I am a huge fan of transparency about platform content moderation. I’ve considered it a top policy priority for years, and written about it in detail (with Paddy Leerssen, who also wrote this great piece about recommendation algorithms and transparency). I sincerely believe that without it, we are unlikely to correctly diagnose current problems or arrive at wise legal solutions.

So it pains me to admit that I don’t really know what “transparency” I’m asking for. I don’t think many other people do, either. Researchers and public interest advocates around the world can agree that more transparency is better. But, aside from people with very particular areas of interest (like political advertising), almost no one has a clear wish list. What information is really important? What information is merely nice to have? What are the trade-offs involved?

That imprecision is about to become a problem, though it’s a good kind of problem to have. A moment of real political opportunity is at hand. Lawmakers in the US, Europe, and elsewhere are ready to make some form of transparency mandatory. Whatever specific legal requirements they create will have huge consequences. The data, content, or explanations they require platforms to produce will shape our future understanding of platform operations, and our ability to respond — as consumers, as advocates, or as democracies. Whatever disclosures the laws don’t require, may never happen.

It’s easy to respond to this by saying “platforms should track all the possible data, we’ll see what’s useful later!” Some version of this approach might be justified for the very biggest “gatekeeper” or “systemically important” platforms. Of course, making Facebook or Google save all that data would be somewhat ironic, given the trouble they’ve landed in by storing similar not-clearly-needed data about their users in the past. (And the more detailed data we store about particular takedowns, the likelier it is to be personally identifiable.)

For any platform, though, we should recognize that the new practices required for transparency reporting comes at a cost. That cost might include driving platforms to adopt simpler, blunter content rules in their Terms of Service. That would reduce their expenses in classifying or explaining decisions, but presumably lead to overly broad or narrow content prohibitions. It might raise the cost of adding “social features” like user comments enough that some online businesses, like retailers or news sites, just give up on them. That would reduce some forms of innovation, and eliminate useful information for Internet users. For small and midsized platforms, transparency obligations (like other expenses related to content moderation) might add yet another reason to give up on competing with today’s giants, and accept an acquisition offer from an incumbent that already has moderation and transparency tools. Highly prescriptive transparency obligations might also drive de facto standardization and homogeneity in platform rules, moderation practices, and features.

None of these costs provides a reason to give up on transparency — or even to greatly reduce our expectations. But all of them are reasons to be thoughtful about what we ask for. It would be helpful if we could better quantify these costs, or get a handle on what transparency reporting is easier and harder to do in practice.

I’ve made a (very in the weeds) list of operational questions about transparency reporting, to illustrate some issues that are likely to arise in practice. I think detailed examples like these are helpful in thinking through both which kinds of data matter most, and how much precision we need within particular categories. For example, I personally want to know with great precision how many government orders a platform received, how it responded, and whether any orders led to later judicial review. But to me it seems OK to allow some margin of error for platforms that don’t have standardized tracking and queuing tools, and that as a result might modestly mis-count TOS takedowns (either by absolute numbers or percent).

I’ll list that and some other recommendations below. But these “recommendations” are very tentative. I don’t know enough to have a really clear set of preferences yet. There are things I wish I could learn from technologists, activists, and researchers first. The venues where those conversations would ordinarily happen — and, importantly, where observers from very different backgrounds and perspectives could have compared the issues they see, and the data they most want — have been sadly reduced for the past year.

So here is my very preliminary list:

• Transparency mandates should be flexible enough to accommodate widely varying platform practices and policies. Any de facto push toward standardization should be limited to the very most essential data.
• The most important categories of data are probably the main ones listed in the DSA: number of takedowns, number of appeals, number of successful appeals. But as my list demonstrates, those all can become complicated in practice.
• It’s worth taking the time to get legal transparency mandates right. That may mean delegating exact transparency rules to regulatory agencies in some countries, or conducting studies prior to lawmaking in others.
• Once rules are set, lawmakers should be very reluctant to move the goalposts. If a platform (especially a smaller one) invests in rebuilding its content moderation tools to track certain categories of data, it should not have to overhaul those tools soon because of changed legal requirements.
• We should insist on precise data in some cases, and tolerate more imprecision in others (based on the importance of the issue, platform capacity, etc.). And we should take the time to figure out which is which.
• Numbers aren’t everything. Aggregate data in transparency reports ultimately just tell us what platforms themselves think is going on. To understand what mistakes they make, or what biases they may exhibit, independent researchers need to see the actual content involved in takedown decisions. (This in turn raises a slough of issues about storing potentially unlawful content, user privacy and data protection, and more.)

