Youth and Gambling: Determinants and Impact on Psychological Resilience in Senegal

Youth and Gambling: Determinants and Impact on Psychological Resilience in Senegal | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Youth and Gambling: Determinants and Impact on Psychological Resilience in Senegal Mamadou Abdoulaye Diallo, Garmy Samb This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7437944/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Over the past decade, gambling has expanded rapidly in Senegal, affecting both adults and youth. This study aims to identify the factors influencing gambling behavior and its impact on psychological resilience among young people in Senegal. Based on a representative survey of 1,560 youths aged 18–35, we performed probit regressions and propensity score matching (PSM) to assess both the determinants of gambling and its causal effect on psychological resilience. The results indicate that males are significantly more likely to gamble than females, post-secondary educated youth exhibit higher gambling propensity than those without formal education, and urban residents are more likely to gamble than rural peers. Tobacco use and sports participation show weaker positive associations, while age and labor market participation are not significant predictors. PSM analysis further reveals that gambling substantially reduces psychological resilience, particularly in perseverance and engagement, positive self-image, emotional regulation, and spirituality, with an overall decline of approximately 12%. These findings underscore the need for targeted interventions, including responsible gambling education, mental health support, and programs offering alternative recreational opportunities, to enhance resilience and mitigate the risks associated with gambling among Senegalese youth. Behavioral Economics Gambling Young people Sports betting Psychological resilience Senegal Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Gambling is generally defined as wagering something of value on an event with an uncertain outcome, with the intention of winning a prize. Three elements are essential: the stake (consideration), the risk (chance), and the reward (prize) (Williams et al., 2017 ). While gambling is popular and often practiced recreationally across societies, it can also have detrimental effects, potentially leading to addiction (Hilbrecht et al., 2020 ) and undermining psychological resilience, understood as the capacity to cope with adversity, stress, or failure (Smith, 2008). Several studies show that gambling can produce multidimensional harms affecting financial, professional, health, psychological, and social domains, and may encourage deviant behaviors such as dishonesty or criminality (Langham et al., 2015 ). Financial stress, debt accumulation, and loss of savings are common consequences (Mathews & Volberg, 2013 ; Muggleton et al., 2021 ). Gambling may also influence cultural values and reduce young people’s interest in education, contributing to school dropout (Wan, 2012 ), while governments bear significant regulatory and treatment costs (Browne et al., 2017 ). Technological advances have accelerated online gambling globally, including in Africa, where mobile penetration, Internet access, and digital payment systems have facilitated participation (Adebisi et al., 2021 ; Bitanihirwe et al., 2022 ). Despite revenue generation, these trends raise concerns about addiction and negative social and health impacts (Blank et al., 2021 ; Uwiduhaye et al., 2021 ; Wardle & McManus, 2021 ), especially in contexts of youth unemployment and poverty (Awo et al., 2023 ). The demand theory of gambling posits that individuals gamble to obtain “something for nothing,” with higher participation among economically vulnerable populations (Nyman, 2004 ). Motivations also include financial aspiration, curiosity, excitement, social interaction, and emotional benefits such as mood improvement and stress relief (Ariyabuddhiphongs & Chanchalermporn, 2007 ; Lam, 2007 ; Tagoe et al., 2018 ; Wickwire et al., 2007). Demographically, men gamble more frequently and spend more than women across age groups (Abbott et al., 2018 ; Appiah & Awuah, 2016 ; Lee & Lee, 2025 ; Kang et al., 2019 ), and lower education levels are linked to higher gambling expenditures (Salonen et al., 2018 ). Income influences participation: lower-income individuals prefer physical venues, while higher-income groups favor online formats (Lind et al., 2022 ). Gambling often co-occurs with other risk behaviors such as smoking and alcohol use (Edgren et al., 2017 ), and recreational gambling is more prevalent among service workers, minorities, and populations in high-unemployment areas (Nyman et al., 2008 ). Despite growing literature on gambling, research in Africa remains limited, particularly on its relationship with psychological resilience. In Senegal, nearly 30% of the population participated in betting in 2022 (Statistics of Gambling, 2022 ), amid high youth unemployment, poverty, and rapid online gambling expansion (Adebisi et al., 2021 ; Bitanihirwe et al., 2022 ). While often perceived as a form of entertainment or an economic opportunity, gambling may weaken the psychological resilience of young people by undermining their stress management, self-esteem, and capacity to cope with adversity. This study aims to identify the determinants of gambling among young Senegalese and assess its effect on psychological resilience. Specifically, it seeks to: (i) describe gambling practices, including frequency, sources of funds, use of winnings, social influences, perceived impacts, and intentions to stop; (ii) identify sociodemographic and behavioral predictors of gambling; and (iii) evaluate the causal effect of gambling on psychological resilience. Using a representative sample of 1,560 youths, we first estimated a probit model to identify gambling determinants. Factor analysis was then employed to operationalize psychological resilience across seven dimensions. Finally, the causal effect of gambling on resilience was assessed using propensity score matching (PSM), with robustness checks via inverse probability-weighted regression adjustment (IPWRA). Results indicate that, beyond traditional sociodemographic predictors such as gender and education, tobacco use, and sports participation are significant determinants of gambling. Overall, gambling has a negative and significant effect on psychological resilience, particularly undermining self-esteem, optimism, emotional regulation, and spirituality. These findings underscore the need for targeted policy interventions, including awareness campaigns, stress management and well-being programs, and regulations governing access to gambling, especially online platforms, to protect vulnerable youth. The remainder of the paper is structured as follows. Section 2 presents data sources and methodology. Section 3 provides the results. Section 4 discusses the findings. Section 5 concludes with policy implications. 2. Methodology 2.1. Data The data used in this study comes from the recent Africa Youth Aspirations and Resilience (AYAR) project which took place in seven African countries: Ethiopia, Ghana, Kenya, Nigeria, Rwanda, Senegal and Uganda. The AYAR project funded by the Mastercard Foundation and coordinated by Partnership for African Social and Governance Research (PASGR) is a three-year initiative that is running between 2021 and 2024 to understand youth aspirations in their own words (Diallo, 2025 ). The data collection was conducted by the Consortium pour la Recherche Economique et Sociale (CRES) . A total of 1,560 young people including 50% women, were interviewed during period within January 05 to 17, 2023 in nine (9) regions of Senegal using a two-stage stratified sample design. A sample of this size yielded country-level results with error margins of ± 2.5 percentage points at a 95% confidence level. 2.2. Conceptualization and measure In this paper, our primary variables of interest are Gambling and Psychological Resilience. The first variable, Gambling, indicates whether an individual participates in games of chance. Specifically, a youth is classified as a gambler if they reported engaging in any form of gambling “Once or twice,” “Several times,” or “Often” during the 30 days preceding the survey. Psychological resilience is generally measured through a set of items encompassing multiple dimensions (Duran et al., 2024 ). To our knowledge, no standardized measure of psychological resilience currently exists for Senegal. In this study, we developed an index based on seven key dimensions: perseverance and commitment, positive self-image and optimism, relationships and regulation, humor and positive thinking, emotional regulation, spirituality and faith, and personal trust and responsibility. Each dimension comprises several items coded on a 5-point Likert scale ranging from “Strongly disagree” (1) to “Strongly agree” (5) (Table A1). Factor analysis was employed to construct the psychological resilience index. The suitability of the scale items for factor analysis was assessed using Bartlett’s Test of Sphericity (BToS) and the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy. Following factor extraction, the internal consistency of the scales was evaluated using Cronbach’s alpha (α), ensuring reliability of the composite indices Table A1 presents the factor loadings, percentages of variance explained, and validity and reliability indicators for the factor model. The results confirm that the items selected for each dimension were suitable for factor analysis, with significant BToS ( \(\:p\:=\:0.00\) ), KMO values above the recommended threshold (0.65–0.87), acceptable internal consistency (α = 0.56–0.87), explained variance ranging from 36.9–58.1%, and factor loadings between 0.31 and 0.76. These results confirm the validity and reliability of the factor model for assessing psychological resilience in this sample. Control variables The control variables used in this study include gender (male and female), age (18–35 years), and education level, ranging from no formal education to post-secondary education (Appiah & Awuah, 2016 ; Lind et al., 2022 ; Lopez-Gonzalez et al., 2024 ). Place of residence (urban vs. rural) is included as a proxy for environmental context (Hamilton-Wright et al., 2016 ). Employment status, reflecting labor market participation, is used as a proxy for income (Díaz et al., 2023 ; Lind et al., 2022 ). Additionally, tobacco use, and sports participation are included as dummy variables to account for risk-taking behaviors (Butler et al., 2020 ; Duran et al., 2024 ). 