Do behavioral drivers matter for healthcare decision-making in times of Crisis? A study of low-income women in El Salvador during the COVID-19 pandemic

Bernal, Pedro; Daga, Giuliana; Kossuth, Lajos; López Bóo, Florencia Working Paper Do behavioral drivers matter for healthcare decision-making in times of Crisis? A study of low-income women in El Salvador during the COVID-19 pandemic IDB Working Paper Series, No. IDB-WP-01513 Provided in Cooperation with: Inter-American Development Bank (IDB), Washington, DC Suggested Citation: Bernal, Pedro; Daga, Giuliana; Kossuth, Lajos; López Bóo, Florencia (2023) : Do behavioral drivers matter for healthcare decision-making in times of Crisis? A study of lowincome women in El Salvador during the COVID-19 pandemic, IDB Working Paper Series, No. IDBWP-01513, Inter-American Development Bank (IDB), Washington, DC, https://doi.org/10.18235/0005094 This Version is available at: https://hdl.handle.net/10419/299480 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/3.0/igo/legalcode Do Behavioral Drivers Matter for Healthcare D ecision-Making in Times of Crisis? A Study of L ow-Income Women in El Salvador During the COVID -19 Pandemic Pedro Bernal Giuliana Daga Lajos Kossuth Florencia Lopez Boo WORKING PAPER No IDB-WP-01513 Inter-American Development Bank Social Protection and Health Division August 2023 Do Behavioral Drivers Matter for Healthcare D ecision-Making in Times of Crisis? A S tudy of L ow-Income Women in El Salvador D uring the COVID -19 Pandemic Pedro Bernal Giuliana Daga Lajos Kossuth Florencia Lopez Boo Inter-American Development Bank Social Protection and Health Division August 2023 Cataloging-in-Publication data provided by the Inter-American Development Bank Felipe Herrera Library Do behavioral drivers matter for healthcare decisionmaking in times of crisis?: a study of low-income women in El Salvador during the COVID-19 pandemic / Pedro Bernal, Giuliana Daga, Lajos Kossuth, Florencia Lopez Boo. p. cm. — (IDB Working Paper Series ; 1513) 1. Health behavior-Decision making-El Salvador. 2. Medical care-Decision making-El Salvador. 3. Poor women-El Salvador. 4. Coronavirus infectionsSocial aspects-El Salvador. I. Brenal, Pedro. II. Daga, Giuliana. III. Kossuth, Lajos. IV. López Boo, Florencia. V. Inter-American Development Bank. Social Protection and Health Division. VI. Series. IDB-WP-1513 http://www.iadb.org Copyright © 2023 Inter-American Development Bank ("IDB"). This work is subject to a Creative Commons license CC BY 3.0 IGO (https://creativecommons.org/licenses/by/3.0/igo/legalcode). The terms and conditions indicated in the URL link must be met and the respective recognition must be granted to the IDB. Further to section 8 of the above license, any mediation relating to disputes arising under such license shall be conducted in accordance with the WIPO Mediation Rules. Any dispute related to the use of the works of the IDB that cannot be settled amicably shall be submitted to arbitration pursuant to the United Nations Commission on International Trade Law (UNCITRAL) rules. The use of the IDB's name for any purpose other than for attribution, and the use of IDB's logo shall be subject to a separate written license agreement between the IDB and the user and is not authorized as part of this license. Note that the URL link includes terms and conditions that are an integral part of this license. The opinions expressed in this work are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank, its Board of Directors, or the countries they represent. [email protected] www.iadb.org/SocialProtection Do Behavioral Drivers Matter for Healthcare Decision-Making in Times of Crisis? A Study of Low-Income Women in El Salvador During the COVID-19 Pandemic Bernal, Pedroi, Daga, Giulianai, Kossuth, Lajosii, Lopez Boo, Florencia Abstract Understanding health-seeking behaviors and their drivers is key for governments to manage health policies. There is a growing literature on the role of cognitive biases and heuristics in health and care-seeking behaviors, but little is known of how they might be influenced during a context of heightened anxiety and uncertainty. This study analyzes the relationship between four behavioral predictors – the internal locus of control, impatience, optimism bias, and aspirations – and healthcare decisions among low-income women in El Salvador. We find positive associations between ’internal locus of control and preventive health behaviors during the COVID-19 pandemic (use of masks, distance, hand washing, and COVID-19 vaccination) and in general (prenatal checkups, iron-rich diets for children and hypertension tests). Measures of impatience negatively correlate with COVID-19 prevention behaviors and mothers’ micronutrient treatment adherence for children, and optimism bias and educational aspirations with healthcare-seeking behaviors during the COVID-19 pandemic. Some associations were more robust during the pandemic, suggesting that feelings of uncertainty and stress could enhance behavioral drivers’ influence on health-related behaviors, a novel and relevant finding in the literature relevant for the design of policy responses for future shocks. JEL Codes: I12, D10, D91, I30. Keywords: