Carole Treibich

Are Survey Risk Aversion Measurements Adequate in a Low Income Context? Carole Treibich Are survey risk aversion measurements adequate in a low income context?

Keywords: Risk aversion measurement, India, Survey design, Risky conducts

Using an original dataset collected among motorcyclists in New Delhi (2011), this paper compares three different survey measures of risk attitudes: self-assessment, hypothetical lotteries and income prospect choices. While previous research on risk aversion measurement methods in developing countries mainly looked at specific groups such as rural farmers or students, the dataset I use covers a large and heterogeneous urban population. I first show that all measurements are positively and highly correlated with one another, this being even more the case within methodologies and within domains. Subsequently, I investigate the predictive power of these different individual risk-aversion measurements on occupation choices and health decisions. Most of my elicited risk preferences appear to predict risky health behaviors well. Puzzling results are found with the lotteries and may be interpreted either as evidence of risk-compensation between domains or as an incapacity to capture the desired characteristic. Finally, thanks to information on religious beliefs and practices, I am able to verify that cultural background does not impact on the relationship between risk preferences and risky conducts. Overall, this analysis highlights that elicitation of risk-aversion measurements through surveys in a developing country like India thus appears possible.

Introduction

Preferences toward risk are a key concept used in economics to explain individual decisionmaking. In the context of low income countries, an individual's risk aversion is often mentioned as a possible explanation for the lack of entrepreneurship or the technological innovation delays maintaining individuals in poverty and impeding growth and development [START_REF] Cardenas | Behavioural development economics: Lessons from field labs in the developing world[END_REF]. However, standard household surveys, such as the World Bank's Living Standard Measurement Surveys, do not ask questions about risk aversion and hence cannot be used to analyze the effect of risk preferences on various economic outcomes. In addition, risk attitude measurement is a difficult challenge which is still being debated in the literature. The various existing methodologies include Likert scales [START_REF] Dohmen | Individual risk attitudes: Measure, determinants, and behavioral consequences[END_REF]; lotteries [START_REF] Holt | Risk aversion and incentive effects[END_REF]; prospect choices on lifetime income [START_REF] Barsky | Preference parameters and behavioral heterogeneity: An experimental approach in the health and retirement study[END_REF]; domain-specific risk-taking scales 1 [START_REF] Weber | A domain-specific risk-attitude scale: Measuring risk perceptions and risk behaviors[END_REF]; scores based on the item response theory [START_REF] Arrondel | Mesurer les préférences individuelles à l'égard du risque[END_REF]; or actual risktaking behaviors (e.g. smoking, seat-belt use). One key aspect of this debate is whether each individual has a single unique preference toward risk or whether his or her risk aversion varies depending on the domain considered. [START_REF] Dohmen | Individual risk attitudes: Measure, determinants, and behavioral consequences[END_REF], [START_REF] Barseghyan | Are risk preferences stable across contexts? evidence from insurance data[END_REF] and [START_REF] Einav | How general are risk preferences? choices under uncertainty in different domains[END_REF] provided evidence against the hypothesis of stable risk preferences across contexts. Furthermore, based on evidence from safety literature, [START_REF] Wilde | Risk homeostasis theory: an overview[END_REF] documented the fact that individuals do not try to minimize risk but rather target a certain level of risk and adapt their behaviors accordingly. Some compensation may thus appear with respect to the risk individuals are willing to take in different domains. The degree of substitution between domains, the role of risk perceptions, but also the formulation of questions makes the measurement of risk-aversion and the study of its influence on risky behaviors quite complex.

Along with this growing literature on the measurement of risk preferences, empirical research has compared many of these risk aversion methods, both in developed and developing countries (see [START_REF] Binswanger | Attitudes toward risk: Experimental measurement in rural india[END_REF][START_REF] Dohmen | Individual risk attitudes: Measure, determinants, and behavioral consequences[END_REF][START_REF] Anderson | Predicting health behaviors with an experimental measure of risk preference[END_REF][START_REF] Ding | Can we measure individual risk attitudes in a survey? IZA Discussion Paper Series[END_REF][START_REF] Hardeweg | Experimental validated survey evidence on individual risk attitudes in rural thailand[END_REF]. This strand of research aims at investigating which measure, if any, best predicts individual risk behavior. Moreover, it also studies whether questions easily implementable in a survey are indeed able to capture individuals' preferences toward risk. [START_REF] Charness | Experimental methods: Eliciting risk preferences[END_REF] surveyed the literature and classified risk-aversion measurement methods according to their complexity. They highlighted that while more complex methods may be able to provide a more precise estimation of the risk aversion parameter, they fail in predicting risky conduct. Finally, the cultural con-1 Such scales measure both risk attitudes and perceived riskiness.

text may modify the impact of the elicited risk aversion parameters on risky decisions.

In particular, religious beliefs like fate, particularly widespread in developing countries, may change the influence of risk aversion on risky conducts. Therefore, a measurement method which has been proven to predict behaviors in one country may not be appropriate in another context. It is therefore interesting to replicate the comparison of risk aversion variables obtained with various methodologies in different regions of the world.

In this paper, I aim to contribute to the debate on risk aversion measurement through the use of an original dataset collected among motorcyclists in Delhi in the year 2011.

While other studies implemented in developing countries focused on particular subgroups of the population (mainly farmers and students, see [START_REF] Charness | Comprehension and risk elicitation in the field: Evidence from rural senegal[END_REF] for a review), I can rely on a more heterogeneous sample of individuals. Methodologies widely used in the literature (self-assessment of risk aversion, hypothetical lotteries and income choice prospects) are included in the survey. I first explore to what extent the information collected differs across methodologies and domains. To do so, I look at the share of riskaverse individuals in each measurement type and at correlations between the different risk aversion variables. It seems that lotteries differ substantially from the other measurement methods regarding the information they actually capture. In a second step, I study the socio-demographic determinants of individuals risk aversion and test whether they are the same as the ones mentioned in the literature. Subsequently, I investigate whether the different survey measurements are appropriate in the Indian context. More precisely, I study whether the various risk preference parameters are good predictors of work sector choices and risky health behaviors, even after controlling for cultural characteristics such as religious beliefs. I find that self-reported risk aversion predicts well the choice to work in the private sector and risky behaviors in health matters. Results found when using the hypothetical lotteries can either be interpreted as evidence for a risk-compensating effect across domains or as proof that such a methodology is not adequate in the Indian context (potentially because of the absence of financial incentives). As for the measurement method based on income prospect choices, this is associated with smoking and drinking behaviors but not with occupational decisions.

The remainder of the paper is organized as follows. Section 2 presents some conceptual considerations on risk-aversion measurements. Section 3 introduces the data and presents the various methodologies used to measure risk preferences. In Section 4, I report the empirical results regarding accuracy of risk aversion measurements in the Indian context and information consistency across survey methods. Section 5 concludes.

Conceptual considerations

Preference toward risk corresponds to an individual-specific characteristic which plays a role in the agents decision-making process under uncertainty. This parameter is usually assumed to be exogenous, i.e. innate and immutable. If a person can choose between two different situations which are identical regarding their expected outcomes but which differ with respect to the probability of the realization of the final state, a risk-averse individual will derive more utility from the behavior without uncertainty. In an expected utility framework, such a definition is reflected in the concavity of his utility function.

Applied research aims at capturing this individual characteristic with the aim of predicting whether a person would engage in risky conduct or not.

A wide range of methods have been used in the empirical literature to proxy or elicit individual risk preferences and study their impact on individual decisions [START_REF] Anderson | Predicting health behaviors with an experimental measure of risk preference[END_REF][START_REF] Charness | Experimental methods: Eliciting risk preferences[END_REF]. Hereinafter I describe some of these methodologies.

In the [START_REF] Holt | Risk aversion and incentive effects[END_REF] setting, agents have to choose between two lottery options which differ in the gap between their two possible outcomes. In this setting, risk aversion is seen as the willingness to avoid a high variation in the outcomes. The lottery option at which the individual switches from the high to the low variance option enables the authors to compute intervals for the risk-aversion parameter. However, the comparison of two lotteries may be quite complicated for certain populations. Instead, in [START_REF] Hardeweg | Experimental validated survey evidence on individual risk attitudes in rural thailand[END_REF], Thai farmers had to choose between different safe monetary amounts and a fixed lottery. Such a simplified method has been implemented in studies in both developed as well as developing countries (see [START_REF] Dohmen | Are risk aversion and impatience related to cognitive ability?[END_REF][START_REF] Palma | Individual and couple decision behavior under risk: evidence on the dynamics of power balance[END_REF][START_REF] Guiso | Time varying risk aversion[END_REF][START_REF] Vieider | Common components of risk and uncertainty attitudes across contexts and domains: Evidence from 30 countries[END_REF].

As for [START_REF] Barsky | Preference parameters and behavioral heterogeneity: An experimental approach in the health and retirement study[END_REF], they used three yes/no questions from the Health and Retirement Study where interviewees were offered job opportunities leading to different lifetime outcomes. Individuals were then classified into four risk-aversion groups without any functional form assumption. However, the non-monetary value of the individuals current job may have biased the results obtained through this methodology.

Self-reported willingness to take risk is extremely easy to introduce in a survey. This has been done in the German Socio-Economic Panel considering general daily life and specific domains such as career, health, finance, or leisure. However, the scale proposed to respondents, as well as the broad definition of domains, have been called into question when comparing two individuals who may interpret the possible answers differently or refer to different situations. In order to counteract this issue, summated rating scales have been developed. [START_REF] Arrondel | Mesurer les préférences individuelles à l'égard du risque[END_REF], for instance, asked in the PATER survey up to 50 questions in domains such as consumption, finance, labor, health or family matters. This method certainly permits a better measure of the global level of individual risk aversion but requires more time to be collected. Furthermore, items must be adapted to the local context, implying an important upstream testing phase.

Finally, [START_REF] Weber | A domain-specific risk-attitude scale: Measuring risk perceptions and risk behaviors[END_REF]; [START_REF] Blais | A domain-specific risk-taking (dospert) scale for adult populations[END_REF] elaborated a risk-taking scale (DOSPERT) to assess both conventional and perceived risk attitudes through scales ranging from 1 to 7 in five specific domains; their argument being that both the willingness to take risk and the perception of a risky situation are important when predicting risky conduct. However, this last method mixes the inconveniencies of the two previous methodologies as it requires asking many questions and inter-individual comparison is subject to biases.

