extfieldsofstudy: Biology Medicine

text:
Determinants of change in fertility pattern among women in Uganda during the period 2006–2011

Background Studies on fertility in Uganda have attributed fertility reduction to a shift in the overall characteristics of women of reproductive age. It is not clear whether the reduction in fertility is due to changing socioeconomic and demographic characteristics over time or stems from the shifts in the reproductive behavior of women. In this paper we examine how fertility rates have changed between 2006 and 2011 and whether these changes have resulted from changing characteristics or from changing reproductive behavior of women. Methods Using the 2006 and 2011 Demographic and Health Survey data for Uganda, Multivariate Poisson Decomposition techniques were applied to evaluate observed changes in fertility. Results Changing characteristics of women aged 15–49 years significantly contributed to the overall change in fertility from 2006 to 2011. The change observed in older age at first marriage was the major contributor to the changes in fertility. The contribution that can be attributed to changes in reproductive behavior was not significant. Conclusions This study finds that the major contribution to the reduction in fertility between 2006 and 2011 was from increased education and delayed marriage among women. Continued improvement in secondary school completion, will lead to older age at first marriage and will continue to be an important factor in Uganda’s declining fertility rates.

Background
Total fertility rate (TFR), the number of live births that a woman would have at the end of her reproductive years if the prevailing age specific fertility rates remained constant [1] has declined in all developing regions of the world. In Asia and Latin America and sub-Saharan Africa, a decline in TFR began in the mid-1970s, and 1990s respectively [2]. Whereas Asia and Latin America have had rapid fertility declines, the declines in Africa and particularly Sub-Saharan Africa have been modest [3]. For example between 1950 and 2010, total fertility rates (TFR) in Asia and Latin America declined from 5.8 and 5.9 children per woman respectively to about 2.3 children per woman, while Africa's fertility declined from 6.6 to 4.9 children per woman in the same period [4]. Kabagenyi, et al. [5] examined the onset of fertility transition in Uganda but found no evidence of a stall. They indicated that Uganda's TFR declined for a period of time and then remained constant. The TFR ranged between 8 in 1970s to 6 in 2010. Current population census results in Uganda indicate that between 2002 and 2014, TFR declined from 7.0 children per woman to 5.8 children per woman [6]. However, the TFR of 5.8 children per woman was the tenth highest in the world [7].
Studies have found fertility changes to be associated with changes in demographic, socioeconomic and cultural factors which particularly influence family size, contraceptive access and use and age at first marriage [8][9][10]. Changes in fertility arise from changing characteristics of women as well as changing reproductive behavior that occur as a result of changing characteristics [9]. The changing characteristics of women and changing reproductive behavior can have varying influences on fertility. For instance, Rutayisire, et al., [9] found that a decrease in the proportion of women who were currently in unions (marriage and cohabitation) contributed much to lowering Rwanda's fertility between 1992 and 2000. On the other hand, changing reproductive behavior revealed that the fertility of the women in unions was higher in 2000 compared to 1992. The decrease in the proportion of women in unions was more than offset by the shift in their reproductive behaviors and thus fertility remained higher [9].
Uganda's persistently high fertility has been attributed to low levels of contraception among women [11]. Previous studies in Uganda have documented differential changes in fertility among population sub groups. For instance faster decline is shown among the most educated women; these reside in urban areas and specific regions of the country [12,13]. These studies have highlighted the influence of women's social, economic and demographic factors in fertility change. The studies however have not isolated the change in fertility that is attributable to changing characteristics of women over time from that which is due to the changing reproductive behavior.
Changing characteristics refer to changes in proportion of the population with particular social, economic and demographic characteristics, while the change in reproductive behavior refers to the changes in the behavior of the population as a result of the change in characteristics [14]. In this study we used number of children ever born (CEB) as the measure fertility to conduct a decomposition analysis of change in fertility in Uganda between 2006 and 2011. The CEB measures the number of children born to a woman reported up to the moment at which the data are collected [1]. Specifically, this study determines variations in fertility between 2006 and 2011 that can be attributed to changing characteristics of women aged 15-49 years and assesses the variations that can be attributed to changes in reproductive behaviors of the women aged 15-49 years.

Methods
Data obtained from the Uganda Demographic and Health Surveys (UDHS) conducted in 2006 and 2011 were used. Surveys were conducted by the Uganda Bureau of Statistics. The surveys were nationally representative cross sectional surveys that collected comparable demographic and health data on women aged 15-49 in the survey periods. The samples were obtained using a two-stage cluster sampling process beginning with the selection of clusters, or enumeration areas, followed by the selection of households from each cluster [15]. Since this study used secondary data, research approval from the Institutional Review Board was not applicable. The DHS data is freely available and the public can access it upon a formal request. We submitted an abstract to Measure DHS seeking permission to use the data and the required access was subsequently permitted.
This study is based on the secondary analysis of data of women aged 15-49 years collected in the 2006 and 2011 demographic and health surveys. The women aged 15-49 years in both the 2006 and 2011 surveys were asked about their birth histories and this provided information on the total number of children ever born. Since we were interested in determining the factors that may explain variations in fertility between 2006 and 2011, we adopted a multivariate decomposition analysis. Multivariate decomposition analysis is used to quantify changes over time into components attributable to changing characteristics and changes in reproductive behavior [14]. We used CEB as the measure of fertility.

Inclusion and exclusion criteria
Women who had ever had sex were included since they were the only ones with the potential for pregnancy and child birth. In the DHS, women were asked "how old were you when you had sexual intercourse for the very first time?" This question was about the sexual activity of women. It is however possible that there was underreporting and misreporting, as the question may be sensitive to young women and especially unmarried adolescents who might not feel comfortable to disclose information related to sexual activities. It is thus possible that some women who had sex but did not declare so were excluded.
From a total of 8531 and 8674 women aged 15-49 years interviewed in the 2006 and 2011 UDHS respectively, we selected a total of 7243 and 7364 women aged 15-49 years who reported having ever had sex in the respective surveys. The weighted sample was 7281 women and 7393 women in 2006 and 2011 respectively. Figure 1 below is a flow chart that shows how the sample was derived.

