Go to cyrussamii.com for ongoing posts.

The CPE@CU blog is in dormancy, and I am now blogging over at cyrussamii.com Please visit me over there. Cyrus

Urquiola & Verhoogen on Tainted RDD

After a bit of a hiatus from blogging, we're back, pointing you to some great research on tainted regression discontinuity designs (RDDs) presented by Eric Verhoogen today at the quantitative political science seminar (paper linked here). The paper looks at RDDs that use class size cut-offs as the basis for identifying the effects of class size on student performance. In many settings, when a class reaches such a cut-off (e.g. 45 students in a classroom), it is somehow mandated to add a new class-room, thus creating a discontinuous decrease in class sizes relative to schools with classes just below the cap. For example, if the 45-student cut-off is rigid, then when the school reaches 46 students, it has to add a class; if it evenly divides students between the (now) two classes, class size falls to 23 per class. So long as no other important factors change discontinuously at the cut-off, then we can compare the class with 45 students to those with the 23 students to identify the effects of class size.

At least, that's what a naive interpretation of such cut-offs imply. Urquiola and Verhoogen show that things are not so simple. They model interactions between schools and households. As they write in the abstract, they find that

[S]chools at the class-size cap [in this case, a maximum of 45 students] adjust prices (or enrollments) to avoid adding an additional classroom, which generates discontinuities in the relationship between enrollment and household characteristics, violating the assumptions underlying regression-discontinuity research designs.

Some schools will try to find ways to avoid crossing the 45 student threshold, others will not, and these choices are correlated with things like the socio-economic status of the communities that the schools serve and the schools' quality, both of which affect the ability of schools to attract more students to compensate for the need to add new classrooms. They are also correlated with student performance, spoiling the RDD.

The upshot for others using RDDs is that behavior near the RDD cut-offs and relevant to the outcomes being studied may involve discontinuities that spoil the design. Clearly researchers using RDD should use whatever data they have to study whether other discontinuities exist near the cut-offs.

Nonetheless, there is something funny going on in the paper. Urquiola and Verhoogen use a model to explain why things like the socio-economic status of households and school quality will vary discontinuously at the cut-offs. But of course, one could probably use less formal reasoning to come to such hypotheses, and then test that with the data. The model seems like overkill in exposing something that is pretty obvious (although hindsight is always 20-20!). So I wonder, rather than using the model to just expose the potential taint that is pretty obvious without the model, why wouldn't we use the model to actually correct our estimation procedure and obtain our "best guess" estimate? It seems that would be carrying a Heckman-type approach to its logical conclusion. But maybe we just don't have that much faith in our models?

More on Change Points

A few months ago I posted on an entry on using estimates of "turning points" in financial time series to study the economic costs of conflict. I've recently learned of a nice little "change point" estimation suite that is part of the MCMCpack for R that allows one to use Bayesian estimation to locate change points and, wonderfully, to compare the fit of various change point models. The suite allows estimation on time series outcomes associated with various distributions. At the EITM seminars, Robert Walker demonstrated how easy it is to use by showing some examples for studying structural breaks in patterns of international exchange rate policies. Very cool. Jong Hee Park also lists a working paper demonstrating applications on his website, although the paper is not linked there. Thus, go forth and discover "structural breaks" in time series data!

Dunning on "endogenous oil rents"

At the comparative politics workshop yesterday, Thad Dunning presented some work in progress on "Endogenous Oil Rents." The puzzle that he is addressing is as follows: oil income is often treated as "manna from heaven" in the political economy literature, but examination of some cases (e.g. Venezuela and Mexico) reveals that the share of oil revenues that governments take for themselves varies over time. Why would leaders choose to limit the share of revenues that they take for themselves? Dunning acknowledges that factors independent of domestic politics account for some of this variation; these include conditions on loans from IFIs, ideology of leaders toward size of government, etc.

But he is convinced that domestic politics also play a role. Here is a simplified version of his logic (he has a much richer model, but the following captures the basics). Suppose that an elected incumbent, call him/her "A", can choose whether the amount of oil revenues that the government can take is "high" or "low", and that this choice cannot be overturned by future leaders. This oil revenue can be used by whomever is in power to buy votes in any forthcoming election. Now consider the possibility that there is some exogenous bias in the electorate that either works in favor or against the electoral chances of A (or A's party) versus some other politician, B. The claim is that when the bias against A is sufficiently strong (i.e. when A is "weak"), A may have incentive to limit the government's take of oil revenues.

