Manthos Delis

Emilios Galariotis

Jerome Monne

Serge Blondel

Nicolas Jacquemet

George Korniotis

Economic condition and financial cognition

Keywords: JEL classification: G40, D90, D91, C83 Financial effects of poverty, Financial literacy, Priming, Cognitive impediment, Financial anxiety, Shame of poverty, Survey experiment JEL classification: G40, D90, D91, C83 Financial effects of poverty, Financial literacy, Priming, Cognitive impediment, Financial anxiety, Shame of poverty, Survey experiment

Are low-income individuals trapped because they are unable to make good financial decisions?

We use a randomized controlled survey experiment to examine how prompting individuals to think about their personal economic condition (priming) affects their scores on a financial literacy quiz. We find that the marginal effect of poverty on financial literacy scores is 3.7 times higher for primed (treated) respondents compared to nonprimed ones. Priming not only worsens the financial literacy scores of low-income individuals, but also improves the scores of highincome individuals. Anxiety and shame are key explanations for our baseline results. Our findings shed light on how economic condition affects financial cognition, especially with regard to cognitive impediments resulting from negative emotions.

Introduction

A person's success in assimilating finance-related information and tackling economic problems relies on cognitive ability. We refer to this as the individual's financial cognition.

Learning finance-related concepts and applying that knowledge to make wise financial decisions are key to growing income and wealth at the individual level, as well as to improving society's general cohesion and welfare.

In this research, we hypothesize and empirically examine whether financial insecurity of low-income people overwhelms mental processes when completing tasks that require financial cognition. Specifically, a difficult financial situation may impair individuals' cognitive abilities and interfere with the performance of financial duties. In other words, a poor financial condition might be linked to poor financial management. In this situation, negative emotions driven by poverty-related concerns grow when low-income people perform a financial task, impeding cognitive function. An examination of this hypothesis is important because people experiencing poverty have, by definition, smaller safety margins. By extension, poverty and inferior financial decision-making may create a vicious cycle that threatens financial market participation, future income, and societal outcome.

The extant literature focuses on establishing a causal link between poverty and difficulty to perform general cognitive tasks, with mixed results (e.g., [START_REF] Carvalho | Poverty and economic decision-making: Evidence from changes in financial resources at payday[END_REF][START_REF] Mani | Poverty impedes cognitive function[END_REF]. The general premise in those studies is that poverty hampers individuals' cognitive abilities in performing several economic and noneconomic tasks. Impoverished people allocate a large share of their cognitive abilities to managing financial scarcity and are distracted from other tasks that require cognitive abilities.

In contrast to the existing literature, our objective is to analyze whether povertyrelated concerns affect financial cognition, especially through the experience of negative emotion. We make two key contributions. First, we apply a financial perspective to the welfare-related economic psychology literature. Second, we place negative emotion at the center of the underlying mechanisms that link poor economic condition with individuals' cognitive abilities in finance-related tasks.

We measure financial cognition and the related quality of financial decision-making using a financial literacy test that includes standard questions on risk and return, calculation of interest rates and credit card debt, portfolio diversification, and inflation. The financial literacy test is a good proxy for the quality of financial decision-making because financial literacy is linked to various positive financial outcomes, such as efficient day-to-day financial management, portfolio management and retirement planning, wealth accumulation, competent debt management, and stock market participation (e.g., [START_REF] Hilgert | Household financial management: The connection between knowledge and behavior[END_REF][START_REF] Hsu | Aging and strategic learning: The impact of spousal incentives on financial literacy[END_REF][START_REF] Bianchi | Financial literacy and portfolio dynamics[END_REF][START_REF] Guiso | How much can financial literacy help?[END_REF][START_REF] Klapper | Financial literacy and the financial crisis[END_REF][START_REF] Van Rooij | Financial literacy and stock market participation[END_REF].

Phrased differently, financial cognition, as financial literacy test performance reflects, is a vital element of household finance. Accordingly, it is important to analyze whether and how poverty affects financial cognition.

Testing our hypotheses implies establishing a causal link between income-related concerns and financial literacy. To this end, we conduct a randomized controlled survey experiment (e.g., [START_REF] Alesina | Intergenerational mobility and preferences for redistribution[END_REF][START_REF] Brown | Behavioral Impediments to Valuing Annuities: Complexity and Choice Bracketing[END_REF][START_REF] Karadja | Richer (and holier) than thou? The effect of relative income improvements on demand for redistribution[END_REF][START_REF] Kuziemko | How elastic are preferences for redistribution? Evidence from randomized survey experiments[END_REF] involving 1,001 respondents who are representative of the French population. The treatment consists of priming half the respondents by asking questions about their financial situations before administering a financial literacy test and surveying their emotions about their financial situations. Priming aims to make poverty-related financial concerns top of mind among low-income respondents, similarly to what it will presumably be in a real-life situations when they are faced with a financial decision. In contrast, we do not prime the control group, which therefore is in a neutral environment. Members of that group answer questions on their financial situations after taking the financial literacy quiz and survey of their emotions about their financial situations. Thus, our baseline model compares financial literacy test scores for the treated and control groups based on income level.

Many studies use priming in experimental protocols and show that subtle changes in the decisional context, despite not affecting the level of information, trigger effective behavioral changes in economic or financial decision-making. For example, priming influences investment behaviors, and particularly the perception of risk [START_REF] Bateman | Risk presentation and portfolio choice[END_REF][START_REF] Carr | Stereotype threat affects financial decision making[END_REF][START_REF] Gilad | Priming the Risk Attitudes of Professionals in Financial Decision Making[END_REF][START_REF] Glaser | Thinking about Prices versus Thinking about Returns in Financial Markets[END_REF]; the search of professional financial advice [START_REF] Lee | Stereotype threat in the marketplace: Consumer anxiety and purchase intentions[END_REF]; and cognitive biases related to economic decisions [START_REF] Liu | Adapting to the Destitute Situations: Poverty Cues Lead to Short-Term Choice[END_REF][START_REF] Mani | Poverty impedes cognitive function[END_REF]). 1 Our priming strategy is close to that of [START_REF] Mani | Poverty impedes cognitive function[END_REF] who examine the impact of poverty-related concerns on general cognitive functions.

The main interest of running a survey experiment on a representative sample of the population is to combine the experiment's causal power with the generalizability of a population-based sample [START_REF] Mullinix | The generalizability of survey experiments[END_REF][START_REF] Mutz | Population-Based Survey Experiments[END_REF]. It is particularly suitable for our study because it enables us to examine how the treatment effect varies over poverty (as measured by the effective income). In the field of financial cognition, a survey experiment based on a representative U.S. sample is by [START_REF] Brown | Behavioral Impediments to Valuing Annuities: Complexity and Choice Bracketing[END_REF] to explore the effect of manipulating the frame of an annuity choice on the value given to the annuity.

Our results show that priming negatively affects financial literacy scores among lowincome respondents, and the magnitude of the effect increases when income decreases. Put another way, when primed, the poorer respondents are, the lower their financial literacy scores are, compared to nonprimed respondents. Economically, the marginal effect of income on financial literacy test scores is 3.7 times higher for primed respondents.

We further hypothesize that negative emotions such as anxiety, shame, and/or guilt associated with economic condition play an important role in impairing individuals' financial abilities. Poverty conditions may trigger these emotions (e.g., Hall et al., 2013;[START_REF] Haushofer | On the psychology of poverty[END_REF][START_REF] Walker | Poverty in global perspective: is shame a common denominator?[END_REF] and may in turn impede cognition (e.g., [START_REF] Brooks | Can Nervous Nelly negotiate? How anxiety causes negotiators to make low first offers, exit early, and earn less profit[END_REF][START_REF] Eysenck | Anxiety: The cognitive perspective[END_REF][START_REF] Eysenck | Anxiety and cognitive-task performance[END_REF]Hall et al., 2013;[START_REF] Shapiro | Measuring financial anxiety[END_REF], especially because the origin of these emotions (i.e., economic distress), is linked with the stakes of the financial task to be performed (e.g., [START_REF] Anderson | The psychology of doing nothing: Forms of decision avoidance result from reason and emotion[END_REF][START_REF] Gino | Anxiety, advice, and the ability to discern: feeling anxious motivates individuals to seek and use advice[END_REF]Hall et al., 2013;[START_REF] Willis | The financial education fallacy[END_REF][START_REF] Willis | Against financial literacy education[END_REF]. Therefore, such negative emotions can be vehicles through which poverty affects financial cognition. These negative emotions and associated financial cognition impediments may be particularly likely to persist while taking a financial literacy test, precisely because the financial questions exacerbate the negative emotions.

We indeed show that feelings of anxiety and shame mediate the significant effect that poverty-related concerns have on financial cognition. We find that priming respondents exacerbates these emotions and in turn explains a significant part of the baseline effect of priming among those experiencing poor economic conditions. In contrast, we do not find a significant role for guilt.

These results on the mediation effect of negative emotions are quite important in the context of the general literature on cognitive impediment. We provide an explanation for why previous literature on general cognitive tasks (not related to financial cognition) finds that poverty has a limited effect (e.g., [START_REF] Carvalho | Poverty and economic decision-making: Evidence from changes in financial resources at payday[END_REF]. Indeed, we also observe no effect when performing the same analysis on a general cognitive test (i.e., the Cognitive Reflection Test) that is unrelated to financial management tasks (e.g., [START_REF] Carvalho | Poverty and economic decision-making: Evidence from changes in financial resources at payday[END_REF][START_REF] Frederick | Cognitive reflection and decision making[END_REF]. Along these lines, our findings imply that poverty-related concerns specifically affect financial cognition rather than general cognition.

Section 2 places our paper within three strands of relevant literature: the first highlights the use of financial literacy tests as measures of the quality of financial decisionmaking and financial cognition, the second shows the nexus between financial literacy and individuals' cognitive abilities, and the third suggests that poverty-related concerns affect general cognitive skills (unrelated to financial decision-making). We then develop our testable hypotheses. Section 3 discusses our survey experiment and how we draw causal inferences about how income affects financial cognition. Section 4 presents the empirical results, analyzing the implications for our hypotheses and their economic significance.

Section 5 concludes the paper and details how our results contribute to the literature on cognitive impediment.