Variables
The dependent variable used in the study was the number of children ever born (CEB) to a female respondent in the two surveys. The independent variables were; age (current age of respondent in 5-year groups), education (highest education level attained by the respondent), residence (type of place of residence of respondent), religion (religion of respondent), wealth quintile (household wealth index), sex of household head (sex of the head of the household), polygamy (whether the respondent is in a polygamous union or was aware of other co-wives), working status of women (whether the respondent is currently working or not), exposure to family planning messages (whether respondent heard about FP on radio, TV and newspaper or not), knowledge about contraceptives (whether respondent has knowledge of any family planning method or not), source of contraceptive (the last source of modern family planning methods for users or respondent is non-user), age at first sex (age of the respondent at first sexual intercourse), ideal family size (Ideal number of children the respondent would have liked to have irrespective of number she already has), contraceptive use (current use of any contraceptive method), age at first marriage (age at first start of marriage or union. Due to the tendency to give birth before marriage, we introduced the "not yet married" category). These independent variables have effects on the outcome in two ways: the distribution effect (changes in characteristics of women) and the behavior effect (this is shown by regression coefficients of the variables on the outcome. For instance, change in fertility may be due to differences in distribution of women by education level attainment and also due to the effects of education on the reproductive behavior. Assuming women with higher education have a lower CEB, a decrease in TFR may be observed if more women in 2011 had a higher level of education when compared to women in 2006. This is an example of how changing characteristics can influence the TFR. In addition, women achieving a higher level of education may purposefully decrease the number of CEB, which would also lead to a decrease in TFR. Therefore changing reproductive behavior can influence the TFR.

Data analysis
Using the 2006 and 2011 DHS data sets for Uganda, a Poisson regression offset by the natural logarithm of the current age of women was done for each survey period to determine the factors associated with number of children ever born. The data were first weighted to ensure representativeness of the sampled data. A weighting variable generated using the sample weight variable in the DHS data was applied in all statistical commands. This variable was used in all the models. The weighting took into account the complex sample design used in the DHS. The coefficients were exponentiated to yield incident rate ratio (IRR) to ease interpretation of the results. The incident rate ratio explains how changes in X (independent variable) affects the rate at which Y (CEB) occurs.
where, μ i is the expected number of children born to a respondent based on the respondent's demographic and socioeconomic characteristics; X i are independent variables; α is a constant and β i represents coefficients associated with the independent variables. ln(age), is the offset variable. This offset variable was generated from the variable age. In addition, we adjusted for marital status since marital status significantly related to the number of children ever born, as  married or formerly married women are more likely to have more children ever born than the never marrieds. To know whether there was a significant change in number of children ever born between the two survey years, a one-way analysis of variance (ANOVA) which reports mean number of children ever born (MCEB) was used.
To determine the factors associated with the change in fertility between 2006 and 2011, a multivariate Poisson decomposition model was used to partition change in mean number of children ever born into components attributable to changing characteristics of women and that due to changing reproductive behavior of the women. The technique also partitions the two components into portions that represent the unique contribution of each predictor to each of the two components in a detailed decomposition (Powers, Yoshioka, & Yun, 2011). The general decomposition equation is as below: The eq. 2 can be further decomposed to eq. 3 below The summarized form of eq. 3 is as in eq. 4 where;

Results
We studied a total weighted sample of 14,674 women aged 15-49 years, 49.6% of whom were from the 2006 survey and 50.4% from the 2011 survey. Our results indicate significant differences in characteristics that include age, education level, religion, current working status, polygamy and exposure to family planning messages (p < 0.05). Other characteristics that impacted CEB were source of modern family planning methods, knowledge of any family planning methods, contraceptive use, age at first sex, family size preference and age at first marriage. The characteristics of women and the associated difference in proportions in 2006 and 2011 are presented in Table 1.
Between 2006 and 2011, there was a significant difference (p = 0.004) in the percentage of women by age. Specifically, the percentage changes in the women aged 15-19, 20-24, 25-29, 30-34, 35-39, 40-44 and 45-49 were; 1.1, − 1.5, 1.8, − 2.1, 1.0, − 0.2 and 0.1 respectively. The percentage of women who had not attained any level of education decreased by 7.4%, while the proportion of women who had attained at least a secondary level of education increased by 6.8%. Similarly, the percentage of women living in urban areas increased by 3.3%. Regarding wealth quintile, there was slight reduction in the proportion of women in the poorest, poorer, and middle categories and a slight increase in proportion of women in the richer and richest categories. The results also indicate that the proportion of women in the "not working" category increased by 11.5%. There was a reduction of 2.5% in the proportion of women who were in a polygamous union.
The proportion of women reached by family planning messages increased by 4.6% in the period 2006-2011. Similarly, the proportion of women who reported getting modern family planning methods from the government facilities increased by 5%. There was a notable change in contraceptive use. The proportion of women who were currently using any contraceptive increased by 4.8% between the two survey periods. The findings further indicate that the proportion of women whose sexual debut was below the age of 20 years reduced by 12% signifying an increase in the age at sexual debut among women aged 15-49 years in 2011. Relatedly, there was a 3.7% reduction in the percentage of women marrying before the age of 20. The proportion of women who desired at least five children reduced by 4% and those who desired 3-4 children increased by 3.7%.

Changes in fertility
Changes in fertility in this study are described by changes in number of children ever born (CEB) to the women. A computation of the mean number of children ever born (MCEB) was done using oneway ANOVA. The results revealed that the MCEB was 4.1 in 2006 and 3.9 in 2011 and that there was a statistically significant difference in MCEB between 2006 and 2011 (p = 0.000) at the 95% confidence level.
A Poisson regression offset by the natural logarithm of the current age of women was done for each survey period to find out the factors associated with CEB. Table 2 reveals that in both 2006 and 2011, the women who had attained at least a secondary level of education had a lower MCEB compared with their counterparts. Relatedly, in both 2006 and 2011, there was generally higher fertility among rural women compared to their urban counterparts. The results also indicate that women in the richest wealth quintile had a lower fertility. Female headed households had lower  After adjusting for age and marital status, women who knew any family planning methods in 2006 were found to have higher fertility compared with their counterparts who did not have any knowledge. Furthermore, women who were currently using a contraceptive method had higher fertility compared to those who were not. However, when adjusted for age and marital status, the influence of contraceptive use was not significant for the two surveys. In both 2006 and 2011, the MCEB was lower among women whose age at first sex was reported to be at least 20 years. Relatedly, the results indicate that in both 2006 and 2011, fertility was lower among women whose age at first marriage was 20 years or older. When adjusted for age and marital status; education, place of residence, wealth quintile, sex of household head, age at first sex and family size preference remained significant while contraceptive use was not significant. Table 2 shows both the crude and adjusted results. Table 3 shows the overall contribution of characteristics and reproductive behavior of the women on the observed variation in number of children ever born. The findings indicate that the overall change in fertility between 2006 and 2011 was attributed to changing characteristics of women. Changing reproductive behavior did not contribute significantly to the observed change in fertility.