Why? Well, the game starts with A choosing whether oil rents should be high or low, weighing consequences over eight possible future states of the world. The first four states are associated with bias being against A's electoral chances. The other four states are associated with bias in favor of A. Consider the first four states, holding the bias against A as fixed. In the future, A is either in power or not, and oil rents can be either high or low. So we have 4 states: (1) A in power, high rents; (2) B in power, high rents; (3) A in power, low rents; and (4) B in power, low rents. For states (3) and (4), rents are low, and so electoral outcomes are determined by the bias in the electorate. So A's chances of retaining office or successfully challenging B are low, but pretty much the same in (3) and (4). For state (2), the electorate's bias combined with B's access to high oil rents means that B will surely retain office. In (1), the oil rents don't do much to compensate for the bias against A in the electorate. Now, note that A's choice at the start of the game determines whether the future alternates between states (1) and (2) or whether it alternates between (3) and (4). In this case, A prefers alternation between (3) and (4), and so A chooses to lock in low oil rents. Therein lies the answer to the puzzle. For the other four states, it is easy to show that A's dominant strategy is to choose high rents. (Try it.)

One comment was that these sorts of counterintuitive outcomes probably occur only rarely, so this probably won't serve as a model of "normal" politics of oil. Another comment suggested that this work echoes work by Terry Moe from the mid-1980s explaining why some leaders seem to create government agencies that went against their interests. Take Nixon and the EPA. Environmental fervor was at an all time high in the early 1970s, so the political payoff of establishing the agency compensated for the offense to supporters from big business; softening the blow even more was that by taking control of the agenda, Nixon could lock in an EPA that would be minimally harmful to the interests of big business in the future. There was some discussion as well about how one could test this kind of logic. Some at the workshop were not convinced by the type of "small N" comparative case study evidence that was presented. Perhaps more convincing would be to collect quotes from people that structure government oil revenue allocation deals, along the lines of the quotes from letters between traders that Avner Greif uses as evidence in his work on medieval traders.

Baliga and Sjostrom on strategic ambiguity and arms proliferation

At the Columbia political economy seminar this week, Tomas Sjostrom presented a paper he co-authored with Sandeep Baliga on "Strategic Ambiguity and Arms Proliferation" (link). A central puzzle that the paper attempts to address is why a leader would want to propagate ambiguity about his country's strategic capabilities. The study is motivated by the use of strategic ambiguity by the leaders of Iraq, Iran, and Israel, for example. It is the last case, Israel, that is particularly puzzling. Here is a country that is generally believed to have strategic weapons but nonetheless has chosen to sustain a level of ambiguity. This would seem to contradict rational deterrence logic, which proposes that the strong should prefer to make it known that they are strong in order to deter attack. The other two cases may be slightly less puzzling, given that incentives to "bluff" are readily admitted by the rational deterrence logic.

In a manner that departs from much conventional wisdom in the popular press, Baliga and Sjostrom do not consider such ambiguity as being the product of attempts to balance against a multitude of possible threats, both internal and external, or attempts to construct a reputation. Rather, they limit their analysis to the stark case of a single interaction between only two actors. The situation is an asymmetric one, where player B decides whether to arm or not and whether or not to allow inspections to reveal its armament status. Player A merely decides whether to attack B or not, perhaps out of an interest in preventing an armed B from passing nuclear weapons to a terrorist organization or threatening allies of A (although the latter are not part of the model).

An important feature of their model is that the players are not certain about each other's "type". Player A can be of a type that would always prefer to attack, to attack only when the opportunity is ripe, to attack reluctantly and only when it seems really necessary, or to never want to attack. Player B can be of the type that inflicts more or less harm to A if B is able to maintain a strategic arsenal (e.g. more or less prone to pass weapons to terrorists, challenge allies of A, etc.).

Given such uncertainty, Baliga and Sjostrom derive a number of conditions under which B would prefer not to allow inspections and thus maintain ambiguity about its weapons status. Some of the results are along the lines of "bluffing" to deter attack from those "opportunists" who would only attack when the situation is ripe. They also show conditions under which something like the curious "Israel scenario" might emerge: Suppose B believes that A is apt to suspect that B is of the type that will cause significant harm to A if B is able to maintain an arsenal. Suppose as well that B places great weight on the possibility that A is of the type that will only attack reluctantly and if it deems it is really necessary to do so. Then B's interests might be best served by arming itself, and thus being able to better fend any attack that might come its way, but to also deny inspections to prevent the "reluctant" A-types from finding sufficient reason to attack B.