Theoretical background and testable hypotheses

Our study examines the relationships among poverty, financial cognition, and financial literacy. As such, it relates to at least three growing strands of interrelated literature. The first measures individuals' financial decision-making ability [START_REF] Klapper | Financial literacy and the financial crisis[END_REF][START_REF] Lusardi | Debt literacy, financial experiences, and overindebtedness[END_REF][START_REF] Van Rooij | Financial literacy and stock market participation[END_REF]2012;[START_REF] Guiso | How much can financial literacy help?[END_REF][START_REF] Lusardi | The economic importance of financial literacy: Theory and evidence[END_REF][START_REF] Bianchi | Financial literacy and portfolio dynamics[END_REF]. This literature shows the importance of skills, measured by quizzes, in explaining respondents' portfolio management, retirement planning, wealth accumulation, debt management, and stock market participation (see [START_REF] Lusardi | The economic importance of financial literacy: Theory and evidence[END_REF]).2

A second strand of literature suggests that cognitive abilities are important parts of financial literacy in predicting optimal financial decision-making [START_REF] Cole | High school curriculum and financial outcomes: The impact of mandated personal finance and mathematics courses[END_REF][START_REF] Hilgert | Household financial management: The connection between knowledge and behavior[END_REF][START_REF] Hsu | Aging and strategic learning: The impact of spousal incentives on financial literacy[END_REF][START_REF] Fernandes | Financial literacy, financial education, and downstream financial behaviors[END_REF][START_REF] Jappelli | Investment in financial literacy and saving decisions[END_REF][START_REF] Lusardi | The economic importance of financial literacy: Theory and evidence[END_REF][START_REF] Willis | Disentangling cognitive function and financial literacy: Implications for financial retirement security research[END_REF]. Similar to financial literacy, a direct link between cognitive abilities and the efficiency of financial decision-making is widely studied in household finance (e.g., [START_REF] Cole | High school curriculum and financial outcomes: The impact of mandated personal finance and mathematics courses[END_REF][START_REF] Grinblatt | IQ, trading behavior, and performance[END_REF]2011;[START_REF] Korniotis | Do older investors make better investment decisions?[END_REF][START_REF] Jappelli | Economic literacy: An international comparison[END_REF]. Specifically, the cognitive skills necessary to make good financial decisions include the ability to perform numerical operations (numeracy), the ability to recall relevant knowledge (memory), and the ability to use both numeracy and memory in a proper way (fluency) [START_REF] Christelis | Cognitive abilities and portfolio choice[END_REF][START_REF] Mcardle | Cognition and economic outcomes in the Health and Retirement Survey[END_REF].

An essential issue in our context, linking the first two strands of literature, is that the results of the financial literacy test (as a measure of efficient financial decision-making) are strongly related to financial cognition. The efficient use of cognitive resources, including those used for financial decision-making, is subject to behavioral biases linked to attention and emotions.3 Such cognitive biases are highly context-dependent and, as a result, financial cognition and the resulting financial literacy scores are likely to vary with the frameworks in which individuals make financial decisions. For instance, two individuals with similar intrinsic levels of financial fluency may equally understand how credit card repayment works.

In practice, however, if one of these individuals is stressed in ways that divert his/her attention, such fluency could be hampered. The two individuals may make different decisions about how much to borrow, when to repay the money, and how they support those debt burdens.

A third strand of literature focuses on how poverty affects general economic and noneconomic outcomes. This literature examines whether poor economic conditions affect risk aversion, time preferences, and self-control, leading to increased chances of trapping people in poverty [START_REF] Banerjee | The shape of temptation: Implications for the economic lives of the poor[END_REF][START_REF] Bernheim | Poverty and self-control[END_REF][START_REF] Carvalho | Poverty and economic decision-making: Evidence from changes in financial resources at payday[END_REF][START_REF] Gloede | Shocks, individual risk attitude, and vulnerability to poverty among rural households in Thailand and Vietnam[END_REF][START_REF] Haushofer | Negative income shocks increase discount rates[END_REF][START_REF] Liu | Adapting to the Destitute Situations: Poverty Cues Lead to Short-Term Choice[END_REF][START_REF] Spears | Economic decision-making in poverty depletes behavioral control[END_REF][START_REF] Tanaka | Risk and time preferences: Linking experimental and household survey data from Vietnam[END_REF]. A key finding is that poverty hampers cognitive abilities, with a direct explanation emerging from the role of scarcity [START_REF] Shah | Some consequences of having too little[END_REF][START_REF] Mullainathan | Scarcity: Why having too little means so much[END_REF]. The literature finds that people experiencing poverty tend to allocate a large share of their cognitive abilities to managing financial scarcity, and this occurs at the expense of other tasks that also require cognitive abilities.

For instance, [START_REF] Mani | Poverty impedes cognitive function[END_REF] show in an experiment that the cognitive processes of the poorest individuals (in IQ points) is impeded when they are exposed to a consumption shock (the respondents were mentally primed with a fictitious expensive car repair).

Similarly, they observe a cognitive impediment among farmers before their harvests but not afterward. The authors argue that before the harvest, farmers tend to focus more on povertyrelated concerns that disappear after the harvest. In contrast, [START_REF] Carvalho | Poverty and economic decision-making: Evidence from changes in financial resources at payday[END_REF], using changes in financial resources at payday, find limited evidence of changes in the cognitive abilities of affected individuals.

In our study, we prime individuals' economic condition and examine how doing so affects financial literacy scores. In line with [START_REF] Mani | Poverty impedes cognitive function[END_REF], priming implies increasing the salience of financial vulnerability. However, there are two important and interrelated differences. First, we ask a subject to deal with a task showing financial cognition, not general cognition. Second, the mechanism is different, as it relies on the role of negative emotions triggered by priming. Also, this mechanism persists during the financial literacy test.

More specifically, priming creates a mental environment that affects the decisionmaking on finance-related issues. In this environment, anticipated difficulties in dealing with the financial literacy test by the relatively poor are psychologically linked to difficulties in dealing with their poor economic condition. Thus, the relatively poor generate vivid negative emotions via priming, perceiving the test's outcome (i.e., success or failure) as more critical.

Persistent negative emotions, in turn, cause cognitive impediment that specifically applies to financial cognition and leads to poor performance in the test. In the words of Willis (2008, p. 234;further developed in Willis, 2011) "thinking about unpleasant facts can bias decisionmaking by inducing fear or anxiety, similar to the negative feelings that can be triggered by high-stakes decision-making. Because the negative feelings occupy attention, a person has a reduced capacity for decision-making." This view is backed by psychology research, which shows that negative emotions yield cognitive impairment [START_REF] Eysenck | Anxiety: The cognitive perspective[END_REF][START_REF] Eysenck | Anxiety and cognitive-task performance[END_REF] that is particularly strong if the task to perform relates to the source of these negative emotions [START_REF] Anderson | The psychology of doing nothing: Forms of decision avoidance result from reason and emotion[END_REF].

Examining this proposition would complement the findings of [START_REF] Mani | Poverty impedes cognitive function[END_REF] because we identify a specific emotional channel affecting financial cognition rather than general cognition. Such negative emotions may be less persistent and hence little overwhelming if primed subjects had to perform a neutral IQ test. This is because the succeeding in such a test is not directly associated by subjects with their difficulty in dealing with financial issues. Individuals should thus feel less pressurized by negative emotions when performing it. Therefore, the financial literacy test may play a key role in measuring the financial cognition impediments some people may experience while living in poverty.

The mechanism occurring for the poor, should also consistently work when priming the rich. [START_REF] Haushofer | On the psychology of poverty[END_REF] review many studies demonstrating a link between high income and positive affects (e.g. happiness, life-satisfaction) or stress alleviation. Thus, whereas priming low-income people with their poor economic condition should increase negative emotions, priming relatively rich individuals should remind them of their financial security, triggering rather positive emotions or at least alleviating any stress related to their finances. In turn, these enhance their financial cognition [START_REF] Anderson | The psychology of doing nothing: Forms of decision avoidance result from reason and emotion[END_REF][START_REF] Eysenck | Anxiety: The cognitive perspective[END_REF][START_REF] Eysenck | Anxiety and cognitive-task performance[END_REF][START_REF] Willis | The financial education fallacy[END_REF].

Taking an opposite view, a few studies suggest that the awareness or the salience of financial difficulties can trigger positive behavioral responses [START_REF] Stango | Limited and varying consumer attention: Evidence from shocks to the salience of bank overdraft fees[END_REF][START_REF] Shah | Money in the mental lives of the poor[END_REF]. [START_REF] Stango | Limited and varying consumer attention: Evidence from shocks to the salience of bank overdraft fees[END_REF] provide evidence that increasing the salience of overdraft fees results in less overdrafting by the same individuals in subsequent surveys.

These are behavioral changes led by more effort towards solving the problems. In our setting, this would imply the poor placing more effort in the financial literacy test. However, the mechanism here is not clear, given that priming is unlikely to bring forward positive emotions to poorer respondents and yield a better outcome in the test.

We thus hypothesize that poverty impedes financial cognition more among primed poor individuals compared to nonprimed poor individuals and individuals who are not poor.

In turn, given that financial literacy is tightly linked to financial cognition, we formulate our main hypothesis as follows:

Hypothesis 1: As poverty increases (decreases), priming people with their financial situation reduces (raises) financial literacy test scores.

We next examine how negative emotions affect poor people who perform financial management tasks. Poverty naturally implies higher economic uncertainty and is thus strongly associated with anxiety or stress [START_REF] Haushofer | On the psychology of poverty[END_REF], which in turn may hinder cognitive capabilities [START_REF] Brooks | Can Nervous Nelly negotiate? How anxiety causes negotiators to make low first offers, exit early, and earn less profit[END_REF][START_REF] Eysenck | Anxiety: The cognitive perspective[END_REF][START_REF] Eysenck | Anxiety and cognitive-task performance[END_REF]. As aforementioned, the psychology literature notes that cognitive impediment due to anxiety or stress is particularly severe in situations where people must deal with the issue that originates this negative emotion [START_REF] Anderson | The psychology of doing nothing: Forms of decision avoidance result from reason and emotion[END_REF]. Experimental work by [START_REF] Gino | Anxiety, advice, and the ability to discern: feeling anxious motivates individuals to seek and use advice[END_REF] suggests that individuals anxious about important decisions are more likely to experience cognitive impediment in the form of reduced ability to discern good from bad advice. 4 On this basis, we further argue that priming poverty has a specific effect on cognitive tasks related to financial literacy compared to "neutral" cognitive tasks, as those in [START_REF] Carvalho | Poverty and economic decision-making: Evidence from changes in financial resources at payday[END_REF] and [START_REF] Mani | Poverty impedes cognitive function[END_REF]. Further, if priming indeed impairs cognition of poorer respondents, an important channel is anxiety. Symmetrically, priming rich people with their positive financial situation could emphasize that they have little to worry about their financial situation and could thus put them in a more positive attitude when faced with the financial literacy test. We thus formulate a second hypothesis as follows:

Hypothesis 2: As poverty increases (decreases), priming people with their financial situation reduces (raises) financial literacy test scores due to increases (decreases) in respondents' feelings of anxiety about their financial situations.