As described above, risk preference elicitation techniques differ in the number of questions to be asked, the complexity of these questions and in the incentives that can be provided to ensure that honest replies are obtained.2 But do all these methods lead to the same information? Or, is one measurement method able to capture an individuals risk preferences better than others? Does the cultural and economic context alter the predictive power of such indicators? Some of these questions have been investigated in the literature. Indeed, researchers have discussed the relevance of experimental vs. survey measurements, as well as the effectiveness of complex vs. simple measurement methods.

Collecting evidence regarding the accuracy and the performance of each method is crucial and of particular interest for survey designers. While survey questions are often more easily extendable to large samples such as national household surveys, incentivized lottery experiments can only be implemented at a reduced scale. [START_REF] Anderson | Predicting health behaviors with an experimental measure of risk preference[END_REF] investigated the relationship between risk preferences, elicited through lotteries, and five health behaviors. They argued, without however relying on a comparison of data, that a survey or hypothetical questions are likely to be biased or noisy unlike an "experimental" lottery where one out of ten of the choices were realized. 3 In another paper, again using U.S. data, the same authors compared the infor-mation provided by lotteries and that obtained through hypothetical gamble questions on inheritance and job choices. 4 Anderson and Mellor (2009) showed that the different techniques did not provide the same individual risk level. [START_REF] Binswanger | Attitudes toward risk: Experimental measurement in rural india[END_REF] measured attitudes towards risks in rural India using both surveys and questions to elicit certainty equivalents and experimental gambling with real payoffs. He highlighted inconsistencies between the two methodologies used and argued that the interview method was the one subject to bias. Yet, in their review of the literature on risk aversion measurements, [START_REF] Charness | Experimental methods: Eliciting risk preferences[END_REF] discussed the differences in the degree of complexity of existing elicitation techniques. These authors stressed that while the multiple price list method [START_REF] Holt | Risk aversion and incentive effects[END_REF][START_REF] Binswanger | Attitudes toward risk: Experimental measurement in rural india[END_REF] allows the precise elicitation of the risk aversion parameter, it seems to have a relatively limited capacity to predict risky conduct. The opposite results are obtained with the self-assessed measurements. For instance, [START_REF] Ding | Can we measure individual risk attitudes in a survey? IZA Discussion Paper Series[END_REF] interviewed Chinese students and elicited their risk preferences via an incentivized lottery and self-assessment risk, asking a general question and questions in five domains. The lottery questions had virtually no predictive power for the risky behaviors the authors selected, whereas the general risk attitude variable gave the best prediction score. Based on [START_REF] Dohmen | Individual risk attitudes: Measure, determinants, and behavioral consequences[END_REF]'s analysis of a German sample, [START_REF] Hardeweg | Experimental validated survey evidence on individual risk attitudes in rural thailand[END_REF] tested the validity of a simple survey item on risk attitude by comparing it to a related field experiment. Using a sample of 900 Thai farmers, they compared three different measurement methods: a survey item based on a Likert scale, lottery questions and a hypothetical investment question. Results indicated that the self-reported measurement method predicted risky conducts such as the purchase of lottery tickets or self-employment better than the two other measurements. The authors thus argued in favor of the use of simple survey measurements which simultaneously provide useful information on risk attitude and are easily implementable on a large scale.

In this paper, I focus on elicitation techniques which are straightforward in their implementation in surveys and which do not require any financial reward to participants. I take advantage of an original dataset collected among motorcyclists in New Delhi in 2011. While previous research on risk aversion measurement methods in developing countries mainly looked at specific groups such as rural farmers or students (see [START_REF] Hardeweg | Experimental validated survey evidence on individual risk attitudes in rural thailand[END_REF][START_REF] Ding | Can we measure individual risk attitudes in a survey? IZA Discussion Paper Series[END_REF][START_REF] Vieider | Common components of risk and uncertainty attitudes across contexts and domains: Evidence from 30 countries[END_REF][START_REF] Charness | Comprehension and risk elicitation in the field: Evidence from rural senegal[END_REF], the dataset I use covers a large and heterogeneous urban population. I explore whether the different risk aversion measurement methods provide the same type of information on individuals and whether they are able to predict risky behaviors in different domains.

Thanks to information on religious beliefs and practices, I am also able to verify that cultural background does not impact on the relationship between risk preferences and risky conducts. To summarize, this analysis provides additional evidence on the capacity of simple survey measurements to capture individual risk preferences in general and in a developing country in particular.

Data

3.1 General presentation of the survey I take advantage of an original survey on the road habits of 902 motorcyclists which was implemented in Delhi in 2011.5 Besides socio-demographic characteristics, information regarding their occupation and different risky health behaviors (smoking, drinking) was collected. We also attempted to measure individual risk-aversion using simple techniques already developed in the literature. Further details on the measurement of individuals risk attitudes are presented below.

Measurements of risk aversion

Several methodologies to measure risk aversion have been included in the survey. In order to be able to compare the different risk-aversion variables, I restrict my sample to individuals who answered all risk-aversion questions. I base my analysis on three elicitation methods (self-reported risk aversion, hypothetical lotteries and income prospect choices) as they are more commonly used in the literature and missing observations for some alternative methods6 considerably reduce the number of respondents for whom I have complete information.

Respondents were first asked to self-assess their risk-aversion in general and in four specific domains (on the road, in sport or leisure, in health, and in finance). They were offered a scale ranging from 0 "I am not ready to take risks at all" to 7 "I am fully ready to take risks". I refer to these measurements as the self-reported risk aversion (SRRA) variables. In addition to domain-specific variables, I compute the average of the five SRRA variables. In a second step, interviewees were offered a choice between a fixed lottery, where they had a fifty percent chance of winning either 12,000 INR7 or nothing, and a safe amount, starting at 0 and increasing by 500 INR up to 9,500 INR. The variable used in the analysis is the number of times the individual opted for the safe amount. The lottery questions were based on [START_REF] Holt | Risk aversion and incentive effects[END_REF] but simplified as in [START_REF] Hardeweg | Experimental validated survey evidence on individual risk attitudes in rural thailand[END_REF]. No financial incentives were provided along with the lottery questions. There are two reasons for this: first, the objective here was to compare elicitation measurements implementable in large surveys, where experimental design is hardly feasible, and second, monetary incentives would have substantially increased the budget necessary to collect our data. Finally, following [START_REF] Barsky | Preference parameters and behavioral heterogeneity: An experimental approach in the health and retirement study[END_REF], I asked questions regarding prospective income choices. These were organized in a two-step procedure which lead to the creation of four different risk-aversion groups.

A detailed presentation of the wording used in the questionnaire and the variables subsequently built for the analysis can be found in the Appendices.

Empirical Analysis

The purpose of the empirical analysis is to study whether risk preferences elicited through non-incentivized and simple methods are able to predict risky behaviors in a low income context.

As previously mentioned, I consider in my analysis only the individuals who gave answers in all three risk-aversion measurement methods. This leads to a restricted sample of 675 observations. A comparison between the characteristics of individuals who gave answers in all three risk aversion measurement methods and the full sample can be found in the Appendices (Table 10).

Table 1 displays the socio-demographic information of my sample. 70% of the respondents are men, they are on average 36 years old. 20% of them are illiterate or only completed primary education, while 47% received some tertiary education. 30% of the interviewed individuals belong to families earning less than 10,000 INR per month.8

[Insert Table 1 here] A "good" measure of risk aversion should be in line with the economic theory and predict risky conducts. In addition, according to the empirical evidence, elicited parameters are expected to be associated with some individual socio-demographic characteristics, such as gender, age, level of education or income. I investigate whether each risk attitude variable included in the survey satisfies these criteria. Furthermore, I compare the predictive power of the three different survey methodologies and explore the existence of domain-specific risk aversion. Different cases, which are described below, may be found in the data.

Case 1 : Risk aversion is not domain-specific

The levels of individual risk aversion defined in different domains are similar and, regardless of its domain, the risk aversion measurement is positively correlated with the fact that the individual engages in a risky behavior.

Case 2 : Risk aversion is (partly) domain-specific A risk aversion measurement defined in a given domain does not predict a risky behavior adopted in another domain or does not predict it as well as a risk aversion measurement defined in the same domain as the risky conduct.

Case 3 : Evidence of risk compensation across domains

A risk aversion measurement defined over a specific domain is negatively correlated with the risk taken in another domain.

Case 4 : Risk aversion is noise

One or several risk aversion measurements defined over the same domain do not predict the risky behavior in that domain.

I start by studying whether the different risk aversion measurements elicited in the survey provide similar information regarding the level of risk aversion of each respondent. I then investigate in which way the risk parameters are associated with specific risky behaviors related either to finance matters or health issues. With these findings, this analysis will be able to shed some additional light on the context-specificity of risk aversion and the relevance of the different survey methodologies.

Do survey measurement methods capture the same information on individuals?

Initial evidence that risk aversion differs across domains and methods will be obtained if I find that the different measurements are not highly positively correlated with one another.

Table 2 presents the distribution of the risk aversion measurements. Given the proposed ladder for the self-assessment of risk preferences, one can define as risk averse any individual who has a score above 4, i.e. in the middle of the scale (cf. [START_REF] Ding | Can we measure individual risk attitudes in a survey? IZA Discussion Paper Series[END_REF].

As for the lottery question, the neutrality point corresponds to choosing the safe choice from 6,000 or 6,500 INR upwards.9 A risk-averse person is therefore someone who chose the safe amount at least nine times. By looking at the median, we note that most of the respondents are risk-averse and this for almost all measurements.10 There is some heterogeneity across measurements though. While at least 75% of individuals are categorized as being risk averse with the lotteries, they are less than 50% so when looking at their self-assessment of risk aversion in finance, in sport or with the income choice measure.

Among the SRRA questions, the highest share of risk-averse individuals is obtained with the general question and the one related to health.

[Insert Table 2 here]

To further investigate the consistency of my different risk-aversion measurement methods, I look at the pairwise correlations. From Table 3, we note that all the risk aversion measurement methods are positively and highly correlated with one another, with the notable exception of the correlation between SRRA in finance and risk preferences derived from lotteries. Moreover, correlation coefficients between lotteries and other variables are quite small in magnitude (less than 0.10) and only two of them are significant (SRRA in sport and income prospect choices). SRRA measurements are more correlated with each other11 than with the measurements based on the other methodologies. The income choice measurement is more correlated with the SRRA in finance, while the risk aversion level derived from lottery questions is more correlated with the income choice measure.

