, you best not miss.” 

The paper provides both cross-national data and an in-depth case study of Cambodia to show how the logic of nearly stunning elections poses a serious dilemma for democracy promoters: When oppositions cannot defeat autocrats, then they must achieve a “sweet spot” of neither too many votes (which scares the incumbent into autocratizing) nor too few (which fails to threaten the incumbent and compel him to make democratic concessions). Yet deliberately planning to hit this sweet spot is simply not possible. Samet thus offers an important challenge to the claim that bigger is better in authoritarian elections.

Cross-National Findings:

Samet’s argument about the pitfalls of nearly stunning elections implies three hypotheses. First, and as previous scholarship suggests, coalitions should outperform individual opposition parties in authoritarian elections. Second, absent the incumbent’s defeat, autocratization is more likely as the opposition’s vote share increases. And third, absent the incumbent’s defeat, countries with high-performing oppositions should witness (a) an increase in state repression and (b) decreases in the quality of elections in the years following a nearly stunning election. Samet then analyzes all elections from 1990 to 2022 in cases where the same authoritarian leader or party had ruled for at least 10 years. This yields 286 elections: 58 (20%) featured coalitions, and 28 (10%) featured electoral turnovers. These numbers alone paint a bleak picture of the prospects for beating dictators. 

The statistical results broadly support Samet’s hypotheses. Coalitions do in fact perform better at the ballot box, winning a median vote share of 36% (compared to just 13% for individual parties). In addition, and consistent with the idea that hitting the “sweet spot” will encourage autocrats to make concessions, Samet finds a positive association between moderately strong opposition performance and democratic change. Importantly, however, levels of democracy decline sharply as the opposition vote share approaches 50%. Nearly stunning elections thus appear to provoke autocratization, both in the short- and medium-term. Finally, the relationship between nearly stunning elections and repression or electoral fraud is somewhat weaker. This may be because the autocrat has more than just these two tools at his disposal — he might limit the number of seats that can be contested in future elections, prevent the opposition from accessing state media, and so on.
 

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FIGURE 2: Opposition performance in authoritarian elections. Note: Density plots display the frequency of specific opposition (opp.) vote shares (left) and vote margins (right) broken down by coalition status.

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FIGURE 3: Opposition performance and electoral democracy change. Note: Displays elections that did not feature turnovers, plotted along two dimensions: opposition vote share and change in electoral democracy. ∙ (dot) denotes election with coalitions; × denotes election without. The gray lines plot LOESS regressions fit to the data, with gray shading indicating 95% confidence intervals.

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FIGURE 6: Predicted change in repression and electoral manipulation, with controls. Note: Displays predicted post-election changes in (1) V-Dem measure of electoral irregularities, comparing election at t with election at t + 1; (2) V-Dem measure of government intimidation of opposition, comparing election at t with election at t + 1; (3) Fariss (2014) physical integrity rights measure 3 years after election, using the lagged score as a baseline. Includes all elections that did not result in turnovers. Dotted lines indicate 95% confidence intervals.
 

The Cambodia Case:

Samet concludes by showing how his theoretical process — oppositions uniting, nearly winning an election, frightening the incumbent, then increasing authoritarianism — is borne out in Cambodia’s recent political history. Throughout the 2000s, Cambodia’s opposition was fragmented, in part due to deliberate actions by its authoritarian Prime Minister, Hun Sen. Ahead of the 2013 elections, in the face of mounting popular dissatisfaction with the government, the two largest opposition parties coalesced. Hun Sen was confident in his position, in part because Cambodia’s strongest opposition party had won just 22% of the vote in 2008. As such, he pardoned Sam Rainsy, one of the country’s most prominent opposition leaders, whom he invited to return from exile. 

The coalition did remarkably well in 2013, winning around 45% of the vote, but then alleged fraud and refused to take their seats in parliament. Opposition supporters then took to the streets in protest, where they were met with state violence. Yet Hun Sen made a number of concessions to successfully quell the protest crisis, including reforming the election commission.

By 2015, however, signs of autocratization became glaring. Opposition lawmakers were publicly beaten by the personal bodyguards of Hun Sen, who withdrew his prior pardon of Rainsy. Other opposition leaders faced politically motivated legal cases. Ahead of the 2018 elections, Hun Sen’s government hired an external survey firm, which found the opposition had become even more popular among Cambodians since 2013. Hun Sen’s fears were aggravated by a strong opposition performance in the 2017 local elections. 