Poverty is also associated with shame, which is another potential source of cognitive impediment. Poverty is often regarded socially as personal failure [START_REF] Reutter | Who do they think we are, anyway? Perceptions of and responses to poverty stigma[END_REF]. Indeed, people living in poverty might be stereotyped as lazy, incompetent, irrational, or lacking self-control, which could lead many people who are experiencing poverty to feel shamed (Hall et al., 2013;[START_REF] Reutter | Who do they think we are, anyway? Perceptions of and responses to poverty stigma[END_REF][START_REF] Vogel | Perceived public stigma and the willingness to seek counseling: The mediating roles of self-stigma and attitudes toward counseling[END_REF][START_REF] Walker | Poverty in global perspective: is shame a common denominator?[END_REF]. Hall et al. (2013) provide evidence that this shame can decrease cognition; they also find that a simple self-affirmation (i.e., remembering something that one can be proud of) significantly increases the fluid intelligence among the poorest individuals. As is the case for anxiety, we expect that dealing with a financial task (here, the financial literacy quiz) is more difficult than dealing with a neutral cognitive task. This should further impair self-esteem and exacerbate financial cognition impairment when taking the test. 5 By symmetry, for the rich, priming should elicit some feelings of social self-satisfaction (i.e. the opposite of social shame) about their own financial situation. As a result, their fluid intelligence could be enhanced (Hall et al., 2013) while performing the financial literacy test. Thus, we formulate our third hypothesis as follows:

Hypothesis 3: As poverty increases (decreases), priming people with their financial situation reduces (raises) financial literacy test scores due to increases (decreases) in respondents'

feelings of shame about their financial situations.

Poverty can also trigger feelings of guilt about consumption. In this way, guilt is related to anxiety because it might originate in difficulty to effectively handling finances [START_REF] Shapiro | Measuring financial anxiety[END_REF]. Similarly, guilt might relate to shame as part of the process of self-stigmatization, where individuals begin to believe negative stereotypes associated with poverty and feel responsible for their difficulties [START_REF] Corrigan | Self-stigma and the "why try" effect: impact on life goals and evidence-based practices[END_REF][START_REF] Vogel | Perceived public stigma and the willingness to seek counseling: The mediating roles of self-stigma and attitudes toward counseling[END_REF]).

Thus, guilt can be an additional channel through which priming the financial situation affects cognitive abilities and in turn financial literacy. Thus, we formulate our fourth hypothesis as follows:

5 Social psychology widely studies self-stereotype (or self-stigma) as a mechanism for decreasing cognitive abilities when dealing with a stereotyped task. See, for instance, [START_REF] Steele | Stereotype threat and the intellectual test performance of African Americans[END_REF] on the role of race in a math test; [START_REF] Kray | Battle of the sexes: gender stereotype confirmation and reactance in negotiations[END_REF] on women's performance in a negotiation task; Carr and Steele (2010) on women's performance in a risk attitude test.

Hypothesis 4: As poverty increases (decreases), priming people with their financial situation reduces (raises) financial literacy test scores due to increases (decreases) in respondents'

feelings of guilt about their financial situations.

Data and methodology

The survey experiment

We collect our data from the 2015 Audencia Barometer -Banque Populaire survey on French Financial Vulnerability that we co-constructed. The survey was conducted online on our behalf by the market research firm Brulé, Ville et Associés group (BVA) between April 28 and May 6, 2015. It involves 1,001 respondents, above 18 years old, who are representative of the French population. The sampling method is the quota sampling method based on respondents' gender, age, occupation, and geographical area. The treatment group includes 500 randomly drawn respondents, and the control group contains 501 respondents. 6Treatment entails priming in order to make poverty-related financial concerns top of mind among low-income respondents. Priming entails asking four questions about the frequency of debit rejection, exceeding overdraft limits, the ability to save money, and vulnerability to income shocks. Table 1 shows the questions. 7 The treatment group answers the four priming questions first and then answers a set of 24 questions, which include the financial literacy test, as well as questions about anxiety, shame, and guilt about their personal financial situations. We also include other questions. 8 We report the 24 questions in section A1 of the Appendix. The control group, on the other hand, answers the four priming questions at the end of the survey (after the 24 questions); thus, the control group is not affected by priming.

Previous studies analyze the effects of priming in a similar way. Papers in finance, examine the effect on investment behavior by priming risk attitudes or just changing the way that assets' past performance and risk are presented [START_REF] Bateman | Risk presentation and portfolio choice[END_REF][START_REF] Gilad | Priming the Risk Attitudes of Professionals in Financial Decision Making[END_REF][START_REF] Glaser | Thinking about Prices versus Thinking about Returns in Financial Markets[END_REF]. Inspired by social psychology literature, other finance papers find significant effects of priming in the form of simply asking people to record their gender before executing an investment experimental task [START_REF] Carr | Stereotype threat affects financial decision making[END_REF] or rating financial preferences [START_REF] Lee | Stereotype threat in the marketplace: Consumer anxiety and purchase intentions[END_REF]. Related to our work, [START_REF] Mani | Poverty impedes cognitive function[END_REF] primed poverty-related concerns with the thought of an expensive car repair and then analyzed the respondents' performance on a test. For the poor, stakes of a typical financial management decision are high since they are more financially vulnerable. Therefore, the objective of priming vulnerability is to recreate a high-stake environment where the quality of financial decisionmaking (reflected here in the financial literacy score) is critical to individuals (see also [START_REF] Liu | Adapting to the Destitute Situations: Poverty Cues Lead to Short-Term Choice[END_REF]. In contrast, the absence of priming leaves individuals in an environment that is freer of any mental threat related to their financial situation. This environment is evidently less likely to exist in a real financial decision-making situation, but it has the advantage for researchers to analyze the real cognitive potential of individuals when dealing with their finance outside a threatening environment.

[Insert Table 1 about here] We remove from our sample individuals who did not report their incomes ( 116observations), because this information is crucial for our analysis. In addition, we drop 197 individuals who answered "I do not know" or "not concerned" to any of the four priming questions. Thus, our end sample includes 688 respondents, of which 335 belong to the control group and 353 to the (primed) treatment group. A t-test of differences in the means indicates that the frequency of dropped observations does not differ across the treatment and control groups (p-value = 0.203).

We first test the random assignment of the treatment by checking that the homogeneity of the treatment and control groups with respect to observed characteristics [START_REF] Imbens | Recent developments in the econometrics of program evaluation[END_REF]. We compare the respondents' characteristics, including gender, age, occupation, geographic area, education, marital status, and dwelling type. We define these variables in Table 2 and report summary statistics in Table 3. We estimate a t-test of the equality of means, and the results in Table 4 essentially show that the two samples are homogeneous. The only exceptions are people who are separated (significant at the 10% level) or surviving spouses (significant at the 5% level). Even in these two cases, however, the normalized differences across treatment (equal to 0.10 and 0.13, respectively) are well below the rule of thumb value of 0.25, suggesting no particular statistical problem from these differences when inferring the average treatment effect [START_REF] Imbens | Recent developments in the econometrics of program evaluation[END_REF].9

[Insert Tables 2,3, & 4 about here]

Our main measure of financial literacy is based on correct answers to a six-question quiz related to basic skills in household finance (questions 7-12 in section A1 of the Appendix). Specifically, we use questions about the calculation of interest (question 7), compounded interest (question 8), credit card debt and settlement (question 9), basic riskreturn concepts (question 10), portfolio diversification (question 11), and inflation (question 12). In general, these questions are similar to those in the related literature (e.g., [START_REF] Guiso | How much can financial literacy help?[END_REF][START_REF] Van Rooij | Financial literacy and stock market participation[END_REF][START_REF] Bianchi | Financial literacy and portfolio dynamics[END_REF]. We take questions 7, 8, 10, 11, and 12 directly from the measure of financial literacy the OECD uses to conduct international comparisons [START_REF] Atkinson | Measuring financial literacy: Results of the OECD[END_REF]. These questions are very close to the so-called big three questions of financial literacy (e.g., [START_REF] Lusardi | The economic importance of financial literacy: Theory and evidence[END_REF]. Also, we obtain question 9 from [START_REF] Lusardi | Debt literacy, financial experiences, and overindebtedness[END_REF] to include a relatively more difficult question on debt repayment. For each of the six questions, we create a binary variable that equals 1 if the respondent answers correctly and 0 otherwise. We measure Financial literacy by adding 1 for every correct answer to the six questions (see also Table 2). A noticeable figure is that only 7 respondents (1.02% of the sample) answered "I do not know" to all financial literacy questions. This signals that very few individuals shirk performing the test.

Baseline specification

We first examine whether the effect of priming on financial literacy varies with poverty level (hypothesis 1). We use the following specification:10

# $ % = & ' + & ) * + + & " , + & -* + × , + & / 0 $ 1 + 2. ( 1
)
We thoroughly define all variables in Table 2. High Income implies low poverty, and low

Income implies high poverty. We define Income as the "effective income" which divides the household income by the squared root of the household size [START_REF] Mani | Poverty impedes cognitive function[END_REF] Given that we are interested in the interaction effect of priming with Income, we check the homogeneity of the observable characteristics in the two groups (treatment and control), this time for the three subsamples reflecting income distribution (bottom, middle, and top tercile per our definition in Table A2 of the Appendix). Following the rule of thumb that the normalized difference should not exceed 0.25 [START_REF] Imbens | Recent developments in the econometrics of program evaluation[END_REF], we conclude that there is a limited heterogeneity of groups across treatment status for these income levels.

Mediation analysis: Introducing anxiety, guilt, and social shame

As suggested by hypotheses 2-4, negative emotions such as anxiety, guilt, and shame about personal finances might affect the relation between anxiety and financial literacy. We measure anxiety about financial situations using question 18, which asks whether "thinking about my personal finances makes me anxious." We generate a binary variable (Anxiety) that equals 1 if the response is "Strongly agree" or "Somewhat agree" (0 otherwise). In turn, we measure guilt about consumption using respondents' answers to question 19 ("I feel guilty and I should have better controlled my spending."). Guilt equals 1 if the respondent answers "Strongly agree" or "Somewhat agree" and 0 otherwise. Similarly, we define Shame using question 20, which states, "When I have financial problems, I prefer not sharing them with anyone, not even my relatives."