It’s time to prioritize. Researchers and civil society should assume we are operating with a limited transparency “budget,” which we must spend wisely — asking for the information we can best put to use, and factoring in the cost. We need better understanding of both research needs and platform capabilities to do this cost-benefit analysis well. I hope that the window of political opportunity does not close before we manage to do that.

Miriam Golden’s presentation in CDDRL’s Research Seminar on April 23, 2026, addressed a central puzzle in democratic politics: why are incumbent reelection rates systematically higher in richer democracies? Drawing on cross-national data, she demonstrates a strong positive relationship between national income and reelection rates, a pattern that is both statistically robust and theoretically unexpected. This empirical finding motivates a reassessment of two dominant frameworks — accountability theory, associated with John Ferejohn, and selection theory, associated with James Fearon. Accountability models suggest that voters reward good performance and punish poor performance, but they do not explain cross-national variation in reelection rates. Selection models argue that elections filter out low-quality politicians, implying that poorer countries with lower reelection rates must have dishonest or incompetent politicians, yet empirical evidence does not align well with these inferences.

Golden proposes an alternative framework centered on “capacity gaps,” introducing the resources that politicians have available and voters' ability to discern political performance as key missing parameters. In poorer countries, both state capacity and voter interpretive capacity are constrained. Governments face fiscal and administrative limitations that restrict policy delivery, while voters struggle to distinguish whether poor outcomes result from incompetence, corruption, or structural constraints. As a result, the informational conditions necessary for effective accountability break down. Golden further argues that informational signals are asymmetric: markers of “bad” types, such as corruption scandals, criminal convictions, or dynastic ties, are visible and salient, whereas markers of “good” types, such as competence or honesty, are diffuse and easily mimicked. In these settings, even honest, competent, and well-intentioned politicians are likely to lose office because they are indistinguishable to voters from the malfeasant and incompetent. Even high-performing politicians may not be rewarded electorally, and good types gain no consistent advantage in reelection. 

To evaluate this framework, Golden presents multiple empirical investigations. First, she examines whether voters reward economic performance using within-country variation in GDP growth. The results show that higher growth increases reelection rates, but only in countries with high literacy levels. Since literacy roughly proxies voter discernment capacity, this suggests that performance matters electorally only when voters can interpret it. Second, she analyzes survey data from legislators in Italy and Pakistan to assess whether elections filter out low-quality politicians. She finds that politicians with “bad-type” markers, such as dynastic backgrounds or long tenure, exhibit higher tolerance for corruption yet continue to survive electorally, contradicting selection theory. Third, she tests whether poorer democracies have lower-quality politicians by examining education levels and relative salaries. She finds no meaningful differences in legislator quality across income levels and no relationship between salaries and reelection rates, further weakening selection-based explanations.

Overall, Golden’s approach reconciles several empirical anomalies: the income–reelection relationship, the conditional effect of economic performance, and the persistence of low-quality politicians. At the same time, important questions remain regarding causal identification and measurement, as proxies like literacy may capture broader development effects. Nonetheless, the framework offers a compelling shift in focus from politicians to voters, highlighting how limits in information processing can undermine both accountability and selection in democratic systems.

On Thursday, April 16, Daniel Kelemen (UC Merced) and CDDRL predoctoral fellow Hanna Folsz discussed the consequential outcome of the April 2026 Hungarian election: the victory of Peter Magyar’s Tisza Party over Viktor Orbán’s Fidesz Party in a Rethinking European Development and Security (REDS) seminar co-hosted by the Center on Democracy, Development and the Rule of Law and The Europe Center.

Daniel Kelemen opened the talk, first offering an overview of Viktor Orbán's rise to power. In 2010, Orbán won Hungary’s nationwide election with over two-thirds majority, a majority large enough to allow him to amend the constitution. Having suffered an electoral defeat in the past, Orbán worked to centralize his power. He captured referees — courts and independent bodies — seized control of the media, and demonized and undermined the opposition. Orbán effectively changed the rules of the game, tilting the electoral playing field. 