Samet argues that this was the last straw: the government responded by promptly arresting and exiling opposition leaders and dissolving their coalition. All of this constituted the most dramatic instance of autocratization in Cambodia since the 1990s. Hun Sen’s allies then ran unopposed in the 2018 elections. By this time, the opposition was once again divided — particularly as regards how to face a government whose elections could barely be characterized as anything other than window-dressing. “When you come at the king” offers an important if distressing lesson for practitioners and scholars of democracy.

*Brief prepared by Adam Fefer

Introduction and Motivation:

Social scientists and philosophers have shown increased interest in the concept of descriptive representation. Indeed, the identities and life experiences of those doing the representing may be critical for enacting the preferences of those being represented. This is especially the case for historically marginalized groups — upper-class, educated men may simply fail to adequately represent lower-class, uneducated, or women voters. In addition to considerations of fairness, there is growing recognition that descriptive representation can improve policy outcomes, such as service delivery or trust in government.

However, the study of descriptive representation has been hampered by data availability. Identifying simple correlations across the world’s democracies — for example, between proportions of working-class legislators and levels of social welfare provision — has hitherto been impossible.

In “The Global Legislators Database,” Nicholas Carnes, Joshua Ferrer, Miriam Golden, Esme Lillywhite, Noam Lupu, and Eugenia Nazrullaeva introduce the largest dataset of biographic and demographic information on national legislators ever assembled. GLD will enable scholars to assess just how much voters elect those with life experiences resembling their own. The authors compile information on five descriptive variables across 97 democracies: legislators’ party affiliation, gender, age, highest level of education, and previous occupation (to assess their social class). By contrast, prior datasets have focused only on heads of state or cabinet members, or on only a selection of more developed democracies.

Some questions around descriptive representation would seem to have intuitive answers: Wouldn’t developed countries have more women representatives, or wouldn’t women legislators feature less prominently in right-wing parties? Scholars can now hope to do more than merely gesture at answers.

Characteristics, Validity Checks, and Applications:

GLD comprises countries that (a) have a population of over 300,000 and (b) meet the standard for what Freedom House calls “Electoral Democracy” — having some minimum of political rights and civil liberties. Excluding six cases of data availability constraints, this yields 97 countries, including India, the United States, Brazil, Mexico, Nigeria, and the Philippines. Scholars of Pakistan, Bangladesh, or Turkey — who would likely characterize these countries as authoritarian during the 2015-17 time period — will be pleased that the authors chose a more forgiving measure of democracy.

Biographic data is drawn from the national legislature in unicameral countries and the lower chamber in bicameral countries. (By contrast, upper chambers are sometimes indirectly appointed or hereditary, which sheds less light on whether voters choose descriptively similar representatives.) This yields over 19,000 individuals who held office during at least one legislative session in 2015, 2016, and 2017. GLD has remarkably complete data for the five variables mentioned above: age and education data are presented for over 90% of legislators in the dataset, for over 93% as regards occupation, and nearly 100% for gender.

In order to assess GLD’s validity, the authors compare select variables to those in comparable datasets. For example, the gender variable is compared with gender data from the Varieties of Democracy (V-Dem) project, which shows that the two measures are nearly identical. So too is the age data nearly identical to an index from 15 affluent democracies.
 

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Figure 1. Shares of women legislators in the GLD and V-Dem. Note: Bahamas, Belize, Fiji, and Kosovo are omitted because of missing data in the V-Dem.
 

For categories like education and prior occupation — where comparable data are unavailable — the authors conduct “face validity” tests: these draw on our intuitions that legislators are, for example, mostly educated and not working class. And indeed, these intuitions are borne out in the distributions of GLD data. In terms of total coverage, GLD includes information on more legislators than the comparable Global Leadership Project database for all but two countries, and in many cases, the differences are large.
 

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Figure 3. Distributions of legislator traits in the GLD. Note: Age is calculated at the time of election. Higher education includes levels beyond primary and secondary education (Bachelors, Masters, PhD, LLB, LLM, JD, MD, and short-cycle tertiary). Data on educational attainments for legislators is unavailable for Côte d’Ivoire.
 

The authors then use GLD in application to a number of questions for which scholars have lacked global data. First, some have hypothesized that in legislatures with more (a) uneducated, (b) female, and (c) working-class representatives, incumbency rates will be lower. This is because individuals from these three groups might have a harder time overcoming challenges relating to expertise, sexism, and fundraising, respectively. Correlating GLD data with a global reelection database, the authors find only evidence for (b), suggesting that women may face higher barriers to remaining in office. These are only correlations, but they point to fruitful areas for exploration: why might women face unique barriers, and what distinguishes countries with lower versus higher barriers?
 