2.3. Estimation Strategy 2.3.1 Probit regression model Since our outcome variable is dichotomous, we estimate a binary probit regression model to identify the determinants of youth gambling in Senegal: $$\:{\text{P}\text{r}(G}_{i}=1\:\left|\:{X}_{i}\right)={\Phi\:}\left({X}_{i}\beta\:\right)$$ where \(\:{G}_{i}\:=\:1\) if young \(\:i\) participates in gambling, \(\:{\Phi\:}\) denotes the standard normal cumulative distribution function, \(\:{X}_{i}\) is a vector of explanatory variables, and \(\:\beta\:\) represents the parameters to be estimated. The covariates include gender, age, educational, labor market participation, residential area, tobacco consumption, and sport practice. To ensure robust inference, we implement a bootstrap procedure with 1,000 replications to compute standard errors. We use STATA (version 16) software to perform all statistics and the probit model. To assess multicollinearity among variables included in the multivariate analyses, the outcome was regressed on all independent variables using the Variance Inflation Factor (VIF). The analyses showed no multicollinearity problems among the variables included in the models (mean of VIF varied 1.07–1.55). 2.3.2 Propensity Score Matching (PSM) To assess the causal impact of gambling on psychological resilience, we adopt a counterfactual approach, which represents what would have occurred in the absence of the treatment (Heckman et al., 1997 ; Rosenbaum & Rubin, 1983 ). Considering gambling participation as the treatment, we estimate the average treatment effect on the treated (ATT), defined as follows: $$\:ATT\:=E[{Y}_{1i}\:-{Y}_{0i}|{T}_{i}\:=\:1]\:=\:E[{Y}_{1i}|{T}_{i}\:=\:1]-E\left[{Y}_{0i}\right|{T}_{i}\:=\:1]$$ Where \(\:Y\) denotes the outcome, in this case psychological resilience. The term \(\:E\left[{Y}_{0i}\right|{T}_{i}\:=\:1]\) represents the counterfactual outcome, that is, the level of resilience the treated group would have experienced had they not received the treatment. Since the counterfactual is not observable, Rosenbaum and Rubin ( 1983 ) propose using propensity score matching (PSM) to approximate it by leveraging the observed outcomes of the untreated group. The propensity score is defined as the probability that a young individual engages in gambling, conditional on observed covariates \(\:X\) . It is expressed as: $$\:p\left({X}_{i}\right)=P\:({T}_{i}=1|{X}_{i})=\frac{\text{exp}\left(\beta\:{X}_{i}\right)}{1+\text{exp}\left(\beta\:{X}_{i}\right)}$$ By conditioning on \(\:p\left({X}_{i}\right)\) , PSM ensures that the treated and untreated groups are balanced in terms of observable characteristics, thereby reducing selection bias in estimating the ATT. However, this estimation is valid only under the Conditional Independence Assumption (CIA), which states that, given a set of observed covariates \(\:{X}_{i}\) ​, treatment assignment is as good as random. $$\:\left\{\begin{array}{c}p\left(X\right)=P\left(T=1|X\right)\:\\\:{Y}_{0}\perp\:T|X\Rightarrow\:{Y}_{0}\perp\:T|p\left(X\right)\end{array}\right.$$ The matching method also assumes the existence of a common support, meaning that for all values of the observables, there must be both treated and untreated units available for comparison: $$\:0\:<\:P\:({T}_{i}\:=\:1|{X}_{i})\:<\:1$$ Several matching techniques can be employed, the most common being nearest-neighbor (NN) matching, the caliper-radius approach, and non-parametric kernel regression matching (Caliendo & Kopeinig, 2008 ; Diallo, 2024 ; Heckman et al., 1997 ). Under the assumptions of conditional independence and common support, and in the case of matching with the M nearest neighbors, the simple estimator of the average treatment effect on the treated (ATT) for the treated observations is given by: $$\:ATT=\frac{1}{{N}_{1}}\left[{\sum\:}_{i=1}^{{N}_{1}}({Y}_{i}-\frac{1}{M}{\sum\:}_{j\in\:{J}_{m}\left(i\right)}{Y}_{j}\:\right]$$ Where \(\:{J}_{m}\left(i\:\right)\) denotes the set of units matched to unit \(\:i\:\) . For each matched unit, a counterfactual outcome for iii is constructed as the average of the observed \(\:Y\) values of its matched units. However, the ATT estimated via PSM may be biased if the propensity score model is mis-specified (Abadie & Imbens, 2006 ; Smith & Todd, 2005 ). One proposed solution is inverse probability weighting (IPW), which first estimates the propensity score and then applies inverse weights to create a pseudo-population balanced on observed confounders (Chesnaye et al., 2022 ). Nonetheless, IPW remains sensitive to correct specifications of the treatment model. To address this limitation, Słoczyński et al. ( 2022 ) recommend the inverse probability-weighted regression adjustment (IPWRA), a doubly robust estimator that combines inverse weighting and regression adjustment, ensuring consistency if either the treatment or outcome model is correctly specified. In this study, we present multiple specifications, namely PSM using the kernel method, five nearest-neighbor matching (NN), and IPWRA. Five variables, gender, age, education, occupation, and residential area are retained as matching covariates. 3. Results 3.1 Betting frequency and harvested earnings Table 1 presents descriptive statistics on youth gambling experience, stakes, and winnings. In a sample of 1,560 youths, 112 reported engaging in gambling, corresponding to a prevalence of 7.18%. On average, young gamblers have 19 months of experience, ranging from one month to eight years, reflecting considerable heterogeneity in gambling trajectories. Weekly stakes average 2,017.8 XOF, with substantial variability reaching up to 25,000 XOF per week. Gamblers place an average of 3.3 bets per week (ranging from 1 to 20), collecting an average of 23,555.4 XOF weekly, with total weekly winnings ranging from 0 to 300,000 XOF, indicating that some participants can achieve substantial gains. Exceptional stakes reach up to 200,000 XOF, while maximum reported winnings can reach 3,000,000 XOF, although the mean of maximum gains remains moderate at 118,060.7 XOF, with high dispersion. This pronounced variability in both stakes and winnings underscores the diversity of gambling behaviors among youths, spanning from occasional participation to high-intensity financial engagement. Table 1 Youth Gambling Experience, Stakes, and Winnings N Mean Min Max SD Experience in Gambling (in month) 112 19.4 1 96 18.7 Weekly stake (in XOF) 112 2 017.8 90 25 000 3 613.5 Number of bets per week 112 3.3 1 20 3.7 Amount collected per week (in XOF) 112 23 555.4 0 300 000 45 458.4 Highest amount bet (in XOF) 112 17 826.4 100 200 000 35 357.9 Highest amount ever collected (in XOF) 112 118 060.7 0 3 000 000 310 706.6 Source: Authors from AYAR project survey in Senegal 2023. Figure 1 presents the distribution of weekly net winnings, calculated by comparing the total amount wagered (computed as the product of the weekly stake and the number of bets per week) with the total amount collected. The results indicate that 21% of gamblers incur negative net gains, meaning they lose more than they win. Among those reporting positive net gains, 33% earn less than 10,000 XOF per week, while 36% earn between 10,000 and 30,000 XOF weekly. Only 10% of gamblers achieve net gains exceeding 30,000 XOF per week. Overall, these figures reveal that many gamblers (69%) earn less than 30,000 XOF per week, with a substantial proportion experiencing net losses, highlighting the limited financial benefits and high risk associated with gambling among youths. 3.2. Sources of Gambling Money and Uses of Winnings Funds used by young people for gambling originate from multiple sources (Fig. 2 ). Pocket money represents the primary source (50%), followed by earned income or salaries (44%). Winnings from previous bets account for 11% of gambling expenditure. Daily allowances and borrowed funds contribute 4% and 3%, respectively, while a marginal 1% derives from illicit sources. Young people were asked about how they use their gambling winnings (Fig. 3 ). Most of the gains are spent on consumer goods (57%), highlighting their role in meeting daily needs. A significant share is also allocated to purchasing mobile phones (20%), reflecting the importance of technological connectivity. Additionally, 18% of the winnings are reinvested in gambling. Gains are also shared among co-gamblers (13%), used to pay for school supplies (6%), to purchase motorcycles (5%), and invested in business or livestock activities (4%). 3.3. Social Influences, Impacts, and Youth Perspectives on Quitting Gambling Figure 4 shows that young gamblers are primarily influenced by friends or classmates (63%). A significant share (27%) engages in gambling independently, while advertising through social media and television influenced 7% of youths. Close parents account for only 3% of the influence, and other sources represent 1%. Figure 5 presents the distribution of perceptions concerning the impact of gambling on academic performance. Among educated youths, 45% report that gambling adversely affects their school results. In contrast, 38% perceive no significant effect, on academic outcomes. A smaller proportion, 17%, view gambling as exerting a positive influence on their academic performance. Figure 6 shows that a large proportion of young people plan to stop gambling soon (47%), followed by 20% who intend to quit immediately. Only 8% of gamblers report having no intention of stopping at all, while 10% plan to reduce their gambling only slightly. Additionally, 15% of respondents are uncertain about their intentions regarding quitting. These results indicate that young people who participate in gambling hold varied views about their future engagement in the activity. 3.4. Sociodemographic Characteristics and Gambling Table 2 presents the descriptive statistics. Although the sample is roughly balanced by gender, gambling is more prevalent among young men (13.3%) than women (1%), with a significant association ( \(\:\chi\:²\:=\:88.7,\:p\:<\:0.001\) ). Nearly 60% of respondents are under 25, and gambling participation is similar across age groups (6.6 − 7.7%). Education shows a strong gradient, with gambling rates increasing from 2.3% among the young with no formal education to 12.4% among those with post-secondary education ( \(\:\chi\:²\:=\:28.1,\:p\:<\:0.001\) ). Employed youth (8.4%) gamble more than the unemployed (5.9%). Urban residents (10.2%) are also more likely to gamble than rural ones (4%), with significant association ( \(\:\chi\:²\:=\:22.4,\:p\:<\:0.05\) ). Tobacco users (19.4%) and sports participants (12.2%) show higher gambling prevalence. Table 2 Descriptive statistics N % % gambling (7.2%) Chi2 test Gender Male 780 50.0 13.3 88.7*** Female 780 50.0 1.0 Age 18–20 years 437 28.0 7.3 21–25 years 486 31.2 6.6 0.4 26–30 years 337 21.6 7.7 31–35 years 300 19.2 7.3 Education No formal education 385 24.7 2.3 Primary 343 22.0 5.5 Middle school 324 20.8 9.0 28.1*** Secondary 291 18.7 9.6 Post secondary 217 13.9 12.4 Labor market participation No 752 48.2 5.9 3.8* Yes 808 51.8 8.4 Residential area Rural 768 49.2 4.0 22.4*** Urban 792 50.8 10.2 Tobacco consumption Yes 67 4.3 19.4 15.7*** No 1493 95.7 6.6 Sport practice Yes 714 45.8 12.2 49.5*** No 846 54.2 3.0 Note: Robust standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1 Source: Authors from AYAR project survey in Senegal 2023. 