Subsequently, we estimate the following equation:

# $ % = & ' + & ) * + + & " , + & -* + × , + & / 0 $ 1 + & 3 4 + $ 5 $ 1 + 2, (3)
where Negative emotions is Anxiety, Guilt, or Shame. Our objective is to perform a mediation analysis, to observe how sensitive the coefficient & -is to changes in the emotions-related variables in the model (i.e., comparing & -in equation 3 and & -in equation 1). If the coefficient on the interaction term is significantly lower when including negative emotions variables, then we can argue that Priming × Income has an indirect effect on Financial literacy via the corresponding Negative emotion [START_REF] Baron | The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations[END_REF][START_REF] Breen | Total, direct, and indirect effects in logit and probit models[END_REF]Carpena and Zia, 2018;[START_REF] Sobel | Asymptotic confidence intervals for indirect effects in structural equation models[END_REF]. The weight of this indirect path in the total effect of the interaction terms is provided by the downward percentage change between & -in equation 1 (total effect) and & -in equation 3 (direct effect).

Working within this framework, we also show that the interaction term (Priming × Income) significantly affects Negative emotions. This is a necessary condition for identifying such indirect effects. As the outcome variables are binary, we use a logit model of the form

*64 +. $ 1 = 19 = & ' + & ) * + + & " , + & -* + × , + & / $ 1 + 2. (4)

Results

Baseline results

In Table 5, we report tests of the differences in the mean of Financial literacy, and the three negative emotions for the primed and nonprimed respondents. The results indeed show no significant differences across the treatment and control groups (irrespective of income levels),

which is an important first result for the validity of our approach.

[Insert Table 5 about here]

Column 1 of Table 6 reports the results from the estimation of equation ( 1). The estimation method is OLS (as in [START_REF] Finke | Old age and the decline in financial literacy[END_REF][START_REF] Guiso | Financial literacy and portfolio diversification[END_REF][START_REF] Jappelli | Economic literacy: An international comparison[END_REF]Lusardi et al., 2014), and all specifications include the demographic control variables defined in Table 2. The coefficient on Priming × Income is positive and significant at the 5% level.

Among primed respondents, a 1% rise in income increases financial literacy scores by 0.0042 points (0.311/100+0.113/100); among nonprimed respondents, the equivalent increase is 0.00113 points (statistically insignificant). Therefore, the marginal effect of income on financial literacy is 3.7 times higher among primed individuals than among nonprimed individuals (0.0042/0.00113 = 3.71).

More informative is Figure 1, which illustrates the change in Financial literacy due to priming for 20 different levels of income (i.e., the linear coefficient of the upward fitted line in this figure equals the estimate of the interaction term in Table 6). Our estimate shows that as Income decreases, the effect of Priming on financial literacy becomes increasingly negative. In fact, Priming has a negative effect on Financial literacy among the relatively poor, whereas it has a positive effect among the relatively rich. In other words, when people are forced to think about their financial situations, those experiencing poverty may find it hard to perform tasks related to financial management. Thus, our baseline results are consistent with hypothesis 1.12 [Insert Table 6 and Figure 1 about here]

We further illustrate this result by comparing individuals above and below the median income. Results of specification 2 in Table 6 show the estimation of Equation 1when replacing Income by a dummy variable that takes the value 1 if individuals have an effective income above or equal to the median; and 0 otherwise (Above median income). The priming effect on the score of financial literacy is -0.26 points amongst the poor (i.e., those below the median) (& ) 9 and +0.20 points (& --& " 9 amongst the rich. Figure 2 illustrates the financial literacy scores obtained for each subsample (Priming × Above median income). Thus, our results suggest not only that priming decreases financial literacy scores amongst poorer individuals but also that priming improves the score amongst wealthy individuals. This validates the theoretical prior that priming the economic condition has symmetric effects depending on the level of Income: a negative one for low-income people having bad feelings about their financial situation and a positive one for high-income people who are reinsured and self-satisfied by their financial situation. This is consistent with our theoretical arguments dealing with finance-related cognitive questions triggers the positive effect of emotions (e.g., stress relief, social self-satisfaction) on the cognitive functioning [START_REF] Anderson | The psychology of doing nothing: Forms of decision avoidance result from reason and emotion[END_REF].

[Insert Figure 2 about here]

We perform a final estimation, interacting Priming with a variable constructed based on the quintile of the Income distribution to which respondents belong. We plot the marginal effect of priming for each quintile in Figure 3. In line with our baseline findings, results show a reversal in the priming effect, from negative among the two low-income categories to positive among the two high-income ones. An interesting finding is that the negative effect of priming is mostly driven by the second lowest-income group (individuals with monthly effective income between 1,061 and 1,500 euros). Symmetrically, the positive effect is mainly driven by the second highest-income category (effective monthly income between 1,768 and 2,475 euros).13 An explanation for this finding is that the priming mechanism is less potent for individuals at the margins of the income distribution. Low-income individuals are used to have their poor economic condition at the forefront, and priming may not have a strong additional impact. Similarly, super high-income individuals are used to abundance and pointing out their comfortable economic situation with priming may hardly affect their emotional state.

[Insert Figure 3 about here]

Our results validate the view developed in the theoretical section that having povertyrelated concerns at the forefront could make financial decision-making more complicated at the cognitive level for relatively poor individuals. In the context of financial decision-making, thinking of their financial vulnerability, the poor might incur difficulties to perform the basic calculations needed to manage savings and debts. This effect can prevail even if financially vulnerable individuals have the potential to do better, as is suggested by the performance of nonprimed poor. Our results further suggest that poverty harms the poor's ability to analyze basic financial issues, related for instance to the effect of inflation on their savings and investments. Thus, poverty contributes to a higher probability of erroneous financial decisions and the discouragement of affected individuals to undertake positive financial actions and use financial products (e.g., opening a remunerated savings account).

We test the sensitivity of our baseline results to a bad controls problem by sequentially adding demographic variables. We report these results in Table A3 of the Appendix. The estimate of the interaction term and our general inferences are not significantly affected. Formally comparing differences in the coefficients (e.g., using suest in Stata) favors the null of no significant differences (results available on request). 14We further use a placebo test to check that our results are not spuriously led by selection bias due to unobservables [START_REF] Athey | The state of applied econometrics: Causality and policy evaluation[END_REF]. Specifically, we estimate our baseline model using as outcome variables the responses to questions asked before priming the treatment group. If there is no sample selection bias, the treatment should have no effect on these outcome variables.

We first use a score from questions addressing respondents' propensity to plan for the use of money (Propensity to plan). Several studies link this trait to financial literacy (e.g., [START_REF] Van Rooij | Financial literacy, retirement planning and household wealth[END_REF][START_REF] Fernandes | Financial literacy, financial education, and downstream financial behaviors[END_REF]. Second, we use a score of the respondents' time preferences for spending money (Present preference). We thoroughly define these variables in Table 2, provide summary statistics in Table 3, and report t-tests of the difference in means across treatment status (not rejecting the null) in Table 7. Results from these placebo tests in Table 8 show that Priming × Income produces insignificant effects on the two outcome variables, providing additional validation that our baseline results do not suffer from endogeneity bias.

[Insert Table 7 and 8 about here]

We perform additional sensitivity tests using variables constructed from answers to the remaining 24 questions (other than those about financial literacy and emotions), as priming may affect those answers too. We aim to show that controlling for variables that capture self-confidence in financial management (Self-confidence financial management), loss aversion (Loss aversion), and positive perceptions of bank advisors (Positive perception bank advisor) do not affect our main results. We define these variables in Table A4 of the Appendix and provide summary statistics in Table A5. In Table A6, we show that differences across the treatment and control groups are statistically insignificant for these variables.

Adding these variables in equation ( 1) suggests that our baseline estimate on Priming × Income remains largely unaffected (see Table A7 of the Appendix).

Heterogeneity due to self-confidence

Existing literature also shows that self-confidence is a strong determinant of financial literacy and financial behavior [START_REF] Barber | Boys will be boys: Gender, overconfidence, and common stock investment[END_REF][START_REF] Bucher-Koenen | How Financially Literate Are Women? An Overview and New Insights[END_REF][START_REF] Fernandes | Financial literacy, financial education, and downstream financial behaviors[END_REF][START_REF] Kramer | Financial literacy, confidence and financial advice seeking[END_REF]. 15 In our setting, an interesting extension is whether loss in selfconfidence due to priming affects our baseline result (the estimate on the interaction term Priming × Income). The fact that the inclusion of Self-confidence financial management enters with a highly significant coefficient but does not affect the coefficient on the interaction term is thus an important result (Table A7): it limits the view that the effect of priming on the poor's (rich's) financial literacy occurs through a loss (gain) in self-confidence triggered by priming. This is further confirmed by a lack of effect of our key interaction 15 Self-confidence in financial management should be distinguished from the above-mentioned feeling of reinsurance (or stress relief) that relates to someone's more general perception that there is no specific reason to be stressed when it comes to think about financial perspectives. It also differs from social self-satisfaction that refers to individuals' increased self-esteem when thinking that their own economic condition is consistent with the social standards of a successful life.

variable on Self-confidence financial management used as a dependent variable in the regression shown in Table A8 of the Appendix (specification 1).

To further check that self-confidence does not affect our baseline result we construct a new dependent variable, adding 1 for each answer "I do not know" in the financial literacy quiz. We name this variable IDK score (exact definition in Appendix Table A4 and summary statistics in Table A5). Following Bucher-Koenen et al. ( 2017), we posit that answering "I do not know" may reflect individuals' lack of self-confidence rather than pure ignorance or cognitive difficulties. 16 However, estimating our baseline equation ( 1) using IDK score as the dependent variable shows that Priming × Income has no effect on this variable (results in specification 2 of Appendix Table A8). This finding implies that our baseline result is not driven by primed poor individuals being more likely to answer "I do not know". The same insignificant result is obtained when conducting the same analysis but using a different dependent variable that takes the value 1 if respondents answered "I do not know" to at least one of the financial literacy questions, and 0 otherwise (the variable is named At least 1 IDK, and its definition is in Appendix Table A4, while summary are in Table A5). This results are in specification 3 of Appendix Table A8. All these results hold when using the above/below median income approach.

