Pamela Jakiela and Owen Ozier
This study estimates the impact of Kenya’s post-election violence on individual risk preferences.
Because the crisis interrupted a longitudinal survey of more than five thousand Kenyan youth, this timing creates plausibly exogenous variation in exposure to civil conflict by the time of the survey.
The study measures individual risk preferences using hypothetical lottery choice questions, which are validated by showing that they predict migration and entrepreneurship in the cross-section. The results indicate that the post-election violence sharply increased individual risk aversion.
Immediately after the crisis, the fraction of subjects who are classified as either risk neutral or risk loving dropped by roughly 26 percent. The findings remain robust to an IV estimation strategy that exploits random assignment of respondents to waves of surveying.
Armed conflict is a source of untold human suffering. Since 1989, more than one million people have been killed in civil and interstate conflicts (Pettersson and Wallensteen 2015). The majority of these episodes are civil wars in low- and middle-income countries; because the scourge of war falls disproportionately on the poorest nations, armed conflict also perpetuates disparities in human and economic development among the living.
The short-term costs of civil conflict are obvious: in addition to the lives lost, war damages or destroys physical capital and deters investment. There is also evidence that war and violence limit the accumulation of human capital (Blattman and Annan 2010) and erode trust (Nunn and Wantchekon 2011). This has led some scholars to refer to civil war as “development in reverse” (Collier, Elliott, Hegre, Hoeffler, Reynal-Querol, and Sambanis 2003). Yet, though the shortterm human and economic costs of conflict are indisputable, many conflict-affected countries — Rwanda and Uganda, for example — have experienced extremely rapid growth in the wake of civil war, and a number of recent papers have challenged the notion that conflict leads to slower growth and development over the long-term (cf. Miguel and Roland 2011). In fact, several studies have found that exposure to civil conflict increases political engagement (Bellows and Miguel 2009,
Blattman 2009), enhances cooperation and pro-sociality (Voors, Nillesen, Verwimp, Bulte, Lensink, and Van Soest 2012, Bauer, Cassar, Chytilov´a, and Henrich 2013), and makes people more willing to bear profitable risks (Voors, Nillesen, Verwimp, Bulte, Lensink, and Van Soest 2012, Callen,
Isaqzadeh, Long, and Sprenger 2014).
These studies share a common empirical approach. First, they take seriously the idea that exposure to conflict is endogenous, and employ a variety of strategies designed to isolate plausibly exogenous variation in victimization and involvement in violence. Second, given their focus on within-conflict variation in exposure and victimization, these papers empirically frame civilians who lived through civil war but were not victimized (or were less exposed to violence) as a comparison group. This strategy enhances the credibility of the estimated treatment effects, but has an obvious drawback: this approach can generate credible estimates of the marginal impact of greater conflict victimization or exposure, but cannot be used to assess the overall impact of conflict unless oneassumes that violence has no impact on the relatively less victimized. If everyone who lives through a period of conflict — regardless of their victim status — is affected, estimates of the marginal impact of greater conflict exposure may present a biased assessment of the overall social cost of violence.
In this paper, we estimate the impact of a specific episode of civil conflict, Kenya’s postelection crisis, on the risk preferences of a broad sample of young adults who lived through it. The post-election crisis was a months-long period of protests, rioting, and ethnic violence that began immediately after a disputed presidential election. The election, in which Raila Odinga challenged incumbent Mwai Kibaki, took place on December 27, 2007. Amidst allegations of electoral fraud by observers, and after three days of uncertainty following the national polls, the incumbent president was both declared the winner and sworn into office on December 30, 2007. Ethnic tensions rose, and rioting ensued. The following two months of civil conflict left more than a thousand people dead and hundreds of thousands more internally displaced. The crisis largely ended when, on February 28, 2008, the two candidates signed a power-sharing agreement.1 We estimate the impact of Kenya’s post-election violence on individual risk preferences, which we measure using lottery choice questions embedded in a longitudinal survey. The Kenyan Life Panel Survey (hereafter KLPS2) is a survey of more than 5,000 young adults who were enrolled in rural primary schools in 1998. The second round of the survey was administered between August of 2007 and December of 2009. 1,180 respondents (23.3 percent) were interviewed prior to the crisis, while the remainder were surveyed after experiencing the period of civil conflict. Thus, Kenya’s post-election violence interacted with the timing of the survey to create a natural experiment in exposure to conflict.
We employ two complementary identification strategies to estimate the impact of the crisis on risk aversion. First, we estimate the impact of the crisis in straightforward linear and nonlinear frameworks, using several strategies to control for any time trends or seasonal shocks. Second, we exploit the fact that survey respondents were randomly assigned to one of two waves of interviews;
Kenya’s election crisis interrupted the first wave of surveys, allowing us to instrument for conflict exposure (pre-survey) using the randomly-assigned survey waves. Both approaches yield similar results: we find that Kenya’s post-election crisis had a large and significant positive impact on individual risk aversion. Specifically, the crisis led to an 11 percentage point increase in the likelihood that a subject always chose the safest, lowest expected value alternative (i.e. lottery) available — this effect constitutes more than an 80 percent increase in the rate of extreme risk aversion. We also observe a 5.7 percentage point (or roughly 26 percent) decrease in the fraction of subjects who are classified as either risk neutral or risk loving. Such substantial impacts highlight an important channel through which civil conflict might affect growth and development: increased risk aversion might lead individuals in post-conflict settings to avoid high-risk, high-return activities (e.g. entrepreneurship) that contribute to economic growth.