I frankly cannot see how this corresponds to the real-world Israel scenario (who are the "reluctant" attackers in this scenario?). There might be other cases that are a better fit. But it is an interesting little result. Another interesting feature of the paper is that they always consider how communication between A and B might affect equilibrium behavior. I feel like this type of analysis of communication is not done often enough.

Ross on Oil, Islam, and Women's Rights

Michael Ross has a provocative article in the current issue of the APSR. He argues that women's rights in the Middle East have been stymied more by oil than by Islamic cultural norms (gated link here). Here's the abstract:

Women have made less progress toward gender equality in the Middle East than in any other region. Many observers claim this is due to the region's Islamic traditions. I suggest that oil, not Islam, is at fault; and that oil production also explains why women lag behind in many other countries. Oil production reduces the number of women in the labor force, which in turn reduces their political influence. As a result, oil-producing states are left with atypically strong patriarchal norms, laws, and political institutions. I support this argument with global data on oil production, female work patterns, and female political representation, and by comparing oil-rich Algeria to oil-poor Morocco and Tunisia. This argument has implications for the study of the Middle East, Islamic culture, and the resource curse.

The evidence is pretty compelling, although I do have some critiques. The regressions in Tables 1 and 2 include income and working age, which are endogenous to the other variables in the model, in which case the coefficients suffer from a type of posttreatment bias. The models reported in Table 4 that include the political system variables have the same problem. It doesn't seem like the results in Table 4 change much as a result, so it might not be a problem. We can't see the consequences in Tables 1 and 2 because all models include the endogenous variables. The concern is that Islam => lower income and lower income => less female labor participation; if so, purging out the effect of income prevents us from seeing all of the possible effect of Islam on depressing labor participation. If some kind of control for income is desired, it would have to be some measure of income that includes variation due to things that are exogenous to Islam and oil. One might also argue that income, working age, and polity are endogenous to the outcomes of female labor force participation and female parliamentary representation. If that is true, then the bias on coefficients on those variables may be substantial, and that bias may propagate bias in the coefficients of interest (on Islam and Oil). (At least I think that may be that case...need to think through that a bit more, perhaps in terms of partial regression...) It's frustrating to see that these problems still arise in top-notch poli sci journals, especially when running the "right" regressions is so easy.

All that being said, though, Figures 3-6 are pretty startling in the patterns that they reveal. So there really does seem to be something to this. A few simple regressions that avoid posttreatment bias could help make the case that much stronger.

Basic Considerations for Modeling Interaction Terms

Some recent discussions in the hallways about modeling interaction terms leads me to put this post up. Interaction terms in regression models can capture many types of joint relationships, particularly when you work with terms that span negative and positive values. Here are some basic examples that show what you can do:

• Interaction term with components that take only positive values. Suppose you have x1 = 1, 2, or 3 and x2 = 1, 2, or 3. Then interaction term, x1*x2 takes values ranging from 1 to 9. The interaction term thus orders observations on the number line in a manner that increases in the same way for both terms. If all values are negative, then the logic is the same, but the ordering is in the opposite direction on the number line. This is the basic case.
  
  
• Interaction term with components that each take positive and negative values. Suppose you have x1 = -1,0, or 1 and x2 = -1, 0, or 1. Then x1*x2 takes values ranging from -1 to 1. The interaction term thus orders observations on the number line according to congruity of direction relative to zero. This type of interaction is useful for theories proposing that congruity of some sort determines the magnitude of an effect. As a basic example, suppose you are modeling amount of legislation passed in a year by a state government as determined by party vote shares in the state assembly and the party of the governor. You could code years with a Democratic governor as 1 and a Republican governor as -1, and then subtract .5 from the Democratic voteshare in Congress, in which case you are left with positive values when the Democrats have a majority and negative values when the Republicans have a majority. Interacting these two terms would result in positive values when the governor and assembly are controlled by the same party, and negative values when there is divided party government.
  
  
• Interaction term with one component always positive and the other spanning positive and negative values. Suppose you have x1 = -1, 0, or 1, and x2 = 1, 2, or 3. Then x1*x2 takes values ranging from -3 to 3. In this case, x1 determines the direction of the joint effect, but x2 determines the magnitude. If x1 included values other than -1 and 1, x1 would both determine the direction and contribute to the magnitude of the joint effect. This could represent some kind of mediating effect.
  