3.5. Determinants of youth gambling The full results of the gambling probability estimation are presented in Table A2 . Figure 7 summarizes the coefficients estimated from the probit model for the entire sample and by residential area. The results indicate that young women are significantly less likely to participate in gambling than young men ( \(\:\beta\:\:=\:-1.124,\:p\:<\:0.01\) ). In rural areas, no female respondent reported gambling during the month preceding the survey. Age was not a significant predictor, although it showed opposite signs depending on the area of residence. Education appears as a factor positively associated with gambling. Youth with primary-level education are more likely to gamble ( \(\:\beta\:\:=\:0.407,\:p\:<\:0.05\) ) than those with no formal education. This trend continues at higher education levels: youth with middle-level ( \(\:\beta\:\:=\:0.508,\:p\:<\:0.05\) ), secondary ( \(\:\beta\:\:=\:0.536,\:p\:<\:0.01\) ), and post-secondary ( \(\:\beta\:\:=\:0.624,\:p\:<\:0.01\) ) education have higher probabilities of engaging in gambling. However, the relationship between education and gambling varies by area of residence. In urban areas, the effect of education is generally stronger, reflecting greater exposure to sports betting networks and digital platforms. Conversely, at the post-secondary level, rural youth exhibit the highest propensity to gamble, likely due to limited economic and recreational opportunities, which leads them to seek gambling as an alternative source of income or leisure. These results align with other contextual and behavioral determinants. Living in an urban area significantly increases the likelihood of gambling ( \(\:\beta\:\:=\:0.413,\:p\:<\:0.01\) ), confirming that cities concentrate most kiosks, digital offers, and sports betting advertisements. Tobacco use is also positively correlated with gambling ( \(\:\beta\:\:=\:0.324,\:p\:<\:0.10\) ), reflecting the co-occurrence of risk behaviors within the same vulnerability profile. Finally, sports participation is another factor associated with gambling ( \(\:\beta\:\:=\:0.225,\:p\:<\:0.10\) ). In rural areas, this effect is particularly strong and significant ( \(\:\beta\:\:=\:0.720,\:p\:<\:0.01\) ), likely because football and sports betting are major collective leisure activities. In contrast, in urban areas, sports participation does not significantly influence gambling probability, suggesting that other urban factors—such as proximity to betting points and digital access—dominate the influence of sports on gambling. 3.6. Impact of gambling on psychological resilience 3.6.1 Distribution of Propensity Scores and Matching Quality Assessment Figure 8 illustrates the distribution of propensity scores for treated (gamblers) and control (non-gamblers). Substantial overlap is observed under the five nearest-neighbor matching, ensuring the existence of common support. This implies that comparable non-gamblers exist for most gamblers in the sample. Kernel matching also confirms this overlap, but the nearest-neighbor method provides a slightly better balance and thus appears more suitable for the analysis. Overall, the common support condition is satisfied, as treated and control groups show significant probability overlap. Table A3 reports the balancing test results before and after propensity score matching using both the five nearest-neighbor and kernel methods. Before matching, significant differences existed between gamblers and non-gamblers in sex \(\:(p<0.01),\) postsecondary education ( \(\:p<0.01\) ), labor market participation ( \(\:p<0.1\) ), and residence ( \(\:p<0.01\) ). After matching, no significant differences remain, indicating that the matched samples are comparable. Table A4 further confirms the quality of matching. The pseudo-R² drops from 0.181 to below 0.05, and the mean standardized bias is reduced by about 87%. Rubin’s R values fall within the acceptable range (0.5–2), supporting that the matching is of good quality, with similar observable characteristics across groups. 3.6.2 Impact of Gambling on Psychological Resilience Table 3 presents the estimated average treatment effects on the treated (ATT) across three specifications and dimensions of psychological resilience. Overall, gambling exerts a negative and statistically significant impact on youth resilience. Estimated effects range from − 0.090 with the Kernel method to − 0.115 with five nearest-neighbor (NN) matching, with relatively small standard errors. The consistency of results across methods reinforces their robustness, suggesting that gambling is systematically associated with a lower capacity for resilience. When disaggregating by dimensions, perseverance and commitment, which capture the ability to pursue goals despite difficulties, persist in undertaken tasks, and learn from adversity, are significantly reduced, with effects between − 0.113 (I \(\:PWR,\:p<0.050\) ) and − 0.124 ( \(\:NN,\:p<0.05\) ). Negative coefficients imply that young gamblers are more likely to give up easily and perceive obstacles as constraints rather than opportunities for growth. Positive self-image and optimism, encompassing self-acceptance, future outlook, and pride in personal achievements, are also adversely affected \(\:(-0.098,\:IPWR,\:p<0.05\) ). Regarding social relationships, measured through the ability to seek help, maintain strong family and friendship ties, and interact harmoniously with others, the effect is negative and weakly significant only under NN matching ( \(\:-0.114,\:p<0.10\) ). Emotional regulation, which captures the ability to control negative emotions such as fear or frustration and to recover from setbacks, is more strongly impaired, with significant effects across NN \(\:(-0.142,\:p<0.05\) ) and IPWR (– \(\:0.113,\:p<0.05\) ). Similarly, spirituality/faith, which includes religious or moral conviction, belief in higher purpose, and the ability to draw strength from personal convictions, shows significant adverse impacts ranging from − 0.118 ( \(\:NN,\:p<0.05\) ) to − 0.094 ( \(\:IPWR,\:p<0.05\) ). By contrast, humor and positive thinking, reflecting the capacity to find optimism and humor in difficult situations, and personal trust and responsibility, reflecting autonomy and accountability, are not significantly affected, suggesting that certain psychological resources may remain more resilient to gambling practices. Overall, the convergence of findings across Kernel, NN, and IPWR methods confirms a robust and unfavorable effect of gambling on multiple key components of psychological resilience among youth. Table 3 Impact of gambling on psychological resilience Kernel NN IPWR Perseverance/Commitment -0.103* -0.124** -0.113** (0.048) (0.050) (0.048) Positive self-image/Optimism -0.091* -0.100** -0.098** (0.048) (0.049) (0.048) Relationship -0.090 -0.114* -0.095 (0.064) (0.067) (0.066) Humor/Positive thinking -0.037 -0.113 -0.050 (0.077) (0.079) (0.079) Emotional regulation -0.105* -0.142** -0.113** (0.056) (0.056) (0.055) Spirituality / Faith -0.087* -0.118** -0.094** (0.047) (0.047) (0.046) Personal trust / responsibility -0.058 -0.076 -0.071 (0.057) (0.053) (0.055) Overall -0.090** -0.115*** -0.099** (0.039) (0.041) (0.039) Note: Robust standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1. 4. Discussions This study investigated the determinants of gambling participation and its impact on the psychological resilience of young Senegalese adults. Results highlight the central role of sociodemographic factors in gambling decisions. Gender emerged as a key predictor, with women significantly less likely to gamble than men. This finding is consistent with international and African studies showing that young men are more exposed to gambling environments and more prone to problematic gambling behaviors (Appiah & Awuah, 2016 ; Kang et al., 2019 ; Lee & Lee, 2025 ). Such patterns may be explained by social and cultural norms that frame gambling as a male activity, while regarding female participation as inappropriate (Abbott et al., 2018 ). Risk attitudes also contribute, as women are generally more cautious and risk-averse (Díaz et al., 2023 ; Harris & Jenkins, 2006 ). Media and marketing campaigns, which disproportionately target men, further reinforce this perception (Guillou-Landreat et al., 2021 ; Kroon, 2022 ). Education was positively associated with gambling participation, suggesting that more educated youths are more likely to gamble. This contrasts with studies showing that lower education is linked to higher gambling engagement and expenditures (Salonen, 2018), suggesting that education can both inform and encourage gambling. This may reflect greater exposure to digital and social environments where gambling is visible and accessible (Lind et al., 2022 ). Although education improves understanding of probabilities and risks, it also enhances exposure and social acceptance of gambling. Educated individuals may also perceive gambling as an opportunity for quick financial gains or a socially valued leisure activity (Tade et al., 2025 ). By contrast, age and occupation were not significant predictors, indicating a relatively homogeneous propensity to gamble among young adults regardless of employment status or income. However, residential areas strongly influenced gambling behavior. Urban youth were significantly more likely to gamble than their rural counterparts, reflecting the greater availability and accessibility of both physical and online gambling opportunities in urban settings (Hamilton-Wright et al., 2016 ). Behavioral correlates included tobacco use and sports participation. Tobacco consumption may capture a general predisposition toward risk-taking (Butler et al., 2020 ). The link between sports and gambling can be explained by several mechanisms. Sporting environments, such as events, clubs, or leagues, facilitate exposure to sports betting, while active participants tend to monitor competitions and odds closely, encouraging betting involvement (Guillou-Landreat et al., 2021 ). Moreover, the competitive and risk-taking culture of sports may translate into gambling behavior. Finally, sports provide an effective channel for gambling marketing, which portrays betting as a fun, profitable, and desirable lifestyle choice (Derevensky et al., 2007 ). Popular sports such as football and basketball, widely practiced by youth, also correspond to the most common betting formats; in our sample, 50% of sports participants reported playing one of these two, reinforcing familiarity and participation. Beyond determinants, the study assessed the causal impact of gambling on psychological resilience. Findings reveal a robust negative effect across all matching methods, with overall resilience scores significantly reduced among gamblers. The strongest impacts were observed for perseverance and engagement, optimism and positive self-image, emotional regulation, and spirituality or religious beliefs, indicating measurable weakening of psychological resources essential for coping with adversity. These results align with international evidence linking gambling to adverse mental health and reduced resilience. For instance, Hamilton-Wright et al. ( 2016 ) showed that for vulnerable youth, gambling simultaneously functions as escape and stressor, undermining long-term goals. Similarly, problematic gambling among adolescents has been associated with lower self-efficacy and impaired emotional regulation (Kang et al., 2019 ). High