Decomposition of fertility change
Results of the detailed decomposition presented in Table 4 reveal that the change in fertility was due to changes in age, education level, place of residence, wealth quintile, polygyny, household headship, exposure to family planning messages, contraceptive use, age at first sex, family size preference and age at first marriage.
The significant variables in the model (age, education level, place of residence, wealth index, sex of household head, polygyny, exposure to family planning messages, contraceptive use, age at first sex, family size preferences and age at first marriage) were tested for confounding. Age was found to be a confounder in the model. The findings indicated that when age was dropped from the model, education was the biggest contributor with 47.8%, followed by age at first marriage (40.9%), women's preferred number of children (29.7%), working status (23.8%), contraceptive use (19.8%), exposure to family planning messages (12.7%), place of residence (11.1%), age at first sex (8.7%) and polygyny (3.4%).

Discussion
Our analysis reveals that of characteristics and reproductive behavior, only changing characteristics of women significantly contributed to the observed change in number of CEB born between 2006 and 2011. The study results highlight importance of change in women's educational attainment and age at first marriage to fertility. With an increase in the proportion of women who have attained at least a secondary level of education, this study highlights significant declines in fertility. The nationwide implementation of the Universal Secondary Education Policy in 2007 increased the proportion of women who attained at least secondary level of education in 2011. The increased attainment of higher level of education could have delayed entry into marriage and also increased the likelihood of using contraceptive methods. This confirms the notion that improvements in education of women is instrumental in fertility decline. Bagavos and Tragaki [16]; Westoff, Bietsch and Koffman [10] as well as Shakya and Gubhaju [17] also observed that increasing women's educational attainment is a key factor contributing to sustained fertility decline. However, this finding partly disagrees with Cai [18] who in a study conducted in China found that improvement in education had no effect on fertility change. This may partly be due to socioeconomic context differences between Uganda and China as well as the different policies that exist in the two countries. In China, the strong government intervention in birth control policy could have suppressed variation in fertility. This is not the case for Uganda since the number of children is largely a personal choice and this can partly explain why education is such a significant contributor to changes in Uganda's fertility. Our findings also point to the need for mechanisms to increase the age at which women marry. The results are in line with other studies that contended that an increase in age at first marriage reduces fertility [5,19,20]. This finding also concurs with Beatty [1] who contended that fertility transition is not likely to begin in a country where age at first marriage for women is still low. An increase in the proportion of women that were exposed to family planning messages between 2006 and 2011 was found to have contributed to the variation in fertility during the period. The finding suggests that if the population experienced increased exposure to family planning messages, a fertility transition can be facilitated. Exposure to family planning messages may lead to changes in attitudes towards large families and use of contraceptive methods which in turn lead to adoption of small family norms such as contraceptive use. Appropriate mass media campaigns on family planning should target high fertility areas rural areas. The importance of exposure to mass media has been reported to be a determinant of the number of children desired and increased use of modern contraceptives [8,10,21]. Although contraceptive use among women in Uganda was still low, our findings indicated that contraceptive use contributed significantly to the change in number of children ever born. This finding points to the need for continued and increased government and international support for quality family planning if sustainable fertility reduction is to be achieved. This finding confirms what numerous studies have asserted about contraceptive use significantly driving fertility transition [9,12,17,19,20,23]. However one study conducted in Uganda reported that modern contraceptive use did not influence the country's fertility rates [5]. This may be because the study by Kabagenyi et al. looked at contraceptive use for one survey period but did not cover the time variations in the effects of contraceptive use. The contribution of family size preference to the observed change in fertility can be linked to the reduction in the proportion of women desiring large family size (at least five children). Family size preferences affect people's fertility behaviors and especially decisions on whether to use or not to use contraceptives. There is need to continue reaching the population especially in rural areas with information about the benefits of smaller families. The importance of shift in desired family size in fertility decline was confirmed in various studies [8,10,12,22,23]. In fact an earlier study asserted that fertility desires and not contraceptive access matter in fertility change [19].
This study also found that increase in the proportion of women who delayed their sexual intercourse to at least 20 years influenced the observed variation in children ever born. Delayed sexual intercourse implies delayed exposure to pregnancy and childbearing. Government and other stakeholders such as parents, local leaders, and religious leaders should keep encouraging young people to delay entry into sex. Most of the sexual abstinence messages in Uganda have focused on the prevention of sexually transmitted infections and especially the Human Immune-deficiency Virus (HIV), it is thus important that such messages incorporate pregnancy and childbearing.
The increases in the proportion of women residing in urban areas significantly influenced the 2006-2011 observed change in fertility. This may be due to improved access to family planning services and information, education and existence of smaller family size norms that usually characterize urban areas. This finding resonates with other studies that have found faster change in fertility among women residing in urban areas compared to rural counterparts [5,10,12,17,20]. Relatedly, the findings have indicated that if household wealth improved, fertility decline. The government of Uganda initiated the "Operation wealth creation" which if well implemented presents an opportunity for the country to achieve faster fertility decline especially in the rural areas where they are based. Dribe, Hacker and Scalone [24] support this as they contended that middle classes and the rich class experience faster fertility transition compared to the poor.
Although the time period for this analysis is very short for detailed explanation of demographic transitions which are known to take longer periods, the two survey years chosen, 2006 and 2011 represented a period in which visible change in fertility was reported. The 2006 and 2011 Uganda Demographic and Health Survey reports indicated that the fertility rate in Uganda reduced from 6.7 children per woman in 2006 to 6.2 children per woman in 2011 [15]. Earlier surveys had indicated that fertility had persisted just over 6.7 children per woman. The study intended to identify the factors that contributed to the observed change in fertility between 2006 and 2011.
In our analysis, we only included women who had ever had sex. Most studies on fertility focus on married and ever married women or all women of reproductive age. By focusing on ever married and married women, such analyses exclude non-marital and premarital fertility which are seemingly increasing in recent times. The current study focused on women who had ever had sex so that only women exposed to the risk of pregnancy and childbirth are included in the analysis. However, this inclusion criteria may represent a limitation of the study as there may have been under reporting or even refusal to report on sexual activities especially among adolescents who may fear to disclose freely disclose their sexual histories. In most cultures, unmarried young people are expected to abstain from sexual intercourse and thus such young people who are not married may decline to disclose their sexual activity status.
There is a possibility that some women who were and interviewed in the 2006 survey were again interviewed in 2011. Even if some women were interviewed both in 2006 and 2011, there could have been changes in characteristics as well as reproductive behavior. For instance, a woman who was interviewed as a non-user of family planning in 2006 could have been interviewed as a "user" in 2011. Relatedly, a woman who was interviewed aged 20 years and who had not yet given birth in 2006 was aged 25 years in 2011 and could have even given birth.
There may be rural and urban disparities in the importance of the factors explored by this study. We propose that future studies explore the determinants of change in the rural and urban areas separately in order to understand the factors influencing fertility change in the two areas.
The strength of this manuscript is that the analysis is based on survey data which is nationally representative. The analysis technique used facilitates the portioning of change in an outcome over time into components attributable to changing socioeconomic and demographic characteristics of women and changing reproductive behaviors.