So, as you can see, there are substantially different qualitative implications to having interaction terms with components that span positive and negative values. Just something to keep in mind.

Kenya Violence in Perspective

News reports say that the number of deaths from the violence in Kenya in the month following the elections reached about 800. Data from the Political Instability Task Force (PITF) Worldwide Atrocities Databases, hosted at the University of Kansas (link here) suggests that this death toll is extraordinary and that there is good reason to be very concerned. Here is a link to a set of graphs showing non-combatant deaths from collective violence in select sub-Saharan African countries from 1995-2007. The graphs were made by aggregating death tolls recorded in the PITF by month for each country.

News agencies reported excess of 800 non-combatant deaths per month in sub-Saharan Africa the following cases: Burundi during the re-escalation of the civil war in the late 1990s, in Cote d'Ivoire as the political crisis in that country escalated in the early 2000s, in DRC (Congo-Kinshasa) during the "first Kabila war" and recently during the crisis in the east, in Nigeria at two point in the past decade, in Rwanda as the RPF pushed through the country, and in Sudan as the Darfur crisis escalated. (Countries not shown in these graphs were never reported to have experienced high levels of deadly violence, according to the data.)

Enduring internal rivalries?

Having recently used the phrase "enduring rivalry" in a recent blog comment on the current crisis in Kenya, I was interested to see that Karl Derouen and Jacob Bercovitch have a paper in the new Journal of Peace Research on "enduring internal rivalries" (gated link here). They import the concept of "enduring rivalry" from the international relations literature and attempt to apply it to civil conflicts. Their coding rules result in a list of 60 enduring internal rivalries.

Enduring rivalries are fruitfully understood as forms of inefficient equilibria. Parties should be cooperating to maximize joint gains, but something prevents them from overcoming costly conflict. These sorts of equilibria may result in large scale violence, but violence may only occasionally punctuate what is otherwise a durable circumstance of conflict. In this way, "enduring rivalries" and long civil wars are not the same thing. One could imagine an enduring rivalry marked by a number of short bouts of violence, for example. To the extent that this is a reasonable characterization of some political circumstances---and I think it is---the challenge is to identify such equilibria when they exist (or have existed).

Enter Derouen and Bercovitch:

[Enduring internal rivalries] denote internal conflicts between a government and an insurgency with at least 10 years of armed conflict in which there are at least 25 deaths – regardless of whether or not these years are consecutive.

They implement this definition by classifying and sometimes lumping together observations from the PRIO armed conflict dataset. They identify 60 enduring rivalries.

Looking at their list, I found the selection of cases to be rough but intuitively reasonable if we are to limit ourselves to situations that have at some point resulted in major violence. But even with this major restriction on cases, the assignment of start and end dates often seemed arbitrary. There also seemed to be some arbitrariness in the decision to define enduring rivalries in terms of factions in some cases and conflicting social groups ("Palestinian insurgents") in others. Perhaps the latter issue is not too important, but it may be indicative of general inconsistencies in the way the original data has been constructed.

More generally, it is dissatisfying to have to use a coding rule that relies on the pre-existence of insurgent groups and a threshold of violence to demarcate cases. This definition would thus exclude years of rivalry that certainly have major socio-economic consequences but precede bouts of violence. In addition, I am not sure what kind of general rule could be used to assign end dates to the more expansive notion of an enduring rivalry that I prefer. If Derouen and Bercovitch are not interested in having us consider such an expansive notion of enduring rivalry, then I am not sure how their effort makes progress over the many civil war duration studies that are already out there.

I was also surprised that the authors chose to study the effects of these enduring rivalries. Bracketing the enormous endogeneity problems here, substantively, do we really need to demonstrate that protracted conflicts are bad or "a distinct class of civil wars"? How about focusing attention on the causes of these enduring rivalries!