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Figure 5. Re-election rates by years of education, gender, and occupational background. Note: The share of working-class legislators is zero for six countries that are dropped from the figure: Albania, Botswana, Cyprus, Estonia, Guatemala, and Mongolia.
 

A second application involves public financing of elections, which is thought to favor more working-class legislators who would have a harder time fundraising. Correlating V-Dem data on public financing with the GLD variable on prior occupation, however, the authors find limited evidence for this conjecture. Finally, some have proposed that countries with a stronger rule of law would favor a higher proportion of lawyers in the national legislature. Looking again at GLD prior occupation data alongside V-Dem rule of law data, the authors find limited evidence for this hypothesis.

These varied applications point to how the Global Legislators Database can serve as a valuable resource for scholars interested in the causes and consequences of descriptive representation. Although the GLD covers only a single point in time, it can serve as a bedrock for additional data-collection efforts. In addition to expanding its temporal coverage, scholars may also wish to gather data on upper chambers. Especially in ethnically diverse countries like Bosnia and Herzegovina, upper chambers are intended to mirror the descriptive composition of specific regions. However, it may be the case that ethnic representatives are still vastly more wealthy or educated than their constituents, thus impeding their ability to represent.

*Brief prepared by Adam Fefer.

Introduction & Contribution:

The social and economic costs of climate change are significant, including damage to infrastructure, loss of agriculture, and disruptions to education. Hurricanes and storms, such as Hurricane Katrina or Myanmar’s Cyclone Nargis, are particularly visible and destructive manifestations of climate change. The incidence of these storms varies across places, suggesting that migration from more- to less-exposed areas could be an important form of climate adaptation, alongside, e.g., building more resilient infrastructure. However, our knowledge of climate migration, particularly its causes and frequency, is limited.

In “Understanding the migratory responses to hurricanes and tropical storms in the USA,” A. Patrick Behrer and Valentin Bolotnyy show — perhaps contrary to expectations — that Americans’ migratory response to storms is limited. Most storms do not result in meaningful out-migration from impacted counties. Meanwhile, when people do migrate, they do not necessarily move to areas with less storm exposure. The paper draws on a range of data sources to highlight the deeply economic drivers of migration, which stem from the concentration of economic opportunity in storm-exposed areas.

The paper highlights tensions between two commonplace assumptions: first, that “rational” migration should reduce the risks of climate change, and second, that migration is driven by economic opportunity. These assumptions are in tension precisely because, as Behrer and Bolotnyy show, hurricane risk and economic opportunity are highly correlated in America. One policy implication is that local governments must invest in storm-resilient infrastructure to prevent the destruction of physical capital and the flight of human capital. In addition, permitting more remote work could reduce the economic appeal of productive but vulnerable migration hubs.

Prior Research:

Scholars have found evidence that hurricanes and storms both do and do not affect migration, which tends to vary based on the places studied and their levels of economic development. These contradictory findings would seem to call for a deeper investigation of the causal mechanisms underlying climate migration, but our understanding is also limited here. Do individuals and families migrate as a consequence of long-term factors (e.g., frequent, medium-intensity flooding) or short-term ones (e.g., a single severe flood)? Do they migrate on the basis of rational, cost-minimizing calculations, or are they influenced by cognitive biases that lead them to overestimate the true costs of one disaster? And what role do certain amenities (e.g., reliable infrastructure) or forms of protective insurance play in decreasing the incentives to migrate?

It is difficult to sustain a purely instrumental account of migration, which is largely driven by existing social networks and occurs over short distances. For example, many survivors of Hurricane Katrina moved to Houston, which is a similarly exposed city just over 300 miles away. Even long-distance migration tends to be driven by social networks and may offer little protection against storms. Finally, migration is costly, not only in terms of moving but because housing prices in less-exposed areas are often bid up for that very reason.

Data, Methods, and Results:

Behrer and Bolotnyy’s empirical analysis is guided by several questions. First, do we observe greater outmigration after storms? Second, do migrants move to less at-risk counties? And finally, has the overall population of high-risk areas declined over the last 25 years? To answer these questions, the authors utilize migration data from the Internal Revenue Service (IRS) as well as storm exposure data from the National Oceanic and Atmospheric Administration (NOAA), the National Hurricane Center, and the Federal Emergency Management Agency (FEMA). Their regression models estimate the extent of migratory change in storm years relative to non-storm years, including lagged models that estimate changes in the years following storms.
 

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Fig. 1C-F: c,d, Coefficients from a panel fixed-effects regression of outmigration (c) and net migration (d) on whether a county experienced a storm. The first bar plots the coefficient from a regression with only contemporaneous storms. The next six bars show coefficients from a separate regression that includes contemporaneous storms and five year lags (L1–L5). The final bar shows the sum of the coefficients from the lags regression. The light grey lines show the 95% CIs. The sample size for these regressions was 52,514 for the outmigration results and 52,448 for the net migration results. e, Migrant-receiving counties in our sample period and the average number of migrants received in non-storm years. f, The same as e but in storm years.
 