Conclusion
The decomposition technique quantified the contribution of changing characteristics and changing reproductive behavior of the women to the observed change in fertility pattern among women in 2006 and 2011. The key contributors to the change in fertility were; changes in age at first marriage, age of women, education level attained, ideal number of children, exposure to family planning messages, age at sexual debut, place of residence, wealth index and contraceptive use.
As Uganda continues to focus on harnessing its demographic dividend resulting from changes in the age structure of the population emanating from rapid fertility decline, it is important that the government continues its support for investments in education and wealth creation programs. The findings point to the need for government and its partners to increase the number of family planning service points and intensified outreaches focusing on fertility control. Increasing support for family planning activities and especially efforts to ensure increased availability and accessibility of quality family planning methods and the intensification of mass media campaign efforts to provide messages on the benefits of family planning and fertility limitation will not only contribute to utilization of contraceptives but would also lead to changes in attitudes towards large families.


extfieldsofstudy: Medicine Economics

s2fieldsofstudy: Economics Psychology

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Are Risk Preferences Stable?

It is ultimately an empirical question whether risk preferences are stable over time. The evidence comes from diverse strands of literature, covering the stability of risk preferences in panel data over shorter periods of time, life-cycle dynamics in risk preferences, the possibly long-lasting effects of exogenous shocks on risk preferences as well as temporary variations in risk preferences. Individual risk preferences appear to be persistent and moderately stable over time, but their degree of stability is too low to be reconciled with the assumption of perfect stability in neoclassical economic theory. We offer an alternative conceptual framework for preference stability that builds on research regarding the stability of personality traits in psychology. The definition of stability used in psychology implies high levels of rank-order stability across individuals and not that the individual will maintain the same level of a trait over time. Preference parameters are considered as distributions with a mean that is significantly but less than perfectly stable, plus some systematic variance. This framework accommodates evidence on systematic changes in risk preferences over the life cycle, due to exogenous shocks such as economic crises or natural catastrophes, and due to temporary changes in self-control resources, emotions, or stress. We note that research on the stability of (risk) preferences is conceptually at the heart of microeconomics and systematic changes in risk preferences have vital real-world consequences.

or perform welfare analyses when individuals' preferences lack complete stability? Can and should policymakers make use of the malleability of risk preferences to promote behavior changes that are deemed desirable (such as giving up smoking or avoiding teenage pregnancies)? Can economists benefit from insights in personality psychology on the degree of stability and malleability of personality traits, a concept somewhat related to economic preferences? We will touch upon these questions before we conclude by pointing out directions for future research.

Measurement of Risk Preferences
In recent years, economists have started to turn attention to validating their measures of risk preferences-that is, to explicitly documenting (instead of assuming) that commonly used measures of risk preferences fulfill crucial criteria such as internal and external validity (for example, Dohmen et al. 2011;Falk, Becker, Dohmen, Huffman, and Sunde 2016). Internal validity (or "convergent validity") implies that different measurement tools of risk preferences (for example, survey-based self-report measures and experimental measures) map into the same underlying construct "risk preferences" and offer a coherent description of the same individual. Measured risk preferences are externally valid (or have predictive or behavioral validity) if they have predictive power for actual risky behaviors. For example, Dohmen et al. (2011) document that self-reported risk preferences are a reliable predictor of investment in stocks, self-employment, participation in sports, and smoking, as well as actual risk-taking in an incentivized lottery experiment.
Well-established tools for measuring risk preferences have emerged that allow for comparing distributions of risk preferences across studies and populations. Two approaches prevail: self-reports and incentivized experiments. 1 A prototypical example of a self-reported questionnaire measure is the corresponding question in the German Socio-Economic Panel that is answered using an 11-point Likert scale: "How do you see yourself: are you generally a person who is fully prepared to take risks or do you try to avoid taking risks?" (Wagner, Frick, and Schupp 2007). Some of the most widely used experimental approaches to measure risk preferences encompass Holt and Laury's (2002) price list approach, Gneezy and Potters' (1997) risky investment task, as well as Binswanger's (1980) and Eckel and Grossman's (2002) choice between different gambles. In these experiments, individuals typically choose between different two-outcome lotteries in which higher expected payoffs come at the cost of a higher variance of payoff (that is, more risk).
There are clear-cut tradeoffs between using self-reported survey or incentivized experimental measures of risk preferences (see also Charness, Gneezy, and Imas 2013). Experimental economists consider experiments as the methodological gold standard for measuring preferences since experiments observe real choices with real incentives in well-controlled decision situations that are comparable across individuals. By assigning probabilities to each outcome, experimental measures precisely quantify the risks under consideration, while survey measures might capture risk perception on top of risk preferences. However, experimental measures are costly and time-consuming to implement in large representative samples. Therefore, they often rely on single-item measurement, making them more prone to measurement error. In Gerhardt, Schildberg-Hörisch, and Willrodt (2017), my coauthors and I measure risk preferences by the monetary amount that is needed to make an individual indifferent between two-outcome lotteries with varying expected payoffs and payoff spreads ("risk premium"). We find pairwise Pearson's correlation coefficients on within-subject risk premia in four different choice lists (that is, within the same experimental task format) that range between 0.27 to 0.57. Administering multiple measures of risk preferences to the same individuals, Frey, Pedroni, Mata, Rieskamp, and Hertwig (2017) document partial correlations among seven different experimental measures that are below 0.1 on average compared to around 0.2 among 22 different self-report measures. From this perspective, it's not a surprise that survey measures tend to outperform experimental measures in terms of external validity (for example, Dohmen et al. 2011; see also Mata, Frey, Richter, Schupp, and Hertwig in this issue, who generally take a more skeptical view on experimental measures).
Typically, economists use measures of risk preferences that aim at eliciting an "overall" risk preference, reflecting the common assumption in economics that a single risk attitude governs risk-taking in all risk-related domains such as financial investments, and health-or job-related risks. However, the existence of a single risk preference across all risk-related domains is not undisputed (for example, Weber, Blais, and Betz 2002;Hanoch, Johnson, and Wilke 2006). 2 In a representative sample of the German population, Dohmen et al. (2011) find correlations of about 0.5 across different risk domains such as financial matters, health, career, sports and leisure, and car driving as well as for general risk preference and domain-specific ones. They take this finding as evidence that risk preferences across domains are correlated strongly, but far from perfectly.
In my view, the recent economic research on measurement of risk preferences lays the groundwork for studying stability of risk preferences (for a more skeptical judgment, see Friedman, Issac, James, and Sunder 2014). Nevertheless, there is still a lot of scope for improvement in measurement tools for (risk) preferences. In particular, economists can benefit from adopting the psychometric standards applied to personality traits in psychology for the case of economic preferences (Borghans, Duckworth, Heckman, and Bas ter Weel 2008). For example, economists should search for the measures with highest test-retest stability in shorter time intervals in panel data in order to reduce measurement error and apply them more broadly. Moreover, if information from multiple survey items, multiple experiments, or both kinds of measurement tools are combined to obtain a single measure of individual risk preference, it may help reduce measurement error.