Another take on resource dependence and conflict

Christa Brunnschweiler and Erwin Bulte have a new working paper on the effects of natural resource dependence on the risk of civil war onset (link). They begin with the observation that "resource dependence" is likely endogenous to politics and conflict. Exploitation of resources is likely to depend on politics and conflict likelihood. Also, the denominator of the resource dependence variable, gross income, is obviously endogenous. Thus, "simple" regressions of conflict onset on resource dependence will not yield reliable estimates; nonetheless, the literature is replete with these types of regressions. They use IV regression on 5-year panels to overcome this endogeneity problem. Their findings:

Our main findings turn received wisdom upside down. We find that resource dependence is indeed an endogenous variable in conflict regressions, and that properly accounting for this endogeneity removes the statistical association between dependence and conflict. In a follow-up regression we demonstrate, not surprisingly, that a country’s history with respect to war and peace is a significant determinant of resource dependence – clenching our main result. Moreover, we find a significant negative relationship between resource abundance and the onset of war, possibly because of an income effect, suggesting that the label “resource curse” seems misplaced. Resource-rich countries have on average a lower propensity to enter a civil war, but countries that do end up with civil strife (possibly resource-poor ones) will experience increasing dependence on the primary sector.

It's good to see people trying to make further strides to deal with the obvious endogeneity problems in predicting conflict based on economic variables. Let's look at their instruments, though:

The main conditioning variables serving as exogenous instruments for [resource dependence] and ln(gdp) are average openness to trade over the previous 5-year period (openness); a dummy variable for a presidential-type system of government; latitude; percent of land area in the tropics; and distance to the nearest coast or navigable river.

Do any of these plausibly satisfy the exclusion restriction as instruments for resource dependence in predicting conflict onset? I'm not so sure. They would do well to conduct sensitivity analysis along the lines of what was mentioned in this post.

Also, there is some circularity in the way they arrive at their conclusion about the endogeneity of resource dependence to conflict. This conclusion comes from the negative and significant coefficient on "number of peace years" in the IV-first stage regression predicting resource dependence. Why shouldn't we be concerned about the endogeneity of "number of peace years" in this regression?

Anyway, the evidence here is far from a slam dunk, but I actually believe their story: politics and conflict drive the macro economy as much as they are responses to it. But the fact is, this is a really hard claim to demonstrate.

"Indigenous" institutions and colonial origins of development

In a new World Bank working paper, Cambridge historian CA Bayly offers a revision of the "colonial origins of comparative development" story (link to paper). Contrasting Indian and African colonial experiences, Bayly notes that Indian commercial and knowledge institutions were quite sophisticated before the arrival of colonists. Indian elites were thus well-prepared to adapt for their own purposes institutions and practices introduced by the colonists. The British colonists would also employ many locals in administration; eventually Indian administrators would be employed throughout the British empire. As such, the colonial experience did more to boost development capacity in the Indian colonies than in the African colonies. Revising the Acemoglu et al line of reasoning, social conditions as much as environmental conditions that explain the difference in the extent to which institutional transfer succeeded, at least in these cases.

Bayly does not offer a theory of why "indigenous" social conditions were so different in the two regions, but he cites others who discuss ecological factors that favored dense, sedentary agriculture in many parts of India versus more expansive and mobile agriculture throughout Africa. So in a way we are back to ecological factors, but with social consequences in pre-colonial times as intervening factors.

Bayly's contribution is useful in providing clear examples of how the success of "interventions"---in this case, colonial institutional transfer---depend on the recipient social environment. On the one hand, there is some danger here of inspiring people to rush to ad hoc judgments about whether recipient social environments were well-suited to absorb innovations in other cases. On the other hand, it would be similarly foolish to disregard the potential constraints associated with low "absorption capacity" of recipient social environments. Grist for the mill...

Evidence of "Perverse Punishment" and "Collectivist Socialization" Effects

Simon Gachter and Benedikt Herrmann present results in a new working paper on their public goods games experiments in urban and rural Russia (link to paper):

Abstract

We report evidence from public goods experiments with and without punishment which we conducted in Russia with 566 urban and rural participants of young and mature age cohorts. Russia is interesting for studying voluntary cooperation because of its long history of collectivism, and a huge urban-rural gap. In contrast to previous experiments we find no cooperation-enhancing effect of punishment. An important reason is that there is substantial punishment of high contributors in all four subject pools. Thus, punishment can also undermine the scope for self-governance in the sense of high levels of voluntary cooperation that are sustained by sanctioning free riders only.

Elinor Ostrom's and Ernst Fehr's experiments in the 1990s caused quite a stir in the social sciences because they showed that people are willing to pay to punish others for not contributing to public goods. This type of pro-social behavior defied the predictions of "Nash"-type rational behavior in public goods games.