healthcare decision-making, behavioral economics, COVID-19, low-income setting, Latin America, El Salvador i InterAmerican Development Bank, Social Protection and Health Division, USA. ii Sloan School of Management, Massachusetts Institute of Technology, USA. 1. Introduction Health-seeking decision making is usually determined by need factors (such as chronic disease status and having a poor health perception) but also by key drivers such as education, health education, income, insurance status and ability to pay by oneself (Abuduxike, et al. 2020). However, the complex nature of healthcare decision-making has made the typical neoclassical economics framework insufficient for its analysis and factors that go beyond observable variables might need to be examined (Frank 2007, Mullainathan y Thaler 2001). For instance, people might delay or avoid seeking preventive medical care in the present because they are myopic to the future gains of these interventions (Bradford 2010). Others, by being overly optimistic, might underestimate their chances of contracting a disease despite it being seriously contagious (Gassen, et al. 2021). While these are general features of decision-making around healthcare, less is known about them in the context of a pandemic, an environment of heightened anxiety and uncertainty. Would people be less rational and rely more on heuristics and their cognitive biases for making decisions? Or, on the contrary, due to the do-or-die nature of the context, would they prioritize “System 2 thinking” (Kahneman 2011), which is more rational and deliberate, but slower and more cognitive resource-intensive. So far, evidence seems mixed in this regard, and almost inexistent in low-income settings or when decisions are made for third parties (like when mothers make decisions on behalf of their children). For instance, while some studies have found no discernible differences between economic preferences and decision making in times of crisis vs. normal times (Drichoutis y Nayga 2021), others report significant changes in risk tolerance and patience (Aragon, et al. 2022, Harrison, et al. 2022). This paper is thus an attempt at bridging that gap in the literature in a low-income setting in a Central American country. We explore four types of behavioral drivers and their associations with the healthcare decisions of a cross-section of women (and, for some, their children) in low-income communities in El Salvador during the COVID-19 pandemic. Specifically, we rely on survey measures of impatience (how people value future versus present), internal locus of control (the belief that one’s life is contingent on own decisions), optimism bias (belief that chances of positive events are higher for us than for our peers), and educational aspirations (for women’s children) which relates to aspirations for the future. We test their predictive power comparing healthcare decisions directly related to COVID-19, and those concerning general healthcare. The former include compliance with COVID-19 non-pharmaceutical preventive measures (masking, social distancing, and hand washing), COVID-19 vaccination, and avoidance of healthcare services for fear of the pandemic. The latter include preventive services for women such as screening for chronic conditions (hypertension and diabetes), and use of health services and nutrition for their children, such as prenatal care check-ups and feeding and supplementation practices. Several key findings emerge from our analysis. First, locus of control is positively associated with multiple COVID-19-related and general health behaviors. For instance, a women’s internal locus of control is positively associated with non-pharmaceutical COVID-19 preventive measures, COVID-19 vaccination, as well as receiving preventive care services (hypertension check-ups) and for their children (prenatal checkups). Interestingly, locus of control has the highest magnitude of its correlation with daily behaviors, for which adherence might be more challenging, such as non-pharmaceutical COVID-19 preventive measures and providing children with an iron-rich diet. This finding, which to the extent of our knowledge has not been reported previously in the literature, is not surprising as those who believe their fate is in their hands will likely take measures to avoid disease in the future. Second, most behavioral drivers are relevant for the most novel behavior such as non-pharmaceutical COVID-19 preventive measures, as impatience, locus of control and optimism bias are all significant and have a meaningful correlation magnitude. This is the only outcome in which all three behavioral drivers are jointly significant. Third, optimism bias and educational aspirations for children, are the only behavioral drivers relevant for health service avoidance during the pandemic. Those with higher optimism bias are less likely to have avoided health services due to fear of the pandemic, as they might have been overconfident of not contracting COVID-19 while receiving health services. In the same vein, mothers with higher educational aspirations for their children were less likely to avoid health care services for their children. This result is probably explained by a