WHY CODE REPARATIONS DIFFERENTLY?

The drawbacks of the current approach to coding reparations are evident when comparing the way that the TJDB, PCJ, and Powers and Proctor each code for reparations in Bosnia. To provide some context: on November 21, 1995, Bosnia signed the Dayton Agreement, a peace accord that ended the 1992–1995 Bosnian War. By signing this agreement, Bosnia confirmed that “all refugees and displaced persons have the right to … have restored to them property of which they were deprived in the course of hostilities since 1991 and to be compensated for any property that cannot be restored to them” (Dayton Agreement 1998). This constitutes a reparations promise, but it is not a reparations payment.

So how do each of the three reparations datasets record this reparations outcome? The TJDB lists reparations as having started on April 4, 1998, and ended on December 31, 2003 (Olsen, Payne, and Reiter Reference Olsen, Payne and Reiter2010b). The TJDB describes this as a “process [that] successfully dealt with over 200,000 property claims for properties lost during the civil war” (ibid.). In contrast, the PCJ Dataset records Bosnia as having yielded three separate cases of property and monetary reparations given to “all refugees and displaced persons,” each of which is listed as starting on November 21, 1995: one case of reparations given to Republika Srpska; one to the Autonomous Province of Western Bosnia; and one to the Croatian Republic of Bosnia and Herzegovina (Binningsbø and Loyle Reference Binningsbø and Loyle2012, 20). Powers and Proctor, who limit their dataset to monetary compensation only and do not include monetary compensation for non-returnable property, record Bosnia as having paid no reparations at all (2015).

These different results—reparations from 1998 to 2003, three instances of reparations in 1995, and no reparations at all—can be resolved by using the coding approach that I propose in this article. First, we need to determine when the Bosnian government promised to pay reparations. As mentioned in the previous paragraph, that happened on November 21, 1995, with the signing of the Dayton Agreement—this is the date recorded in the PCJ Dataset. The next step is to identify the first year that reparations were paid. This occurred when a small number of returnees moved back to homes in Bosnia in 1998—the year of the reparations start date given in the TJDB (Philpott Reference Philpott2006; Hastings Reference Hastings2001).

That leaves Powers and Proctor, who code reparations as “not paid.” If we disaggregate reparations outcomes by type of violation, it is easy to reconcile this assessment of “not paid” with the PCJ and TJDB’s decisions to code reparations as present. Looking at the reparations promise and payment outcomes for each of the most common state-sanctioned human rights violations committed during the conflict in Bosnia reveals that, at the national level, the Bosnian government has made only two reparations promises, which correspond to three types of human rights violations: property loss, displacement, and forced disappearance (Hronešová Reference Hronešová2018; Džidić and Dzidic Reference Džidić and Denis2015; Gajin Reference Gajin2015, 25). Bosnia’s first reparations promise was the one discussed above, given in 1995 for displaced persons and refugees’ loss of property, which yielded reparations payments in the form of property restitution or monetary compensation for non-returnable property. The second promise was made in a 2004 law that mandated compensation for relatives of missing persons (Hronešová Reference Hronešová2018). The Bosnian government has not yet established a victim’s fund to disburse the direct monetary compensation required by this law, but in 2005 it did create the Missing Persons Institute as a result of the 2004 law, and this organization has paid for some missing persons’ burials and memorial ceremonies (Salvioli Reference Salvioli2021; Hronešová Reference Hronešová2018; Women Organizing for Change 2018).

By moving the level of analysis from the abusive episode (e.g., “did Bosnia pay reparations for the 1990s war”) down to each specific type of human rights violation (e.g., “did Bosnia pay reparations for the displacements in the 1990s war,” “did Bosnia pay reparations for the property lost in the 1990s war,” and so on), we are able to understand why Powers and Proctor (Reference Powers and Proctor2015) coded Bosnia as not having paid reparations. Property restitution does not meet Powers and Proctor’s definition of “monetary compensation,” and neither do the burials and commemorations that have resulted from the 2004 law. As a result, Powers and Proctor code Bosnia as never having paid reparations. Thus, the coding discrepancies in the TJDB, PCJ, and Powers and Proctor datasets are all resolvable, but reconciling these differences is possible only with a level of detail about reparations that current coding approaches lack.

Clearly, there are problems with our current quantitative measurements of reparations. However, this does not mean that we should abandon our attempts to quantify reparations; after all, purely qualitative work has limitations, too, and qualitative data can be misinterpreted just as easily as quantitative data.Footnote ³ In the same way that statistics can be misleading, subjectivity influences how qualitative data is presented and understood, as well, which makes it important to set clear definitions, measurements, and scope parameters for all work, not just quantitative work. There are ways to reduce the effects of subjectivity, of course, including by engaging in mixed-methods research. Sally Engle Merry makes the point that the best research combines both quantitative and qualitative approaches, as “both methods of research taken separately contain hazards,” but the ability to engage in mixed-methods research requires good quantitative data as well as good qualitative data (2016, 21). Thus, if we increase the amount of good quantitative data available to researchers by creating better quantitative measurements of reparations, we would expand the number of research questions that scholars can ask, facilitate mixed-methods and quantitative projects that complement qualitative work, and deepen our understanding of this widely used public policy tool.