A line of skeptical inquiry since then has been to look into whether the results of these experiments are themselves endogenous to the social environment. Maybe Fehr's subjects---students from a Swiss university---were not products of a social environment that would make them representative of humankind, for example. Gachter and Herrmann's paper provides more evidence that this is indeed the case. For Russia, Gachter and Herrmann propose that varying exposure of "collectivization" programs would leave varying marks in the behavior of subjects. They find some supporting evidence: older people and rural people, both of whom were exposed to higher "doses" of collectivization, tend to contribute at higher rates in the public goods games.

The experiments thus become measuring devices for the effects of aspects of the social environment. With this being the case, researchers are now faced with an additional challenge. Not only do they have to work out the challenges of designing and implementing the experiment, but they have to do so in places where the effects of particular aspects of the social environment are identifiable. Behavioral experiments themselves do not produce generalizable results if the effects measured in the experiments are conditioned by unidentifiable environmental variables. While Gachter and Herrman's results are intriguing, should we accept that age and rurality map uniquely to collectivization experiences? Probably not. The lesson for me: when we are interested in developing generalizable knowledge about human behavior, behavioral experiments are strong measuring devices, but are nonetheless subject to identification problems associated with observational studies.

Kenya elections and violence, III

An article from the IRIN news service (linked here) suggests that the following factors were central in motivating people to participate in the violence:

(1) Frustration associated with high socio-economic inequality throughout the country, as documented in a report by Kenya's Society for International Development (link here), using UNDP socio-economic data.

(2) Perceptions of unfair distribution of opportunities. The article claims,

Ethnicity came into play during the election violence because of the widespread perception that those who fared best under Kibaki were his own Kikuyu group, the country’s largest, which dominated politics and the economy both under his administration and that of founding president Jomo Kenyatta.

(3) Frustrated ambition of the current young adult generation, which has received more education than previous generations:

Kenya’s youth in particular, who make up a majority of the population - and of those who rioted - feel the most let down. Improved education gave them hope of a better life than their parents’, hope that was dashed, according to Kwamchetsi Makokha of Nairobi-based communications consultancy Form and Content.

“Under colonialism, it was almost a slave labour system which grew up in the early days of the coffee estates. After independence [in 1963], the white master was simply replaced by the black master. A lot of young people who got a bit of education could not see themselves working for pittances as farm labourers. They started drifting to the cities where the opportunities are not enough to accommodate all of them. You have this massive influx of people who just can’t find work,” he told IRIN.

Nor can they find a political voice, he added. “The common Kenyan citizen who does not have money or property does not have a say in how Kenya is organised. They never have. It’s always been about what car you drive, where you live, and then you have more rights than other people.”

"Huntington 1968" should be ringing in peoples minds as they read that.

(4) Frustration with the corrupt practices of the Kibaki regime: "Another ingredient in this combustible mix is corruption, which Kibaki pledged to eradicate but which under his rule, according to analyst and author Gerard Prunier, 'reached new heights, matching some of the excesses of the Moi years'. "

One thing that strikes me in reading this is the extent to which grievances are perceptions. That's where the challenge comes in conducting rigorous analysis of the link between inequality, favoritism/discrimination, and political upheaval. Perceptions may cause participation in an uprising, but the perceptions themselves are caused by strategies of "political entrepreneurs" and objective conditions. I sense that a lot of debate among analysts and researchers over "true" motivations centers on disentangling the relative contribution of these factors in causing perceptions injustice. Of course, there is another view prevalent these days that much of the participation in upheaval is somehow "opportunistic", but to me, those interpretations have to first explain where from the "opportunity" for an uprising comes. I think any serious thought on that question would lead the analyst back to considering perceptions of injustice.

Kenya elections and violence, II

Discussion of events in Kenya on Ryan Sheeley's blog continue. Ryan discussed the idea that the restoration of order may need to flow from the local to the national. I posted a comment that raises some questions about this view:

I put a comment on Chris's blog yesterday that may challenge the faith put into "local solutions." First, if participation in violence is largely determined by the extent to which one is frustrated by discrimination, favoritism, or other barriers to realizing one's potential, then "local solutions" are only available to the extent that the relevant barriers operate at the local level. But is this the case here? Or are we talking about macro level barriers (e.g. mass discrimination organized across ethnic lines)?

Second, in my own examination of conflict histories in developing societies, I have found that "local solutions" tend to be conservative. That is, local institutions tend to police the barriers against which mass protest is rallying. Without being too teleological, one could say that their erosion is part of the modernization process. Thus, CDF mobilization in Sierra Leone, for example, was a conservative, counter-insurgent response to the RUF. The "local solution" was effective because this conservative movement was both allied with foreign interveners and sufficiently effective in mobilizing people to preserve the status quo. As a contrasting example, rebel mobilization in Burundi thoroughly transgressed "local institutions" precisely because these local institutions were erected to police, at the local level, the type of mass discrimination that rebels sought to overthrow. No "local solution" was imaginable in this case. Thus, whose interests would "local solutions" in Kenya satisfy?