We include two more tests regarding the role of self-confidence. First, we examine the effect of Priming interacted with Self-confidence financial management and find an insignificant effect (column 4 of Table A8). This result implies that the effect of priming is independent of the level of self-confidence of respondents. Second, we include the triple interaction term Priming × Income × Self-confidence financial management and again find an insignificant effect (results in column 5 of Table A8). This finding suggests that the individuals' level of self-confidence does not interact with our baseline result to affect Financial literacy. 17

Interaction of Priming with acute financial difficulty

We deepen our analysis by looking at the interplay of priming with acute financial difficulty.

We construct Extreme financial difficulties by adding one when individuals give extreme negative answers to each of the four priming questions (a score of 4 means extreme negative answers to all four questions). We provide an exact definition in Table 2 and summary statistics in Table 3. The results, reported in specification 2 of Table 9, show that primed individuals with acute financial difficulties indeed have more negative financial literacy scores compared to their non-primed peers.

[Insert Table 9 about here]

We next examine the extent to which this result underlays our baseline result. In specification 3, we include both Priming × Income and Priming × Extreme financial difficulties. Both terms remain statistically significant, but lose part of their economic significance compared to their separate inclusion in the model (specifications 1 and 2). 18 We thus conclude that two priming effects coexist, one based on structural low-income conditions (Income) and the other based on acute financial distress. 19 Whereas the latter effect is intuitive, the former implies that the hampered financial cognition of primed low-income 17 We conduct similar tests with the variables capturing gender and education (Woman and Low educ), as these are often associated to low self-confidence in financial decisions, mathematics, etc. [START_REF] Barber | Boys will be boys: Gender, overconfidence, and common stock investment[END_REF][START_REF] Bucher-Koenen | How Financially Literate Are Women? An Overview and New Insights[END_REF][START_REF] Lusardi | Financial literacy and retirement planning in the United States[END_REF]. We find that the interaction of these variables with priming yields insignificant coefficients. This further suggests that priming affects financial literacy of individuals depending on their level of income but not depending on the lack of self-confidence that women and low-educated individuals are more likely to exhibit. We also find insignificant coefficients when interacting these two variables with Priming × Income (i.e., three-way interactions), suggesting that the specific harmful effect of priming on financial literacy among low-income respondents is not influenced by gender and education. 18 Wald tests confirm the significance of the drop in the value of coefficients (both at the 5% level). 19 A triple interaction term (i.e., Priming × Income × Extreme financial difficulties) has no impact on financial literacy scores.

people is not solely based on current acute difficulties but also based on the fear of experiencing such difficulties. In other words, for low-income individuals, the thought of poor economic condition is a mental threat even if their financial situation is not dramatic. 20

Mediation analysis: The role of negative emotions

Turning to hypotheses 2-4, we estimate equation ( 3) by sequentially adding Anxiety, Guilt,

and Shame (the results are in columns 3-5 of Table 6). With these estimations, we identify whether controlling for emotions decreases Priming × Income, essentially suggesting that the differential effect of priming poor economic condition for different income levels on the financial literacy score is at least partially due to negative emotions.

The results of the mediation analysis show that whereas the coefficients on the three negative emotions are statistically significant at the 1% level, the coefficient on Priming × Income is only affected by including Anxiety and Shame (columns 3 and 4, respectively). In these specifications, the estimate on Priming × Income is both statistically and economically weaker compared to the baseline result in column 1. This implies an indirect effect of Priming × Income on Financial literacy through Anxiety and Shame. In contrast, our baseline estimate remains unaffected when including Guilt (column 5). In fact, including both Anxiety and Shame in the same equation (column 6) further decreases the coefficient on Priming × Income.

In Table 10, we report formal mediation analyses in the form of testing the significance of the differences between column 1 and columns 3-6 in Table 6 (i.e., the table

showing baseline results). The drop in the coefficient on Priming × Income is statistically significant between columns 1 and 3 (at the 10% level), between columns 1 and 4 (at the 10% 20 We draw consistent conclusions when performing similar analyses using dummies based on the most negative answer to each of the four priming questions.

level), and between columns 1 and 6 (at the 5% level). We conclude that there is a significant indirect effect of Priming × Income on financial literacy through Anxiety and Shame.

Economically, the differences in the estimates are substantial. The drop in the coefficient of the interaction term after introducing Anxiety and Shame equals 0.061 (from 0.0311 to 0.025), which represents a 20% decrease. In other words, 20% of the total effect of Priming × Income on financial literacy is mediated via the effect of Anxiety and Shame.

[Insert Table 10 about here] So far, our findings are consistent with hypotheses 2 and 3. However, following our discussion on the estimation of equation ( 4), the picture is incomplete unless we also show that Priming × Income directly affects Anxiety and Shame and does not significantly affect

Guilt. Given that the outcome variables are binary, we use a logit model. Table 11 shows average marginal effects from the interaction term Priming × Income. 21 The results are fully consistent with those in Tables 6 and8: Anxiety and Shame significantly respond to priming (at the 10% level), but Guilt does not respond significantly. In line with hypotheses 2 and 3, we conclude that among those with relatively low effective income, priming hinders financial literacy test performance by increasing respondents' anxiety and shame about their financial situations.

[Insert Table 11 about here]

The role played by emotions in impeding/enhancing the financial literacy score of the poor/rich distinguishes our findings from those of [START_REF] Mani | Poverty impedes cognitive function[END_REF], who analyze general cognition using an IQ test. We complement the findings by [START_REF] Mani | Poverty impedes cognitive function[END_REF] by providing 21 To obtain a linear probability metric of the interaction effect between a discrete and a continuous variable in a logit regression, we should take into account that this metric can vary over the different values of the continuous variable, here Income [START_REF] Ai | Interaction terms in logit and probit models[END_REF]. Therefore, the coefficient on the interaction term reported in Table 11 is an average of the interaction effects estimated for different levels of Income. Following [START_REF] Ai | Interaction terms in logit and probit models[END_REF], we examine if this negative and significant average estimation is consistent across different values of Income. For specification 1 (Anxiety), we find that it is the case for 85% of the distribution of Income and insignificant for only 15% of the distribution reflecting extreme values of Income. For specification 3 (Shame), we also find that the average estimation is consistent across different values of Income.

evidence that priming impedes cognitive functions when dealing with a finance-related cognitive task.

Discussion and conclusions

Our analysis provides the first evidence that poverty-related concerns affect the cognitive abilities needed for financial literacy (i.e., financial cognition). We show that priming hampers financial literacy scores of the poor (people who are below the median effective income) but also improves scores of the rich. Our results point out a "cognitive inequality" between the rich and the poor when it comes to optimal financial decision-making. We find that this effect is only partially related to the level of financial difficulties experienced by primed individuals. This suggests that for low-income individuals, the thought of poor economic condition is a mental threat even if their financial situation is not dramatic. We show that such cognitive impediment for relatively poor individuals is related to increased anxiety and shame when these individuals' financial difficulties are primed. The loss of financial cognition, as evident by the drop in financial literacy scores, implies that concerned individuals have higher probability to incorrectly perform the basic calculations needed to manage savings and debts. It also means that individuals can be confused when trying to recollect the functioning of basic mechanisms linked to personal financial management (e.g. inflation, exponential growth of interests, diversifications, etc.). Therefore, these results shed light on the existence of a vicious behavioral cycle, whereby those that are more vulnerable to negative economic shocks are also more likely to make mistakes in their financial decisions.

Ultimately, this may trap people in poverty.

The external validity of our analysis is important because priming arguably replicates a ubiquitous situation, whereby individuals are mechanically led to think of their own financial situation and associated consequences. In contrast, nonpriming is a neutral environment that might scarcely be existent. This environment gives researchers a benchmark on the real cognitive potential of the poor if the thought of poverty was not so threatening.

Debt-related decisions and potential mistakes are those for which negative emotions should have large effects for the poor. In fact, even more positive financial decisions, like saving or investing, require poor individuals to think about their financial capacity and this triggers emotions. In contrast, richer individuals might view these activities as constructive and even exciting, triggering a cognitive ease when thinking of the settings of the decision and the potential for significant financial benefits.

These considerations are not just semantics because they imply that poor individuals might make mistakes on selecting between basic financial products. Using financial advice (e.g., debt counselling or consulting with the bank advisor) could be a way to overcome the financial cognition problems [START_REF] Gennaioli | Money doctors[END_REF]. However, these are costly services that also require extra attention to avoid any stigmatization linked to individuals' economic condition. As we show, social shame can amplify the impediment of financial cognition, making the advising process difficult.

The paper's conclusion is part of an ongoing discussion in the literature. Especially, the significant role that negative emotions play is a key difference of our findings compared to previous work on the link between poverty and general cognition. [START_REF] Mani | Poverty impedes cognitive function[END_REF] show that stress plays no role in their findings on general cognitive abilities, whereas we show that specific negative emotions (shame and anxiety) are important when studying financial cognition. This is because the nature of the tasks in the financial literacy test directly relates to the source (i.e., poor economic condition) of anxiety and shame. Thus, these povertyrelated negative emotions are vivid when performing the test, impeding a normal cognitive process. Regarding the role of shame, our results echo the literature analyzing the harmful effects of negative self-stereotyping on self-esteem and in turn on cognitive ability (e.g., [START_REF] Carr | Stereotype threat affects financial decision making[END_REF][START_REF] Steele | Stereotype threat and the intellectual test performance of African Americans[END_REF]. Our findings on the role of emotions are consistent with [START_REF] Fernandes | Financial literacy, financial education, and downstream financial behaviors[END_REF] and [START_REF] Willis | Against financial literacy education[END_REF][START_REF] Willis | The financial education fallacy[END_REF] in that policies aiming to increase financial literacy through education programs might not be as efficient. At the very moment of financial decision-making, negative emotions of those most in need of good judgement prevail, lowering financial cognition and the quality of decision-making.

Overall, our paper contributes to the growing literature on household finances and how households make financial decisions. Our results particularly relate to literature exploring why individuals tend to deviate from rational financial behavior (e.g., [START_REF] Beshears | Behavioral household finance[END_REF]. From a policy-making perspective, increasing individuals' responsibility for their financial security might pose a serious behavioral issue for the most economically vulnerable citizens. Our results imply that the economically vulnerable should receive extra advice to help them make sound financial decisions and allow them to escape the vicious cycle of poverty. Spreading the access and the use of web or mobile applications designed to make financial decisions simpler may also represent an interesting avenue to explore (e.g., [START_REF] Abiona | Financial Inclusion, Shocks, and Poverty: Evidence from the Expansion of Mobile Money in Tanzania[END_REF].