In addition, I perform Pearson Chi 2 tests.12 I find that all p-values are inferior to 0.001.

Nevertheless, the Pearson Chi 2 statistics are much lower when the lottery question is one of the two measurements considered. These results thus reveal a higher correlation within methods and domains. However, the use of a different scale across methodologies may partly explain the greater correlation found across self-reported risk-aversion measurements. We can thus invalidate Case 1.

[Insert Table 3 here] 4.2 Are the personal characteristics of respondents related with risk attitudes?

To further investigate the accuracy of my different survey measurement methods in the Indian context, I investigate whether gender, age, education and income are correlated with the individuals level of risk aversion. Indeed, numerous studies, though mainly conducted in developed countries, found that men as well as younger, more educated and wealthier individuals13 are less risk averse (see [START_REF] Outreville | Risk aversion, risk behavior and demand for insurance: A survey[END_REF], for a recent review). Here I therefore tease out the correlations between socio-demographic characteristics and risk attitudes for the case of India and assess whether these fit the evidence found for other countries.

Again I consider the restricted sample of respondents for which I have answers for all three methodologies of risk aversion measurement: self-assessment, hypothetical lotteries and job income choices. As dependent variables, I consider each of my risk aversion measurement methods. I run ordered logit estimations for the self-reported risk aversion measurements in different domains and the job income choices (8 and 4 ordered values respectively). As for the average of SRRA measurements, I perform an ordinary least square regression. Finally, given that I consider the number of safe choices made in the series of lottery questions, I use a negative binomial model for this particular risk attitude measurement.14 Table 4 shows the coefficients obtained with the different specifications used. Similar results are found when using ordinary least square estimations. Results vary from one dependent variable to the other. A gender effect (males being less risk averse) is found with SRRA on the road and in finance. Using a sample of rural farmers in Senegal, [START_REF] Charness | Comprehension and risk elicitation in the field: Evidence from rural senegal[END_REF] found that women were more risk-tolerant when considering their declared willingness to take a risk in general, and thus invalidated this measure for developing countries. However, I do not find such inconsistency with this Indian sample.

An age effect (older individuals being more risk averse) is found for SRRA in general, on the road, in sport as well as with lotteries. An education effect (more highly educated individuals being less risk averse) is detected for SRRA in sport, in health, in finance and for income prospect choices. Finally, the income gradient (wealthier individuals being less risk averse) appears only for job income choices. Income and the two other risk aversion measurements in finance are not significantly correlated, even if these coefficients are negative. It is interesting to note that the average SRRA measurement is significantly correlated with gender, age and level of education.

Overall, the same relationships between socio-demographic characteristics and risk attitudes as the ones already presented in the literature on developed countries are found with this sample.

[Insert Table 4 here] 4.3 Do survey measurements predict the risky conduct adopted by respondents?

In this survey, respondents were asked about both the sector in which they work and their conduct relative to different health behaviors. Looking at Table 5, we see that 23% and 11% of individuals work in the private and public sectors respectively, 20% state they are self-employed and 44% inactive. 15 Moreover, 13% of the interviewees smoke and 16.5% drink alcohol. These various decisions appear, in the literature, to be linked to individuals risk preferences. In particular, [START_REF] Dohmen | Individual risk attitudes: Measure, determinants, and behavioral consequences[END_REF] showed that smoking habits were associated with a higher willingness to take risks. [START_REF] Bonin | Cross-sectional earining risk and occupational sorting: the role of risk attitudes[END_REF] found that individuals who are more risk-averse, are more likely to work in occupations with low earning variation risks. 16 Cramer et al. (2002) highlighted that self-employment is considered a more risky occupation than being an employee and that there is a negative relationship between risk aversion and entrepreneurship. However, in the specific context of India, where public jobs are scarce, occupation may not be exclusively the result of an individual's choice.

[Insert Table 5 here]

15 Only 34% of the inactive respondents are men. 75% of them are between 15 and 25 years old. 16 Both studies used self-assessed risk aversion from the German Socio-Economic Panel data.

I look at the predictive power of my different risk aversion variables for measuring the probability of an individual being self-employed and of working in the private or the public sectors, as well as of the probability of smoking and drinking. Table 6 reports the results of probit estimations for the different measurements of risk attitudes. Sociodemographic controls are always included in the regressions and robust standard errors are considered.

When looking at the choice of employment sectors, self-reported risk aversion on the road, in sport, in finance and the average of the different SRRA variables, are significantly and negatively correlated with working in the private sector. For instance, a marginal change in the self-assessed risk attitudes in finance decreases by 2.4% the probability of working in the private sector (marginal effects are not reported in Table 6). No significant relationship between the public sector and risk aversion is found for any of the risk attitudes considered, except for the SRRA in health. In this latter case, a more riskaverse individual is less likely to work in the public sector, which contradicts previous research on occupational choice. Nonetheless, when restricting the sample to individuals who contribute to the household income, 17 this coefficient is no longer significant and I find that SRRA in finance and sport are positively correlated with the probability of working in the public sector (cf. Table 7). Finally, the average of the SRRA measurements is positively and significantly correlated with being self-employed. Similar results are found with SRRA in health and finance when restricting the sample to income contributors. In other words, the more risk-averse individuals are the more likely they are to work in the public sector or be self-employed. The unexpected positive relationship between self-employment and risk attitudes might be refined when taking into account the Indian context where opportunities to get a safe job in the public sector are scarce and the informal sector represents an important share of the labor market. Under these circumstances, private sector work and self-employment are not necessarily the result of the preferred choice by less risk-averse individuals. I note that neither lottery questions nor income choice prospects appear to be associated with occupational choices in the expected way. Moreover, the preference toward risk based on the lotteries are positively correlated with the probability of working in the private sector. In relation to Case 4, one is inclined to conclude that lottery questions are not able to capture the preferences toward risk. Nevertheless, in the case of occupational choices, a reverse causality issue may appear. More precisely, once revenues are secured through a safe public or formal 17 I.e. 56% of respondents, which corresponds to a sample of 372 persons.

sector job with a monthly salary, individuals may be more willing to take risks in finance and in hypothetical lotteries (see Case 3 ).

In order to further explore the issue of better job opportunities mentioned above, I introduce interaction terms between risk aversion and both education and caste. Indeed, one would expect that more highly educated individuals would have more job alternatives and consequently that their occupational choice is more likely to result from a preference. As for individuals belonging to a high caste, they are likely to have more opportunities, even if the effect of caste may be balanced by the existence of national positive discrimination schemes. When introducing these interaction terms in the selfemployment regressions, the coefficient of risk aversion becomes negative for all measures and is significant for the income prospect choice variable. The level of education seems to reduce the effect of risk aversion on the probability of being self-employed. Individuals belonging to a scheduled caste or tribe are less likely to be working in the public sector.

However, the magnitude of this effect is lowered by the individuals level of risk aversion.

The more risk-averse a low caste person is, the more likely he is to work in the public sector. This result may reflect the amount of effort these persons may actually put to take advantage of the positive discrimination policy implemented by the State (cf. Table 8 in the Appendices).

Regarding health behaviors, all the SRRA measurements as well as the job income choice variable are highly and negatively correlated with the probability of smoking or drinking. For instance, a marginal change in the average individual's self-reported risk aversion decreases by respectively 3.8% and 2.9% the probability of smoking and drinking. Notably, the risk-aversion variable derived from the lottery questions is unexpectedly positively correlated with the probability of engaging in any of the risky health behaviors considered. Again, a first interpretation of these results could be that in the context of India, this methodology is not appropriate to measure individuals' preferences toward risk, as such a measurement is not able to predict risky behaviors while other risk measurements in finance are (see Case 4 ). 18 Nevertheless, if one starts from the premise that lottery questions are the gold standard, such results may invalidate other elicitation methods and reflect a risk compensation effect across domains (see Case 3 ). In other words, individuals who already take risks with their health by smoking or drinking may be less willing to play with their money. In any case, this observation being made, when 18 I acknowledge that the irrelevance of the lottery measurement might come from the absence of incentives provided to respondents. Nonetheless, the budget constraints usually faced by large-scale survey designers and the purpose of this study to derive achievable recommendations justify the choice we made to only include hypothetical lotteries in the questionnaire.

looking at descriptive statistics, the discrepancy between the information provided by the lotteries and that provided by the other risk attitude variables is confirmed.

Additionally, it is interesting to study whether one particular risk aversion measurement method has better predictive powers than others. This kind of analysis will allow me to make conclusions about the existence of domain-specificity in risk preferences (see Case 2 ). The Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), Pseudo R 2 and the Log-likelihood are reported in Table 6. All these statistics provide information regarding the quality of each statistical model and enable me to compare the predictive power of different risk aversion measurement methods on a given dependent variable. 19 More precisely, the lowest AIC, BIC and Log likelihood values and the highest Pseudo R 2 indicate the best model. When considering labor choices, self-assessed risk aversion in finance is the variable which seems to be the most relevant. Job income choice measurement, SRRA on the road and the average of the five SRRA variables are the measurements with the highest power of explanation of risky health behaviors. Similarly to [START_REF] Ding | Can we measure individual risk attitudes in a survey? IZA Discussion Paper Series[END_REF], I find that SRRA in health and in sport perform quite well for cigarette and alcohol consumption respectively. Nevertheless, unlike them I do not find that SRRA in general is the best predictor. The average score of self-reported risk aversion in general and in the four specific domains introduced in the survey is actually among the two SRRA measurements with the lowest AIC, BIC and Log likelihood scores and the highest Pseudo R 2 for all risky health behaviors. Finally, lotteries seem also to be quite a good predictor of smoking behaviors, even if the found relationship contradicts the results obtained using other measurements of risk aversion in finance and seem to reflect some risk compensation effect.