UPDATE: See the comments linked below for a very informative response from Ryan.

Kenya elections and violence

On their blogs, Chris Blattman and Ryan Sheeley have been offering updates and links to other blogs on the Kenyan electoral crisis. I posted the following comment on Chris's blog:

I wonder about the deeper background to the current crisis.

One gets the sense that an all too common story might be at work here:

Undiversified, aid-dependent economy means that control of the state equals control over significant assets and opportunities. Access is conditional on relationship to incumbent, who is thus custodian of exclusionary political economy. For some reason, incumbent loses control over electoral dynamics, which presents a secular opportunity for the excluded to seize control of assets and opportunities. Recognizing what is at stake, incumbent tries to prevent control from being pried from his grasp. Fighting ensues.

Something like this story is what we heard about Burundi in 1993 during the course of our research there over the past 2 years. It's also like the story one reads about Rwanda in 1959 or even Congo-Brazzaville since 1997. It echoes recent stories told about Bolivia, heck even Venezuela for some... It bears resemblance to the "revolutionary politics" story that has been formalized by Carles Boix, Daron Acemoglu and James Robinson, and Thad Dunning, among others.

I wonder whether this "exclusion" or "mass discrimination" lens is relevant here. If one were to peer into the records in the education system, for example, would one find overrepresentation of one group or another? If so, there are implications for how external aid should be used as leverage for dealing with the type of exclusion that may be at the heart of the crisis.

I'd be interested in hearing responses to the applicability of this lens.

Survey Experiments: Dictator Game in a Mass Survey

Another clever way of embedding a behavioral experiment into a survey:
Rene Bekkers. 2007. "Measuring Altruistic Behavior in Surveys: The All-or-Nothing Dictator Game." Survey Research Methods, Vol 1, No 3 (open access link here). In surveys, respondents are often given some kind of compensation for their participation. This survey did so as well, but Bekkers also gave respondents the opportunity to donate their payment to a charity rather than keep it for themselves. Here is the abstract:

A field study of altruistic behaviour is presented using a modification of the dictator game in a large random sample survey in the Netherlands (n=1,964). In line with laboratory experiments, only 5.7% donated money. In line with other survey research on giving, generosity increased with age, education, income, trust, and prosocial value orientation.

Bekkers's method here also differs from common dictator game approaches in that the subjects were allocating "earned" endowments rather than windfall endowments. Past research suggests that such determinants of "asset legitimacy" affect what subjects do with their endowments. Note again that it is the survey process itself that allows for this approach to be taken. Of course, since there is no variation in this treatment, we are limited in how much we can learn about such phenomena. Maybe that was a missed opportunity?

In a complementary analysis of self-reported contributions to charities, Bekker follows Smith, Kehoe, and Cremer (1995) ("The private provision of public goods") in using 2SLS to estimate correlates of giving conditional on having given at all. But he claims to have assigned some kind of randomized treatment in the survey that was used to satisfy the exclusion restriction for the first stage of 2SLS. There seemed to be a bit of hand waving on this, and results from the first stage are not included in Table 2. I found this presentation of 2SLS results to be too vague.

Costs of Conflict

An important issue in the study of violent conflict is the nature of the economic costs that are imposed. On the one hand, instability drives away investment, and resources committed to fighting and the destruction wrought by conflict both steal from productive capacity. On the other hand, it is conceivable that political change ushered in via conflict can result in redistribution of assets and opportunities that increase long run efficiency. The economic costs of conflict are conventionally understood as fundamental in determining whether protagonists decide to fight.

A major difficulty in addressing this issue is measurement. A new paper by Zussman, Zussman, and Oregaard in the current Economica (gated link here) shows how asset market data can be used to measure the economic effects of conflict. Their key methodological contribution is a way of identifying "turning points" in financial time-series. They apply this method to data from Israeli and Palestinian asset market series. They find that their methodology does a good job at identifying turning points in the market series that correspond to key events in the Israeli-Palestinian conflict. Aggregates over periods between turning points can be used as summaries of economic costs of conflict. Doing so, they find that "rough calculation based on the results of our analysis yields a drop of 22% in the value of the Tel Aviv Stock Exchange resulting from the outbreak of the Intifada and an increase of 25% in market value arising from the adoption of the Road Map peace plan."