Definition of Preference Stability in Economics
In economic theory, stability of preferences is defined as stability at the level of the individual (as opposed to stability of the distribution of preferences in a given population). Stability of risk preferences implies that, in the absence of measurement error, one should observe the same willingness to take risks when measuring an individual's risk preferences repeatedly over time. Indeed, a standard approach in economics is to attribute any changes in measured risk preferences to measurement error and to consider them as meaningless noise.
In the common economic risk paradigm, 3 a single parameter is sufficient to characterize an individual's risk preferences. The value of this single parameter differs across individuals, spanning the continuum from risk proclivity over risk neutrality to risk aversion, with a large majority of individuals being risk-averse (for example, Dohmen et al. 2011). In the subjective expected utility theory framework (Savage 1954), risk preferences are completely characterized by a parameter that describes the curvature of an individual's utility function. Similarly, in the modelindependent concept of risk apportionment (Eeckhoudt and Schlesinger 2006), an individual is classified as risk-averse if that individual prefers a particular lottery to a mean-preserving spread of that lottery. Conversely, an individual who prefers the mean-preserving spread over the original lottery is classified as risk seeking. The intensity of a subject's risk attitude is measured by the monetary amount-the "risk premium"-that is needed to make the subject indifferent between a lottery and a given mean-preserving spread of that lottery. In both approaches, the standard economic definition of stability of risk preferences implies that, in the absence of measurement error, one should obtain the same estimate of the parameter of interest (curvature parameter or risk premium) when measuring an individual's risk preferences repeatedly.

Concepts of Stability of Personality Traits in Psychology
In personality psychology, "traits" are defined as enduring patterns of behavior, thought, and emotion that are relatively stable over time but differ across individuals (Roberts 2009). 4 Personality traits and economic preferences are related in the sense that both are characteristics of an individual that are assumed to be important predictors for individual decision-making. Almlund, Duckworth, Heckman, and Kautz (2011) and Borghans et al. (2008) provide thoughtful introductions to personality psychology for economists and discuss the relation between economic preferences and personality traits in more depth.
Personality traits are considered stable if they meet the criterion of rank-order stability. Rank-order stability implies consistency in the rank ordering of individuals according to the intensity of a given trait across repeated measurements and is typically measured by correlations: in this setting, those who are most risk-averse at one time also tend to be most risk-averse at later times. While a high level of rankorder stability is a defining aspect of a trait, mean-level stability is not. Mean-level stability refers to consistency in the average level of a trait over time. It is important to stress that the concept of mean-level stability refers to an "average level" or central tendency in repeated measurement, not an exactly constant parameter value. However, personality psychologists acknowledge the existence of systematic changes in the average level of a trait within individual over time. Such changes might occur due to aging, new experiences, or traumatic events, for example.
Opposing the more traditional conception of personality traits being characterized by a mean or central tendency (measured with some error), Fleeson (2001) argues that personality traits should be conceived as density distributions. He offers three lines of evidence that within-person variability in traits is too large and too systematic to be ignored. First, on average, within-person variability in traits over time is as large as variability in traits between individuals. Second, within-person variability is not only measurement error but represents predictable individual differences in reactions to changes in the situation ("conditional traits"). Finally, for a given individual and a given personality trait, both mean and standard deviation of the distribution are parameters that are stable over time. According to this view, "trait concepts are not threatened in their usefulness by the existence of within-person variability, because of the equally large degree of distributional stability" (p. 1019).

Conceptual Framework and Empirical Evidence
Most studies that report results on the stability of adults' risk preferences in panel data document correlations of an individual's risk preferences across time that are significantly positive but typically moderate (Dohmen, Lehmann, and Pignatti 2016; for a detailed and excellent review, see Chuang and Schechter 2015). The reported correlations range from 0.18 to 0.68 for time horizons varying from a few days to five years. About half of the studies use incentivized experiments as opposed to hypothetical experiments or survey questions. In contrast, correlations of risk preferences over time in Chuang and Schechter (2015), Horowitz (1992), Lönnqvist, Verkasalo, Walkowitz, and Wichardt (2015)  This evidence about the stability of risk preferences can be interpreted as the glass being half-full or half-empty. It is half-full in the sense that the available empirical evidence implies that individual risk preferences do represent a persistent characteristic of an individual that is at least moderately stable over time: correlations over time in panel data are nearly exclusively positive, typically significant, and of medium or large size. It is half-empty because the correlations of risk preferences over time are low enough to cast doubt on the empirical validity of the strict stability definition typically put forward in economics. However, it is important to stress that less-than-perfect correlations in panel data do not provide unequivocal evidence in favor of changing risk preferences. Even if risk preferences were perfectly stable, measurement error in risk preferences could cause low correlations in panel data over time. But given the additional evidence on systematic changes in risk preferences as individuals advance in age, as well as due to exogenous shocks such as economic crises or temporary variations in self-control, emotions, or stress, it seems unlikely that measurement error alone is responsible for the substantial deviations from perfect correlations. Instead, it seems plausible that people's risk aversion changes.

A Conceptual Framework for Preference Stability
In the following, we suggest a framework for studying several possible reasons why an individual's risk aversion may change. The standard economic definition of stability of an individual's risk preferences-absolute stability of a single parameter that is sufficient to characterize an individual's risk preferences-is relaxed in several respects, building on concepts from personality psychology. In particular, we extrapolate Fleeson's (2001) concept of personality traits as distributions to the case of economic preferences and replace the single, constant parameter by a distribution that is characterized by mean and variance (ignoring further moments for the sake of simplicity). Moreover, we do not assume perfect mean-level stability (consistency in the average level of a trait over time). Figure 1 illustrates this framework. For expositional clarity, it focuses on a representative individual and ignores the substantial heterogeneity in risk preferences across individuals. The sketched effect sizes are inspired by empirical findings, and we provide some detailed estimates for each source of variation in risk preferences from selected studies below.
First, the solid line in Figure 1 shows continuous change in the mean-level of risk preferences, reflecting empirical evidence that individuals become more risk-averse over the life cycle. Second, the figure also allows for abrupt mean-level changes in individual risk preferences-as observed in the presence of exogenous shocks like economic crises, natural catastrophes, or violent conflict. This possibility is shown by the dashed line that represents a downward shift of the solid line. Third, the figure displays a distribution around that mean that could be represented by a variance. The variance of the preference distribution allows for temporary variation in risk preferences, which is in line with empirical evidence that temporary variations in emotions, self-control, or stress cause temporary variation in risk preferences around a baseline or average level.
Before I discuss the evidence concerning these three reasons for instability of risk preferences, I should stress that this framework does not propose to treat risk preferences as completely stochastic. Age-related changes in adults' risk preferences are modest in size and take place slowly. Exogenous shocks are rare events. Temporary changes in self-control, stress, and emotions induce only temporary, typically small changes in risk preferences. As a result, individual risk preferences are moderately stable over time and sufficiently persistent to be considered an individual trait. However, their degree of stability is too low to be reconciled with the assumption of perfect stability in neoclassical economic theory. Moreover, because change in risk preferences is systematic, it should not be dismissed as meaningless noise.