To summarize, on the one hand, echoing results found in Germany [START_REF] Dohmen | Individual risk attitudes: Measure, determinants, and behavioral consequences[END_REF] but also in China or Thailand [START_REF] Ding | Can we measure individual risk attitudes in a survey? IZA Discussion Paper Series[END_REF][START_REF] Hardeweg | Experimental validated survey evidence on individual risk attitudes in rural thailand[END_REF], selfreported risk aversion in different domains, in particular the average score, appears to provide appropriate information on individual risk aversion as it is good at predicting risky conduct. On the other hand, in this specific context, hypothetical lottery questions may not capture a parameter correlated with the risky behaviors adopted by individuals.

There may be several explanations for this finding, ranging from respondents limited understanding of the questions, to differences in cultural norms (20% of the respondents said they prefer the zero amount to the lottery 20 ). However, I acknowledge that an alter-native explanation may be put forward: such results would be also found if individuals decided to compensate the risk they take in a certain domain by adopting safer conduct in another area or if they were more willing to take a risk in finance if their financial situation was secure. In these cases, I would conclude that lotteries actually capture the risk preferences of individuals and some context-specificity well and that our data can detect risk compensation effects. Finally, while SRRA in finance is more strongly related to labor decisions, results found with risky health behaviors do not really allow me to make any conclusions on the existence of context-specificity (see Case 2 ) as SRRA on the road and in sport provides better explanations, than SRRA in health, for smoking and drinking habits.

[Insert Table 6 here] [Insert Table 7 here] [Insert Table 8 here]

Do cultural specificities bias the influence of risk aversion?

Religion definitively punctuates the daily life of Indians. For instance, in my sample, 87% of the respondents believe their life is in God's hands and 73% pray daily. These religious beliefs and practices may either prevent individuals from engaging in prevention activities or compel them to adopt safe conducts. However, these individual characteristics would bias my previous estimates if, and only if, they were correlated with risk aversion. In order to check the existence of a potential omitted variable bias, I look at the pairwise correlation coefficients between the religious variables and the risk aversion parameters. No significant correlations are found. Furthermore, I include religious beliefs and practices in the set of explanatory variables of individuals preference toward risk. Believing that one's life is in the hands of God reduces only the self-reported risk aversion parameters, while praying every day is significantly negatively correlated only with the self-reported risk aversion in the health domain and with the income choice prospect measurements. When including religious beliefs and practices in the regressions, similar results are found regarding the relationship between the various risk attitudes and the different risky decisions, with the exception of the income choice variable which, in this context, is negatively and significantly correlated with working in the private sector.

Moreover, individuals who pray daily are more likely to work in the private sector and less likely to work in the public sector. Respondents who believe in fate are more likely to evolve in a private firm. As for a belief in fate and praying, these are positively correlated with risky health behaviors. In other words, individuals who believe that their life is in the hands of God or who prey on a daily basis are more likely to smoke and drink. Despite differences in religious practices between India and developed countries, the relationship between individual risk aversion and risky conducts found previously remains (cf. Table 9).

[Insert Table 9 here]

Conclusion

In this paper, I am interested in the elicitation of individual risk-aversion through surveys in general and in the context of India in particular. More precisely, I study whether simple survey risk-aversion parameters as measured in developed countries are appropriate when investigating risky conducts by a population with a different cultural background.

For this purpose, I take advantage of an original dataset implemented among motorcyclists in Delhi in 2011. I compare three different survey measurements of risk attitudes (self-reported risk aversion, hypothetical lotteries and income prospect choices). I first show that all measurements are positively and highly correlated with one another but that this is even more the case within methodologies and within domains. In a second step, I detect the gender, age and education effects on the level of risk-aversion, confirming the results found in surveys conducted in developed countries.

Subsequently, I investigate the predictive power of these different individual riskaversion measurements on occupation and health decisions. Most of my elicited risk preferences appear to predict risky health behaviors well. This is particularly the case of the average of SRRA variables. Regarding labor decisions, when restricting the results to the sample of income contributors, SRRA in finance is shown to be the variable with the strongest effect. The unexpected positive relationship observed between self-reported risk-aversion measurements and entrepreneurship may be specific to the Indian context where public job opportunities are scarce and occupation may not always reflect an individual's preference. As for the lottery questions, the results are puzzling and may be interpreted either as evidence of risk compensation between domains or as an incapacity to capture the desired characteristic. Finally, while religious practices and beliefs increase the likelihood of engaging in risky health behaviors, they do not modify the influence of risk preferences on an individual's conduct. The elicitation of risk-aversion measurements through surveys in developing countries thus appears possible.

Appendices Appendix A. Risk aversion measures

Self reported risk aversion

Based on the methodology used in particular in the German Socio-Economic Panel (G-SOEP), risk aversion in general and in four domains (on the road, in sport or leisure, in health and in finance) have been assessed using an 8 level scale. 21 The formulation used in the questionnaire was the following: "People behave differently in different situations.

On a risk scale going from 0 (not at all ready to take risk) to 7 (fully ready to take risk), how would you describe yourself [in each domain]?"

To build my risk-aversion measurements, I inverse the scale so that the lowest value corresponds to a risk lover and the highest value to the most risk-averse individuals.

The following variables were created: srra [domain] 8: can take 8 different values, increasing with risk aversion.

srra score 8: is the average of the level of risk aversion in each domain, The following variable was created:

safe choice: can take 21 different values, indicates the number of times the person has chosen the safe amount.

Income prospect choices

This measure is based on the hypothetical income prospect choices developed by [START_REF] Barsky | Preference parameters and behavioral heterogeneity: An experimental approach in the health and retirement study[END_REF]. All respondents answer to a first 'yes/no' question. According to their response, a second question is asked by the interviewer.

(qA) "Would you take the opportunity of a 50 percent chance of doubling your income and a 50 percent chance of reducing your income by one third?" (qB) if answer to (qA) is 'yes': "Would you take the opportunity of a 50 percent chance of doubling your income and a 50 percent chance of reducing your income by one half ?" (qC) if answer to (qA) is 'no': "Would you take the opportunity of a 50 percent chance of doubling your income and a 50 percent chance of reducing your income by one fifth?" I create the following variable: ra gamble 4: 4 categories, increasing with risk aversion, takes value 1 if the individual would gamble on his income despite a risk of reducing his income by one half; 2 if he agrees to take the job opportunity if the risk of reducing his income is of one third but not of one half; 3 if the risk of reducing his income is of one fifth but not of one third; 4 if he won't take the job opportunity even for a risk of one fifth only.

Appendix B. Comparing individuals from restricted and full samples [Insert Table 10 here] When comparing individuals who answered to all three risk-aversion measurement methods and those who did not, it appears that the former are more likely to be men, Tables Table 1: Notes: * 'average' is the average of the five SRRA measurements. † 'perc. 25 th ' stands for the 25 th percentile.

In the case of SRRA in general, less than 25% of individuals report a risk aversion lower than 4. -197.58 -197.13 -196.99 -196.06 -196.81 -197.52 -197.29 -197

  [START_REF] Holt | Risk aversion and incentive effects[END_REF] procedure in order to derive an individuals constant relative risk-aversion parameter.[START_REF] Holt | Risk aversion and incentive effects[END_REF] captured individual risk preferences through differences in volatility; more precisely, they offered 10 choices between two lotteries which differed in the level of gain, with the safe choice being the lottery with the smallest difference outcomes. The complexity of a such task, in particular for individuals with low math skills, have been raised by[START_REF] Dohmen | Are risk aversion and impatience related to cognitive ability?[END_REF],[START_REF] Hardeweg | Experimental validated survey evidence on individual risk attitudes in rural thailand[END_REF] or[START_REF] Vieider | Common components of risk and uncertainty attitudes across contexts and domains: Evidence from 30 countries[END_REF]. Specifically, the previous procedure has been simplified through the use of a set of 20 choices between an increasing safe amount and a fixed lottery instead of the comparison of two lotteries.. By choosing the lottery, the individual had a 50% chance of winning 12,000 INR and a 50% chance of receiving nothing. The safe amount was increased by 500 INR from 0 to 9,500 INR. The point at which subjects switch from the lottery option to the safe choice can be used to classify them according to their degree of risk aversion. 27 individuals provided inconsistent answers. More precisely, they switched from a safe amount to lottery. I thus exclude these observations from the analysis.

Table 2 :

 2 Socio-demographic characteristics of respondents Descriptive statistics of our risk-aversion measuresObservations Mean Standard Deviation Median perc. 25 th † Min Max

	observations %/mean

Table 3 :

 3 Pairwise correlation between our risk-aversion measures Pairwise correlation coefficients are computed over the restricted sample of 675 observations. * 'average' is the average of the five SRRA measurements., and stands for 1%, 5% and 10% significance respectively.

		(1)	(2)	(3)	(4)	(5)	(6)	(7)
	Self-reported risk aversion
	in general	(1) 1.000
	on the road	(2) 0.585	1.000
	in sport or leisure	(3) 0.222	0.343	1.000
	in health	(4) 0.246	0.347	0.548	1.000
	in finance	(5) 0.332	0.499	0.631	0.408	1.000
	average *	(6) 0.610	0.733	0.782	0.718	0.799	1.000
	Lottery questions	(7) 0.018	0.013	0.085	0.036	0.025	0.051	1.000
	Income prospect choices (8) 0.174	0.261	0.316	0.243	0.329	0.367	0.175

Notes:

Table 4 :

 4 Determinants of risk aversion measures 'average' is the average of the five SRRA measurements.

			Self-reported risk aversion			Income
								Lottery	prospect
		in general on the road in sport in health in finance average * questions choices
		ordered	ordered	ordered ordered	ordered		negative	ordered
	Specification	logit	logit	logit	logit	ologit	ols	binomial	logit
	Male (=1)	-0.208	-0.369	-0.198	0.055	-0.322	-0.291	-0.055	-0.176
		(0.145)	(0.139)	(0.164)	(0.155)	(0.150)	(0.123)	(0.035)	(0.161)
	Age (in years)	0.017	0.016	0.020	0.008	0.006	0.014	0.003	0.008
		(0.005)	(0.005)	(0.005)	(0.005)	(0.005)	(0.004)	(0.001)	(0.005)
	Education level (3 groups) -0.102	-0.170	-0.296	-0.212	-0.171	-0.194	-0.006	-0.374
		(0.107)	(0.104)	(0.096)	(0.096)	(0.102)	(0.081)	(0.021)	(0.100)
	Household monthly income, ref: Less than 10,000 INR
	From 10,000 to 20,000 INR -0.197	-0.050	-0.162	-0.087	-0.145	-0.157	-0.018	-0.325
		(0.190)	(0.177)	(0.180)	(0.171)	(0.181)	(0.153)	(0.041)	(0.184)
	More than 20,000 INR	-0.353	-0.048	-0.029	-0.222	-0.070	-0.158	0.041	-0.073
		(0.216)	(0.227)	(0.216)	(0.234)	(0.225)	(0.188)	(0.047)	(0.224)
	Pseudo R 2 /R 2	0.015	0.011	0.016	0.011	0.014	0.072	0.003	0.020
	Observations	665	665	665	665	665	665	665	665
	Notes: Robust standard errors are reported in parentheses.	, and stands for 1%, 5% and 10% significance respectively.