A. Dependent variable

Financial literacy

The sum of correct answers to the six financial literacy questions (questions 7 to 12 in Appendix 1). It equals 0, 1,…, 6, corresponding to the number of correct answers.

B. Main explanatory variables

Priming A dummy variable indicating whether the respondent answers questions about his/her economic condition early in the questionnaire (priming) or later in the questionnaire. It equals 1 for primed (treated) respondents and 0 for nonprimed (nontreated) respondents.

Income

The natural logarithm of the effective income, where Effective income = household income / squared root (household size). The respondents report the range of their monthly disposable income (below €1,000; between €1,000 and €2,000; between €1,000 and €2,000; between €2,000 and €3,000; between €3,000 and €4,000; between €4,000 and €5,000; and above €5,000). For the calculation, we use the middle ranges (e.g., €1,500 for the range "between €1,000 and €2,000"). For the upper range, which has no upper bound, we assign a virtual income of €10,000.

The calculation of the standard of living by INSEE is household income/# of units of consumption (uc) where the first adult counts for 1 uc, another person above 14 counts for 0.5, and children below 14 counts for 0.3 uc. Our data does not allow calculating the standard of living because the age of the children in the household was not asked in the survey. However, the premise is fairly the same than that of calculating the effective income. In both measures, the idea is to reduce a household's income by the size of the household and to decrease the weight of an additional member.

Above median income A dummy variable based on the level of Income. It takes the value 1 if Income is above or equal the median, and 0 otherwise.

Extreme financial difficulties

Score variable adding one when respondents report the worst financial outcome (extreme negative answer) for each of the four priming questions.

C. Mediating variables: Negative emotions about personal finances

Anxiety A dummy variable based on answer to question 18 (see Appendix 1). It equals 1 if outcome = "1. Strongly agree" or "2. Somewhat agree," and 0 otherwise.

Guilt

A dummy variable based on answer to question 19 (see Appendix 1). It equals 1 if outcome = "1. Strongly agree" or "2. Somewhat agree," and 0 otherwise.

Shame

A dummy variable based on answer to question 20 (see Appendix 1). It equals 1 if outcome = "1. Strongly agree" or "2. Somewhat agree," and 0 otherwise.

D. Other variables

Age A continuous variable equal to the respondent's age.

Woman A dummy variable equal to 1 for female respondents and 0 for male respondents. Marital status This is a vector of 5 dummy variables (Single, Divorced, Married or civil union, Separated, Widow(er)) that equal 1 if the respondent reports this marital status, and 0 otherwise.

Dwelling situation This is a vector of 7 dummy variables (House owner (with mortgage), House owner (no mortgage), Hosted, Tenant, Tenant (low-cost housing), Other (dwelling), Don't know (dwelling)) that equal 1 if the respondent reported this dwelling situation, and 0 otherwise.

E. Outcome variable for the placebo test

Propensity to plan This is a score derived from the following five-item scale. Respondents were asked to rate whether they strongly agree, somewhat agree, somewhat disagree, strongly disagree, or are not concerned with the following statements:

1. I decide beforehand how my money will be used in the next one to two months.

2. I have considered future expenses (taxes, school fees, etc.) beforehand in order to stick to my budget in the next one to two months.

3. I like to review my budget for the next one to two months in order to get a better picture of my spending in the future. 4. I consult my budget to see how much money I have for the next one to two months. 5. It makes me feel better to have my finances planned out for the next one to two months.

Propensity to plan equals the arithmetic average of the rating for each item (coding of ratings were reversed). It takes a missing value if the respondent answers "Not concerned."

Present preference This is a score derived from the following three-item scale. Respondents were asked to rate whether they strongly agree, somewhat agree, neither agree nor disagree, somewhat disagree, or strongly disagree with the following statements:

1. I find it more satisfying to spend money than to save it. 2. I tend to live for today and let tomorrow take care of itself. 3. Money is there to be spent.

Present preference equals the arithmetic average of the rating for each item (coding of ratings were reversed).

The 24 questions

We report here the 24 questions that follow the priming questions for the treatment group and are followed by the priming questions for the control group. The order of the questions appears as in the actual survey (headers are not included in the questionnaire).

Self-confidence in financial management (six questions)

Overall, would say that you are comfortable in doing calculations? (question 1)

1. Strongly agree 2. Somewhat agree 3. Somewhat disagree 4. Strongly disagree On a scale of 1 to 5, where 1 is "not at all" and 5 is "yes, exactly", assess whether your financial knowledge is sufficient to deal with each of the following situations.

Loss aversion

Let's imagine that you are endowed with 10€, would you accept to participate in the following lottery (question 14):

Perception of bank advisor (four questions)

Would you say that you "totally agree", "rather agree", "rather disagree", or "totally disagree" with the following statements? Negative emotions about the financial situation (three questions) Would you say that you "totally agree", "rather agree", "rather disagree", or "totally disagree" with the following statements?

Budget behavioral intentions (three questions)

Would you say that you "totally agree", "rather agree", "rather disagree", or "totally disagree" with the following statements? 4. When I subscribe a loan, I usually choose to repay the highest monthly settlements, even though it requires an important budget effort. (question 22) 5. I would accept to implement a standing orders towards a blocked savings account that would constraint me to save more (question 23) 6. I take measures to save energy in order to improve my budget situation (question 24)

Table A2. Randomization checks on demographics -Breakdown by tercile of Income distribution

The table reports the results from t-tests (p-value) of the difference in means and the normalized difference of the demographic variables across treatment statuses, for the bottom, middle and top tercile of Income distribution. All variables are defined in Table 2. t-tests and normalized differences are not reported when the mean of the variable is 0 across treatment statuses.

Figure 1 .

 1 Figure 1. Priming effect on the financial literacy score This figure illustrates the change in Financial literacy (y-axis) due to priming for 20 different levels of Income (xaxis). The figure is obtained after the estimation of specification 1 in Table 6 (i.e., equation 1). The bars indicate 95% confidence intervals, and the black vertical line reflects the median value of Income.

Figure 2 .Figure 3 .

 23 Figure 2. Priming effect below and above the median IncomeThis figure illustrates the score of Financial literacy (y-axis) depending on the priming status for individuals having an effective income (Income) below/above the median (x-axis). The figure is obtained from the estimation of specification 2 in Table6. The bars indicate 95% confidence intervals.

2.

  Assess the return and the risk associated with financial investments. (question 2) 3. Discuss with your financial advisor to optimize your financial management. (question 3) 4. Read your account statement. (question 4) 5. Subscribe a credit. (question 5) 6. Buy a house. (question 6) Financial literacy quiz (six questions) 1. Suppose you put $100 into a savings account with a guaranteed interest rate of 2% per year. You don't make any further payments into this account and you don't withdraw any money. How much would be in the account at the end of the first year? [Open response: $102] (question 7) 2. and how much would be in the account at the end of five years? (question 8) is impossible to tell from the information given e) other response (open) f) do not know 3. You owe $3,000 on your credit card. You pay a minimum payment of $30 each month. At an Annual Percentage Rate of 12% (or 1% per month), how many years would it take to eliminate your credit card debt if you made no additional new charges? (question 9) a) Less than 5 years b) Between 5 and 10 years c) Between 10 and 15 years d) Never, you will continue to be in debt* e) Do not know 4. An investment with a high return is likely to be high risk. [True/False] (question 10) 5. It is usually possible to reduce the risk of investing in the stock market by buying a wide range of stocks and shares. [True/False] (question 11) 6. High inflation means that the cost of living is increasing slowly. [True/False] (question 12) Cognitive reflection test (CRT) A bat and a ball cost €1.10 in total. The bat costs €1 more than the ball. How much does the ball cost? [5cts] (question 13)

1 .

 1 I worry that the bank advisor would think I'm ignorant if I come into their office with a minor financial concern. (question 15) 2. Describing to my bank advisor how I spend money on frivolous or unnecessary items is not embarrassing for me. (question 16) 3. The bank advisor is the right person to talk about financial distress. (question 17) 4. When I have to take an important decision, I do not hesitate to take some advice from by bank officer. (question 18)

1 .

 1 Thinking about my personal finance makes me anxious. (question 19) 2. I can feel guilty by thinking that I should have better controlled my spending. (question 20) 3. When I have financial problems I prefer not evoking them with anyone, not even my relatives. (question 21)

  ; we use its natural logarithm. The calculation of effective income follows the calculation of the standard of living by the French National Institute for Statistics (INSEE), which employs this variable to define people living in poverty. Comparing the two, we observe a mean effective income of €1,984 and a median of €1,750 in our sample, which are very close to the equivalent by the INSEE: €1,953 and € 1,692, respectively in 2015. 11 Due to randomization, including control variables should have a limited effect on the coefficient of the interaction term[START_REF] Angrist | Mostly harmless econometrics: An empiricist's companion[END_REF]. A correlation matrix with all variables of interest in this study is in the Appendix (TableA1).In Equation (1), & -reflects the differential effect of priming on financial literacy for unitary increases in Income. If & -is statistically and economically significant, then priming has different effects on the outcome variable for individuals with lower incomes compared to primed individuals with higher incomes and nonprimed respondents with lower incomes. If

& -is not statistically different from zero, then priming does not yield a heterogeneous effect of poor financial condition on the outcome variable.

Table 1 . Priming questions

 1

	Question number	Question text
	1.	In the last two years, how frequently have you have experienced a direct debit rejection?
		Never / rarely / from time to time / often / N/A (no bank account)
	2.	In the last two years, how frequently have you have experienced a bypassing of your overdraft
		authorization?
		Never / rarely / from time to time / often / N/A (no bank account)
	3.	Over the last 12 months, did you:
		• Save money from your income
		• Meet your current expenses with nothing to spare
		• Tap into your savings to meet current expenses
		• Tap into your savings and borrow money to meet current expenses.
		• I do not know
	4.	If you lost your main source of income, how long would you be able to cover your current expenses
		without borrowing money or being evicted?
		• Less than a week
		• Between one week and one month
		• Between one month and three months
		• Between three months and six months
		• More than six months
		• I do not know

Table 2 . Variable definitions

 2

	Variable

  Ile de France, North, East, East of Parisian Basin, West of Parisian Basin, West, Southwest, Southeast, Mediterranea) that equal value 1 if the respondent reports this living area, and 0 otherwise.