In addition to the methodological advantages of being able to conduct quantitative studies of reparations, having a better understanding of reparations, facilitated by more quantitative reparations data that is measured with greater precision and increased clarity, would have benefits beyond academia. This is because reparations have real-world consequences. They have been used, avoided, recommended, and debated on every inhabited continent, they are increasingly being implemented by subnational entities and outside of transitional contexts, United Nations General Assembly Resolution 60/147 of 2005 states that anyone who was subjected to state-sponsored human rights is entitled to reparations, and public interest in the topic is growing rapidly (City of Evanston 2021; ICTJ 2021; McCormick Reference McCormick2021; Wright Reference Wright2021).

The rising level of interest in reparations is, in part, a reflection of how reparations are becoming an international norm (Powers Reference Powers2016). Eighty years ago, it was unthinkable that a government would pay its own citizens after having subjected them to state-sponsored human rights abuses. Now, it is becoming increasingly untenable for governments to avoid at least discussing reparations, even if actual reparations payments stall in the debate, legislation, or implementation phase. Given the global relevance of reparations, the number of human lives affected by them, their political implications, and the massive financial stakes involved, possessing a fuller understanding of the global reparations landscape—something that is possible only with a combination of rigorous qualitative and quantitative research—is imperative. We need to think about how to measure reparations more effectively, and we should do so promptly.

In this article, I make a case for rethinking and improving quantitative reparations indicators in two key ways: First, we need to be explicit about whether we are coding reparations promises or reparations payments; and second, we should disaggregate reparations data by type of human rights violation. Due somewhat to space constraints but largely to limited data availability, I focus more on reparations promises than on reparations payments in this article, but I do present evidence to show that there is a distinction between these two categories and that there is a benefit to coding them separately.Footnote ⁴ I also offer evidence that demonstrates the value of coding promises for different types of human rights violations.Footnote ⁵

This new coding approach has merit from both a positivist perspective and a normative perspective, because our current approach to quantifying reparations not only yields misleading results, but also entrenches existing power dynamics and reinforces persistent inequalities by systematically overlooking certain crimes more than others (Merry Reference Merry2016). I support this argument by presenting the literature’s first analysis of quantitative reparations data that is disaggregated by type of human rights violation and show how, if future work distinguishes between reparations promises and reparations payments, and if it uses data on reparations promises and reparations payments made over time that record these outcomes by violation type rather than treating reparations as a monolith,Footnote ⁶ then it will avoid some of the damaging aspects of the “seductions of quantification” that can affect quantitative reparations research (Merry Reference Merry2016).

My argument proceeds in five stages. First, I assess the state of our knowledge about reparations and show that, although quantitative work on reparations is scarce, qualitative work on reparations already indicates that promises are not the same as payments, that certain kinds of abuses are more likely to receive reparations promises and reparations payments than other abuses, and that there is reason to expect that the promise and payment likelihood for different types of abuses has changed over time. Second, I discuss the original reparations data that I use in this article, which is the only quantitative reparations data to code reparations outcomes by violation type. This data covers the reparations promises—that is to say, governments’ public reparations commitments as expressed in laws, peace treaties, or binding truth commission reports—and selected reparations payments made between 1945 and 2020 for nine different types of state-sponsored human rights violations that occurred in twenty-seven countries in Europe in the context of internal conflicts or dictatorships that took place from 1939–2006. Third, I use this data to show that reparations promises are not the same thing as reparations payments, and that reparations promises do indeed vary across different kinds of abuses. In this section, I also explain why overlooking this systematic variation biases quantitative analyses of reparations. Fourth, I present results showing that reparations promises for different types of human rights abuses also vary over time. I discuss how this temporal variation is likely a result of expanding human rights norms and how the growth of human rights norms complicates the creation of quantitative, cross-temporal reparations indicators. Finally, I conclude by offering suggestions for future research.

THE RISE OF REPARATIONS

The study of reparations is relatively new, partly because 1953 marked the first national-level program where a government committed to paying reparations to their own citizens in the wake of state-sponsored human rights abuses (Torpey Reference Torpey2006; Teitel Reference Teitel2003). Currently, most reparations research consists of qualitative work, with a number of detailed single-country case studies (Barton-Hronešová Reference Barton-Hronešová2020; Firchow Reference Firchow2017; Reference Firchow2013; Dixon Reference Dixon2016; Moffett Reference Moffett2016; Laplante Reference Laplante2007; Magarrell Reference Magarrell2007; de Greiff Reference de Greiff2006). This type of work is foundational, as it establishes a baseline understanding of reparations dynamics and their political, cultural, and economic dynamics. And although it cannot answer all of the questions we have about reparations, it provides numerous examples of domestic reparations programs that cover some types of human rights abuses and not others, as well as a rationale for why, as time goes on, we are likely to see reparations programs expand to cover increasingly more types of human rights abuses.