*

Table 5 :

 5 Risky behaviors adopted by respondents

		Sector of work choices
		Private	Public	Self-
		sector	sector	employment
	Mean (%)	23.41	11.26	19.56
	Std. dev.	42.37	31.63	39.69
	Observations	675	675	675
		Health risky behaviors
				Smoking
		Smoking Drinking or/and drinking
	Mean (%)	12.93	11.88	16.51
	Std. dev.	33.58	32.38	37.16
	Observations	673	648	648

Table 6 :

 6 Influence of risk aversion on risky behaviors

			Self-reported risk aversion			Income
								Lottery prospect
		in general on the road in sport in health in finance average * questions choices
	Probability of working in the private sector † , observations = 665
	Coefficient	-0.008	-0.060	-0.070	-0.023	-0.095	-0.102	0.020	-0.073
	Robust SE	(0.032)	(0.029)	(0.027)	(0.027)	(0.027)	(0.041)	(0.011)	(0.045)
	Log-likelihood	-309.31	-307.28	-305.64	-308.95	-302.84	-305.72	-307.82	-308.05
	Pseudo R 2	0.146	0.152	0.156	0.147	0.164	0.156	0.150	0.150
	AIC	634.62	630.55	627.29	633.91	621.67	627.43	631.65	632.10
	BIC	670.62	666.55	663.29	669.90	657.67	663.43	667.65	668.10
	Probability of working in the public sector † , observations = 665
	Coefficient	-0.005	0.036	0.034	-0.053	0.041	0.017	-0.011	-0.035
	Robust SE	(0.041)	(0.035)	(0.032)	(0.029)	(0.030)	(0.044)	(0.013)	(0.052)
	Log-likelihood

Table 7 :

 7 Influence of risk aversion on risky behaviors -sample of income contributors

			Self-reported risk aversion			Income
								Lottery	prospect
		in general on the road in sport in health in finance average * questions choices
	Probability of working in the private sector † , observations = 372
	Coefficient	-0.063	-0.089	-0.087	-0.020	-0.127	-0.147	0.023	-0.044
	Robust SE	(0.040)	(0.035)	(0.030)	(0.033)	(0.033)	(0.047)	(0.014)	(0.053)
	Pseudo R 2	0.090	0.098	0.101	0.086	0.118	0.106	0.091	0.086
	Probability of working in the public sector † , observations = 372
	Coefficient	-0.012	0.036	0.059	-0.049	0.063	0.039	-0.009	0.008
	Robust SE	(0.046)	(0.039)	(0.034)	(0.033)	(0.033)	(0.049)	(0.015)	(0.058)
	Pseudo R 2	0.078	0.079	0.086	0.083	0.085	0.079	0.078	0.077
	Probability of being self-employed † , observations = 372
	Coefficient	0.045	0.045	0.035	0.052	0.067	0.093	-0.013	-0.008
	Robust SE	(0.041)	(0.036)	(0.029)	(0.031)	(0.031)	(0.046)	(0.013)	(0.053)
	Pseudo R 2	0.029	0.030	0.029	0.032	0.036	0.035	0.028	0.027

Notes: Gender, age, education level (3 category variable) and household monthly income (dummies) are controlled for in all regressions. † Probit estimations.

, and stands for 1%, 5% and 10% significance respectively.

Table 8 :

 8 Influence of risk aversion on occupational choices with heterogenous effects Gender, age and household monthly income (dummies) are controlled for in all regressions. † Probit estimations. Robust standard errors are reported in parentheses., and stands for 1%, 5% and 10% significance respectively. 'average' is the average of the five SRRA measurements.

				Self-reported risk aversion			Income
								Lottery	prospect
		in general on the road in sport in health in finance average * questions choices
	Probability of working in the public sector † , observations = 657
	Risk aversion coefficient	-0.032	-0.088	0.021	0.069	0.173	-0.025	0.079	0.350
		(0.148)	(0.135)	(0.148)	(0.145)	(0.123)	(0.181)	(0.056)	(0.217)
	Education level (3 groups)	0.272	0.223	0.519	0.664	0.688	0.490	0.901	0.797
		(0.284)	(0.271)	(0.267)	(0.306)	(0.235)	(0.327)	(0.347)	(0.250)
	RA × Education level	0.010	0.025	-0.027	-0.064	-0.068	-0.026	-0.041	-0.174
		(0.055)	(0.050)	(0.052)	(0.052)	(0.045)	(0.065)	(0.021)	(0.080)
	Belongs to a low caste (=1)	0.021	-0.957	-1.059	-1.072	-0.534	-1.521	-0.434	-0.346
		(0.438)	(0.424)	(0.397)	(0.560)	(0.382)	(0.509)	(0.420)	(0.325)
	RA × Belongs to a low caste 0.010	0.202	0.256	0.217	0.128	0.326	0.039	0.168
		(0.083)	(0.077)	(0.077)	(0.095)	(0.071)	(0.099)	(0.029)	(0.112)
	Pseudo R 2	0.151	0.165	0.187	0.182	0.169	0.175	0.168	0.169
	Probability of being self-employed † , observations = 657
	Risk aversion coefficient	-0.099	-0.095	-0.096	-0.139	-0.115	-0.202	-0.023	-0.378
		(0.117)	(0.112)	(0.098)	(0.100)	(0.07)	(0.147)	(0.040)	(0.167)
	Education level (3 groups)	-0.630	-0.556	-0.483	-0.655	-0.589	-0.814	-0.395	-0.661
		(0.252)	(0.232)	(0.184)	(0.223)	(0.189)	(0.276)	(0.236)	(0.183
	RA × Education level	0.067	0.056	0.047	0.071	0.070	0.112	0.008	0.144
		(0.047)	(0.043)	(0.036)	(0.038)	(0.037)	(0.054)	(0.016)	(0.064)
	Belongs to a low caste (=1)	-0.380	-0.456	-0.393	-0.673	-0.489	-0.610	0.038	-0.383
		(0.432)	(0.403)	(0.289)	(0.390)	(0.323)	(0.469)	(0.368)	(0.288)
	RA × Belongs to a low caste 0.015	0.031	0.020	0.062	0.038	0.061	-0.028	0.029
		(0.080)	(0.074)	(0.060)	(0.068)	(0.063)	(0.092)	(0.026)	(0.105)
	Pseudo R 2	0.204	0.203	0.201	0.206	0.209	0.209	0.209	0.203

Notes: *

Table 9 :

 9 Influence of risk aversion on risky behaviors controlling for religious practices and beliefsSelf-reported risk aversion Income Lottery prospect in general on the road in sport in health in finance average * questions choices Probability of working in the private sector † , observations = 661 Gender, age and household monthly income (dummies) are controlled for in all regressions. † Probit estimations. Robust standard errors are reported in parentheses., and stands for 1%, 5% and 10% significance respectively. 'average' is the average of the five SRRA measurements.

	Risk aversion coefficient	-0.007	-0.058	-0.061	-0.027	-0.093	-0.097	0.020	-0.087
		(0.003)	(0.029)	(0.028)	(0.028)	(0.028)	(0.042)	(0.011)	(0.046)
	Pray daily (=1)	-0.238	-0.248	-0.222*	-0.247	-0.252	-0.252	-0.232	-0.259
		(0.126)	(0.126)	(0.127)	(0.126)	(0.127)	(0.127)	(0.125)	(0.126)
	Believes his life is in God's hands 0.688	0.660	0.654	0.687	0.666	0.652	0.675	0.709
		(0.236)	(0.236)	(0.235)	(0.235)	(0.231)	(0.234)	(0.237)	(0.238)
	Pseudo R 2	0.165	0.171	0.173	0.167	0.182	0.174	0.170	0.170
	Probability of working in the public sector † , observations = 661
	Risk aversion coefficient	-0.005	0.034	0.032	-0.051*	0.048	0.019	-0.009	-0.013
		(0.042)	(0.036)	(0.032)	(0.030)	(0.030)	(0.046)	(0.013)	(0.053)
	Pray daily (=1)	0.458	0.462	0.453	0.445	0.465	0.460	0.456	0.455
		(0.182)	(0.184)	(0.183)	(0.182)	(0.184)	(0.183)	(0.182)	(0.182)
	Believes his life is in God's hands	-0.288	-0.274	-0.267	-0.288	-0.279	-0.280	-0.283	-0.283
		(0.215)	(0.215)	(0.211)	(0.220)	(0.216)	(0.214)	(0.218)	(0.216)
	Pseudo R 2	0.176	0.177	0.178	0.182	0.180	0.176	0.177	0.176
	Probability of being self employed † , observations = 661
	Risk aversion coefficient	0.050	0.043	0.016	0.040	0.045	0.070	-0.013	-0.037
		(0.037)	(0.032)	(0.028)	(0.029)	(0.029	(0.042)	(0.012)	(0.050)
	Pray daily (=1)	0.178	0.168	0.156	0.174	0.163	0.170	0.156	0.155
		(0.138)	(0.139)	(0.139)	(0.139)	(0.139)	(0.139)	(0.139)	(0.139)
	Believes his life is in God's hands	-0.153	-0.155	-0.165	-0.171	-0.160	-0.150	-0.170	-0.166
		(0.186)	(0.187)	(0.187)	(0.186)	(0.187)	(0.187)	(0.186)	(0.186)
	Pseudo R 2	0.196	0.196	0.194	0.196	0.197	0.198	0.195	0.194
	Probability of smoking † , observations = 659
	Risk aversion coefficient	-0.115	-0.213	-0.097	-0.089	-0.099	-0.226	0.089	-0.172
		(0.034)	(0.036)	(0.031)	(0.027)	(0.031)	(0.050)	(0.014)	(0.057)
	Pray daily (=1)	0.389	0.381	0.440	0.376	0.391	0.386	0.456	0.377
		(0.161)	(0.169)	(0.164)	(0.161)	(0.161)	(0.166)	(0.165)	(0.161)
	Believes his life is in God's hands 0.884	0.935	0.870	0.942	0.917	0.865	0.971	0.984
		(0.337)	(0.371)	(0.343)	(0.346)	(0.330)	(0.352)	(0.326)	(0.340)
	Pseudo R 2	0.162	0.221	0.163	0.160	0.163	0.194	0.214	0.163
	Probability of drinking † , observations = 635
	Risk aversion coefficient	-0.109	-0.130	-0.099	-0.063	-0.105	-0.188	0.033	-0.272
		(0.035)	(0.034)	(0.030)	(0.028)	(0.030)	(0.045)	(0.013)	(0.060)
	Pray daily (=1)	0.128	0.122	0.186	0.128	0.139	0.130	0.154	0.101
		(0.158)	(0.158)	(0.160)	(0.157)	(0.158)	(0.160)	(0.156)	(0.160)
	Believes his life is in God's hands	0.587	0.613	0.575*	0.640	0.626	0.585	0.630	0.662
		(0.296)	(0.299)	(0.300)	(0.294)	(0.287)	(0.299)	(0.286)	(0.304)
	Pseudo R 2	0.138	0.151	0.142	0.129	0.144	0.156	0.133	0.171