How Do Risk Preferences Evolve over the Lifetime?
There is a clear pattern of risk preferences over the life cycle: as individuals grow older, they become less willing to take risks. Empirical evidence on risk preferences in childhood and adolescence is largely based on cross-sectional data and  documents systematic changes as children grow. At younger ages, children are more willing to take risks than adults, and a larger share of them behave in a riskseeking manner (Deckers, Falk, Kosse, and Schildberg-Hörisch 2015;Levin, Hart, Weller, and Harshman 2007;Moreira, Matsushita, and Da Silva 2010;Paulsen, Platt, Huettel, and Brannon 2011). As children grow they become less willing to take risks and in adolescence their risk preferences converge to those of adults (Levin, Hart, Weller, and Harshman 2007;Levin, Weller, Pederson, and Harshman 2007;Paulsen et al. 2011). 5 For example, in Deckers et al. (2015, correlations range from 0.12 to 0.24 for a period of 16 months with an average initial age of 7.8 years, while Levin, Hart, Weller, and Harshman (2007) present a correlation of 0.38 over a three-year period with age ranges of 6-8 and 9-11. These systematic changes in risk preferences during childhood and adolescence are in line with a standard model of skill formation (Cunha and Heckman 2007). The skills in this model include both cognitive and noncognitive skills-such as risk preferences, patience, self-control, and perseverance. Skill formation is modeled as a dynamic, multistage process. In contrast to a model of stable preferences, children's skills in this model change over time as the result of accumulating investments and the self-reinforcing and cross-fertilizing nature of skills.
From the onset of adulthood to old age, the trend to greater risk aversion continues but is less pronounced (Bucciol and Zarri 2015;Dohmen, Falk, Golsteyn, Huffman, and Wagner 2017;Josef, Richter, Samanez-Larkin, Wagner, Hertwig, and Mata 2016;Sahm 2012;Schurer 2015). Three studies have used panel datasets and disentangle birth cohort and period effects from age effects. Using large representative panel datasets like the DNB Household Survey from the Netherlands and the German Socio-Economic Panel in Germany, which include self-reported measures of the willingness to take risks, Dohmen et al. (2017) find that the willingness to take risks decreases linearly from early adulthood until approximately age 65, after which the slope becomes flatter. In terms of effect size, risk attitudes decrease by about 0.023 standard deviations for each additional year of age. Translating this age effect into an effect on life outcomes, Dohmen et al. (2017) predict that an increase in society's median age of 10 years implies 6 percent less self-employment or 2.5 percent less investment in stocks, ceteris paribus. Building on their work, Schurer (2015) documents that risk tolerance declines strongly for all socioeconomic groups from late adolescence up to age 45. From age 45 onwards, however, the risk tolerance of individuals with high socioeconomic status stabilizes or even increases, while the risk tolerance of individuals with low socioeconomic status continues to drop. Using panel data on hypothetical gambles on lifetime income from the US Health and Retirement Study, Sahm (2012) finds a modest decline in risk tolerance in a sample of older adults (age 45-70).
Because all three of these studies rely on self-reported measures of risk aversion, they do not allow for disentangling whether it is risk preferences, risk perceptions, or constraints (for example, having a higher number of dependents to worry about) that changes with age. Cross-sectional evidence based on hypothetical choices between lotteries (Donkers, Melenberg, and Van Soest 2001) and choices between safe payoffs and lotteries (reviewed in Mata, Josef, Samanez-Larkin, and Hertwig 2011) tends to confirm that risk aversion increases in age, although effect sizes are rather small. This line of research focuses on lotteries with given probabilities and well-defined, rather low payoffs, suggesting that changes in willingness to take risk by age at least partially reflect changes in risk preferences.