While there are no quantitative studies that examine abuse-specific variation in reparations outcomes, a great deal of work, largely (but not solely) single-country case studies, describes reparations programs that make provisions for people harmed by some abuses and that exclude or disadvantage others (see Adhikari, et al. Reference Adhikari, Hansen and Powers2012; Ludi Reference Ludi2012; de Greiff Reference de Greiff2006; Goschler Reference Goschler2005; Buford and van der Merwe Reference Buford and van der Merwe2004; Margalit Reference Margalit2002). Sometimes this exclusion is intentional, as happened in Brazil, when it passed a law in 1995 that offered compensation to relatives of individuals who, for political reasons, were killed and/or disappeared by the country’s military regime between September 1961 and August 1979 (Diário Oficial da União de 5.12.1995). The scope of the human rights violations committed by Brazil’s former military regime extended far beyond politically motivated murders and disappearances, but none of these abuses were included in the 1995 law. It took years of dedicated pro-reparations campaigning until Brazil passed its next reparations law, in 2002, which compensated individuals whose careers had, for political reasons, been interrupted or ended by the military regime. (Mezarobba Reference Mezarobba2010)

Other times, governments exclude certain types of abuses unintentionally, as happened in reunified Germany, where reparations laws addressing the crimes of the East German dictatorship did not provide reparations for adolescents who were sent to group care homes due solely to their parents’ political activity. These children could be eligible for reparations if their care home experience met other criteria, but the sheer act of being placed in group foster care because of their parents’ politics was not enough. The law was not designed to discriminate against these children; the lawmakers who wrote and passed the original bill either did not think through this chain of events or did not understand the East German context well enough to realize that once these children were subjected to a politically motivated punishment, they qualified as political persecutees themselves. (Thurm Reference Thurm2016; Reininghaus and Schabow Reference Reininghaus and Schabow2013) There are many such examples of instances where governments exclude some human rights violations from their reparations laws while including others. Thus, by triangulating the information from these single-country case studies, it becomes apparent that there are indeed abuse-specific trends in reparations outcomes.

While the limitations imposed by current quantitative measures of reparations mean that there is currently no quantitative work examining how reparations outcomes for specific human rights violations vary over time—or even if they do at all—qualitative work suggests that such variation does exist. Just as the literature demonstrates that we should expect to find systematic variation in reparations outcomes from one type of human rights violation to another, the literature, specifically literature about the justice cascade, indicates that we should expect to find that these variations change over time. According to the justice cascade theory, norm creation and diffusion are the result of concerted efforts by norm entrepreneurs, who create transnational advocacy networks to disseminate and promote new norms, which, over time, spread internationally (Risse and Sikkink Reference Risse, Sikkink, Risse, Ropp and Sikkink1999; Finnemore and Sikkink Reference Finnemore and Sikkink1998). The justice cascade—that is, a swell of international interest and citizen participation in human rights, which resulted in new norms and the diffusion of these norms—began in the 1980s, sparked a global increase in the use of transitional justice mechanisms, and accelerated the ongoing expansion of what qualifies as a “human right” (Horne Reference Horne2014; Parker Reference Parker2008; Williams, Fowler, and Szczerbiak Reference Williams, Fowler and Szczerbiak2005; Tsutsui and Wotipka Reference Tsutsui and Min Wotipka2004; Lutz and Sikkink Reference Lutz and Sikkink2000; Reference Lutz and Sikkink2001; Finnemore and Sikkink Reference Finnemore and Sikkink1998).

The influence of the justice cascade, as well as its relevance to reparations, is particularly obvious when examining attitudes and actions around sexual violence. Historically, women in conflicts or authoritarian regimes have had their roles reduced to being passive victims or wives and mothers waiting patiently at home, not active agents with a range of experiences, and the sexual violence they endured in these contexts was seen as an expected cost of conflict, not as a human rights violation (Berry Reference Berry2015; Rubio-Marín Reference Rubio-Marín2012; Bell Reference Bell2009). However, recent work has started examining the diverse and gendered experiences of women in authoritarian regimes and conflicts, which in turn has helped spread new norms that promote gender equality, condemn sexual violence, and prompt increased attention to reparations for crimes that disproportionately affect women (Hovil Reference Hovil and Duthie2012; Rubio-Marín Reference Rubio-Marín2006; Finnemore and Sikkink Reference Finnemore and Sikkink1998). These new egalitarian norms have given women a larger role in the story of reparations by allowing victims, practitioners, and politicians to seriously discuss sexual violence as a distinct type of human rights abuse that deserves reparations (Rubio-Marín Reference Rubio-Marín2012; Purdon Reference Purdon2008).

While it is clear that norms around sexual violence have changed, we do not know if the increase in the number of governments discussing reparations for sexual violence has translated into an increased number of promises to pay reparations for state-sponsored sexual violence. As I mentioned earlier, the quantitative data we have on reparations codes reparations as a monolith that is either paid or unpaid, present or absent, which prevents us from identifying abuse-specific trends in reparations outcomes. Furthermore, extant quantitative data does not indicate whether state-sanctioned or state-permitted sexual violence was prevalent during a conflict or dictatorship, which is information that we would need in order to assess if there is variation over time in the level of need for reparations addressing sexual violence. While the TJDB and PCJ datasets have codebooks that state who was the target of a reparations program or what incident the reparations are responding to, even this does not allow us to identify temporal, systematic variation in multiple countries’ reparations outcomes for sexual violence specifically (or for any other specific type of human rights violation). Thus, the discipline’s current approach to measuring reparations makes it impossible to determine whether the reparations outcomes for sexual violence have changed in the way that norm diffusion theories would predict.