rates of problematic gambling have also been linked to depression, anxiety, and stress, which further weaken perseverance and optimism (Lee & Lee, 2025 ; Tran et al., 2024 ; Yimam et al., 2024 ;). In our sample, reported losses (21% of gamblers) and relatively low winnings (69% earning less than 30,000 XOF) exacerbate stress, thereby reducing the capacity to manage frustration and recover from setbacks. The cultural and moral dimensions are equally significant. Although gambling is embedded in youth practices, it often leads to financial and emotional losses that foster feelings of failure (Appiah & Awuah, 2016 ; Tade et al., 2025 ). The negative effect on spirituality and religious beliefs may reflect the perceived incompatibility of gambling with dominant moral and religious norms. In a highly religious context—97% of respondents identified as Muslim—gambling may induce guilt or cognitive dissonance, eroding spiritual anchoring and the perception of a meaningful life. This finding confirms previous studies that reporting negative associations between religiosity and gambling (Billah et al., 2025 ; Mutti-Packer et al., 2017 ). Limitations This study has several limitations. First, the cross-sectional nature of the data prevents establishing definitive causal relationships or examining the evolution of gambling effects over time. Second, all measures of gambling behavior, weekly gains or losses, and psychological resilience are self-reported, which may introduce recall bias, social desirability bias, or underreporting, particularly for sensitive behaviors. Third, while several socio-demographic and behavioral factors were included, other relevant determinants such as family stress, peer influence, access to online gambling platforms, or pre-existing mental health conditions, were not considered. Fourth, the psychological resilience indicators rely on self-reported scales, which, although validated, may not fully capture all facets of resilience, particularly behavioral responses and situational adaptability in real-life contexts. Finally, the low participation of women and the lack of distinction between gambling types limit the generalizability of the findings. Future research should combine longitudinal data, objective measures, and disaggregation by gambling type to better understand the mechanisms linking gambling to psychological resilience. 5. Conclusions and Policy Implications This is among the first comprehensive analyses of gambling behavior and its impact on the psychological resilience of young people in Senegal. It highlights several significant factors influencing participation in gambling. Gambling is highly prevalent among young men and is positively associated with educational attainment, urban residence, and, to a lesser extent, sports participation and tobacco use. The findings also indicate that most gamblers experience limited financial gains, with 21% reporting net losses and 69% earning less than 30,000 XOF per week. Causal analysis reveals that gambling significantly reduces psychological resilience, particularly affecting perseverance and engagement, self-esteem and optimism, emotional regulation, as well as spirituality and faith. These results suggest that gambling is not merely a leisure activity but a serious issue that undermines individuals’ adaptive capacities in the face of life challenges. By compromising key dimensions of psychological well-being, it increases both social and mental vulnerability, especially among young people and already at-risk populations. Several policy implications emerge from this study. First, there is a pressing need to strengthen education and awareness regarding the risks of gambling. The strong link between educational level and gambling behavior underscores the importance of prevention strategies targeting not only less-educated youth but also those with higher educational resources, promoting critical understanding of gambling-related risks. This could include school-based programs and targeted public awareness campaigns. Second, gambling should be integrated into public health policies. Given its negative effects on key well-being dimensions (optimism, emotional regulation, spirituality), gambling should be treated as a mental and social health issue. Institutional monitoring, stronger regulatory frameworks, and psychological support services are necessary, particularly for most young people willing to stop gambling (67%, including 20% immediately and 47% soon). Specific programs should also be developed for addicted youths not intending to quit in the near term (18%, including 8% not at all). Third, promoting healthy, accessible leisure alternatives can enhance youth resilience without resorting to gambling. This could involve developing cultural and digital spaces for young people, especially in rural areas and among better-educated youth. Finally, limiting aggressive gambling advertising across traditional and social media is essential. Gambling companies should be encouraged to adopt responsible marketing practices, minimize exposure of vulnerable populations, and promote safe and responsible gambling behaviors. Declarations Ethics approval and informed consent The survey protocol was approved by the Technical Committee on Statistical Programs (clearance No. 0001A2023). Informed consent was obtained from all participants. References Abadie A, Imbens GW (2006) Large sample properties of matching estimators for average treatment effects. econometrica 74(1):235–267 Abbott M, Binde P, Clark L, Hodgins D, Johnson M, Manitowabi D, Williams R (2018) Conceptual framework of harmful gambling: An international collaboration. https://doi.org/10.11575/PRISM/43603 . Gambling Research Exchange Ontario Adebisi T, Alabi O, Arisukwu O, Asamu F (2021) Gambling in transition: assessing youth narratives of gambling in Nigeria. J Gambl Stud 37(1):59–82 Appiah MK, Awuah F (2016) Socio-cultural and environmental determinants of youth gambling: Evidence from Ghana. Br J Psychol Res 4(4):12–23 Ariyabuddhiphongs V, Chanchalermporn N (2007) A test of social cognitive theory reciprocal and sequential effects: Hope, superstitious belief and environmental factors among lottery gamblers in Thailand. J Gambl Stud 23(2):201–214 Awo LO, Amazue LO, Eze VC, Ekwe CN (2023) Mediating role of impulsivity in the contributory roles of upward versus downward counterfactual thinking in youth gambling intention. J Gambl Stud 39(1):33–48 Billah MA, Sofiah D, Arifiana IY (2025) The Relationship Between Religiosity and Self-Control with Online Gambling Addiction in Online Gamblers. J Sci Res Educ Technol (JSRET) 4(1):279–288 Bitanihirwe BK, Adebisi T, Bunn C, Ssewanyana D, Darby P, Kitchin P (2022) Gambling in sub-Saharan Africa: traditional forms and emerging technologies. Curr Addict Rep 9(4):373–384 Blank L, Baxter S, Woods HB, Goyder E (2021) Interventions to reduce the public health burden of gambling-related harms: a mapping review. Lancet Public Health 6(1):e50–e63 Browne M, Greer N, Armstrong T, Doran C, Kinchin I, Langham E et al (2017) The social cost of gambling to Victoria. CQUniversity. Report. https://hdl.handle.net/10018/1219773 Butler N, Quigg Z, Bates R, Sayle M, Ewart H (2020) Gambling with your health: Associations between gambling problem severity and health risk behaviours, health and wellbeing. J Gambl Stud 36(2):527–538. https://doi.org/10.1007/s10899-019-09902-8 Caliendo M, Kopeinig S (2008) Some practical guidance for the implementation of propensity score matching. J Economic Surveys 22(1):31–72 Chesnaye NC, Stel VS, Tripepi G, Dekker FW, Fu EL, Zoccali C, Jager KJ (2022) An introduction to inverse probability of treatment weighting in observational research. Clin kidney J 15(1):14–20 Derevensky J, Sklar A, Gupta R, Messerlian C, Laroche M, Mansour S (2007) The effects of gambling advertisements on child and adolescent gambling attitudes and behaviors. Chapitre 5:144 Diallo MA (2024) Covid-19 et distribution de kits alimentaires: quels impacts sur le bien-être des ménages de la région de Dakar? Revue d'économie du développement 32(2):95–133 Diallo MA (2025) Labor Market Participation and Gender Wage Gap: The Case of Young Workers in Senegal. Rev Dev Econ. https://doi.org/10.1111/rode.13244 Díaz A, García J, Pérez L (2023) Gender differences in the propensity to start gambling. J Gambl Stud 39(4):1799–1814 Duran S, Demirci Ö, Akgenç F (2024) Investigation of gambling behavior, self-confidence and psychological resilience levels of university students. J Gambl Stud 40(4):1937–1949 Edgren R, Castrén S, Alho H, Salonen AH (2017) Gender comparison of online and land-based gamblers from a nationally representative sample: Does gambling online pose elevated risk? Comput Hum Behav 72:46–56 Guillou-Landreat M, Gallopel-Morvan K, Lever D, Le Goff D, Le Reste JY (2021) Gambling marketing strategies and the internet: What do we know? A systematic review. Front Psychiatry 12:583817 Hamilton-Wright S, Woodhall-Melnik J, Guilcher SJ, Schuler A, Wendaferew A, Hwang SW, Matheson FI (2016) Gambling in the landscape of adversity in youth: reflections from men who live with poverty and homelessness. Int J Environ Res Public Health 13(9):854 Harris CR, Jenkins M (2006) Gender differences in risk assessment: Why do women take fewer risksthan men? Judgm Decis Mak 1(1):48–63 Heckman JJ, Ichimura H, Todd PE (1997) Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme. Rev Econ Stud 64(4):605–654 Hilbrecht M, Baxter D, Abbott M, Binde P, Clark L, Hodgins DC, Williams RJ (2020) The conceptual framework of harmful gambling: a revised framework for understanding gambling harm. J Behav addictions 9(2):190–205 Kang K, Ok JS, Kim H, Lee KS (2019) The gambling factors related with the level of adolescent problem gambler. Int J Environ Res Public Health 16(12):2110 Kroon Å (2022) Moderate gendering in Swedish gambling advertisements. Feminist Media Stud 22(7):1817–1836 Lam D (2007) An exploratory study of gambling motivations and their impact on the purchase frequencies of various gambling products. Psychol Mark 24(9):815–827 Langham E, Thorne H, Browne M, Donaldson P, Rose J, Rockloff M (2015) Understanding gambling related harm: A proposed definition, conceptual framework, and taxonomy of harms. BMC Public Health 16(1):80 Lee HJ, Lee G (2025) Pathways to understanding problem gambling among adolescents. BMC Public Health 25(1):2144 Lind K, Marionneau V, Järvinen-Tassopoulos J, Salonen AH (2022) Socio-demographics, gambling participation, gambling settings, and addictive behaviors associated with gambling modes: A population-based study. J Gambl Stud 38(4):1111–1126 Lopez-Gonzalez H, Granero R, Fernández-Aranda F, Griffiths MD, Jiménez-Murcia S (2024) Perceived impact of gambling advertising can predict gambling severity among patients with gambling disorder. J Gambl Stud 40(4):1787–1803 Mathews M, Volberg R (2013) Impact of problem gambling on financial, emotional and social well-being of Singaporean families. Int Gambl Stud 13(1):127–140 Muggleton N, Parpart P, Newall P, Leake D, Gathergood J, Stewart N (2021) The association between gambling and financial, social and health outcomes