Do Exogenous Shocks Affect Risk Preferences Lastingly?
The literature on how exogenous shocks such as natural disasters, violent conflict, or economic crises affect risk preferences is relatively new, but growing quickly.
For negative economic shocks such as the financial crisis in 2008-2009, the evidence rather consistently documents an increase in risk aversion, using a variety of methods. In terms of our framework, such an increase in risk aversion represents an abrupt mean-level change in individual risk preferences. Dohmen, Lehmann, and Pignatti (2016) document this result based on self-reported questionnaire measures of general risk preferences and representative survey data from Germany and Ukraine. Gerrans, Faff, and Hartnett (2015) work with data on financial investors from Australia, the United Kingdom, and the United States who filled out a psychological trait scale that also covers self-assessed financial risk tolerance. Guiso, Sapienza, and Zingales (forthcoming) measure the rise in Italian investors' risk aversion using both a measure of self-assessed financial risk-taking and hypothetical choices between a constant gamble paying €10,000 or zero with equal probability and a sequence of safe payments, similar to the price list approach. In terms of effect size, they find that the risk premium required to accept the risky gamble increased from €1,000 to €2,500 following the crisis. Similarly, the fraction of respondents who say they do not want to take any financial risk rises from 16 to 43 percent. Necker and Ziegelmeyer (2016), working with representative panel data on self-reported financial risk attitudes from Germany, document that households attributing losses to the crisis decreased their risk tolerance.
A potential concern about studies that estimate risk aversion by using the willingness to take financial risks is that it is hard to disentangle whether changes in the willingness to take financial risk reflect changes in risk preferences or beliefs about returns. For example, Weber, Blais, and Betz (2002) document reduced risk-taking of investors due to the financial crisis, which they attribute to changes in subjective expectations of risk and return as opposed to changes in risk preference-which they argue is rather stable in their data. Malmendier and Nagel (2011) provide evidence that is consistent with a beliefs channel, but does not rule out an effect of experiences on risk preferences. All studies suggest that changes in risk preferences due to the financial crisis are not mostly driven by changes in income or wealth (see also Sahm 2012;Bucciol and Miniaci 2018). 6 Guiso, Sapienza, and Zingales (forthcoming) and Necker and Ziegelmeyer (2016) point at negative emotions as a possible channel for increased risk aversion.
Another strand of the literature studies the relationship between macroeconomic conditions and risk preferences in general, instead of focusing on shocks only. This literature provides evidence for continuous, typically modest, mean-level changes in risk preferences due to continuously changing macroeconomic conditions. For example, Bucciol and Miniaci (2018) use panel data from the representative Dutch Household Survey, self-reported attitudes towards financial risk-taking, and GDP, market returns, and unemployment rates as contemporaneous macroeconomic indicators. Sahm (2012) uses panel data on older adults (age 40-75), hypothetical gambles on lifetime income from the US Health and Retirement Study (HRS) as a measure of risk preferences, and an Index of Consumer Sentiment to proxy the business cycles. Both studies document that individuals are willing to take substantially larger risks during periods of economic growth and are more risk-averse during periods of recession. Malmendier and Nagel (2011) take a more long-term view and examine whether households differ in their willingness to take financial risks depending on the macroeconomic history they experienced over the whole course of their lives. Using repeated cross-section data from the US Survey of Consumer Finances from 1960 to 2007 and controlling for age and year effects, they show that households with higher experienced stock market returns express a higher willingness to take financial risk. More recent experiences receive higher weights, but even returns experienced decades earlier still have some impact. Based on their estimates, the authors extrapolate that for those aged 30 in 2008, the effect of the financial crisis will only have died away after 30 years, pointing at a rather slow fade-out of large shocks.
Research on how natural catastrophes or violent conflict affect risk preferences is inconclusive. The literature review by Chuang and Schechter (2015) finds that natural disasters such as earthquakes, famines, floods, droughts, hurricanes, and tsunamis have been found to either increase risk aversion, or decrease risk aversion, or to have no (consistent) effect on risk preferences. Likewise, the effects of conflict such as civil wars, riots, or political violence show contradictory results, suggesting that conflict may decrease risk aversion (Voors, Nillesen, Verwimp, Bulte, Lensink, and Van Soest 2012) or increase risk aversion (Callen, Isaqzadeh, Long, and Sprenger 2014;Kim and Lee 2014;Moya 2015). Currently, we can only guess about the reasons for these inconsistent results. A large share of the papers that document contradictory effects of violent conflict or natural disasters use experimental data from developing countries, but these tools were typically developed in the context of high-income countries. They may be more likely to produce noisy results in samples that are less educated, partly illiterate, or less used to abstract thinking (for example, Vieider forthcoming; Chuang and Schechter 2015). Moreover, the literature on the effects of natural catastrophes or violent conflict is suffering from a lack of theoretical predictions about the circumstances under which we should expect an increase or decrease in an individual's willingness to take risks. This literature is growing quickly, and in the future, it may become possible to do a meta-analysis that could shed light on the reasons behind the divergent findings.

Are There Systematic but Temporary Variations in Risk Preferences?
A rapidly growing body of research has investigated factors in the decision environment that go beyond prices and constraints, and which might cause systematic but temporary deviations from underlying "baseline risk preferences." This research can be broadly grouped into two areas: temporary variations in an individual's self-control resources, or temporary variation in emotions and stress.
In the area of self-control, a recent class of economic models posits that economic decisions are shaped by the interaction of "dual selves" or "dual systems": a deliberative or long-run and an affective or short-run system (for a review, see Alós-Ferrer and Strack 2014). Several of these models explicitly address decisionmaking under risk. In particular, lower current levels of self-control resources are assumed to shift the balance of power in favor of the risk-averse short-run self, at the cost of the risk-neutral long-run self. Thus, lower self-control is predicted to induce stronger risk aversion for stakes within a particular range (for example, Fudenberg and Levine 2006, 2011, 2012Fudenberg, Levine, and Maniadis 2014). 7 Several laboratory experiments provide causal evidence on the link between self-control and risk preferences by using so-called "ego-depletion tasks" from social psychology (for meta-analyses, see Hagger, Wood, Stiff, and Chatzisarantis 2010;Carter and McCullough 2014;Hagger et al. 2016). Depletion tasks are based on the notion that exerting self-control in one activity consumes self-control resources, thereby increasing self-control costs in subsequent activities (Baumeister, Bratslavsky, Muraven, and Tice 1998). Increased self-control costs in turn will result in lower levels of self-control exertion and thus increase risk aversion according to the models of Fudenberg and Levine (2006,2011,2012) and Fudenberg, Levine, and Maniadis (2014). Measuring risk preferences via finely graduated choice lists, in Gerhardt, Schildberg-Hörisch, and Willrodt (2017), my coauthors and I explicitly test hypotheses from the Fudenberg-Levine model. We do not find any evidence for increased risk aversion after self-control depletion, but a small, consistent tendency towards increased willingness to take risks. The same tendency of increased risktaking under ego-depletion is observed in Friehe and Schildberg-Hörisch (2017), where we measure risk preferences using the risky investment task by Gneezy and Potters (1997). Similarly, Stojic , Anreiter, and Carrasco Martinez (2013) do not find a significant effect of ego-depletion on risk preferences measured via price lists. Benjamin, Brown, and Shapiro (2013, Study 3) and Gerhardt, Biele, Heekeren, and Uhlig (2016) use cognitive load manipulations (specifically, memorizing large numbers) that are expected to decrease self-control resources at the time of risky decision-making. Risk preferences are measured by a price list or multiple pairwise lottery choices, respectively. Both studies show that cognitive load induces significantly more risk-averse behavior. In terms of effect sizes, the riskier of two lotteries is chosen in 54 percent of choices in the cognitive load condition and 57 percent in the absence of cognitive load in Gerhardt et al. (2016).
Predictions on how emotions or stress should plausibly influence risk preferences are conflicting. In psychology, the Mood Maintenance Hypothesis (Isen and Patrick 1983) suggests that positive affect induces greater risk aversion, while negative affect leads to a higher willingness to take risks. (The term "affect" refers to conscious, subjectively experienced aspects of an emotion, apart from bodily changes.) The intuition is that individuals in good mood try to protect their good mood by avoiding risks, while individuals in bad mood take risks, trying to improve their mood. The Affect Infusion Model (Forgas 1995) posits the opposite effects. Since risk-related decision-making is a complex process that requires deliberation, this model posits an "affect-priming-mechanism" to be at work-that is, affect may indirectly influence decisions through its influence on selective attention to information or via associative processes. In particular, affect-priming predicts that subjects in bad mood will be more risk-averse than subjects in good mood since they are more attentive to downside risks.
With a few exceptions, empirical evidence on temporary shifts of risk preferences due to changes in emotions or stress tends to be in line with the Affect Infusion Model: that is, negative emotions like fear or stress are typically found to increase risk aversion in studies that establish causal relationships. Most empirical studies on the relation between risk preferences and emotions use a priming approach, so that the resulting shift in emotions can be treated as exogenous. For example, Guiso, Sapienza, and Zingales (forthcoming) induce fear by having students watch a horror movie, then measure risk preferences using a hypothetical choice-list format and find that on average treated students have a 27 percent higher risk premium than untreated ones. Using an incentivized, adapted risky investment task, Cohn, Englemann, Fehr, and Maréchal (2015) prime financial professionals with a financial boom-or-bust scenario and document that subjects are more fearful and substantially more risk-averse in the bust than in the boom condition. In particular, they invest on average 22 percent less into the risky asset in the bust condition than in the boom condition. Moreover, they expose university students to low or high levels of fear by threat of painless or painful electric shocks and show that those with lower levels of fear are willing to take significantly higher risks in ambiguous risky decisions. In Kandasamy et al. (2014), subjects are randomly assigned to taking placebo or hydrocortisone capsules which induce chronic stress over an eight-day period and make incentivized pairwise choices between lotteries. While there is no effect of acute stress (shown by the cortisol response 90 minutes after the first hydrocortisone capsule was taken) on risk preferences, sustained elevation of cortisol leads to greater risk aversion. Cahlíková and Cingl (2017) expose a randomly chosen subset of participants to a psychosocial stressor in a standard laboratory stress-induction procedure, the Trier Social Stress Test for Groups, and measure risk preferences via multiple price lists. They find that acute psychosocial stress significantly increases risk aversion, a result that is mainly driven by men. 8