positive cost-benefit assessment regarding the risks of detecting children’s health needs and acting on time, even in the context of a pandemic. Finally, in line with the literature, those with higher impatience are less likely to engage in healthy behaviors such as non-pharmaceutical COVID-19 prevention measures, prenatal check-ups, and micronutrient adherence. These results provide evidence of the relevance of certain behavioral traits in healthcare decision-making in times of a crisis. The paper is structured as follows. In the next section, we provide definitions a brief literature review on the behavioral predictors we use in this study and their relationship with decision-making in general and related to health. In Section 3, we described the data used for the estimations. In Section 4, we present the econometric specifications and the main results. Finally, Section 5 provides a brief discussion and a conclusion. 2. Behavioral drivers and their relevance in the health literature In this section we provide a brief overview of the behavioral predictors we use to study healthcare decisionmaking in the context of the COVID-19 pandemic in El Salvador. We provide definitions and contextualize them in the healthcare decision-making literature. 2.1 Internal Locus of control A person with a high internal locus of control believes that life events are contingent on their own decisions and behaviors instead of other forces like fate or luck (Rotter 1966). Several studies have demonstrated its positive relationship with different human capital investments, such as educational, job-seeking, and labor markets decisions and outcomes (Heckman, Stixrud, and Urzua 2006; Caliendo et al. 2022; McGee and McGee 2016). In the health domain, the evidence shows that individuals with a higher internal locus of control prefer to be more present in the decision-making process and have a more active and collaborative role with the doctors (Marton et al. 2021; Nazareth 2016). Likewise, empirical research supports positive associations with healthy living and well-being and negative associations with risky behavior such as tobacco, alcohol, and drug use (Cobb-Clark, Kassenboehmer and Schurer 2014, Heckman, Stixrud and Urzua 2006, Lassi, et al. 2019). They also exhibit better physical and mental health and are less likely to suffer long-term health conditions (Kesavayuth et al. 2020). In the prenatal and maternal health field, a recent study in Nigeria shows that having a higher internal locus of control was a significant predictor of utilization of antenatal care, skilled birth care, and completion of child vaccination (Aikpitanyi et al. 2022). According to this evidence, individuals with a higher locus of control are more likely to follow preventive health-related behaviors. 2.2 Impatience Impatience measures how an individual values the future relative to the present, and this preference affects inter-temporal decision-making. The positive association between patience, human capital investments, and healthy lifestyles is well-stated in the literature (Hunter et al. 2018). Further, impatience has been positively associated with obesity (Courtemanche, Heutel and McAlvanah 2015), and negatively with preventive health checkups such as fewer mammograms, pap and prostate examinations, dental visits, and flu shot usage (Bradford 2010). In sum, more impatient individuals should be associated with less desirable healthrelated decisions since most preventive e health investments are realized in the future. 2.3 Optimism Bias Optimism bias occurs when people think their chances of experiencing positive (negative) events are higher (lower) than that of their peers or of the public (Weinstein 1980). A typical example is that a majority of people claim they are less likely than the average driver to be involved in an automobile accident, which is mathematically unfeasible. This behavioral feature is also present in the health domain. Individuals with optimism bias tend to believe they are less likely to experience negative health outcomes (Nezlek and Zebrowski 2001), which may hinder efforts to promote preventive, risk-reducing behaviors (Weinstein 1989). This is especially true among the youth and among those with no active medical symptoms (Sandroni and Squintani 2004). Moreover, in the context of the pandemic, this bias may help explain why many people refused to wear masks in health facilities and continued to attend large gatherings (Lehmann y Lehman 2021, Sassano, y otros 2020). However, evolutionary models suggest that individuals with optimism bias could have higher chances to survive if benefits of optimism outweigh the cost of inaccurate risk estimation (Johnson and Fowler 2011, Gassen, et al. 2021). In this line, there is a wide body of literature finding that optimistic individuals had lower probabilities to suffer blood pressure and cardiovascular, explained by increased physical activity and better diet (Rozanski, et al. 2019, Räikkönen, et al. 1999). 