This is not to say that we have no information whatsoever. There are case studies documenting innovative new laws and programs that include reparations for sexual violence, such as Croatia’s 2015 Law on the Rights of Victims of Sexual Violence, which was developed in concertation with many of the law’s eventual beneficiaries and is therefore unusually well-adapted to the needs and circumstances of individuals harmed by sexual violence (Clark Reference Clark2016). There has also been a fair amount of attention paid to recently passed laws that offer reparations to people who were forcibly sterilized by state agents (Amnesty International 2021; McCormick Reference McCormick2021). Thus, there are certainly indications that reparations programs are increasingly likely to cover sexual violence, but, as discussed above, qualitative work is not well equipped to determine if these examples represent global or regional systematic trends or if they are isolated incidents. In order to assess whether the reparations outcomes for specific human rights violations do indeed vary over time, and if they do so systematically, we need quantitative data from many different countries. In the next section, I discuss how I gathered the data that this article uses to evaluate whether there are abuse-specific patterns in reparations outcomes and how those patterns (if there are any) vary over time.

DATA AND METHODS

Currently, quantitative datasets that include information on reparations do not differentiate by type of abuse, they utilize different inclusion criteria, and, with the exception of Powers and Proctor (Reference Powers and Proctor2015), they do not focus specifically on reparations and were not intended to be used to analyze reparations specifically. This limits the representativeness, validity, and generalizability of results acquired using these datasets. Consequently, in order to assess if and how governments’ reparations decisions vary across different categories of human rights abuses, I gathered data on reparations promise decisions made by governments after sixty-five cases of conflict and/or abusive dictatorships (nine post-conflict cases, nineteen post-dictatorship cases, and thirty-seven that are both post-conflict and post-dictatorship) that occurred in twenty-seven countries in Europe between 1939 and 2006. I collected reparations promise results for the nine most common categories of state-sponsored human rights violations committed these countries. I start the coverage in 1939 so as to include the precedent-setting reparations programs created in response to the Holocaust. The cutoff date is 2006 because that was the end date for many of the datasets that I used to identify dictatorships and conflicts in which state-sponsored human rights abuses were widespread and systematic. For a list of the cases in this article, see the Appendix. Each case is structured by victim group—conflict/dictatorship—country, and the reparations promise outcome is recorded for each case. For example, the reparations outcomes for Jewish Austrians harmed by the Holocaust is one case, and the reparations outcomes for ethnic Slovenian Austrian citizens who were persecuted by the Nazis is a separate case. Table 1 provides a visual representation of the data structure.Footnote ⁷

TABLE 1. Data Structure Example

I have chosen to look at Europe in particular because many European countries established domestic reparations programs soon after the Holocaust, thereby providing a wider timespan over which I can assess how reparations outcomes change, and because these countries make their laws (and many other government documents) freely available online, which ensures that I can code reparations promises comprehensively and without systematic missingness. Reparations programs on other continents were not generally established until the 1980s at the earliest, which limits the temporal coverage and, thus, my ability to demonstrate that reparations outcomes change over time, and governments in other regions are often less likely to have online legislative databases. As mentioned above, this article focuses mainly on reparations promises, not both promises and payments, due to the difficulty of finding comprehensive, reliable, and systematic data for reparations payments. However, I plan to expand my data collection efforts in the future so that I have wider geographic coverage and more reparations payments outcomes by violation type.

I classified countries as a dictatorship when they were scored as a non-democracy for a given time period on at least two of three regime classification scales that I consulted.Footnote ⁸ The internal conflicts consist of cases in the Correlates of War Dataset (COW) (Sarkees and Wayman Reference Sarkees and Wayman2010) included on the list of intra-state wars in which the government was a participantFootnote ⁹ and cases in the UCDP/PRIO Armed Conflict Dataset (Gleditsch, et al. Reference Gleditsch, Peter Wallensteen, Sollenberg and Strand2002) of state governments fighting internal armed conflicts against one or more domestic opposition groups. I also read primary and secondary literature about each case to see if reputable qualitative sources stated that state-sponsored or state-sanctioned human rights abuses had occurred. In this way, I was able to ensure that every case in the dataset is one where there is evidence that the state actively and systematically abused, abetted, or failed to protect its citizens. In addition to using this information to determine whether any human rights violations were occurring in a specific country in a given time period, I also used it to establish which types of groups were targeted and what specific kinds of violations the state committed in each conflict.