Implications and Conclusion
We started from the premise that it is ultimately an empirical question whether risk preferences are stable over time. The evidence stems from diverse strands of literature, covering the stability of risk preferences in panel data over shorter periods of time, life-cycle dynamics in risk preferences, the possibly long-lasting effects of exogenous shocks on risk preferences as well as temporary variations in risk preferences. Individual risk preferences appear to be persistent and moderately stable over time, but their degree of stability is too low to be reconciled with the assumption of perfect stability in neoclassical economic theory. Inspired by research in personality psychology, we have proposed a framework for preference stability that considers preference parameters as distributions and relies on rank-order stability without imposing mean-level stability. This framework is able to accommodate the empirical evidence on stability and change in risk preferences. Important next steps in research on the stability of risk preferences involve empirical, theoretical, and policy issues.
For empirical research, an important next step would be to seek ways to reduce measurement error in risk preferences. There is a largely unexplored potential for economists to benefit from applying psychometric standards from personality psychology to measures of economic preferences (Borghans et al. 2008;Golsteyn and Schildberg-Hörisch 2017). In particular, it should become standard to measure a single construct like risk preferences with multiple items (experiments and/ or questionnaire measures) and to average over those items in order to reduce measurement error. Economists should search for measures of risk preferences with the highest test-retest stability in panel data over shorter periods of time. Moreover, researchers should use measurement tools of risk preferences that have been validated in the context under consideration: for example, a study seeking to measure risk aversion in a largely illiterate sample should use a tool that has been validated in a similar sample.
As far as theory is concerned, research on the stability of risk preferences might ultimately result in an overarching model of endogenous risk preferences in which risk preferences evolve over time as a function of, among others, aging, exogenous shocks, and changes in the decision environment that encompasses situational factors such as the current level of self-control, stress, or emotions. By acknowledging that individual risk preferences are not carved in stone but may change under specific circumstances, economists would take an important step towards the view commonly held in psychology that the decision environment (beyond incentives and constraints) affects individual decisions.
For many research questions and circumstances, it will still be fine to adopt the simpler textbook model of stable risk preferences as an as-if approach and to investigate how individuals with given preferences react to changes in incentives and constraints. However, systematic changes in risk preferences over time severely complicate policy advice and welfare analysis. After all, policy advice is typically based on predicting (or in case of evaluation, retrospectively documenting) behavioral reactions to institutional changes under the assumption that preferences are stable. Preference changes are a major threat to that approach. To give just one example, consider policymakers who react to a financial crisis with a new regulation. If the financial crisis has induced the population to become more risk-averse, but public policy assumes constant risk preferences before and after the crisis, those proposing and analyzing the new policy will fail to predict behavioral responses adequately.
On the other side, acknowledging these systematic instances of changes in risk preferences opens up new possibilities for public policy. If exogenous shocks affect risk preferences lastingly, policymakers could expose individuals to positive "shocks" in their social environment. For example, such interventions might aim at reducing the high levels of risk proclivity that are associated with drug addiction, criminal activities, or teenage pregnancies. Gutman and Schoon (2013) and Kautz, Heckman, Diris, ter Wel, and Borghans (2014) survey evidence on the effects of interventions such as mentoring programs, social and emotional learning programs, center-based care, residential-based education programs, or programs aimed at improving parenting practices. Some of the surveyed interventions have been found to decrease overly risky behaviors, but no evaluation directly measures risk preferences. An important prerequisite for the successful timing of interventions in childhood and adolescence is identifying critical periods in which risk preferences are especially malleable (Cunha and Heckman 2007), an open challenge for future research.
Finally, a recognition that risk aversion may shift seems to imply that people have multiple preferences, leaving open the question of which kind of preferences public policy and welfare analysis should rely on. While this article has focused on the stability of risk preferences, similar lines of reasoning apply to other dimensions of economic preferences-in particular time preferences and social preferencesand empirical evidence concerning their stability and systematic instances of changes in these areas is starting to accumulate as well. Evidence of systematic preference changes over time deprives economics of a clean analytical foundation for assessing the welfare impact of policies and behaviors, and there is no consensus for how to deal with the issue. For example, O'Donoghue and Rabin (2003), Thaler and Sunstein (2003), Glaeser (2006), O'Donoghue and Rabin (2006), Bernheim (2009), Bernheim and Rangel (2009), and Chetty (2015 propose highly divergent approaches for how to do welfare economics if choices do not unambiguously reveal preferences. Others have proposed turning away from welfare criteria that are based on preference satisfaction, suggesting an opportunity-based approach to welfare (as in Sugden 2004) or approaches rooted in happiness economics (for example, O'Donnell, Deaton, Durand, Halpern, and Layard 2014). Addressing the issue of how to conduct research and policy while acknowledging the reality of changing preferences seems certain to present difficult challenges. ■ I am grateful to Bart Golsteyn, Gordon Hanson, Enrico Moretti, and Timothy Taylor for helpful comments.