in big financial data. Nat Hum Behav 5(3):319–326 Mutti-Packer S, Hodgins DC, Williams RJ, Konkolÿ Thege B (2017) The protective role of religiosity against problem gambling: Findings from a five-year prospective study. BMC Psychiatry 17(1):356 Nyman JA, Welte JW, Dowd BE (2008) Something for nothing: A model of gambling behavior. J Socio-Econ 37(6):2492–2504 Nyman JA (2004) A Theory of Demand for Gambles. Retrieved from the University Digital Conservancy. https://hdl.handle.net/11299/55890 Rosenbaum PR, Rubin DB (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70(1):41–55 Salonen AH, Kontto J, Perhoniemi R, Alho H, Castrén S (2018) Gambling expenditure by game type among weekly gamblers in Finland. BMC Public Health 18(1):697 Słoczyński T, Uysal SD, Wooldridge JM (2022) Doubly robust estimation of local average treatment effects using inverse probability weighted regression adjustment. IZA Institute of Labor Economics Discussion Paper No. 15727 Smith BW, Dalen J, Wiggins K, Tooley E, Christopher P, Bernard J (2008) The brief resilience scale: assessing the ability to bounce back. Int J Behav Med 15(3):194–200 Smith JA, Todd PE (2005) Does matching overcome LaLonde's critique of nonexperimental estimators? J Econ 125(1–2):305–353 Statistics of Gambling (2022) TGM Gamling and Sports Betting Survey. https://tgmresearch.com/gambling-sports-betting-market-research-in-senegal.html Tade O, Dinne CE, George OI (2025) I have lost more than I have won’: sports betting and bettors experiences in Nigeria. Afr Identities 23(2):364–377 Tagoe VN, Yendork JS, Asante KO (2018) Gambling among youth in contemporary Ghana: understanding, initiation, and perceived benefits. Afr today 64(3):53–69 Tran LT, Wardle H, Colledge-Frisby S, Taylor S, Lynch M, Rehm J, Degenhardt L (2024) The prevalence of gambling and problematic gambling: a systematic review and meta-analysis. Lancet Public Health Uwiduhaye MA, Niyonsenga J, Muhayisa A, Mutabaruka J (2021) Gambling, family dysfunction and psychological disorders: a cross-sectional study. J Gambl Stud 37(4):1127–1137 Wan YKP (2012) The social, economic and environmental impacts of casino gaming in Macao: The community leader perspective. J Sustainable Tourism 20(5):737–755 Wardle H, McManus S (2021) Suicidality and gambling among young adults in Great Britain: results from a cross-sectional online survey. Lancet Public Health 6(1):e39–e49 Wickwire Jr EM, Whelan JP, West R, Meyers A, McCausland C, Leullen J (2007) Perceived availability, risks, and benefits of gambling among college students. J Gambl Stud 23(4):395–408 Williams RJ, Volberg RA, Stevens RM, Williams LA, Arthur JN (2017) The definition, dimensionalization, and assessment of gambling participation. Canadian Consortium for Gambling Research Yimam TP, Mkpem N, Ayangeawam MJ, Terseer HJ, Ene AB (2024) Depression, anxiety, stress and gambling behaviour of young people in makurdi metropolis. Afr J Social Behav Sci, 14 (3) Additional Declarations The authors declare no competing interests. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7437944","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":505989695,"identity":"abe66f2b-5fed-4727-be83-b6c1c4202b4e","order_by":0,"name":"Mamadou Abdoulaye Diallo","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-7946-4777","institution":"Consortium pour la recherche économique et sociale (CRES)","correspondingAuthor":true,"prefix":"","firstName":"Mamadou","middleName":"Abdoulaye","lastName":"Diallo","suffix":""},{"id":505989696,"identity":"86b3167e-bc0a-4624-9ed9-0dccef54890b","order_by":1,"name":"Garmy Samb","email":"","orcid":"","institution":"Ecole nationale de la Statistique et de l'Analyse Economique (ENSAE)-Pierre Ndiaye","correspondingAuthor":false,"prefix":"","firstName":"Garmy","middleName":"","lastName":"Samb","suffix":""}],"badges":[],"createdAt":"2025-08-23 01:55:35","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7437944/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7437944/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90317791,"identity":"32e437eb-7ca1-4880-95f4-a7d1d8e4f7a1","added_by":"auto","created_at":"2025-09-01 10:28:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":54640,"visible":true,"origin":"","legend":"\u003cp\u003eNet winnings per week\u003c/p\u003e\n\u003cp\u003eSource: Authors from AYAR project survey in Senegal 2023.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7437944/v1/254edf085e276eb81a5ac7b1.png"},{"id":90317784,"identity":"6f31972b-14af-451a-9100-b91aeccefda3","added_by":"auto","created_at":"2025-09-01 10:28:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61816,"visible":true,"origin":"","legend":"\u003cp\u003eSource of money used for gambling\u003c/p\u003e\n\u003cp\u003eSource: Authors from AYAR project survey in Senegal 2023.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7437944/v1/e6577856488d5638c7589b3e.png"},{"id":90317787,"identity":"816ba15e-2511-41e1-b4e9-6020925bdb0d","added_by":"auto","created_at":"2025-09-01 10:28:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":75982,"visible":true,"origin":"","legend":"\u003cp\u003eUse of gambling winnings\u003c/p\u003e\n\u003cp\u003eSource: Authors from AYAR project survey in Senegal 2023.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7437944/v1/52891ed88c5239fc1eb8309a.png"},{"id":90317795,"identity":"865211a1-c228-4679-9792-2d84cfc70e17","added_by":"auto","created_at":"2025-09-01 10:28:52","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":55286,"visible":true,"origin":"","legend":"\u003cp\u003eSources of First Exposure to Gambling Practices (%)\u003c/p\u003e\n\u003cp\u003eSource: Authors from AYAR project survey in Senegal 2023.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7437944/v1/3a788b0f3719f0d76f64bbc5.png"},{"id":90317797,"identity":"30df8245-03e7-4f31-92bb-58a47a806a27","added_by":"auto","created_at":"2025-09-01 10:28:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":39862,"visible":true,"origin":"","legend":"\u003cp\u003ePerceived impact of gambling on academic performance\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: The question was asked of educated people only.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSource: Authors from AYAR project survey in Senegal 2023.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7437944/v1/6d752d483268fbbe37cfeacc.png"},{"id":90318785,"identity":"1bec0fbb-6971-4026-9bca-f0ccac6a43f3","added_by":"auto","created_at":"2025-09-01 10:36:52","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":42838,"visible":true,"origin":"","legend":"\u003cp\u003eIntentions to Stop Gambling\u003c/p\u003e\n\u003cp\u003eSource: Authors from AYAR project survey in Senegal 2023.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7437944/v1/6794363fd0d35af82d9911aa.png"},{"id":90320432,"identity":"39959077-21f9-4c42-980c-cb898c4b7a24","added_by":"auto","created_at":"2025-09-01 10:44:52","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":77625,"visible":true,"origin":"","legend":"\u003cp\u003eDeterminants of youth gambling\u003c/p\u003e\n\u003cp\u003eNote: Coefficient effects plot of the probit model estimates for the probability of gambling. The reference modalities are as follows: Sex (Male); Education level (No formal education); Residential area (Rural). Complete model estimates are presented in Table A2 in the Appendix.\u003c/p\u003e\n\u003cp\u003eSource: Authors from AYAR project survey in Senegal 2023.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7437944/v1/cc9cf9c820c327ac6caea7e1.png"},{"id":90318789,"identity":"55caf41c-e2f5-4dfc-9044-2e51e8e52c66","added_by":"auto","created_at":"2025-09-01 10:36:52","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":111777,"visible":true,"origin":"","legend":"\u003cp\u003eDensity of propensity scores before and after matching\u003c/p\u003e\n\u003cp\u003eNote: Scores were estimated using a logistic model controlling age, gender, education, occupation and place of residence.\u003c/p\u003e\n\u003cp\u003eSource: Authors from AYAR project survey in Senegal 2023.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7437944/v1/041c620c7d32c1ee10f8e9bf.png"},{"id":90321827,"identity":"0beb80f8-6640-4b0c-bd35-3d7559a4637c","added_by":"auto","created_at":"2025-09-01 11:00:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1379991,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7437944/v1/b4c8cc78-b8b4-4da4-9920-3dd4d41ed05d.pdf"},{"id":90318782,"identity":"7b1f111f-9992-4ddd-b4c1-f6f7a6988b4c","added_by":"auto","created_at":"2025-09-01 10:36:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29464,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-7437944/v1/2feb661b305bb82f011cfa42.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eYouth and Gambling: Determinants and Impact on Psychological Resilience in Senegal\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGambling is generally defined as wagering something of value on an event with an uncertain outcome, with the intention of winning a prize. Three elements are essential: the stake (consideration), the risk (chance), and the reward (prize) (Williams et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). While gambling is popular and often practiced recreationally across societies, it can also have detrimental effects, potentially leading to addiction (Hilbrecht et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and undermining psychological resilience, understood as the capacity to cope with adversity, stress, or failure (Smith, 2008).