2.4 Aspirations Parental aspirations about their children’s future play an important role in overall human capital investments. For example, mothers´ aspirations are important determinants of their daughter´s schooling decisions (Attanasio y Kaufmann 2014) and in their children´s aspirations, achievements, and overall well-being (Lekfuangfua y Odermatt 2022). Further, parental educational aspirations might lead adolescents to participate in health-promoting activities, like exercise or healthy eating (Whitehead et al. 2015). According to this evidence, we expect mothers in our sample to engage in pro-healthy behaviors for their children if they show high levels of educational aspirations for them. 3. Data Our main data source comes from a household and facility survey conducted in June 2021 in low-income areas in El Salvador. At the time, the country was rolling out COVID-19 vaccinations and about one in five had received at least one dose while restrictions were being eased, and COVID-19 cases and mortality had stabilized after the Delta variant surge in January 2021.3 The main respondents for the household survey were women aged 15 to 49 years old and the questionnaire focused on utilization of health services for them and their children (for those that had children younger than 60 months) as well as other health-related behaviors during the COVID-19 pandemic. The survey is unique in that it also includes measures on four behavioral predictors that could be relevant for healthcare decision-making in this context: impatience, internal locus of control, optimism bias, and the mother’s educational aspirations for their own children. In addition to the household survey, a facility survey was conducted to the coordinator of the public primary care facility which served women in our sample4. Results from the facility survey were merged to the household survey. Overall, we collected information on 848 women, which are representative of 14 of the poorest municipalities in El Salvador.5 3.1 Main covariates Covariates in our dataset are categorized in two groups, as we show in Table 1. Panel A includes individual characteristics of the women and children, as well as proxies for their socioeconomic status. In our sample, women are on average around 31 years old. The majority (68.27%) are either married or live in a de facto union, 27.94% have secondary or tertiary education, 57.43% perceive they had good health, only around 15% are first-time mothers and 2.95% of them lived in a household were someone had been diagnosed with COVID-19 previously.6 The children in our sample are approximately 1.87 years old on average and are equally distributed by gender. Further, less than half of them (42.53%) were or are still breastfed, and their mothers are highly optimistic about their health status. Finally, we use adequate housing conditions (i.e., access to electricity, owning a restroom, roof and floor materials, etc.) and access to treated water as proxies for socioeconomic status. In our sample, around 21% of households have three or less adequate housing conditions present, and only 34.67% have access to treated water, which underscores the low-income setting of the population in our sample. 3 The statement is based on data compiled in a COVID-19 Situational Update report for Latin American and the Caribbean by the Inter-American Development Bank as of June 15, 2021 which can be accessed at http://www.iadb.org/document.cfm?id=EZSHARE-2024879176-650. 4 Sampling for the facility survey was conducted on two stages. First facilities were selected and then a random sample of dwellings in the catchment area of the facility were selected to interview women. Women were interviewed regardless of whether they received services in the facility. Public health facilities are the main provider in the locations of the survey. For example, according to UNICEF’s Multiple Indicator Cluster Survey (2014), in 2014, the MoH provided around 93 percent of postnatal care services in rural areas, which are the main type of areas captured in our survey. 5 The survey is representative of women living in 14 municipalities in El Salvador: Chiltiupán, Tacuba, El Sauce, Sociedad, Ilobasco, Sensuntepeque, Monte San Juan, San Cristóbal, San Antonio Masahuat, Santa María Ostuma, Apastepeque, San Esteban Catarina, San Ildefonso, and Tecoluca. 6 It refers only self-reported COVID-19 diagnosis of woman or someone in the household wither by test or health personnel prior to the survey. If a household had a member with COVID-19 at the time of the survey, it was not include in the sample due to the health security protocol. fourth behavioral predictor, 𝐴𝐴𝐿𝐿𝐼𝐼𝐼𝐼𝐴𝐴𝐼𝐼𝐼𝐼𝐼𝐼𝐿𝐿𝐼𝐼𝐿𝐿𝑖𝑖𝑖𝑖𝑖𝑖. We use the same base specification for both outcomes related to COVID-19 and general health behaviors, except that we do not include health facility controls for general health behaviors given that their time frame is different: our health facility controls focus on how services were affected during 2020, whereas the reference period for the general health behaviors is 2021. We conduct robustness checks of results for the first set of outcomes without these controls (Table A2) and clustering standard errors at the municipality level (Tables A3 and A414). 