Kelemen states that there are cases in which smaller authoritarian groups within a larger system are tolerated or protected by national parties because they deliver votes. Orbán operated with the support of Angela Merkel, the former Chancellor of Germany, who largely stopped the EU from taking action against Orbán. Orbán’s party, the Fidesz Party, was a part of Merkel’s EU-wide party, the European People’s Party (EPP), a center-right, Christian party. This support, along with the emigration of dissatisfied voters and continued funding from the EU, helped Orbán stay in power. 

However, Orbán’s Fidesz Party was kicked out of the EPP in 2021. Merkel, who was a strong supporter of Orbán, left office in 2022. Orbán’s policy also became more extreme, raising more concern from European member states. In 2022, the EU Commission cut funding to Hungary, suspending 32 billion euros. Kelemen identifies this suspension of funds as an effective step against Hungary’s regime. 

Kelemen then outlined the implications of Orbán’s fall for Hungary, the EU, and international actors, including Russia and the United States. For Hungary, it means full regime change, as the Tisza Party will likely take efforts to undo Orbán’s autocratic policy changes. For the EU, it means that policy on Ukraine and Russia will be different, because Orbán was using his veto to prevent support for Ukraine and sanctions on Russia. For the US and Russia, Russia lost its supporter and ear in the EU, and the Trump administration lost its closest ally in Europe. On a global note, Orbán was a key figure in trying to bring together far-right populists. After he was kicked out of the EPP, he formed a more autocratic-focused party called MEGA (Make Europe Great Again). 

In a Rethinking European Development and Security (REDS) seminar held on April 9, 2026,  and co-hosted by CDDRL and The Europe Center, Konstantin Sonin, a John Dewey Distinguished Service Professor at the University of Chicago Harris School of Public Policy, presented his research on “The Reverse Cargo Cult: Why Authoritarian Governments Lie to Their People,” offering a theoretical explanation for why regimes such as the Soviet Union would knowingly tell citizens visibly false statements. According to Sonin’s research, authoritarian propaganda is much more complex than simple misinformation or manipulation, as it is often designed not to convince people of a single claim, but to shape how they evaluate information more broadly. 

Sonin begins with a personal anecdote, reflecting on his own experience participating in Soviet elections where there was only one candidate on the ballot, despite the process being presented as a meaningful choice. Using this example, he questions why regimes like the Soviet Union invest so heavily in clearly staged elections or exaggerated portrayals of Western life, even when citizens recognize these distortions. From this, he introduces the idea that such actions are not meant to persuade citizens of a specific falsehood, but instead to influence how they interpret all incoming information. Drawing on the metaphor of a “reverse cargo cult,” he suggests that just as some communities misinterpret the source of Western goods, citizens in authoritarian systems may come to believe that institutions in other countries are equally performative or deceptive. In this sense, narratives about foreign countries become an integral tool for reinforcing domestic political stability. 

He further explores how citizens evaluate elections and the decision to replace an incumbent under uncertainty about both competence and trustworthiness. He recognizes that in these regimes, citizens are not entirely naïve and may often recognize when a leader is lying. However, Sonin shows that even obvious lies can be effective. When a domestic leader lies about conditions that citizens already know to be bad, it signals not only that the leader is untrustworthy but also raises the perceived likelihood that foreign leaders are similarly dishonest. As a result, citizens downgrade their expectations of potential replacements, concluding that alternatives may not be any better. This dynamic ultimately reduces the incentive to replace the incumbent. 

As his theory suggests, negative information about conditions abroad, or even skepticism toward foreign success, can benefit authoritarian leaders. For example, Sonin points to Soviet reactions to the American National Exhibition in Moscow, where displays of a typical American home were dismissed by officials as unrealistic or misleading. This kind of framing encouraged citizens to question whether life in the United States was truly better, reinforcing the idea that shortcomings at home were not unique. As a result, domestic failures appear less exceptional, helping explain why authoritarian propaganda frequently emphasizes criticism of other countries and why such narratives often reinforce one another. 

Sonin concludes by emphasizing that lying in this context is not primarily about persuading citizens of a particular false claim, but about shaping their broader beliefs about the reliability of information. By weakening trust in information overall, leaders can make bad conditions at home seem like the safer or more reliable option compared to the uncertainty of change.