Notes: *

Table 10 :

 10 Socio-demographic characteristics of respondents -restricted, missing, full samplesSampleAll answers Missing p-value Full

	Observations	675	227		902
	Male (%)	69.73	60.35	0.009 67.37
	Age (years)	36.06	37.67	0.118 36.47
	Married (%)	71.98	77.53	0.102 73.39
	Education
	Illiterate (%)	6.31	8.85	0.194	6.95
	Tertiary education (%)	46.7	38.94	0.043 44.73
	Education (3 groups)	2.26	2.11	0.012	2.22
	Low caste (SC/ST, %)	36.43	28.77	0.039 34.54
	Household monthly income
	below 10,000 INR	31.41	31.72	0.931 31.49
	10,000 -20,000 INR	34.07	28.19	0.102 32.59
	above 20,000 INR	17.63	15.42	0.444 17.07
	Contribute to income (%)	55.87	52.42	0.367 55.00

It is common in experimental literature implementing lotteries, to draw randomly one of the questions and pay the respondents according to the results of the lottery and the choice they previously expressed.

[START_REF] Wölbert | Measuring time and risk preferences: Reliability, stability, domain specificity[END_REF], using monetary incentives, actually found that lotteries were not correlated with non-financial risky behaviors.

The hypothetical gamble questions are similar to the questions used by[START_REF] Barsky | Preference parameters and behavioral heterogeneity: An experimental approach in the health and retirement study[END_REF].

The following sampling design was adopted: (i) New Delhi was divided into five zones, (ii) in each zone, ten polling booths were randomly drawn, (iii) the locations of these polling booths represented the starting points from which every fifth household was selected for the interview. Around each polling booth, 30 households were interrogated. In total 1,502 households were interviewed. In 545 households at least one member had traveled by motorbike in the past four weeks. Up to three drivers or passengers per household could answer the survey.

The willingness to invest in a risky business, to pay for a lottery ticket or to take a product which may be beneficial or detrimental to the respondent's health was also measured in the survey.

12,000 INR corresponds to 220 EUR or 1,310 EUR in PPP 2011.

10,000 INR corresponds to 183 EUR in 2011 or 1,092 EUR in PPP 2011.

Indeed, the expected gain of the lottery is 6,000 INR. A risk-neutral agent is thus indifferent between playing a lottery with a 6,000 INR expected gain and being guaranteed of getting 6,000 INR. Therefore he may opt for the safe amount at 6,000 INR or still choose the lottery at that point but switch to the safe amount the next time, i.e. for a safe amount of 6,500 INR.

While a risk aversion tendency is always found in developed countries,[START_REF] Vieider | Common components of risk and uncertainty attitudes across contexts and domains: Evidence from 30 countries[END_REF] pointed out that this is not the case in developing regions.

[START_REF] Dohmen | Individual risk attitudes: Measure, determinants, and behavioral consequences[END_REF] also compared self-reported risk aversion in different domains. They found that risk attitudes were not perfectly correlated across contexts and that the pairwise correlation coefficients were around 0.5, which is similar to the results I obtain.

The Pearson Chi 2 test is a test of independence between two variables. In my case, rejecting the null hypothesis means that the two different risk aversion measurement methods are not independent.

For instance, Tanaka et al. (2010) showed the existence of a negative relation between income and risk aversion in the Vietnamese context.

The Pearson goodness-of-fit test results indicate that the distribution of the number of safe choices significantly differs from a Poisson distribution, according to the p-value of 0.000.

This comparison can be made if the sample of respondents does not vary and there is a fixed set of controls.

Nonetheless, similar findings are reported byDe Palma et al, 2001.

In the literature[START_REF] Dohmen | Individual risk attitudes: Measure, determinants, and behavioral consequences[END_REF][START_REF] Ding | Can we measure individual risk attitudes in a survey? IZA Discussion Paper Series[END_REF], for instance), a scale going from 0 to 10 is most commonly used.

educated but also to belong to a low caste. No significant difference is detected regarding income.


Thai Hoang

Nguyen Thuy Chinh

Thi Thu Nguyen

Trang

Thi Xuan To

Hang

Thi Mai Dinh

Thanh

Dang Viet

Chang-Sik Ha

Maëlenn Aufray

Effects of Maleic Anhydride Grafted Ethylene/Vinyl Acetate Copolymer (EVA) on the Properties of EVA/Silica Nanocomposites

Keywords: ethylene/vinyl acetate copolymer (EVA), nanosilica, nanocomposite, mechanical properties, thermal stability, weatherability

Ternary nanocomposites based on ethylene/vinyl acetate copolymer (EVA), maleic anhydride-grafted EVA (EVAgMA), and nanosilica were prepared in a Haake Rheomixer. The structure of the EVA/EVAgMA/silica nanocomposites was characterized by Fourier transform infrared spectroscopy and field emission scanning electron microscopy. The blending sequence was found to have a significant effect on the microstructure of EVA/EVAgMA/silica nanocomposites and the dispersion behavior of the nanosilica in the EVA matrix. The tensile properties (tensile strength and elongation at break), thermal behavior, crystalline structure and weatherability of the nanocomposites were also studied. The results showed that the above properties of the nanocomposites were enhanced remarkably using 1 wt% EVAgMA.

Introduction

Ethylene/vinyl acetate copolymer (EVA) is used widely in electrical insulation, cable jacketing, encapsulation, packaging and corrosion protection, etc. The low cost and relatively good electrical and barrier properties of EVA have led to the continuous expansion of its applications, replacing other engineering plastics. On the other hand, the low tensile strength, thermal and UV stability of EVA have limited its applications in some fields. 1 To overcome these disadvantages, nanoparticles were added to this copolymer to improve the mechanical properties and thermal stability. [2][3][4][5] The dispersion of silica nanoparticles (SNP) and the interfacial interaction between the SNP and polymer matrix play important roles to determine the properties of nanocomposites. [6][7][8][9] SNP exhibit high surface activity and large agglomeration due to the lack of coordinate atoms on the surfaces. Moreover, the poor compatibility between the SNP and EVA matrix leads to the formation of nanocomposites that cannot achieve the expected properties. Therefore, it is important to use suitable compatibilizers to improve the dis-persibility of SNP in EVA, and the interfacial adhesion of SNP and EVA. Several studies related to SNP/polymer composites have focused on using amine and maleic anhydridemodified polypropylene (PP) as compatibilizers in PP/SNP nanocomposites to enhance the mechanical properties of the nanocomposites. [9][10][11][12] Hitherto, there has been little research on the use of a maleic anhydride modified EVA (EVAgMA) as a compatibilizer in EVA/SNP nanocomposites.

The main aim of this work was to examine the effects of EVAgMA on the rheological, mechanical properties, crystalline structure, thermal stability and weatherability of EVA/ SNP nanocomposites. The interactions in the EVA/EVAgMA/ SNP nanocomposites might include hydrogen bonding and dipole-dipole interactions between the carbonyl and C-O-C groups of MA in EVAgMA, as well as between the hydroxyl groups on the SNP surface and the carbonyl and C-O-C groups in EVA. These interactions might improve the dispersion of SNP in the EVA, which would enhance the properties of the EVA/silica nanocomposites.

Experimental

Ethylene/vinyl acetate copolymer (EVA) containing 10 wt% vinyl acetate (VAc) in granular form with a density of 0.93 *Corresponding Authors. E-mails: hoangth@itt.vast.vn or csha@pusan.ac.kr g/cm 3 and melt flow index of 1.3 g/10 min/190 o C/2.16 kg was purchased from Hanhwa Co., Korea. Silica nanoparticles (SNP) with a purity of 99.8%, mean particle size of 12 nm, and specific surface area of approximately 175-225 m 2 /g (BET) were obtained from Sigma-Aldrich Co. EVA grafted with 0.5 wt% maleic anhydride (MA) was purchased from Hanhwa Co., Korea.

Preparation of EVA/EVAgMA/SNP Nanocomposites (EMS). The nanocomposites containing 2 to 5 wt% SNP and 0 to 2 wt% EVAgMA were prepared by the melt mixing of EVA and SNP in a Haake Rheomixer (Germany) at a mixing temperature of 160 o C, mixing time of 5 min, and rotor speed of 50 rpm. After melt mixing, the nanocomposites were molded using a hot pressured machine (Toyoseiki Co.) at 160 o C at a 15 MPa pressure for 3 min to form samples with a thickness of approximately 1 mm. The samples examined are abbreviated in Table I. The EVAgMA and SNP contents were based on EVA by weight %.