To code reparations promises, I started by looking at documents where governments make those promises (i.e., public, formal commitments to pay reparations): laws, peace agreements, and truth commission reports. To compile this information, I consulted the University of Ulster Transitional Justice Peace Agreements Database (2015), the UCDP Peace Agreements database (Melander, Petterson, and Themnér Reference Melander, Petterson and Themnér2016; Gleditsch, et al. Reference Gleditsch, Peter Wallensteen, Sollenberg and Strand2002), the Notre Dame Peace Accords Matrix (2015), and the United Nations Peacemaker Database (2015), as well as countries’ own databases of past legislation, their press archives, their truth commission reports, parliamentary debates, speeches, budgets, and ministerial records. I also consulted domestic and international news outlets, field reports from human rights organizations such as Human Rights Watch and Amnesty International, the LexisNexis database, the NewsBank database, The Handbook of Reparations (de Greiff Reference de Greiff2006), Keesing’s Record of World Events, and primary and secondary sources about each case. These are all standard sources used for compiling transitional justice datasets (Powers and Proctor Reference Powers and Proctor2015; Binningsbø, et al. Reference Binningsbø, Loyle, Gates and Elster2012a; Olsen, Payne, and Reiter Reference Olsen, Payne and Reiter2010a). I also searched for sources in both English and each country’s respective language, and I did so using both the word “reparations” and a variety of synonyms for reparations, including “indemnification,” “compensation,” “redress,” and “restitution,” so as to avoid missing relevant information due to translation issues or variations in preferred terminology.

I recorded a “yes” for a reparations promise when there was evidence of a reparations promise for each particular type of abuse. I recorded a “no” when I was able to find reparations legislation regarding a particular abusive episode and none of that legislation included provisions for the type of abuse I was examining and when one of two conditions held: (1) I found no other literature indicating that the government had made an official reparations promise for that type of abuse; or (2) I found literature stating that the government had failed to make a promise for that type of abuse. If, due to language ability and/or data availability, it was possible that I had overlooked a reparations law, or, if I could not find information confirming or disconfirming a reparations promise but was not certain that reparations were not promised for a specific type of violation, then I coded the data as missing. Because I coded promises as “missing” unless primary and secondary sources allowed me to decisively confirm that there were no reparations promised, there are no cases coded as “no” that should have been “yes.” There is also no patterned missingness in the data, because legislation for all of the countries in this sample, including defunct countries, such as Czechoslovakia, is available online, and the time coverage for these legislative databases dates back to either the creation of the country for states that gained independence after 1945 or to at least 1945 (if not earlier) for older countries.

I used the same sources as above to establish which types of violations appeared most often during dictatorships and internal conflicts in general, as well as which of these common abuses occurred specifically in each of my cases. Based on my reading, I decided to include the following categories of abuses in my dataset: death/injury; internal displacement/refugees/exile/forced resettlement/deportation; property violations (damaged, stolen, confiscated); forced labor; torture; forced disappearance; political persecution; and sexual violence. Then, I coded whether or not each kind of abuse was present in each of my cases. If a certain type of abuse was widespread and systematic during a case of dictatorship or internal conflict, I then coded whether and when reparations had been promised for that specific violation.

I include material reparations only. I exclude symbolic reparations for two reasons: they are intangible and lack the fungibility of material reparations, making them fundamentally different from reparations in the form of goods, services, or payments; and they are also extremely difficult to measure systematically, which would seriously impact the reliability and validity of the data. I include both individual and communal reparations, because although there are differences between them, they both have concrete value and their disbursements can be tracked. Furthermore, because it can be difficult to determine whether people have received goods, services, or money through a reparations program designed for individuals or for communities, it is easier to present information on reparations payments if both individual and communal reparations are included. As for my methods, because my goal in this article is simply to identify whether certain trends exist in the data, I present summary statistics.

VARIATION IN REPARATIONS OUTCOMES BY TYPE OF HUMAN RIGHTS VIOLATION

As seen in Table 2, there is indeed systematic—and sizable—variation across Europe in terms of which common human rights abuses governments include in reparations laws and which ones they exclude. For example, after dictatorships and internal conflicts where death or injury was one of the prevalent forms of state-sponsored human rights abuses inflicted upon citizens, governments promised to give reparations to affected individuals 69 percent of the time. In contrast, governments pledged to pay reparations for sexual violence to only 6 percent of the groups who were victimized in dictatorships or conflicts where state-sponsored sexual violence was a widespread type of human rights violation. Clearly, there is a large disparity between the likelihood of receiving a reparations promise for having been subjected to death or injury as opposed to having been subjected to sexual violence, and it would be impossible to see this variation using an indicator that subsumed the reparations outcomes for all types of human rights violations.

TABLE 2. Reparations Promises by Abuse Type

There is a wrinkle in this assessment, however; many reparations laws promise compensation and/or healthcare for people who were injured during a period in which they were unjustly imprisoned or deprived of liberty by the government, and this phrasing could conceivably include the psychological and physical injuries resulting from sexual violence. However, almost all reparations laws place some burden of proof on claimants to show that their injury was the direct result of actions for which the state was in some way responsible, and many people who have been harmed by sexual violence are unwilling to discuss it, lack documentation proving that they were subjected to sexual violence, and/or do not realize that what they experienced was a human rights abuse (Clark Reference Clark2016; Paterson Reference Paterson2016; Woodcock Reference Woodcock2014). All of these things create barriers to receiving compensation for the physical and psychological damage inflicted by sexual violence. Furthermore, even if someone who experienced sexual violence does not want to admit or does not feel that they were injured by that event, there is still a normative argument to be made for why such a person deserves reparations.