4.1 COVID-19-related health behaviors Our dataset contains the following COVID-19-related outcomes: i) avoidance of health services (for the women, child, or another household member) for fear of the pandemic; ii) compliance with COVID-19 non-pharmaceutical prevention measures; and iii) having been vaccinated against COVID-19 or willingness to do so. All women in the sample responded to the avoidance of health services for the woman or another household member and the vaccination and compliance with prevention methods survey items. However, only women who are mothers of children between the ages of 0 and 5 responded to the question related to the avoidance of health services for the child. Table 6 presents the results of this first set of estimations. All behavioral predictors (impatience, internal locus of control, optimism bias, and educational aspirations) are standardized with mean 0 and standard deviation of 1 to make their association with the outcome variables comparable. We start by looking at impatience. The literature predicts that more impatient individuals tend to show lower adherence to health care guidelines. Indeed, we find that a one standard deviation increase in impatience is associated with 0.118 fewer points in the number of COVID-19 non-pharmaceutical prevention measures, which ranges from 0 to 4 (0 meaning no prevention at all). Internal locus of control, on the other hand, is positively associated with COVID-19 non-pharmaceutical prevention measures, COVID-19 vaccination status, and healthcare avoidance: a one standard deviation increase in internal locus of control predicts 0.277 more points in preventive behaviors, a 0.034 rise in the likelihood of having gotten vaccinated or willing to be vaccinated, and an increase of 0.020 in the likelihood of avoiding health services for fear of the pandemic. These results also go in line with the predictions made by the literature: a higher degree of internal locus of control should be associated with believing everyone is more in control of their destiny and thus that their health status is mainly their responsibility. Optimism bias negatively predicts whether a woman or a household member avoided attending a healthcare facility for themselves or their child for fear of COVID-19. We interpret these results as women overly optimistic about not contracting COVID-19 during their health visit. If they suffer from optimism bias about maintaining a healthy status despite the COVID-19 associated risk of infection, they might still decide to go to the health center. Moreover, the aim of optimistic people in maintaining a healthy status might help explain why optimism bias is positively associated with non-pharmaceutical prevention measures compliance. This surprising result might also be related to different types of behaviors for novel sources of risk, such as the COVID-19 pandemic. Finally, the educational aspirations a mother has for their children are negatively associated with avoiding health services for the latter because of fear of COVID-19. Mothers 14 Overall, our results hold when conducting these robustness checks. Only the negative association between impatience and having at least four prenatal visits, which is predicted by the theory is now significant, at a 99% level of confidence. with higher aspirations for their children are 0.032 less likely to avoid or postpone healthcare services for their children, which is in line with the literature. Table 6: Behavioral predictors of COVID-19-related health behaviors COVID-19-Related Health Behaviors (1) (2) (3) (4) Women avoided health care for herself or someone in the household for fear of COVID-19 Women followed COVID-19 nonpharmaceutical prevention measures Women got the COVID-19 vaccine or is willing to get it Mothers avoided health care for their children for fear of COVID-19 Impatience (z score) 0.001 -0.118*** 0.005 0.008 (0.008) (0.038) (0.013) (0.013) Internal locus of control (z score) 0.020** 0.277*** 0.034** -0.014 (0.009) (0.043) (0.014) (0.014) Optimism bias (z score) -0.031*** 0.089** 0.001 -0.025* (0.009) (0.044) (0.013) (0.014) Educational aspirations (z score) -0.032** (0.015) Individual Controls YES YES YES YES Household Controls YES YES YES YES Health Services Controls YES YES YES YES Municipality FE YES YES YES YES Observations 848 848 848 518 R2 0.059 0.138 0.058 0.124 Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. OLS estimations with robust standard errors in parentheses. In column (1), the outcome of interest is an indicator variable that takes a value of 1 if the mother reports herself or any member of the household having avoided access to health services because of fear of contracting COVID-19. In column (2), the outcome of interest is the number (out of 4) of taken actions related to COVID-19 non-pharmaceutical prevention measures. In column (3), the outcome variable of interest is an indicator variable that takes a value of 1 if the mother is willing to get vaccinated against COVID-19 or if she already did get vaccinated. In column (4), the outcome of interest is an indicator variable that takes a value of 1 if the mother reports having avoided access to health services for her children because of fear of contracting COVID-19. The behavioral explanatory variables of interest are described as follows. Impatience is the standardized measure of the Present bias Index; Internal locus of control is the standardized measure of the adapted Locus of control Index; optimism bias is the standardized measure of the general Optimism bias Index; and Educational aspirations is the standardized measure of the Educational aspirations Index. 