Characterization. The relative melt viscosity, which is expressed as the torque values in the mixing process of EVA, SNP and EVAgMA, was recorded using Polylab 3.1 software connected to a Haake Rheomixer. The Fourier transform infrared (FTIR) spectra were recorded on a FTIR-Nexus infrared spectrometer using thin films of the samples prepared by compression molding in the range, 4000-400 cm -1 , at room temperature. The tensile properties, such as the tensile strength and elongation at break of EVA and EMS were measured on a Zwick Tensile 2.5 Machine according to the ASTM D638 standard. The morphology of the nanocomposites was analyzed by field emission scanning electron microscopy (FESEM, S-4800 Hitachi). X-Ray diffraction (XRD, Siemens D5000) was performed using CuK α radiation (λ=0.154 nm) at 40 kV and 30 mA. The XRD data was collected between 5 o and 60 o 2θ at room temperature with a scanning speed of 0.7 o /s and a step size of 0.03 o . The thermal properties were measured on a DTG-60H and DSC-60 thermogravimetric analyzer (Shimadzu Co.) under an argon atmosphere from room temperature to 600 o C and at a heating rate of 10 o C/min. The relative crystallinity (χ c ) of the samples was calculated using the following equation: 10,13

χ c =∆H f ×100/∆H * f
where ∆H * f is the fusion enthalpy of the perfectly polyethylene crystal (298 J/g) and ∆H f is the enthalpy of fusion of the samples.

The weatherability of the nanocomposites was analyzed using an accelerated weathering test on a UV-CON 327 (USA) according to the ASTM 793-91 (G32) method. Every cycle of the accelerated weathering test includes: 8 h of UV irradiation at 70 o C and 4 h of humidity condensation at 50 o C. The total testing time is 168 h (corresponding to 14 cycles). The tensile properties of the nanocomposites were determined before and after 6 and 14 cycles of the accelerated weathering test.

Results and Discussion

IR Spectra. Figure 1 presents the FTIR spectra of the SNP, EVA, E0M3S, and E1M3S nanocomposites. The spectrum of SNP showed the characteristic peaks, such as Si-O asymmetric and symmetric stretching vibrations (1110 and 794 cm -1 ), Si-O bending vibrations (461 cm -1 ) and Si-OH stretching vibrations (955 cm -1 ), and the OH stretching and bending vibration (3448 and 1633 cm -1 , respectively). 14,15 The spectra of E0M3S and E1M3S clearly showed the char- acteristic peaks of EVA, such as the peaks at 1737 cm -1 for carbonyl and 1242 and 1025 cm -1 for C-O groups in the MA and acetate groups, as well as peaks at 2924, 1456, 1368, and 724 cm -1 due to the CH groups. Relative Melt Viscosity. Figure 2 shows the relative melt viscosity of the EVA/SNP nanocomposites, which is expressed by the torque in the melt mixing of EVA, SNP, and EVAgMA. The torque of the nanocomposites was higher than that of EVA. The internal friction generated in the mixing process between the SNP and EVA matrix causes an increase in torque. Normally, the torque of the nanocomposites depends on the SNP content and increase with increasing SNP content. In the presence of EVAgMA, the torque of the nanocomposites increased due to the fine SNP dispersion in EVA and the adhesion between the SNP and EVA. The interactions between EVAgMA and other components in the nanocomposites reduce the mobility of EVA chains. This leads to an increase in the torque of the E1M2S and E1M4S nanocomposites compared to EVA. 9 At the "equilibrium" state, the stable torque moment of EVA, E0M2S, E0M4S, E1M2S and E1M4S samples approached 1.8; 7.3; 9.2; 10.0 and 10.3 MPa, respectively. The stable torque moment of the nanocomposites using EVAgMA increased by between 12 and 37% compared to that of the nanocomposites without EVAgMA at the same SNP content.

Morphology. The interactions between the SNP and EVA matrix with and without EVAgMA in the EVA/SNP nanocomposites were evaluated by FESEM (Figure 3). FESEM of the cryo-fractured surface of the nanocomposites without EVAgMA (Figure 3(a)-(d)) indicated an irregular dispersion of SNP in the EVA matrix. The SNP agglomerated to form large clusters in the EVA matrix, from 100 nm-2 µm, which causes a decrease in the mechanical properties of the nanocomposites presented in Tensile Properties section.

For nanocomposites containing EVAgMA, the particle agglomerate sizes were less than 200 nm (Figure 3(e)-(h)). This suggests that the SNP are well dispersed and adhered to EVA due to the presence of EVAgMA. The decrease in particle agglomerate size can explain the increase in the mechanical properties and thermal stability of the nanocomposites containing EVAgMA. 9,15 Tensile Properties. Figure 4 shows the tensile strength of the EVA/SNP nanocomposites. The tensile strength of the nanocomposites at different EVAgMA and SNP contents was higher than that of the neat EVA (17.3 MPa). SNP can enhance the tensile strength of the nanocomposites due to interactions between the nanoparticles and EVA matrix at the molecular level. 7 The tensile strength of the EVA/SNP nanocomposites with EVAgMA was higher than that of the and poly(ethyl methacrylate-cohydroxyethyl acrylate) (P(EMA-co-HEA))/silica nanocomposites. 11 Presumably, EVAgMA localized at the interface between the SNP and matrix polymer is responsible for the enhanced mechanical properties of the nanocomposites.

The interactions and good dispersion between EVA and SNP can improve the mechanical properties of the nanocomposites. On the other hand, the tensile strength showed a decrease tendency when the EVAgMA content in the nanocomposites exceeded 1 wt%. Similarly, at a constant EVAgMA content (1 wt%), the tensile strength of the nanocomposites increased with increasing SNP content. For example, at 2, 3, 4, and 5 wt% of SNP, the maximum tensile strength of the nanocomposites using 1 wt% EVAgMA was 23.2, 23.7, 24.1, and 25.4 MPa, respectively, showing a corresponding increase of 27.5, 28.8, 25.5, and 30.1%, compared to the samples without EVAgMA. This can be explained by the role of EVAgMA in inducing intermolecular interactions between EVA and SNP leading to improved compatibility of the EVA and SNP phases. At an EVAgMA content less than 1 wt%, the amount of EVAgMA might not be sufficient to induce inter-molecular interactions between EVA and SNP. Therefore, the change in the tensile strength of the nanocomposites was not great. In contrast, the agglomeration of EVAgMA can occur when EVAgMA content exceeds 1 wt%, leading to an irregular dispersion of SNP into the EVA matrix. Therefore, the tensile strength of the nanocomposites can decrease.

Figure 5 shows the elongation at break of the nanocomposites with various EVAgMA and SNP contents. The elongation at break of EVA/SNP nanocomposites decreased with increasing SNP content in the EVA matrix because the hard and rigid SNP act as stress concentrators, providing less ductility to the nanocomposites. In particular, when the SNP content was increased to 4-5 wt%, the size of the agglomerated particles increased, indicating a higher stress concentration, more extensive cavitation and faster breaking. 9,11,12 The elongation at break of the EVA/SNP nanocomposites with EVAgMA was higher than that of the nanocomposites without EVAgMA and reached the maximum at 1 wt%. Using a constant EVAgMA content (1 wt%), the maximum elongation at break of the nanocomposites decreased with increasing SNP content.

Thermal Properties and Thermal Stability. The melting temperature of all EVA/silica nanocomposites with and without EVAgMA was observed at approximately 96 o C with marginal differences, as shown in Table III. On the other hand, when adding SNP and EVAgMA into EVA and the nanocomposites, the chain segments possessing vinyl acetate (VAc) units in the amorphous and ethylene chain segments in the secondary crystallization region could re-arrange. 1,10,13,16 This might explain the slight decrease in overall crystallin-ity (χ c %).

Figure 6 and Table IV respectively show TGA curves and the TG characteristic data of EVA and the EVA/SNP nanocomposites. The weight loss of all samples is related to two steps of EVA degradation corresponding two temperatures with the maximal degradation rate (T p1 , T p2 ). The first stage was complete at approximately 400 o C, involving mainly autocatalytic deacetylation in the VAc moieties. The second stage is chain scission of the residual main polyethylene chains within an interval of 405-500 o C. 1,10,17 When adding SNP to EVA, the weight loss in the 1 st stage (∆W 1 ) of the nanocomposites was lower than that of EVA, and the onset temperatures (T On-d ), which are the temperatures corresponding to 10%, 20% and 50% weight loss (T 10 , T 20 , and T 50 ), were higher than those of EVA. This can be explained by the high thermal stability of SNP when the SNP are dispersed regularly in the EVA matrix, which have a protective agent and barrier effect for EVA at high temperature. Therefore, the thermal stability of EVA increases in the presence of SNP. 8,10 The T On-d , T 10 , T 20 , and T 50 were shifted to higher temperatures, in range of 2-10 o C, due to the presence of EVAgMA in the EVA/SNP nanocomposites (Table IV). This suggests that EVAgMA can enhance the thermal stability of the EVA/ SNP nanocomposites. The EVA/SNP nanocomposites containing 1 wt% EVAgMA have lower ∆W 1 values and higher T p1 and T p2 than those of the nanocomposites without EVAgMA. The peak positions of T p1 and T p2 (DTG data not shown) might change with different EVAgMA content. 10 X-Ray Diffraction. Figure 7 shows XRD patterns of SNP, EVA and different EVA/SNP nanocomposites, showing that SNP is amorphous. The broad peak might be due to the small size and incomplete inner structure of the particles. 18 EVA possesses both crystalline and amorphous regions. Therefore, two sharp XRD peaks at 21.26 o 2θ (110 plane) and 23.60 o 2θ (200 plane) were observed for EVA. Accordingly, the XRD patterns of all EVA containing nanocomposites showed an intense peak at approximately 21 o 2θ, which corresponds to the crystalline regions, whereas the weak peak approximately 23 o 2θ was assigned to the amorphous regions in EVA. The EVA crystalline or amorphous structural state appears to depend on the fraction of components in the nanocomposites. For the nanocomposites, however, the positions of the XRD peaks did not shift significantly compared to those of neat EVA. This suggests that the crystalline structure of EVA remains unchanged upon the addition of different SNP and EVAgMA contents during preparation of the EVA/SNP nanocomposites. No additional peaks were observed, which suggests no third phase in the nanocomposites. 19 The relative intensity of these peaks decreased in all nanocomposites, corresponding to a decrease in their overall crystallinity, as reported elsewhere. 2,13,18 The change in the relative intensity of the peaks in the nanocomposites might be due to the crystal alignment during the preparation pro-cess. Interestingly, the relative intensity of the diffraction peaks of the E1M3S samples was higher than the other samples, indicating an enhancement of diffuse amorphous scattering and an increase in overall crystallinity. This is in accordance with the DSC results reported above.