	Area	This is a vector of 9 dummy variables (
	Education	This is a vector of 4 dummy variables (Low education, Intermediate education, High
		education, Don't know) that equal 1 if the respondent reports a level of education and 0
		otherwise.
	Occupation	This is a vector of 9 dummy variables (Agriculture; Artisan, merchant, firm director;
		Inactive; Executive or intellectual profession; Student; Employee; Factory worker;
		Intermediate profession; Retiree) that equal 1 if the respondent reports this type of
		occupation, and 0 otherwise.

Table 3 .

 3 Summary statisticsThe table reports the number of observations, mean, standard deviation, minimum, and maximum for the variables used in our empirical analysis. All variables are defined in Table2.

		Obs.	Mean	Std. Dev.	Min.	Max.
	Financial literacy	688	3.47	1.54	0	6
	Anxiety	688	0.59	0.49	0	1
	Guilt	688	0.58	0.49	0	1
	Shame	688	0.47	0.50	0	1
	Propensity to plan	665	3.09	0.68	1	4
	Present preference	688	2.84	0.87	1	5
	Effective income (original variable)	688	€1,983.60	€1,422.80	€204.12	€10,000
	Income	688	7.38	0.67	5.32	9.21
	Above median income	688	0.54	0.5	0	1
	Extreme financial difficulties	688	0.21	0.53	0	4
	Age	688	47.50	16.35	18	88
	Woman	688	0.50	0.50	0	1
	Low education	688	0.30	0.46	0	1
	Intermediate education	688	0.18	0.39	0	1
	High education	688	0.51	0.50	0	1
	Don't know (education)	688	0.00	0.054	0	1
	Agriculteur	688	0.02	0.14	0	1
	Artisan, merchant, company director	688	0.03	0.18	0	1
	Inactive	688	0.07	0.26	0	1
	Executive or intellectual profession	688	0.11	0.31	0	1
	Student	688	0.04	0.20	0	1
	Employee	688	0.17	0.37	0	1
	Factory worker	688	0.12	0.33	0	1
	Intermediate profession	688	0.14	0.35	0	1
	Retiree	688	0.29	0.46	0	1
	Ile de France	688	0.19	0.39	0	1
	North	688	0.06	0.23	0	1
	Est	688	0.08	0.27	0	1
	East of Parisian Basin	688	0.08	0.27	0	1
	West of Parisian Basin	688	0.09	0.29	0	1
	West	688	0.14	0.34	0	1
	Southwest	688	0.11	0.31	0	1
	Southeast	688	0.13	0.34	0	1
	Mediterranean	688	0.13	0.34	0	1
	Single	688	0.27	0.44	0	1
	Divorced	688	0.07	0.25	0	1
	Married or civil union	688	0.60	0.49	0	1
	Separated	688	0.02	0.15	0	1
	Widow(er)	688	0.04	0.19	0	1
	House owner (with mortgage)	688	0.20	0.40	0	1
	House owner (no mortgage)	688	0.40	0.49	0	1
	Hosted	688	0.06	0.23	0	1
	Tenant	688	0.24	0.43	0	1
	Tenant (low-cost housing)	688	0.08	0.27	0	1
	Other (dwelling)	688	0.01	0.12	0	1
	Don't know (dwelling)	688	0.00	0.04	0	1

Table 4 . Randomization checks on demographics

 4 The table reports the results from t-tests of the difference in means, as well as the normalized difference of the demographic variables. All variables are defined in Table2.

		Control group	Treatment group
		N= 335	N= 353
						t-test	Normalized
		Mean	Std. Dev.	Mean	Std. Dev.	p-value	difference
	Income	7.36	0.68	7.40	0.67	0.45	-0.04
	Above median income	0.53	0.5	0.54	0.5	0.8	-0.01
	Age	47.47	16.46	47.54	16.28	0.95	0.00
	Woman	0.51	0.50	0.49	0.50	0.54	0.03
	Low education	0.30	0.46	0.30	0.46	0.90	0.01
	Intermediate education	0.21	0.41	0.16	0.37	0.13	0.08
	High education	0.48	0.50	0.54	0.50	0.15	-0.08
	Don't know (education)	0.01	0.08	0.00	0.00	0.15	0.08
	Agriculteur	0.02	0.13	0.02	0.15	0.66	-0.02
	Artisan, merchant, company director	0.03	0.17	0.04	0.19	0.61	-0.03
	Inactive	0.07	0.25	0.08	0.27	0.58	-0.03
	Executive or intellectual profession	0.10	0.29	0.12	0.32	0.38	-0.05
	Student	0.04	0.19	0.05	0.21	0.55	-0.03
	Employee	0.17	0.38	0.16	0.37	0.68	0.02
	Factory worker	0.14	0.35	0.11	0.31	0.19	0.07
	Intermediate profession	0.13	0.34	0.15	0.36	0.55	-0.03
	Retiree	0.30	0.46	0.28	0.45	0.49	0.04
	Ile de France	0.17	0.38	0.20	0.40	0.40	-0.05
	North	0.06	0.23	0.06	0.24	0.88	-0.01
	Est	0.08	0.27	0.08	0.27	0.83	-0.01
	East of Parisian Basin	0.07	0.26	0.08	0.28	0.52	-0.04
	West of Parisian Basin	0.10	0.30	0.08	0.27	0.31	0.05
	West	0.15	0.36	0.12	0.33	0.35	0.05
	Southwest	0.13	0.33	0.09	0.29	0.18	0.07
	Southeast	0.12	0.32	0.14	0.35	0.28	-0.06
	Mediterranean	0.13	0.33	0.13	0.34	0.85	-0.01
	Single	0.27	0.45	0.27	0.44	0.94	0.00
	Divorced	0.06	0.23	0.08	0.27	0.24	-0.06
	Married or civil union	0.61	0.49	0.60	0.49	0.83	0.01
	Separated	0.01	0.11	0.03	0.18	0.06	-0.10
	Widow(er)	0.05	0.23	0.02	0.14	0.02	0.13
	House owner (with mortgage)	0.20	0.40	0.21	0.41	0.75	-0.02
	House owner (no mortgage)	0.39	0.49	0.42	0.49	0.50	-0.04
	Hosted	0.06	0.24	0.05	0.22	0.51	0.04
	Tenant	0.25	0.43	0.24	0.43	0.76	0.02
	Tenant (low-cost housing)	0.08	0.27	0.08	0.27	0.84	0.01
	Other (dwelling)	0.02	0.13	0.01	0.11	0.47	0.04
	Don't know (dwelling)	0.00	0.05	0.00	0.00	0.30	0.05

Table 6 .

 6 Baseline resultsThe table reports the results (coefficient estimates and t-statistics in brackets) from the estimation of equation 1 in specification 1 and 2, and equation 3 in specifications 3-6. Specification 2 replicates specification 1 but replaces Income by the dummy variable Above median income. The estimation method is OLS with robust standard errors (clustered by individual). The dependent variable is Financial literacy. Income and all demographic variables are mean centered for easier interpretation of the intercept and main term. All variables are defined in Table2. The regression includes the demographic variables defined in Table2(Controls). The lower part of the table reports the number of observations (N),

	the R-squared and the pseudo R-squared. The *, **, and *** marks denote statistical significance at the 10%, 5%, and
	1% levels, respectively.
		1	2	3	4	5	6
		Financial	Financial	Financial	Financial	Financial	Financial
		literacy	literacy	literacy	literacy	literacy	literacy
	Priming	-0.016	-0.261*	-0.019	-0.006	-0.006	-0.010
		[-0.16]	[-1.68]	[-0.19]	[-0.06]	[-0.06]	[-0.10]
	Income	0.113		0.0656	0.100	0.0672	0.0661
		[1.02]		[0.60]	[0.92]	[0.60]	[0.60]
	Priming × Income	0.311**		0.278*	0.269*	0.316**	0.250*
		[2.12]		[1.91]	[1.86]	[2.16]	[1.74]
	Above median income		-0.0208
			[-0.13]
	Priming × Above median inc.		0.469**
			[2.26]
	Anxiety			-0.347***			-0.263**
				[-3.04]			[-2.27]
	Shame				-0.404***		-0.350***
					[-3.88]		[-3.30]
	Guilt					-0.410***
						[-3.73]
	Constant	3.470***	3.480***	3.677***	3.654***	3.704***	3.786***
		[47.78]	[31.24]	[36.86]	[41.04]	[36.81]	[35.39]
	Controls	YES	YES	YES	YES	YES	YES
	N	688	688	688	688	688	688
	R 2	0.32	0.31	0.33	0.33	0.33	0.34

Table 9 -The influence of extreme financial difficulties

 9 The table reports the results (coefficient estimates and t-statistics in brackets) from OLS estimations with robust standards errors (clustered by individual). The dependent variable is Financial literacy (definition in Table2). All demographic variables are mean centered for easier interpretation of the intercept and main term. The regression includes the demographic variables defined in Table2(Controls). The lower part of the table reports the number of observations (N) and the R-squared. The *, **, and *** marks denote statistical significance at the 10%, 5%, and 1% level, respectively.

	1	2	3
	(Baseline)

Table A3 . Sensitivity to demographic variables

 A3 This table reports coefficient estimates and standard errors (in brackets) for specifications that differ based on the control variables. Estimation method is OLS with robust standards errors (clustered by individual). Dependent variable is the Financial literacy score. Income and all demographic variables are mean centered for easier interpretation of the intercept and main term. All variables are defined in Table2. Specification 9 is the specification with the full set of controls, used as the baseline specification 1 of Table6. The lower part of the table reports the number of observations (N) and the R-squared. The *, **, and *** marks denote statistical significance at the 10%, 5%, and 1% level, respectively.