For all of these reasons, individuals harmed by sexual violence benefit from having a law that explicitly offers reparations for having experienced sexual violence. Unfortunately, out of the forty-six cases in this sample where sexual violence was a widespread, systematic human rights abuse, only five of them (11 percent) resulted in a reparations promise specifically for this crime: West Germany’s 1980 law that recognized forcible sterilization as a “typical National Socialist crime” that was therefore eligible for reparations; Austria’s decision in 1995 to accept reparations claims from women who were forcibly sterilized by the Nazi regime (men had to wait until 2005); Germany’s 2011 law granting reparations to people persecuted under the label “homosexual” who were forcibly sterilized by the Nazis; Croatia’s 2015 law promising reparations to people harmed by sexual violence in the 1990s Yugoslav War, and Czechia’s promise, first made in 2021, to pay reparations to Romani women who were subjected to forced sterilization in a program initiated by the communist regime (Bundesministerium der Finanzen 2022, 13; Ryšavý Reference Ryšavý2021a; Merkel and Schäuble Reference Merkel and Schäuble2011; Spring Reference Spring2009).

This data reveals a glaring mismatch between the number of people who need reparations that address sexual violence and the number of governments who promise to pay reparations that address sexual violence. The data also shows that reparations for sexual violence are a recent phenomenon—although many of the conflicts and dictatorships in the dataset occurred in the 1940s, the only reparations laws in the sample that specifically compensate people for sexual violence are the five mentioned above, which were passed in 1980, 1995, 2011, 2015, and 2021. Clearly, by measuring reparations outcomes by type of human rights abuse, we can identify abuse-specific trends that, in turn, provide information about the relationship between international norms and governments’ reparations decisions, expose gaps between reparations rhetoric and reparations reality, and alert us to potential divisions within affected communities where survivors of one type of abuse are eligible for reparations, when survivors of another kind of abuse are not.

The data in Table 2 also show that just because a government made a reparations promise for one type of violation does not mean that it also made a reparations promise for other types of violations for which it is liable. People who are subjected to torture, who are disappeared, or who are subjected to sexual violence are unlikely to receive a reparations promise specifically for having experienced one of these crimes. Thus, if a researcher codes reparations as either “promised” or “not promised” without indicating what crimes occurred in a conflict, what crimes the reparations addressed, and what crimes the reparations did not address, a country that perpetrated multiple violations and promised reparations for only one of those violations for will seem to be just as compliant with its international reparations obligations as a country that perpetrated multiple violations and promised reparations for all of them. Consequently, studies using quantitative reparations indicators that either do not clarify which human rights violations are included or excluded or that fail to explain that the indicator is a general one, not one that accounts for different types of human rights violations, will yield systematically biased results.

Making these distinctions enables us to see reparations trends that were previously obscured, to confirm or disprove expectations derived from qualitative work, and to better identify ways in which reparations are either reinforcing or dismantling power structures, inequalities, and socioeconomic disparities. For example, we know that women are more likely to be subjected to sexual violence than men and to have a lower socioeconomic status than men (Björkdahl and Mannergren Selimovic Reference Björkdahl, Mannergren Selimovic and Simić2017; UN Women 2014; Potter and Abernethy Reference Potter, Abernethy, Simić and Volčič2013; Rubio-Marín Reference Rubio-Marín2006, Reference Rubio-Marín2012; UN Report 2011;). Having quantitative data, such as the data provided above, that concretely shows that individuals who were harmed by state-sponsored sexual violence—generally women—are much less likely to be eligible for reparations than individuals subjected to other forms of violence allows us to see that, at least on this dimension, reparations are reinforcing gender inequalities rather than reducing them.

This information, in turn, could prove useful to organizations that seek to empower women after conflict, or politicians who care about women’s rights, or transitional justice activists who want to know how to direct their advocacy efforts. It can also help us understand persistent gender inequality after conflict and authoritarianism, provide insight into post-conflict reconciliation dynamics, and enable us to conduct comparative analyses of cases where reparations for sexual reparations were promised (and perhaps paid) and cases where reparations for sexual violence were not promised. The more nuance we have in our quantitative reparations measurements, the better we can understand the complicated realities of fragile transitional contexts and facilitate efforts to make reparations reparative, not retraumatizing.

As Table 2 shows, reparations laws do not guarantee money, healthcare, or other measures to everyone who has been victimized in a given internal conflict or dictatorship. Instead, they are highly specific about which crimes a victim had to have suffered in order to be eligible for reparations. Governments are unlikely to include all of those eventualities in their reparations laws, and they are systematically more likely to promise reparations for some abuses than for others. These variations—and the fact that these variations are systematic, not random—have a substantive impact on people and societies, and if quantitative researchers are truly seeking to avoid the seductions of quantification, then they must take these factors into account (Merry Reference Merry2016).