T On-d ( o C) T 10 ( o C) T 20 ( o C) T 50 ( o C) ∆W 1 (%) T p1 ( o C) T p2 ( o C
Table V lists the characteristic Bragg angles (2θ) and corresponding d-spacing (d) determined from the Bragg's equation (eq. ( 9) in ref. 13), as well as the lateral crystal size calculated using the Scherrer's equation (eq. ( 10) in ref. 13) based on the XRD patterns. The addition of SNP and EVAgMA to EVA can reduce slightly the d-spacing and crystal domain sizes of EVA. The result might also be due to the role of EVAgMA in enhancing the compatibility between the SNP and EVA matrix. The decrease in the bulk crystallization rate and crystal domain sizes might result in a lower degree of crystallinity in EVA. 18 Weatherability. Figure 8 shows the FTIR spectra of EVA and the EVA/SNP nanocomposites before and after the A comparison of the non-polar ethylene chain segments showed that the VAc units are more vulnerable to heat, oxygen and UV light radiation, which can form reactive radicals or unstable hydro peroxide easily, and facilitate further irreversible chemical reactions. Along with the accumulation of unstable structures and oxygen penetration, degradation of the molecules extends from the surface to the entire sample (see Figure 10 later).

Figure 9 shows the retention of the tensile strength of the nanocomposites after the 72 and 168 h accelerated weathering test. The addition of EVAgMA and SNP to EVA led to an increase in the percentage retention of the tensile strength of the nanocomposites compared to that of neat EVA (24.3% after 72 h and 10.5% after 168 h compared to that of neat EVA before testing (17.3 MPa)).

At low EVAgMA contents (0-0.5 wt%), the retention in the tensile strength of all nanocomposites was similar. The retention of the tensile strength of the nanocomposites using EVAgMA tended to decrease except for the nanocomposites containing 3 wt% SNP. The increase in the accelerated weathering test time resulted in a decrease in retention in the tensile strength of all nanocomposites. This can be explained by a rearrangement in the crystalline region leading to cracks on the surface of the material (Figure 10). The chain scission gives rise to a stress concentration and crack propagation in the material, which leads to a further decrease in the mechanical properties. 10,22 The nanocomposites containing 3 wt% SNP at all EVAgMA contents showed the highest retention in tensile strength due to the highest crystalline percentage, as shown in Table III.

Figure 10 presents FESEM images of the surface of the nanocomposites after the 168 h accelerated weathering test. The EVA film showed large, deep and linear cracks on both the surface and deep inside of the sample (Figure 10(a) and (d)). The E0M3S and E1M3S samples, however, showed some niches on the surface (Figure 10(b) and (e), Figure 10(c) and (f)). This suggests that the SNP can limit the decomposition of the EVA in the presence of UV radiation. In addition, the E1M3S sample showed no niches, whereas the E0M3S sample showed only a few small niches. Therefore, the E1M3S sample has better weatherability than that the E0M3S and EVA samples (Figure 9).

Conclusions

This study examined the effects of the addition of EVAgMA on the rheological, mechanical properties, thermal stability, crystalline structure and weatherability of the EVA/SNP nanocomposites. The use of EVAgMA produced remarkable enhancement in the relative melt viscosity, tensile strength, elongation at break, thermal stability and weatherability of the EVA/SNP nanocomposites. The overall crystallinity of EVA decreased when SNP and EVAgMA were added. Furthermore, the SNP dispersed evenly into EVA in the presence of EVAgMA might act a good UV stabilizer for EVA. In particular, the weatherability of the EVA/SNP nanocomposites was clearly improved using only 1-1.5 wt% EVAgMA and 3 wt% SNP.

Figure 1 .

 1 Figure 1. FTIR spectra of the EVA and EVA/SNP/EVAgMA nanocomposites.

2 , 5

 25 Some peaks observed at 795 and 804 cm -1 (Si-O-Si symmetric stretching vibration), and 463 and 482 cm -1 (Si-O-Si bending vibration) in the E0M3S and E1M3S spectra, respectively, were assigned to Si-O groups in nanosilica. A slight shift (6-23 cm -1 ) was observed in the peaks of Si-O stretching and bending vibrations, and the C-O and C=O stretching vibrations, which was caused by the incorporation of EVAgMA in E1M3S (TableII). This suggests that the C=O and C-O-C groups of MA in EVAgMA interact with the hydroxyl groups on the SNP surface as well as carbonyl and C-O-C groups in EVA by hydrogen bonding and dipole-dipole interactions. Bikiaris et al. confirmed the aforementioned interactions between the carboxyl groups of PP-g-MA and the surface hydroxyl groups of SNP.9

Figure 2 .

 2 Figure 2. Torque of EVA and the nanocomposites during melt mixing.

Figure 4 .

 4 Figure 4. Eect of the EVAgMA and SNP content on the tensile strength of the EVA/SNP nanocomposites: 2 wt% SNP: (-•-), 3 wt% SNP: (-̰-), 4 wt% SNP: (-̵-), 5 wt% SNP: (-̷ -).

Figure 3 .

 3 Figure 3. SEM images of the cryo-fractured surface the EVA/ SNP nanocomposites; (a) E0M2S; (b) E0M3S; (c) E0M4S; (d) E0M5S; (e) E1M2S; (f) E1M3S; (g) E1M4S; (h) E1M5S.

Figure 5 .

 5 Figure 5. Eect of the EVAgMA and SNP content on the elongation at break of the EVA/SNP nanocomposites: 2 wt% SNP: (-•-), 3 wt% SNP: (-̰-), 4 wt% SNP: (-̵-), 5 wt% SNP: (-̷ -).

Figure 6 .

 6 Figure 6. TGA curves of EVA, E0M4S and E1M4S.

Figure 7 .

 7 Figure 7. XRD patterns of the SNP, EVA and EVA/SNP nanocomposites with and without 1 wt% EVAgMA.

Figure 8 .

 8 Figure 8. FTIR spectra in the range 2000-1550 cm -1 of EVA and the nanocomposites after the 168 h accelerated weathering test.

Figure 9 .

 9 Figure 9. Retention of the tensile strength after the 72 h (a) and 168 h (b) accelerated weathering test; 2, 3, 4, 5: 2, 3, 4, 5 wt% SNP.

Figure 10 .

 10 Figure 10. SEM images of the nanocomposites after the accelerated weathering test; (a) EVA; (b) E0M3S; (c) E1M3S (magnification 100 times); (d) EVA; (e) E0M3S; (f) E1M3S (magnification 10,000 times).

Table I .

 I Abbreviation of the EVA/EVAgMA/SNP Nanocomposite Samples

	EVAgMA Content (wt%)
		0	0.5	1	1.5	2
	SNP Content (wt%)
	2	E0M2S	E0.5M2S	E1M2S	E1.5M2S	E2M2S
	3	E0M3S	E0.5M3S	E1M3S	E1.5M3S	E2M3S
	4	E0M4S	E0.5M4S	E1M4S	E1.5M4S	E2M4S
	5	E0M5S	E0.5M5S	E1M5S	E1.5M5S	E2M5S

Table II .

 II Characteristic Wavenumbers of the SNP, EVA, E0M3S, and E1M3S Samples

	Samples	ν OH	ν CH	ν C=O	δ CH	Wavenumbers (cm -1 ) ν C-O	ν Si-O	ν Si-OH	γ CH	δ Si-O
	SNP	3448	2921 2847	-	-	-	1110 794	955	-	461
	EVA	-	2924	1737	1456 1368	1242 1025			724	-
	E0M3S	3500	2921 2852	1750	1463 1370	1241 1021	795		723	463
	E1M3S	3612 3456	2925	1760	1457 1368	1244 1031	1127 804	-	724	482

EVA/SNP nanocomposites without EVAgMA. The maximum tensile strength of the nanocomposites was observed at 1 wt% EVAgMA. Similar results were also obtained in the poly(ethylene 2,6-naphthalate)/silica nanocomposites, 6 PP/ SiO 2 nanocomposites

9,12

Table III .

 III DSC Data of EVA and the EVA/SNP Nanocomposites a

	Samples	T m-m ( o C)	∆H f (J/g)	χ c (%)
	EVA	96.1	67.7	22.7
	E0M2S	96.1	61.5	20.6
	E0M3S	97.0	64.1	21.5
	E0M4S	96.1	62.6	21.0
	E0M5S	96.2	63.3	21.2
	E1M2S	95.7	55.6	18.7
	E1M3S	97.4	66.5	22.3
	E1M4S	95.9	59.5	20.0
	E1M5S	96.0	55.3	18.6

a

T m-m : melting temperature; ∆H f : enthalpy of fusion; χ c : relative crystallinity.

Table IV .

 IV TG and DTG Results of EVA and the EVA/SNP Nanocomposites a

	Samples	TG	DTG

Table V .

 V Diffraction Angle (2θ), d-Spacing (d), and Lateral Crystal Size (L) of SNP, EVA and EVA/SNP Nanocomposites with and without 1 wt% EVAgMA, Obtained from XRD

	Sample	2θ 110 ( o )	d 110 (nm)	d 200 (nm) L 110 (nm)
	EVA	21.26	4.176	3.774	0.511
	E0M2S	21.27	4.175	3.778	0.506
	E0M3S	21.33	4.162	3.767	0.505
	E0M4S	21.37	4.156	3.762	0.483
	E0M5S	21.32	4.165	3.774	0.505
	E1M2S	21.32	4.165	3.765	0.480
	E1M3S	21.38	4.153	3.752	0.491
	E1M4S	21.20	4.187	3.777	0.510
	E1M5S	21.32	4.165	3.777	0.508

Acknowledgments. The authors would like to thank the National Foundation for Science and Technology Development of Vietnam (NAFOSTED, 104.04-2010.02) and Viet-nam Academy of Science and Technology (VAST-CNRS of France Project of Corrosion and Protection of Materials in period 2011-2012) for the financial support. CSH also thanks to the National Research Foundation of Korea (NRF) Grant funded by the Ministry of Science, ICT & Future Planning, Korea (Acceleration Research Program (2013) and Pioneer Research Center Program (2013)).