	Single	0.36	0.29	0.24	0.11	0.34	0.31	0.59	0.05	0.10	0.20	0.03	-0.21
	Divorced	0.06	0.06	0.80	0.02	0.06	0.10	0.24	-0.11	0.05	0.08	0.30	-0.10
	Married or civil union	0.52	0.59	0.28	-0.10	0.57	0.52	0.50	0.06	0.75	0.70	0.37	0.09
	House owner (with mortgage) Separated Widow(er) Priming	Mean control group 0.22 0.02 1 0.03 0.0383	Bottom tercile of Income Mean treatment group t-test p-value Normalized difference 0.15 0.20 0.11 0.07 0.12 -0.14 2 3 0.00 0.04 0.18 0.0368 0.0209	Mean control group 0.18 0.00 4 0.04 -0.00950	Middle tercile of Income Mean treatment group t-test p-value Normalized difference 0.22 0.41 -0.08 0.03 0.06 -0.18 5 6 0.04 0.89 -0.01 0.0290 0.0312	Mean control group 0.19 0.01 7 0.10 0.0269	Top tercile of Income Mean group p-value 0.25 0.31 treatment t-test Normalized difference -0.10 0.00 0.31 8 9 0.10 0.02 0.01 0.24 0.0190
	Income House owner (no mortgage)	6.69 0.19	6.74 0.28	0.51 0.13	-0.06 -0.14	7.45 0.39	7.44 0.43	0.54 0.51	0.06 -0.06	8.05 0.63	8.10 0.56	0.42 0.31	-0.08 0.10
	Age Hosted	39.60 0.11	40.01 0.07	0.82 0.19	-0.02 0.12	46.41 0.05	48.83 0.03	0.25 0.59	-0.11 0.05	57.82 0.02	54.61 0.06	0.13 0.17	0.15 -0.13
	Woman Tenant	0.58 0.31	0.59 0.34	0.84 0.56	-0.02 -0.05	0.53 0.30	0.47 0.22	0.39 0.18	0.08 0.13	0.41 0.12	0.39 0.14	0.72 0.77	0.03 -0.03
	Low education Tenant (Low cost housing)	0.36 0.14	0.39 0.14	0.66 0.98	-0.04 0.00	0.32 0.08	0.33 0.08	0.88 0.84	-0.01 -0.02	0.22 0.02	0.17 0.00	0.32 0.15	0.10 0.14
	Intermediate education Other (dwelling)	0.21 0.02	0.19 0.02	0.66 0.99	0.04 0.00	0.24 0.01	0.16 0.01	0.14 0.93	0.14 0.01	0.17 0.02	0.14 0.00	0.50 0.15	0.07 0.14
	High education Don't know (dwelling)	0.41 0.01	0.42 0.00	0.86 0.32	-0.02 0.09	0.44 0.00	0.51 0.00	0.30 .	-0.10 .	0.61 0.00	0.70 0.00	0.18 .	-0.13 .
	Don't know (Education)	0.02	0.00	0.16	0.13	0.00	0.00	.	.	0.00	0.00	.	.
	Farmer	0.02	0.05	0.15	-0.13	0.03	0.01	0.25	0.11	0.01	0.01	0.98	0.00
	craftsman, merchant, company director	0.04	0.05	0.75	-0.03	0.01	0.04	0.14	-0.14	0.04	0.02	0.38	0.08
	Inactive	0.14	0.16	0.58	-0.05	0.02	0.05	0.21	-0.12	0.03	0.01	0.30	0.10
	Executive or intellectual profession	0.05	0.07	0.57	-0.05	0.07	0.08	0.64	-0.04	0.18	0.21	0.58	-0.05
	Student	0.09	0.11	0.65	-0.04	0.02	0.02	0.76	-0.03	0.00	0.01	0.33	-0.10
	Employee	0.27	0.21	0.31	0.09	0.23	0.19	0.50	0.06	0.01	0.07	0.02	-0.23
	Factory worker	0.16	0.16	0.98	0.00	0.23	0.10	0.01	0.25	0.03	0.06	0.34	-0.09
	Intermediate profession	0.14	0.12	0.72	0.03	0.16	0.18	0.67	-0.04	0.10	0.15	0.36	-0.09
	Retiree	0.10	0.07	0.38	0.08	0.25	0.32	0.20	-0.12	0.60	0.47	0.05	0.19
	Ile de France	0.14	0.17	0.47	-0.07	0.15	0.18	0.54	-0.06	0.24	0.25	0.87	-0.02
	North	0.06	0.07	0.99	0.00	0.07	0.09	0.49	-0.07	0.04	0.02	0.38	0.08
	Est	0.09	0.10	0.81	-0.02	0.07	0.08	0.64	-0.04	0.08	0.06	0.73	0.03
	East of Parisian Basin	0.06	0.07	0.99	0.00	0.11	0.12	0.95	-0.01	0.04	0.07	0.26	-0.11
	West of Parisian Basin	0.07	0.10	0.48	-0.06	0.15	0.07	0.04	0.19	0.09	0.07	0.74	0.03
	West	0.17	0.14	0.50	0.06	0.12	0.11	0.72	0.03	0.15	0.13	0.62	0.05
	South-West	0.16	0.06	0.01	0.24	0.09	0.13	0.37	-0.08	0.11	0.09	0.59	0.05
	South-East	0.13	0.13	0.98	0.00	0.13	0.14	0.85	-0.02	0.09	0.17	0.08	-0.17
	Mediterranean	0.11	0.18	0.14	-0.13	0.10	0.08	0.59	0.05	0.17	0.14	0.50	0.07

For a review on the increasing use and importance of priming strategy in the field of economics see[START_REF] Cohn | Priming in economics[END_REF]

More generally, several studies in household finance examine how education and human capital affect financial decision-making (e.g.,[START_REF] Calvet | Measuring the financial sophistication of households[END_REF][START_REF] Haliassos | Why do so few hold stocks?[END_REF]. Other studies examine how financial advisors alleviate the harmful effects of financial illiteracy (e.g.,[START_REF] Calcagno | Financial literacy and the demand for financial advice[END_REF][START_REF] Hackethal | Financial advisors: A case of babysitters?[END_REF][START_REF] Gaudecker | How does household portfolio diversification vary with financial literacy and financial advice[END_REF].

This relates to the behavioral household finance literature (e.g.,[START_REF] Beshears | Behavioral household finance[END_REF][START_REF] Lerner | Emotion and decision making[END_REF][START_REF] Guiso | Household finance: An emerging field[END_REF], as well as more general emotions in the decision-making literature (e.g.,[START_REF] Lerner | Emotion and decision making[END_REF].

[START_REF] Shapiro | Measuring financial anxiety[END_REF] suggest that individuals can exhibit anxiety related to difficulties in managing their finances. They find that those reporting such financial anxiety display reaction latencies in processing financial information. However, unlike us, their work addresses trait financial anxiety, that is a feeling that is intrinsic to individuals and unrelated to the context. In this paper we examine state anxiety that is a feeling which can vary with external stimuli (i.e., poor economic condition or its salience).

A growing number of studies uses randomized survey experiment designs in economics and finance (e.g.,[START_REF] Alesina | Intergenerational mobility and preferences for redistribution[END_REF][START_REF] Brown | Behavioral Impediments to Valuing Annuities: Complexity and Choice Bracketing[END_REF][START_REF] Karadja | Richer (and holier) than thou? The effect of relative income improvements on demand for redistribution[END_REF][START_REF] Kuziemko | How elastic are preferences for redistribution? Evidence from randomized survey experiments[END_REF].

The four priming questions follow several other questions on topics related to respondents' management of their finances.

Beyond the financial literacy quiz and questions about anxiety, guilt, and shame, the rest of the 24 questions address self-confidence in financial capability (six questions), the use of heuristics (two questions), the respondents' perception of bank officers (four questions), and budget behavioral intentions (three questions).

The formula is = Δ =, where is the mean and ! " is the variance of each sample (treatment and control).

For similar specifications, see[START_REF] Mani | Poverty impedes cognitive function[END_REF] and[START_REF] Wicherts | Comment on "Poverty impedes cognitive function[END_REF].

We provide information on the calculation of the standard of living and its similarities with the effective income in Table2.

The estimation of an ordered logit model confirms these results, showing that the coefficient of the interaction term (estimating the average marginal effect for each of the seven outcomes of the variables; for example, Financial literacy =0,…, 6) is negative and significant when Financial literacy is low (≤ 39, whereas it is positive and significant when Financial literacy is high (> 3). Results are available on request.

The change in the priming effect across these two categories (2 nd quintile and 3 rd quintile) is significant at the 1% level.

Income per se should not influence financial literacy when including proper control variables in the model, except via the psychological effect put forward in the paper. The drop of the effect of Income when introducing Age (specification 2 of TableA3), is due to the fact that older people are both richer and have more financial literacy (since they have more experience in financial decision-making). We can make similar arguments for the decline in the effect of income observed when including the gender dummy variable (specification 3), the education dummy variables (specification 4), the occupation dummy variables (specification 5), and the housing dummy variables (specification 8). We note that introducing Occupation (specification 5) has the strongest downward effect on the coefficient of Income. However, the addition of all the relevant controls is what makes Income insignificant.

[START_REF] Bucher-Koenen | How Financially Literate Are Women? An Overview and New Insights[END_REF] show that women are more likely than men to answer "I do not know" to financial literacy questions even if they know the correct answers. The results imply that women tend to be less self-confident than men in financial decision-making.

thanks Banque Populaire for funding this research.

Online Appendix

Economic condition and financial cognition

This Appendix, intended for internet use only, reports the following:

1. The 24 questions that include the six questions of the financial literacy quiz, the cognitive reflective test and the questions on emotions.

2. The correlation matrix of variables of interest in this study.

3. The test that treatment and control groups are homogeneous as regards the observable characteristics in the three subsamples reflecting terciles of the distribution of Income.

4. The results from sensitivity tests on the use of demographic control variables.

5. Definitions and summary statistics of additional variables used for analyses reported in the Appendix.

6. Test of the main effect of priming for additional dependent variables used for analyses reported in the Appendix 7. Tables of results corresponding to additional empirical developments in section 4.2

Table A1. Correlation matrix

The

Table A4. Definitions of additional variables

Variable Definition Self-confidence financial management

The average rating in questions on self-confidence in financial management (questions 2 to 6 in Appendix 1).

Loss aversion

A binary variable equal to 1 if respondents answered "No" to items 1 and 2 of question 14 in Appendix 1 (Loss aversion question), and 0 otherwise.

Positive perception bank advisor

The average rating in questions on the perception of the respondents' bank advisor (questions 15 to 18 of Appendix 1, coding of questions 16-18 were reversed in order to obtain a coherent score).

IDK

Score measuring the number of answers "I do not know" to questions from the financial literacy tests. It is obtained by adding one for each answer "I do not know" to financial literacy questions. At least 1 IDK Binary variable taking the value 1 if respondents answered "I do not know" to at least 1 question from the financial literacy test, and 0 otherwise.

Table A5. Summary statistics for additional variables

The table reports the number of observations, mean, standard deviation, minimum, and maximum for the variables used in additional empirical tests reported in the Appendix. The variables are defined in Table A4.