Croatia is an instructive example here: While Croatian civilians who were harmed by wartime sexual violence became eligible for reparations starting in 2015, Croatian civilians who experienced other wartime human rights abuses, such as death or injury, were technically eligible for reparations thirteen years earlier, in 1992 (Bužinkić Reference Bužinkić and Bužinkić2020 ; Croatia’s Law 1992). Although not all eligible individuals have received those payments—for either violation—both of those payment processes have started (Bužinkić Reference Bužinkić and Bužinkić2020; Vladisavljevic, et al. Reference Vladisavljevic, Lakic and Begisholli2019).Footnote ¹⁰ The same cannot be said of Czechia’s promised reparations to Romani Czechs who were subjected to forced sterilization—at least not yet.

At the time of writing (April 2022), Czechia has not yet paid the reparations for forced sterilization that it promised in 2021, but this will likely change in the next few months. Czechia’s president did not sign the reparations bill into law until August 3, 2021, and the compensation program started accepting claims on January 1, 2022, so payments are expected to follow soon (Ryšavý Reference Ryšavý2021a; Reference Ryšavý2021b). Still, as the earlier examples of Paraguay and Panama show, sometimes it takes years for governments to convert their promises into payments, if they ever do at all. Just as the example of Croatia shows that a reparations promise for one human rights violation is not the same thing as a reparations promise for all human rights violations, the example of Czechia shows that reparations promises are not the same as reparations payments. Consequently, all of these pieces of information should be coded separately.

It is important to keep in mind that even if we code reparations in this more nuanced way, qualitative data will continue to play a key role in reparations research. Quantitative indicators are simply not able to capture central concerns, such as how widely payments are being disbursed, or the level of reparatory intent in a given reparations promise, as easily as qualitative data can. This is especially true regarding the nature of reparations payments—e.g., how many people have been paid or not, how fast are payments being made, are payments being disbursed in a discriminatory way—because the vast majority of governments either do not keep or do not release information about the amount of reparations each recipient has gotten, how many eligible claimants have not gotten reparations, reparations claim denial rates, or which abuse types the reparations in a given year were paid to address. This makes it impossible to code for partial payments in any large-N, systematic way, at least with the data available at present.

While it is potentially easier to create a quantitative measurement of reparatory intent, this is still not a simple endeavor. Even if reparations promises and payments meet the United Nations’ standards of being “reparations,” these reparations can be framed or disbursed in ways that undermine or negate the reparative aims that distinguish reparations from other types of assistance, such as humanitarian aid or government-funded development. Although all of the promises in this dataset are intended to ameliorate the harm caused by human rights violations, assessing whether each law is truly reparative or merely window-dressing is beyond the scope of this project. Thus, this data should be seen as an indicator of the extent to which states are doing the bare minimum required by them under international law, not whether states are accomplishing or even aiming to accomplish reparative goals. Although we can improve our quantitative reparations data by making the changes I suggest in this article, reparations are simply too complicated to be captured through quantitative indicators alone.

VARIATION IN REPARATIONS OUTCOMES BY TYPE OF HUMAN RIGHTS VIOLATION: ADDING IN THE JUSTICE CASCADE

The other aspect of quantitative reparations measurements considered in this article is the cross-temporal variation in the types of abuses for which governments make reparations promises and payments. It is first worth stating that most of the cases in my sample are Holocaust, World War II, or Eastern European Communist dictatorship cases, which helps explain why so many promises in this sample were made in the 1940s, 1950s, and 1990s—these are promises made in the near-aftermath of the end of World War II in 1945 and after the collapse of communism, which occurred around 1989. Even overlooking these trends, which are less indicative of norms and more indicative of waves of democratization, there is clear variation over time in terms of which human rights abuses are likely to receive reparations promises and when.

One temporal reparations trend made visible in Table 3 is that there are only two reparations promises made specifically for torture until the 1990s, in which decade many formerly communist countries promised reparations to individuals who were unjustly imprisoned in psychiatric institutions. There certainly were various forms of torture occurring prior to 1990, but governments almost never explicitly mentioned it in their reparations legislation. Instead, governments made reparations promises for death and injury. While these were likely intended to cover injuries inflicted by torture, they did not always accomplish that purpose. For example, some individuals who survived the Holocaust suffered lasting mental damage from what they endured in concentration camps and death camps, which amounted to psychological torture. West Germany’s reparations legislation did make provisions for reparations for death and injury resulting from the concentration camps, but, because of prejudice and the state of psychiatric research at the time, many people who applied for disability benefits because of their camp-induced debilitating depression had those applications denied (Frankfurter Rundschau 1956). Without a visible disability to examine and document, many doctors were unwilling to certify these applicants’ claims for the reparations that had been promised to injured people (ibid.). A reparations law compensating people expressly for the experience of being tortured—regardless of whether an affected individual could prove that the torture had a lasting negative impact on them—could have avoided this situation.