The key strength of our study is that we are able to estimate the impact of civil conflict on the risk preferences of the general population, as opposed to the specific (marginal) effect of being victimized or more exposed to violence. Very few KLPS2 respondents were themselves victims of violence during the unrest: more than three quarters of respondents were temporarily deprived of basic necessities because it was not safe to visit markets or other public places, but less than 4 percent indicated that anyone in their household was physically assaulted during the crisis.
Thus, KLPS2 respondents experienced the conflict, but were not, by and large, among the most impacted Kenyans; they therefore provide an important window into the impacts of civil conflicts on the preferences of the general population.
Our results differ from several recent studies. For example, Voors, Nillesen, Verwimp, Bulte,
Lensink, and Van Soest (2012) find that greater exposure to violence leads to an increase in riskseeking behavior, while Callen, Isaqzadeh, Long, and Sprenger (2014) find that priming subjects with recollections of violent events increases their risk tolerance in situations involving only uncertainty (but also increases their certainty premia). However, these differing results can be reconciled by the difference in estimands, for example, if those who narrowly escape being exposed to violence or victimized become more risk averse. Our aim in this paper is to estimate the overall impact of violence on the population as a whole; doing so, we may not find the same effect. Understanding these overall impacts is of critical importance as we seek to characterize the ways that conflict may change a country’s overall growth trajectory.This paper contributes to several strands of literature. First, most obviously, we add to the evidence on the impacts of conflict. As discussed above, this literature has expanded rapidly in recent years as increasingly high-quality micro data from post-conflict settings has become available.2 Second, we contribute to a growing body of evidence that individual preferences, one of the key determinants of individual behavior in all economic domains, are not as immutable as has long been assumed (cf. Stigler and Becker 1977); instead, individual preferences appear to be shaped by life experiences. For example, Malmendier and Nagel (2011) and Fisman, Jakiela, and Kariv (2015) show that exposure to economic downturns makes people more risk averse and more efficiency-focused, respectively; while Eckel, El-Gamal, and Wilson (2009), Cameron and Shah (2013), and Hanaoka, Shigeoka, and Watanabe (2015) estimate the impact of natural disasters on individual risk preferences (and arrive at different conclusions). As discussed above, several papers (cf. Voors, Nillesen, Verwimp, Bulte, Lensink, and Van Soest 2012) have estimated the impact of conflict on the preferences of those most affected, but, to our knowledge, no work to date has estimated the impact of violence on the risk preferences of the general population.
Finally, our study contributes to the growing body of evidence documenting the validity and predictive power of laboratory and lab-style measures of individual preferences. Though some scholars have questioned whether individual decisions in choice experiments predicts behavior outside of the lab (cf. Levitt and List 2007, Voors, Turley, Kontoleon, Bulte, and List 2012), numerous studies document the explanatory power of experimental measures of risk, time, and social preferences.
For example, Liu (2013) and Liu and Huang (2013) show that experimental measures of risk preferences predict the crop choice and investment decisions of Chinese farmers. In the domain of time preferences, Meier and Sprenger (2012) show that experimental measures of patience predict creditworthiness. Fisman, Jakiela, and Kariv (2014) show that experimental measures of equalityefficiency tradeoffs predict the voting behavior of adult Americans, while Fisman, Jakiela, Kariv, and Markovits (2015) show that the same measure predicts the post-graduation career choices of law students. Jing (2015) shows that experimental measures of fair-mindedness predict medical students’ choices regarding field of specialization. To date, the majority of work linking choices in decision experiments to behavior outside the lab has used incentivized measures of individual preferences, but there are notable exceptions (cf. Ashraf, Karlan, and Yin 2006). Existing evidence suggests the use of incentives shifts individual responses toward greater risk aversion (cf. Camerer and Hogarth 1999, Holt and Laury 2002), leading many to question the broad applicability of hypothetical approaches to risk preference elicitation. We contribute to this literature by demonstrating that hypothetical measures of risk preferences, interpreted as an ordinal index of risk tolerance, predict real world behaviors in an internally consistent way. In particular, our measure of risk preferences is associated with real-world behaviors that involve risk: migration and entrepreneurship.
The rest of this paper is organized as follows. In Section 2, we describe the KLPS2 data collection effort and Kenya’s post-election crisis. In Section 3, we describe our measure of risk preferences and assess the extent to which our hypothetical lottery choice questions predict behaviors likely to depend on risk aversion. In Section 4, we explain our analytic approach and present our main results. Section 5 concludes.