Their results indicate that American outmigration has not increased at statistically significant levels after storms. In addition, there is no evidence that migrants in storm years move to less exposed areas compared to migrants in non-storm years. The most damaging storms are indeed followed by increased outmigration, but there is no evidence that migrants move to low-risk areas. In fact, they often migrate to other high-risk areas and to places with high economic activity. This is because the majority of American GDP is generated in coastal areas where storms are more prevalent. The authors thus uncover a tradeoff, namely that places in the U.S. with more opportunity face more risk. GDP is substantially more predictive of migration than storm risk. The economic and social benefits of moving to high-risk areas appear to outweigh any incentives to reduce one’s storm exposure via relocation. Finally, the authors find that overall population exposure to storms has increased.
 

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Fig. 4: GDP versus net migration and number of storms. a, Correlation between net migration and GDP. The Z-score of total net migration is the Z-score across all counties of the sum of net migration (in-migration minus outmigration) for each county across all years in the sample. The Z-score of GDP is based on county GDP in 2019, as measured by the Bureau of Economic Analysis. All points are shaded equally, with darker areas on the graph indicating a greater density of counties. We omitted three outliers with GDP Z-scores >10. We show a version of this figure that includes the outliers in Supplementary Fig. 3. b, Correlation between the number of storms and GDP. Total storms is the sum of storms hitting each county across all years in our sample. ln(2019 GDP) is the natural log of county GDP in 2019, as measured by the Bureau of Economic Analysis. All points are shaded equally, with darker areas on the graph indicating a greater density of counties. The x-axis units are log points.
 

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Fig. 3: Trend in population-weighted exposure and correlation between net migration and total storms. a, Trend in population-weighted exposure. We plotted the weighted average number of storms across all 2,387 counties in our sample. Weights are the county population in each year. The number of storms in each county is the sum over the sample and so remains constant across years. The change in the trend line is due to changes in where people live. The flat grey lines show the weighted average if populations had not changed from 1990 levels—that is, if no one had moved. The solid lines show all storms. The dashed lines show storms with at least US$10 million in damages according to FEMA. b, Correlation between net migration and total storms. The Z-score of total net migration is the Z-score across all counties of the sum of net migration (in-migration minus outmigration) in the county across all years in the sample. The Z-score of total storms is the Z-score across all counties of all storms over our sample period. All points are shaded equally; darker areas on the graph indicate a greater density of counties. The dashed line is the linear best fit line of the plotted data points.
 

The authors caution that these findings may be driven by (a) those Americans most impacted by storms being least able to move, this despite their preferences to do so, and (b) those with the means to insure themselves against climate risks having weaker preferences to move. In addition, migration within the same county — for example, moving from lower to higher sea level areas — may be a significant but hidden process that enables climate adaptation. The findings may also be less relevant to understanding migration dynamics in low- and middle-income countries, especially in places with less comprehensive insurance and less resilient infrastructure.

Behrer and Bolotnyy deepen our understanding of the importance and “stickiness” of geography. Indeed, many people do not or cannot move, even if they want to and even if staying in place puts them at risk. One wonders about how these processes interact with politics. For example, climate change has coincided with the powerful forces of climate change denial. Perhaps skepticism about storms as systemic phenomena is blunting migratory pressures, leading those affected to view them as one-off occurrences. Similarly, social scientists have coined the term “petro-masculinity” to describe an identity that views the climate change consensus as an attack on, e.g., driving large trucks or eating meat. It may be that when this identity is salient, people view climate migration as a form of weakness or betrayal.

*Brief prepared by Adam Fefer.

Introduction & Contribution:

Employment contracts vary widely, not only in terms of salary and wage levels but also in rewards (punishments) for good (bad) performance and in the discretion exercised by employers. As employment continues to globalize and transform — for example, with rising levels of gig work or feelings of burnout — our knowledge is partial about the kinds of contracts employers are willing to propose, and the levels of effort employees are willing to exert. In particular, it is not clear that incentives — whether positive or negative — actually encourage significantly more effort from employees; it might be that a non-negligible proportion of workers are intrinsically dedicated to their jobs or intrinsically unmotivated. Meanwhile, it is not clear whether similar contracting scenarios will elicit similar behaviors among employers and employees from different parts of the world. 