\u003c/p\u003e\u003cp\u003eSeveral studies show that gambling can produce multidimensional harms affecting financial, professional, health, psychological, and social domains, and may encourage deviant behaviors such as dishonesty or criminality (Langham et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Financial stress, debt accumulation, and loss of savings are common consequences (Mathews \u0026amp; Volberg, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Muggleton et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Gambling may also influence cultural values and reduce young people\u0026rsquo;s interest in education, contributing to school dropout (Wan, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), while governments bear significant regulatory and treatment costs (Browne et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Technological advances have accelerated online gambling globally, including in Africa, where mobile penetration, Internet access, and digital payment systems have facilitated participation (Adebisi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bitanihirwe et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Despite revenue generation, these trends raise concerns about addiction and negative social and health impacts (Blank et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Uwiduhaye et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wardle \u0026amp; McManus, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), especially in contexts of youth unemployment and poverty (Awo et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe demand theory of gambling posits that individuals gamble to obtain \u0026ldquo;something for nothing,\u0026rdquo; with higher participation among economically vulnerable populations (Nyman, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Motivations also include financial aspiration, curiosity, excitement, social interaction, and emotional benefits such as mood improvement and stress relief (Ariyabuddhiphongs \u0026amp; Chanchalermporn, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Lam, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Tagoe et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wickwire et al., 2007). Demographically, men gamble more frequently and spend more than women across age groups (Abbott et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Appiah \u0026amp; Awuah, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lee \u0026amp; Lee, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and lower education levels are linked to higher gambling expenditures (Salonen et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Income influences participation: lower-income individuals prefer physical venues, while higher-income groups favor online formats (Lind et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Gambling often co-occurs with other risk behaviors such as smoking and alcohol use (Edgren et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and recreational gambling is more prevalent among service workers, minorities, and populations in high-unemployment areas (Nyman et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite growing literature on gambling, research in Africa remains limited, particularly on its relationship with psychological resilience. In Senegal, nearly 30% of the population participated in betting in 2022 (Statistics of Gambling, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), amid high youth unemployment, poverty, and rapid online gambling expansion (Adebisi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bitanihirwe et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While often perceived as a form of entertainment or an economic opportunity, gambling may weaken the psychological resilience of young people by undermining their stress management, self-esteem, and capacity to cope with adversity.\u003c/p\u003e\u003cp\u003eThis study aims to identify the determinants of gambling among young Senegalese and assess its effect on psychological resilience. Specifically, it seeks to: (i) describe gambling practices, including frequency, sources of funds, use of winnings, social influences, perceived impacts, and intentions to stop; (ii) identify sociodemographic and behavioral predictors of gambling; and (iii) evaluate the causal effect of gambling on psychological resilience. Using a representative sample of 1,560 youths, we first estimated a probit model to identify gambling determinants. Factor analysis was then employed to operationalize psychological resilience across seven dimensions. Finally, the causal effect of gambling on resilience was assessed using propensity score matching (PSM), with robustness checks via inverse probability-weighted regression adjustment (IPWRA). Results indicate that, beyond traditional sociodemographic predictors such as gender and education, tobacco use, and sports participation are significant determinants of gambling. Overall, gambling has a negative and significant effect on psychological resilience, particularly undermining self-esteem, optimism, emotional regulation, and spirituality. These findings underscore the need for targeted policy interventions, including awareness campaigns, stress management and well-being programs, and regulations governing access to gambling, especially online platforms, to protect vulnerable youth.\u003c/p\u003e\u003cp\u003eThe remainder of the paper is structured as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents data sources and methodology. Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides the results. Section \u003cspan refid=\"Sec17\" class=\"InternalRef\"\u003e4\u003c/span\u003e discusses the findings. Section \u003cspan refid=\"Sec18\" class=\"InternalRef\"\u003e5\u003c/span\u003e concludes with policy implications.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Data\u003c/h2\u003e\u003cp\u003eThe data used in this study comes from the recent Africa Youth Aspirations and Resilience (AYAR) project which took place in seven African countries: Ethiopia, Ghana, Kenya, Nigeria, Rwanda, Senegal and Uganda. The AYAR project funded by the Mastercard Foundation and coordinated by Partnership for African Social and Governance Research (PASGR) is a three-year initiative that is running between 2021 and 2024 to understand youth aspirations in their own words (Diallo, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe data collection was conducted by the \u003cem\u003eConsortium pour la Recherche Economique et Sociale (CRES)\u003c/em\u003e. A total of 1,560 young people including 50% women, were interviewed during period within January 05 to 17, 2023 in nine (9) regions of Senegal using a two-stage stratified sample design. A sample of this size yielded country-level results with error margins of \u0026plusmn;\u0026thinsp;2.5 percentage points at a 95% confidence level.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Conceptualization and measure\u003c/h2\u003e\u003cp\u003eIn this paper, our primary variables of interest are Gambling and Psychological Resilience. The first variable, Gambling, indicates whether an individual participates in games of chance. Specifically, a youth is classified as a gambler if they reported engaging in any form of gambling \u0026ldquo;Once or twice,\u0026rdquo; \u0026ldquo;Several times,\u0026rdquo; or \u0026ldquo;Often\u0026rdquo; during the 30 days preceding the survey.\u003c/p\u003e\u003cp\u003ePsychological resilience is generally measured through a set of items encompassing multiple dimensions (Duran et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To our knowledge, no standardized measure of psychological resilience currently exists for Senegal. In this study, we developed an index based on seven key dimensions: perseverance and commitment, positive self-image and optimism, relationships and regulation, humor and positive thinking, emotional regulation, spirituality and faith, and personal trust and responsibility. Each dimension comprises several items coded on a 5-point Likert scale ranging from \u0026ldquo;Strongly disagree\u0026rdquo; (1) to \u0026ldquo;Strongly agree\u0026rdquo; (5) (Table A1).\u003c/p\u003e\u003cp\u003eFactor analysis was employed to construct the psychological resilience index. The suitability of the scale items for factor analysis was assessed using Bartlett\u0026rsquo;s Test of Sphericity (BToS) and the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy. Following factor extraction, the internal consistency of the scales was evaluated using Cronbach\u0026rsquo;s alpha (α), ensuring reliability of the composite indices\u003c/p\u003e\u003cp\u003eTable A1 presents the factor loadings, percentages of variance explained, and validity and reliability indicators for the factor model. The results confirm that the items selected for each dimension were suitable for factor analysis, with significant BToS (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\:=\\:0.00\\)\u003c/span\u003e\u003c/span\u003e), KMO values above the recommended threshold (0.65\u0026ndash;0.87), acceptable internal consistency (α\u0026thinsp;=\u0026thinsp;0.56\u0026ndash;0.87), explained variance ranging from 36.9\u0026ndash;58.1%, and factor loadings between 0.31 and 0.76. These results confirm the validity and reliability of the factor model for assessing psychological resilience in this sample.\u003c/p\u003e\u003cp\u003e\u003cb\u003eControl variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe control variables used in this study include gender (male and female), age (18\u0026ndash;35 years), and education level, ranging from no formal education to post-secondary education (Appiah \u0026amp; Awuah, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lind et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lopez-Gonzalez et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Place of residence (urban vs. rural) is included as a proxy for environmental context (Hamilton-Wright et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Employment status, reflecting labor market participation, is used as a proxy for income (D\u0026iacute;az et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lind et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, tobacco use, and sports participation are included as dummy variables to account for risk-taking behaviors (Butler et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Duran et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Estimation Strategy\u003c/h2\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1 Probit regression model\u003c/h2\u003e\u003cp\u003eSince our outcome variable is dichotomous, we estimate a binary probit regression model to identify the determinants of youth gambling in Senegal:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\text{P}\\text{r}(G}_{i}=1\\:\\left|\\:{X}_{i}\\right)={\\Phi\\:}\\left({X}_{i}\\beta\\:\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{i}\\:=\\:1\\)\u003c/span\u003e\u003c/span\u003e if young \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e participates in gambling, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\Phi\\:}\\)\u003c/span\u003e\u003c/span\u003e denotes the standard normal cumulative distribution function, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}\\)\u003c/span\u003e\u003c/span\u003e is a vector of explanatory variables, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e represents the parameters to be estimated. The covariates include gender, age, educational, labor market participation, residential area, tobacco consumption, and sport practice. To ensure robust inference, we implement a bootstrap procedure with 1,000 replications to compute standard errors. We use STATA (version 16) software to perform all statistics and the probit model.\u003c/p\u003e\u003cp\u003eTo assess multicollinearity among variables included in the multivariate analyses, the outcome was regressed on all independent variables using the Variance Inflation Factor (VIF). The analyses showed no multicollinearity problems among the variables included in the models (mean of VIF varied 1.07\u0026ndash;1.55).