4.2 General health behaviors We use the following general health outcomes as general health behaviors: i) test for hypertension in the last six months; ii) test for diabetes in the last six months; iii) at least four prenatal visits; iv) children’s consumption of micronutrients in the last 6 months; and v) amount of iron-rich food items consumed by children. Again, it is worth noting that, while the survey is responded by women in general, the number of observations in each regression will depend on the type of outcome. The hypertension and diabetes survey items are responded by women in general. The rest of the outcomes (child-related) are responded by women who are mothers of children between the ages of 0 and 5, except for the question related to an iron-rich diet, which was restricted to mothers of children older than 1 year. Table 7: Behavioral predictors of general health behaviors General Health Behaviors Children Women (1) (2) (3) (4) (5) At least 4 prenatal care visits Micronutrients adherence Iron-rich diet Hypertension screening Diabetes screening Impatience (z score) -0.019 -0.035** -0.045 0.023 0.015 (0.013) (0.017) (0.044) (0.015) (0.012) Internal locus of control (z score) 0.028* -0.007 0.124** 0.058*** -0.008 (0.016) (0.014) (0.048) (0.016) (0.013) Optimism bias (z score) -0.010 0.008 0.040 -0.009 0.012 (0.016) (0.015) (0.053) (0.016) (0.013) Educational aspirations (z score) 0.020 0.019 0.062 (0.015) (0.013) (0.051) Individual Controls YES YES YES YES YES Household Controls YES YES YES YES YES Municipality FE YES YES YES YES YES Observations 534 536 425 848 848 R2 0.133 0.136 0.181 0.071 0.061 Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. OLS estimations with robust standard errors in parentheses. In column (1), the outcome of interest is an indicator variable that takes a value of 1 if the mother had at least 4 prenatal visits to the doctor. In column (2), the outcome of interest is an indicator variable that takes a value of 1 if the child has consumed micronutrients more than 60 days in the last 6 months. In column (3), the outcome of interest is the number of iron-rich food items the child has consumed (out of 7). In column (4), the outcome variable of interest is an indicator variable that takes a value of 1 if hypertension has been detected in the mother during the last 6 months. In column (5), the outcome of interest is an indicator variable that takes a value of 1 if diabetes has been detected in the mother during the last 6 months. The behavioral explanatory variables of interest are described as follows. Impatience is the standardized measure of the Present bias Index; Internal locus of control is the standardized measure of the adapted Locus of control Index; Optimism bias is the standardized measure of the general Optimism bias Index; and Educational aspirations is the standardized measure of the Educational aspirations Index. Table 7 presents the results of this second set of estimations. Starting with impatience, we observe it negatively predicts feeding the child with micronutrients for 60 days in a space of 6 months. Internal locus of control, on the other hand, positively predicts having gone to at least 4 prenatal visits, an increased number of iron-rich food items consumed by children and having been tested for hypertension in the last 6 months. Especially salient is that one standard deviation increase in internal locus of control is associated with consuming 0.124 additional iron-rich food items, which are measured in a scale of 0 to 6. What these correlations are likely indicating is that women who believe their destiny is in their own hands might try to guarantee the best possible health status for their children and themselves15. This time we find no significant associations between optimism bias and educational aspirations for the child and the outcome variables of interest. All associations were more robust in behaviors related to COVID-19, suggesting that feelings of uncertainty and stress could enhance the predictive power of our chosen behavioral predictor and that they may play an important role in novel behaviors. 5. Conclusions Decision making about the utilization of healthcare historically shows evident disparities by socio-economic status and have always been shaped by need factors and observable drivers such as education, income, insurance status and ability to pay. This study aims to go beyond traditional determinants and analyzes four types of behavioral predictors – impatience, internal locus of control, optimism bias, and aspirations – and their associations with the decisions around healthcare among lowincome women in El Salvador in the context of the COVID-19 pandemic. Our results provide some novel insights. First, we find that our behavioral predictors show more significant associations with healthcare decisions when these are related to the pandemic. For example, impatience and locus of control have higher magnitudes and significance for COVID-19 prevention