TABLE 3. Reparations Promises for Different Types of Human Rights Abuses, by Decade

By measuring reparations promises for specific types of abuses, and in doing so by decade, it becomes apparent that there are many historical crimes, such as torture, that were committed during an abundance of dictatorships and conflicts and yet have never explicitly received reparations promises. This more nuanced measurement approach alerts us to the fact that there are likely many people who were tortured and who now lack government assistance that they need—and to which they are legally entitled. Complementing this quantitative data with qualitative case study information allows us to identify gaps in reparations coverage, observe how the growth of human rights norms is resolving some of these disparities, and figure out what types of human rights abuses are still largely unaddressed by post-conflict and post-authoritarian governments.

Table 3 also clearly demonstrates the influence of changing norms on reparations for sexual violence. Forty-six of the sixty-five cases that I analyzed did have widespread sexual violence, including thirty-two cases that occurred in the context of the Holocaust and World War II, and yet three of the five reparations promises for sexual violence were made from 2011 onward. This dramatic shift shows that the justice cascade has indeed changed the landscape of reparations for sexual violence and underscores the value of measuring reparations outcomes by type of abuse. Although we could, based on qualitative evidence, reasonably expect changing norms to induce more reparations promises for sexual violence, we would not be able to confirm the generalizability of that assumption without quantitative data that measured reparations outcomes by type of abuse and date of promise.

The dearth of reparations promises for sexual violence also points to the importance of being aware that changing norms affect historical records, data collection efforts, and governments’ sense of what crimes they could and/or should include in reparations laws. Sexual violence has long been used as a coercive tool in dictatorships and conflicts, but it was not recorded as such until relatively recently. For example, memoirs and qualitative historical work confirm that victims of the Holocaust were subjected to sexual violence, but decision makers and victims’ advocates in the 1940s and 1950s generally did not discuss or classify sexual violations as their own category of crime that deserved reparations. For this reason, historical biases must be accounted for when assembling quantitative data, and, when possible, quantitative findings should be corroborated with qualitative evidence. Although all data collection efforts are limited by what historical contemporaries decided to record, being aware of period-specific blind spots can help avoid misleading inferences based on unintentionally censored data. This is glaringly true of sexual violence, which, despite its lack of reparations promises, was certainly prevalent before the 2010s.

The mismatch between incidences of sexual violence and reparations promises for sexual violence is not an isolated complication; as the international conception of human rights continues to expand, we will continue to encounter difficulties in analyzing the dynamics and development of reparations over time. For example, human rights experts have recently begun to push governments to start paying reparations for violations of economic and sociocultural rights (Roht-Arriaza Reference Roht-Arriaza and Dustin2014; Muvingi Reference Muvingi2009). Economic and sociocultural rights were not conceived of as rights for most, if not all, of the twentieth century, which means that there has been no effort to document and count such violations in a way that would permit a rigorous cross-temporal analysis. As economic and sociocultural rights become entrenched as a global human rights norm, new reparations programs will likely reflect that perspective by promising reparations for violations of economic and sociocultural rights, while past reparations programs will either need to be expanded or will continue to reflect the less-expansive norms of the past.

This is one of the most difficult aspects of measuring reparations quantitatively, because creating a cross-temporally comparable indicator necessitates deciding how to handle the fact that human rights norms are not static. Many quantitative researchers do not encounter these difficulties, because they use indicators measure information that is essentially static. Unless a researcher or statistics bureau makes an initial calculation error that has to be corrected later, then the election results, levels of educational attainment, and taxation rates reported for a given month will be the same now as they were five years ago. The percentage of votes won by a candidate in a given election does not change over time. Reparations expectations, however, do.

Thus, as human rights norms continue to expand, the different types of abuses that are included in reparations programs will likely expand, too, and newer reparations laws are likely to include reparative measures for crimes that older laws overlooked. This type of variation over time, which we see laid out in Table 3, means that reparations are dynamic. One approach to handling this difficulty is revisiting the data and revising it to meet evolving human rights standards. The thinking here is that if reparations are dynamic, then the measurements we use to understand them should be dynamic, as well. Creating dynamic measurements is a multi-step process. First, researchers would have to periodically identify new human rights norms. Second, they would need to evaluate all cases in a given dataset to decide whether these “new” human rights were violated during those abusive episodes to the extent that a government would need to pay reparations for those violations. Third, they would need to code the reparations outcomes for those newly-added human rights violations for historical as well as contemporary cases. Finally, they would need to update any composite reparations indicators to reflect these additions. In essence, this means that reparations researchers must periodically revisit their measurements and revise them to reflect current human rights norms—or note when they choose not to do this.

The most obvious objection to this approach is that it applies modern standards to historical cases. Should we downgrade the rating of a historical reparations program because it was created in a time when certain violations were not considered to be human rights abuses? Are these types of cross-temporal comparisons valid enough to justify creating this kind of measure? What are the costs and benefits of retroactively raising our expectations of formerly abusive governments? Democracy researchers have grappled with this problem for decades and have yet to settle on an answer; reparations scholars are unlikely to agree on this issue, either. Suffice it to say that some researchers will prefer to update their measurements, while others will not. Either way, scholars should be clear about their choice so that their data is used to answer only those research questions that are compatible.