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2 Propensity Score Matching (PSM)\u003c/h2\u003e\u003cp\u003eTo assess the causal impact of gambling on psychological resilience, we adopt a counterfactual approach, which represents what would have occurred in the absence of the treatment (Heckman et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Rosenbaum \u0026amp; Rubin, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). Considering gambling participation as the treatment, we estimate the average treatment effect on the treated (ATT), defined as follows:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:ATT\\:=E[{Y}_{1i}\\:-{Y}_{0i}|{T}_{i}\\:=\\:1]\\:=\\:E[{Y}_{1i}|{T}_{i}\\:=\\:1]-E\\left[{Y}_{0i}\\right|{T}_{i}\\:=\\:1]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Y\\)\u003c/span\u003e\u003c/span\u003e denotes the outcome, in this case psychological resilience. The term \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\left[{Y}_{0i}\\right|{T}_{i}\\:=\\:1]\\)\u003c/span\u003e\u003c/span\u003e represents the counterfactual outcome, that is, the level of resilience the treated group would have experienced had they not received the treatment.\u003c/p\u003e\u003cp\u003eSince the counterfactual is not observable, Rosenbaum and Rubin (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) propose using propensity score matching (PSM) to approximate it by leveraging the observed outcomes of the untreated group. The propensity score is defined as the probability that a young individual engages in gambling, conditional on observed covariates \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:X\\)\u003c/span\u003e\u003c/span\u003e. It is expressed as:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:p\\left({X}_{i}\\right)=P\\:({T}_{i}=1|{X}_{i})=\\frac{\\text{exp}\\left(\\beta\\:{X}_{i}\\right)}{1+\\text{exp}\\left(\\beta\\:{X}_{i}\\right)}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBy conditioning on \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\left({X}_{i}\\right)\\)\u003c/span\u003e\u003c/span\u003e, PSM ensures that the treated and untreated groups are balanced in terms of observable characteristics, thereby reducing selection bias in estimating the ATT. However, this estimation is valid only under the Conditional Independence Assumption (CIA), which states that, given a set of observed covariates \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}\\)\u003c/span\u003e\u003c/span\u003e​, treatment assignment is as good as random.\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:\\left\\{\\begin{array}{c}p\\left(X\\right)=P\\left(T=1|X\\right)\\:\\\\\\:{Y}_{0}\\perp\\:T|X\\Rightarrow\\:{Y}_{0}\\perp\\:T|p\\left(X\\right)\\end{array}\\right.$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe matching method also assumes the existence of a common support, meaning that for all values of the observables, there must be both treated and untreated units available for comparison:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:0\\:\u0026lt;\\:P\\:({T}_{i}\\:=\\:1|{X}_{i})\\:\u0026lt;\\:1$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSeveral matching techniques can be employed, the most common being nearest-neighbor (NN) matching, the caliper-radius approach, and non-parametric kernel regression matching (Caliendo \u0026amp; Kopeinig, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Diallo, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Heckman et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnder the assumptions of conditional independence and common support, and in the case of matching with the M nearest neighbors, the simple estimator of the average treatment effect on the treated (ATT) for the treated observations is given by:\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:ATT=\\frac{1}{{N}_{1}}\\left[{\\sum\\:}_{i=1}^{{N}_{1}}({Y}_{i}-\\frac{1}{M}{\\sum\\:}_{j\\in\\:{J}_{m}\\left(i\\right)}{Y}_{j}\\:\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{J}_{m}\\left(i\\:\\right)\\)\u003c/span\u003e\u003c/span\u003e denotes the set of units matched to unit \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\:\\)\u003c/span\u003e\u003c/span\u003e. For each matched unit, a counterfactual outcome for iii is constructed as the average of the observed \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Y\\)\u003c/span\u003e\u003c/span\u003e values of its matched units.\u003c/p\u003e\u003cp\u003eHowever, the ATT estimated via PSM may be biased if the propensity score model is mis-specified (Abadie \u0026amp; Imbens, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Smith \u0026amp; Todd, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). One proposed solution is inverse probability weighting (IPW), which first estimates the propensity score and then applies inverse weights to create a pseudo-population balanced on observed confounders (Chesnaye et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Nonetheless, IPW remains sensitive to correct specifications of the treatment model. To address this limitation, Słoczyński et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) recommend the inverse probability-weighted regression adjustment (IPWRA), a doubly robust estimator that combines inverse weighting and regression adjustment, ensuring consistency if either the treatment or outcome model is correctly specified.\u003c/p\u003e\u003cp\u003eIn this study, we present multiple specifications, namely PSM using the kernel method, five nearest-neighbor matching (NN), and IPWRA. Five variables, gender, age, education, occupation, and residential area are retained as matching covariates.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Betting frequency and harvested earnings\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents descriptive statistics on youth gambling experience, stakes, and winnings. In a sample of 1,560 youths, 112 reported engaging in gambling, corresponding to a prevalence of 7.18%. On average, young gamblers have 19 months of experience, ranging from one month to eight years, reflecting considerable heterogeneity in gambling trajectories. Weekly stakes average 2,017.8 XOF, with substantial variability reaching up to 25,000 XOF per week. Gamblers place an average of 3.3 bets per week (ranging from 1 to 20), collecting an average of 23,555.4 XOF weekly, with total weekly winnings ranging from 0 to 300,000 XOF, indicating that some participants can achieve substantial gains. Exceptional stakes reach up to 200,000 XOF, while maximum reported winnings can reach 3,000,000 XOF, although the mean of maximum gains remains moderate at 118,060.7 XOF, with high dispersion. This pronounced variability in both stakes and winnings underscores the diversity of gambling behaviors among youths, spanning from occasional participation to high-intensity financial engagement.\u003c/p\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eYouth Gambling Experience, Stakes, and Winnings\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExperience in Gambling (in month)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeekly stake (in XOF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 017.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3 613.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of bets per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmount collected per week (in XOF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23 555.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e300 000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45 458.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighest amount bet (in XOF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17 826.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200 000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35 357.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHighest amount ever collected (in XOF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e118 060.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 000 000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e310 706.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eSource: Authors from AYAR project survey in Senegal 2023.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the distribution of weekly net winnings, calculated by comparing the total amount wagered (computed as the product of the weekly stake and the number of bets per week) with the total amount collected. The results indicate that 21% of gamblers incur negative net gains, meaning they lose more than they win. Among those reporting positive net gains, 33% earn less than 10,000 XOF per week, while 36% earn between 10,000 and 30,000 XOF weekly. Only 10% of gamblers achieve net gains exceeding 30,000 XOF per week. Overall, these figures reveal that many gamblers (69%) earn less than 30,000 XOF per week, with a substantial proportion experiencing net losses, highlighting the limited financial benefits and high risk associated with gambling among youths.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Sources of Gambling Money and Uses of Winnings\u003c/h2\u003e\n \u003cp\u003eFunds used by young people for gambling originate from multiple sources (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Pocket money represents the primary source (50%), followed by earned income or salaries (44%). Winnings from previous bets account for 11% of gambling expenditure. Daily allowances and borrowed funds contribute 4% and 3%, respectively, while a marginal 1% derives from illicit sources.\u003c/p\u003e\n \u003cp\u003eYoung people were asked about how they use their gambling winnings (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Most of the gains are spent on consumer goods (57%), highlighting their role in meeting daily needs. A significant share is also allocated to purchasing mobile phones (20%), reflecting the importance of technological connectivity. Additionally, 18% of the winnings are reinvested in gambling. Gains are also shared among co-gamblers (13%), used to pay for school supplies (6%), to purchase motorcycles (5%), and invested in business or livestock activities (4%).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Social Influences, Impacts, and Youth Perspectives on Quitting Gambling\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows that young gamblers are primarily influenced by friends or classmates (63%). A significant share (27%) engages in gambling independently, while advertising through social media and television influenced 7% of youths. Close parents account for only 3% of the influence, and other sources represent 1%.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e presents the distribution of perceptions concerning the impact of gambling on academic performance. Among educated youths, 45% report that gambling adversely affects their school results. In contrast, 38% perceive no significant effect, on academic outcomes. A smaller proportion, 17%, view gambling as exerting a positive influence on their academic performance.