measurements, and especially salient is the case of optimism bias: it seems to predict the lack of avoidance of health services for fear of the pandemic but exhibits no significant correlations with other healthcare decisions related to general health services attendance. It is possible that the scale and nature of the event could have enhanced the influence of behavioral predictors on healthcare decisions to the detriment of a more rational approach to decisionmaking, something worth considering for future shocks—e.g., due to natural disasters, health emergencies or situations of social unrest. Second, most of the correlations found in this paper go in the direction of what theory would have predicted. Still, additional evidence is needed to support this, especially in a low-income setting. For instance, on average higher internal locus of women is associated with healthier behaviors for them and their children. Likewise, women with optimism bias were less likely to avoid attending health facilities for fear of COVID-19, signaling overconfidence of not contracting the disease. Moreover, impatience negatively predicts prevention measures, which denotes the tension between present costs and future health benefits. Finally, we find significant correlations between mothers having educational aspirations for their children and behaviors intending to improve their health status during the pandemic. However, there is a lack of significant correlation between this behavioral driver and general health decisions. Even though all our coefficients have the expected sign, we do not find statistical significance. The literature predicts that mothers have incentives to invest in their children’s human capital, especially when educational aspirations are high, but the connection between health services decision making and future human capital might not be as clear as for educational decisions. 15 Although we do not find any significant association between internal locus of control and diabetes detection. Perhaps this might be because of the disease being less frequent than hypertension. In sum, understanding reasoning processes behind healthcare decision-making is key to improving policy design. Our study aims at adding to the evidence on this topic with data from disadvantaged women in a developing country context. To the best of our knowledge, it is also one of the first to compare healthcare decisions related to COVID-19 pandemic with those deemed as general health behaviors. In addition, we contribute to the literature by analyzing how these behavioral predictors affect third parties: the children of some of the women in our sample. Our results emphasized the necessity of further research providing specific strategies informed by behavioral sciences to improve health seeking behaviors and establish causal associations, which is one of the limitations of this study. Our findings also shed light play on the potential effective role of behavioral strategies to improve the healthcare-seeking behaviors for the most vulnerable populations, whose locus of control, impatience, optimism, and aspirations might differ from the general population. 6. 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Table A1: Behavioral drivers’ distribution by demographic characteristics (1) (2) (3) (4) (5) (6) Mean p-value Mean p-value Value=0 Value=1 Value=0 Value=1 Locus of control Impatience Married / De facto union 3.71 3.80 0.00 -0.06 0.02 0.32 Secondary or Tertiary Education 3.73 3.88 0.03 0.01 -0.04 0.75 Self-reported good health 3.73 3.80 0.26 0.06 -0.06 0.74 Three or less household assets 3.79 3.70 0.44 0.00 -0.02 0.43 Woman has children 3.70 3.81 0.33 0.00 -0.01 0.73 Optimism bias total Optimism bias health Married / De facto union 5.89 6.05 0.10 5.82 6.11 0.06 Secondary or Tertiary Education 6.02 5.95 0.14 6.04 5.98 0.01 Self-reported good health 5.84 6.12 0.23 5.83 6.17 0.13 Three or less household assets 5.99 6.02 0.69 6.03 6.00 0.71 Woman has children 5.83 6.11 0.00 5.76 6.20 0.02 Total observations=848 Educational aspirations Married / De facto union 0.74 0.74 0.97 Secondary or Tertiary Education 0.65 0.95 0.00 Self-reported good health 0.70 0.76 0.14 Three or less household assets 0.76 0.67 0.07 Woman has children - 0.73 - Total observations=507 Notes: Columns (1) and (4) report the mean of the behavioral driver if the demographic variable is equal to 0; and Columns (2) and (5) report the mean when the demographic variable is equal to 1. Columns (3) and (6) report Pearson’s chi squared test of the difference. The behavioral explanatory variables of interest are described as follows. Internal locus of control was measured on a scale from 1 to 5, where 5 is a high internal locus of control; impatience was measured on a scale from 1 to 32, being 32 with high impatience, its standardized version with mean 0 and standard deviation of 1 is reported; optimism bias was measured on a scale from 0 to 10 being scores higher than five moreoptimistic; finally, educational aspirations were measured with a binary variable that takes a value of 1 if the mother aspires their children to have a educational career such as medical doctors, lawyers, engineers, architects, or educators.