In “Material incentives and effort choice,” Elwyn Davies and Marcel Fafchamps conduct a series of experimental online games with participants from India, the United States, and five African countries. The experimental component involves randomizing which kinds of contracts the employer players can offer to employee players. After employers propose a contract, employees then choose a level of effort to exert in response. The different interactions between players shed light on how incentives may (or may not) motivate workers and how these motivations differ across cultures.

The authors find a lot of commonality across regions: the majority of participants do not respond to incentives or wage levels when choosing their effort level; a sizable fraction of participants increase effort in response to a higher wage, even in the absence of incentives. The proportion of participants who choose high effort only when incentivized is small in all regions. 

The authors also find some stark differences across regions. For example, employee players from India and Africa were more intrinsically motivated, so performance-based incentives were less relevant to them than to American players. Not only were American employees more self-interested, but American employers were more prone to renege on providing bonuses to employees. The authors do not find evidence of cross-country stereotyping or prejudice, meaning that players did not form biased expectations or provide less effort when paired with players from other countries. 

“Material incentives and effort choice” greatly deepens our understanding of the (in)effectiveness of incentives. At the same time, the authors take an important step by extending this research program into the Global South, where employer-employee relations are less often studied and where different norms around work may operate. By providing evidence about how people from different countries negotiate and think about effort, the authors draw our attention to important connections between culture and the economy. 

Recruitment, Game Rules, and Archetypes:

Participants in the experiments hailed from India, the US, South Africa, Senegal, Kenya, Malawi, and Morocco. They were recruited via Amazon Mechanical Turk, a digital labor platform, and Facebook advertisements. Participants play four games as employers and four as employees, never against the same person, and most of the time against players from their home country. The experiment takes 15 minutes and does not involve any real effort from employees. Before playing the game, participants were asked questions regarding their beliefs about the effectiveness of incentives and the acceptability of different kinds of rewards and punishments. 

The games begin with employers making a non-negotiable contract offer. Contracts can take one of six forms: a fixed contract with (1) low wages or (2) high wages; those with (3) a bonus for high effort or (4) malus for low effort; finally, contracts (5) and (6) are the same as (3) and (4) but with an employer option to renege on the bonus or malus, respectively. Players attempt to maximize their points. These are calculated based on wage rates and effort levels, which affect the value of what is produced for employers. The games differ in terms of which offers an employer can make — these options, between one and three in total (including making no offer), are randomly selected. The game begins with an employer either making an offer or making no offer; in the latter case, the game ends. Workers then choose a level of effort, in which case the game ends, except for in contracts (5) and (6), where employers choose whether or not to renege.
 

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The authors introduce four behavioral “archetypes” to guide their expectations about which kinds of offers will be proposed and accepted, whether employees will exert effort, and whether employers will renege. The first type, Archetype 1, always provides high effort. A2s provide effort only if they are paid a high fixed wage. A3s provide effort only if their contract includes performance incentives. And A4s never provide effort. Of course, a “rational,” benefits-maximizing employee will always exert low effort under contracts (1-2) and high effort for (3-4), while rational employers will always renege on contract (5). To what extent, then, are different archetypes and behaviors borne out in actual interactions?
 

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Findings:

To give just a snapshot of the authors’ many findings, most employers offered contracts rather than ending the games. American employers reneged more often than Indian or African ones. And when employers could renege, employee effort declined sharply. Despite this, employee effort levels remained higher than expected if all employers were selfish, suggesting that workers anticipated some fair or altruistic behavior.

Employee effort varied significantly by contract type and region, as mentioned above. Data from the Amazon Mechanical Turk sample shows that American employees were much less likely to embody the A1 archetype than Indians and much more likely to embody As2-4. Across all samples, roughly one-third to one-half of workers never exerted effort (A4), which poses a dilemma for assumptions about the effectiveness of incentives. The authors find sizable proportions of each archetype on all three continents. Most common are archetypes A1, then A4, A2, and A3. This is an important descriptive finding, as material incentives are clearly not the most significant motivators.
 

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Another set of findings relates to the concepts of stereotyping, defined as employers expecting workers from different countries to work more or less than their co-nationals, and prejudice, whereby employees exert low effort when matched with employers from different countries. The authors found virtually no evidence of stereotyping and no evidence of prejudice behind their results. 

In the final part of the paper, the authors investigate whether differences in behavior across regions can be predicted from differences in observable characteristics. They find strong evidence that they cannot. This leaves open the possibility that behavioral differences across regions reflect cultural differences.

Ultimately, “Material incentives and effort choice” underscores the need to rethink our assumptions about workplace motivation, which will be key for designing effective contracts in the evolving global labor market.

*Brief prepared by Adam Fefer.