So can we interpret this as saying that about a quarter of Israeli asset market activity was sensitive to the conflict? If so, one wonders what factors contribute to the extent to which markets are sensitive to conflict. In addition, to what extent does this number characterize general economic sensitivity to conflict in Israel? To the extent that markets are made more resistant to conflict, are incentives to fight altered?

Volunteering, Happiness, and a Clever Natural Experimental Design

Here's the abstract from an interesting paper by Stephen Meier and Alois Stutzer in the current issue of Economica (gated link here)

Stephen Meier and Alois Stutzer. "Is Volunteering Rewarding in Itself?" Economica 75(297):39–59.

Abstract

Volunteering constitutes one of the most important pro-social activities. Following Aristotle, helping others is the way to higher individual wellbeing. This view contrasts with the selfish utility maximizer, who avoids helping others. The two rival views are studied empirically. We find robust evidence that volunteers are more satisfied with their life than non-volunteers. The issue of causality is studied from the basis of the collapse of East Germany and its infrastructure of volunteering. People who lost their opportunities for volunteering are compared with people who experienced no change in their volunteer status.

That's a clever research design.

Error Correction Models for Elections

Virginia and I were looking at how to implement error correction models (ECMs) to study the stability of vote shares for parties and incumbents. Thought I would share some of the material in case others are working on these topics too. The main paper that we looked at was the McDonald and Best (2006) Political Analysis paper (gated link here). They use ECMs to study variation in the stability of incumbent vote shares under different electoral systems. It's an interesting approach, although Virginia and I agreed that they should have used seemingly-unrelated regressions to estimate the trends for the parties in the different countries (you have to look at the paper for that to be meaningful).

Also, here's a link to a little tutorial that I wrote up that goes through an ECM example in Wooldridge Introductory Econometrics (a.k.a. "baby Wooldridge"). It shows full implementation of the Engle-Granger two-step method as well as the Banerjee et al one-step method. The example in the tutorial is a standard type of ECM for two conitegrated series. It is a bit more sophisticated than what is going on in the McDonald and Best paper, which only evaluates error correction in a single series.

Does "war in mixed strategies" make sense?

At yesterday's political economy workshop, our own Massimo Morelli presented a working paper that he co-authored with Matthew Jackson entitled "Strategic Militarization, Deterrence, and Wars" (paper is here). The centerpiece of the paper is an analysis of a mixed strategy equilibrium involving states playing "hawkish," "dovish", and "deterrence" strategies, with wars occurring when "hawkish" and "dovish" strategies interact.

This is the second paper that we've seen at the political economy workshop this Fall where fighting has occurred only in mixed strategies. The other paper was a working paper by Ernesto Dal Bo and Robert Powell on civil conflict resulting from a situation where governments hold private information on the size of a centrally controlled endowment from which contenders seek a share (paper is here).

This raises all the questions about whether such mixed strategy "fighting" equilibria are plausible characterizations of behavior. In the Dal Bo and Powell, paper, contenders mix over fighting and not fighting to "keep the government honest". However, the contender's decision to fight comes after she has already received her share of the endowment from the government. For the equilibrium to be believable, we have to accept that even after the contender's share has been paid out, she would still with positive probability initiate an insurgency that offers no additional gain. This strikes me as implausible.

In the Morelli and Jackson paper, things are a little better insofar as the timing of the game does not conflict so much with the logic behind the mixed strategy. But it's still a case where wars are initiated as the result of joint randomization of strategies.

However, Massimo argued for another interpretation. Rather than thinking in terms of randomizing strategies in the context of a bilateral interaction, think of one central actor facing many adversaries. Then, we can imagine a mixed strategy as being one where the central actor is playing a different strategy with each adversary such that the distribution of strategies conforms with the equilibrium "mix". My thought was that this interpretation was stretching things a bit. This interpretation would seem to require that the adversaries all act as if they are playing a bilateral game with the central actor; but clearly in any real world situation, the adversaries would condition their behavior on what the central actor is doing vis-a-vis the other adversaries. So, to be convinced, I would have see that the same equilibrium "mix" holds in a compelling, respecified game with one central actor and multiple adversaries and with the central actor choosing war with a subset of the adversaries in pure strategies. I just don't think that something like war makes any sense as an element of a mixed strategy.