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e shows that a large proportion of young people plan to stop gambling soon (47%), followed by 20% who intend to quit immediately. Only 8% of gamblers report having no intention of stopping at all, while 10% plan to reduce their gambling only slightly. Additionally, 15% of respondents are uncertain about their intentions regarding quitting. These results indicate that young people who participate in gambling hold varied views about their future engagement in the activity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Sociodemographic Characteristics and Gambling\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the descriptive statistics. Although the sample is roughly balanced by gender, gambling is more prevalent among young men (13.3%) than women (1%), with a significant association (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\chi\\:\u0026sup2;\\:=\\:88.7,\\:p\\:\u0026lt;\\:0.001\\)\u003c/span\u003e\u003c/span\u003e). Nearly 60% of respondents are under 25, and gambling participation is similar across age groups (6.6 \u0026minus;\u0026thinsp;7.7%). Education shows a strong gradient, with gambling rates increasing from 2.3% among the young with no formal education to 12.4% among those with post-secondary education (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\chi\\:\u0026sup2;\\:=\\:28.1,\\:p\\:\u0026lt;\\:0.001\\)\u003c/span\u003e\u003c/span\u003e). Employed youth (8.4%) gamble more than the unemployed (5.9%). Urban residents (10.2%) are also more likely to gamble than rural ones (4%), with significant association (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\chi\\:\u0026sup2;\\:=\\:22.4,\\:p\\:\u0026lt;\\:0.05\\)\u003c/span\u003e\u003c/span\u003e). Tobacco users (19.4%) and sports participants (12.2%) show higher gambling prevalence.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e% gambling\u003c/p\u003e\n \u003cp\u003e(7.2%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChi2 test\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.7***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;20 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u0026ndash;25 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u0026ndash;30 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u0026ndash;35 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.1***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost secondary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLabor market participation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.8*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidential area\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.4***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTobacco consumption\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.7***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSport practice\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.5***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: Robust standard errors in parentheses; *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eSource: Authors from AYAR project survey in Senegal 2023.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Determinants of youth gambling\u003c/h2\u003e\n \u003cp\u003eThe full results of the gambling probability estimation are presented in Table \u003cspan class=\"InternalRef\"\u003eA2\u003c/span\u003e. Figure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e summarizes the coefficients estimated from the probit model for the entire sample and by residential area. The results indicate that young women are significantly less likely to participate in gambling than young men (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\:=\\:-1.124,\\:p\\:\u0026lt;\\:0.01\\)\u003c/span\u003e\u003c/span\u003e). In rural areas, no female respondent reported gambling during the month preceding the survey. Age was not a significant predictor, although it showed opposite signs depending on the area of residence.\u003c/p\u003e\n \u003cp\u003eEducation appears as a factor positively associated with gambling. Youth with primary-level education are more likely to gamble (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\:=\\:0.407,\\:p\\:\u0026lt;\\:0.05\\)\u003c/span\u003e\u003c/span\u003e) than those with no formal education. This trend continues at higher education levels: youth with middle-level (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\:=\\:0.508,\\:p\\:\u0026lt;\\:0.05\\)\u003c/span\u003e\u003c/span\u003e), secondary (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\:=\\:0.536,\\:p\\:\u0026lt;\\:0.01\\)\u003c/span\u003e\u003c/span\u003e), and post-secondary (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\:=\\:0.624,\\:p\\:\u0026lt;\\:0.01\\)\u003c/span\u003e\u003c/span\u003e) education have higher probabilities of engaging in gambling. However, the relationship between education and gambling varies by area of residence. In urban areas, the effect of education is generally stronger, reflecting greater exposure to sports betting networks and digital platforms. Conversely, at the post-secondary level, rural youth exhibit the highest propensity to gamble, likely due to limited economic and recreational opportunities, which leads them to seek gambling as an alternative source of income or leisure.\u003c/p\u003e\n \u003cp\u003eThese results align with other contextual and behavioral determinants. Living in an urban area significantly increases the likelihood of gambling (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\:=\\:0.413,\\:p\\:\u0026lt;\\:0.01\\)\u003c/span\u003e\u003c/span\u003e), confirming that cities concentrate most kiosks, digital offers, and sports betting advertisements. Tobacco use is also positively correlated with gambling (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\:=\\:0.324,\\:p\\:\u0026lt;\\:0.10\\)\u003c/span\u003e\u003c/span\u003e), reflecting the co-occurrence of risk behaviors within the same vulnerability profile. Finally, sports participation is another factor associated with gambling (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\:=\\:0.225,\\:p\\:\u0026lt;\\:0.10\\)\u003c/span\u003e\u003c/span\u003e). In rural areas, this effect is particularly strong and significant (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\:=\\:0.720,\\:p\\:\u0026lt;\\:0.01\\)\u003c/span\u003e\u003c/span\u003e), likely because football and sports betting are major collective leisure activities. In contrast, in urban areas, sports participation does not significantly influence gambling probability, suggesting that other urban factors\u0026mdash;such as proximity to betting points and digital access\u0026mdash;dominate the influence of sports on gambling.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6. Impact of gambling on psychological resilience\u003c/h2\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e3.6.1 Distribution of Propensity Scores and Matching Quality Assessment\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e illustrates the distribution of propensity scores for treated (gamblers) and control (non-gamblers). Substantial overlap is observed under the five nearest-neighbor matching, ensuring the existence of common support. This implies that comparable non-gamblers exist for most gamblers in the sample. Kernel matching also confirms this overlap, but the nearest-neighbor method provides a slightly better balance and thus appears more suitable for the analysis. Overall, the common support condition is satisfied, as treated and control groups show significant probability overlap.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003eA3\u003c/span\u003e reports the balancing test results before and after propensity score matching using both the five nearest-neighbor and kernel methods. Before matching, significant differences existed between gamblers and non-gamblers in sex \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:(p\u0026lt;0.01),\\)\u003c/span\u003e\u003c/span\u003e postsecondary education (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\u0026lt;0.01\\)\u003c/span\u003e\u003c/span\u003e), labor market participation (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\u0026lt;0.1\\)\u003c/span\u003e\u003c/span\u003e), and residence (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\u0026lt;0.01\\)\u003c/span\u003e\u003c/span\u003e). After matching, no significant differences remain, indicating that the matched samples are comparable. Table \u003cspan class=\"InternalRef\"\u003eA4\u003c/span\u003e further confirms the quality of matching. The pseudo-R\u0026sup2; drops from 0.181 to below 0.05, and the mean standardized bias is reduced by about 87%. Rubin\u0026rsquo;s R values fall within the acceptable range (0.5\u0026ndash;2), supporting that the matching is of good quality, with similar observable characteristics across groups.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e3.6.2 Impact of Gambling on Psychological Resilience\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the estimated average treatment effects on the treated (ATT) across three specifications and dimensions of psychological resilience. Overall, gambling exerts a negative and statistically significant impact on youth resilience. Estimated effects range from \u0026minus;\u0026thinsp;0.090 with the Kernel method to \u0026minus;\u0026thinsp;0.115 with five nearest-neighbor (NN) matching, with relatively small standard errors. The consistency of results across methods reinforces their robustness, suggesting that gambling is systematically associated with a lower capacity for resilience.\u003c/p\u003e\n \u003cp\u003eWhen disaggregating by dimensions, perseverance and commitment, which capture the ability to pursue goals despite difficulties, persist in undertaken tasks, and learn from adversity, are significantly reduced, with effects between \u0026minus;\u0026thinsp;0.113 (I\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:PWR,\\:p\u0026lt;0.050\\)\u003c/span\u003e\u003c/span\u003e) and \u0026minus;\u0026thinsp;0.124 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:NN,\\:p\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e). Negative coefficients imply that young gamblers are more likely to give up easily and perceive obstacles as constraints rather than opportunities for growth. Positive self-image and optimism, encompassing self-acceptance, future outlook, and pride in personal achievements, are also adversely affected \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:(-0.098,\\:IPWR,\\:p\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eRegarding social relationships, measured through the ability to seek help, maintain strong family and friendship ties, and interact harmoniously with others, the effect is negative and weakly significant only under NN matching (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-0.114,\\:p\u0026lt;0.10\\)\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eEmotional regulation, which captures the ability to control negative emotions such as fear or frustration and to recover from setbacks, is more strongly impaired, with significant effects across NN \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:(-0.142,\\:p\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e) and IPWR (\u0026ndash;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:0.113,\\:p\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e). Similarly, spirituality/faith, which includes religious or moral conviction, belief in higher purpose, and the ability to draw strength from personal convictions, shows significant adverse impacts ranging from \u0026minus;\u0026thinsp;0.118 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:NN,\\:p\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e) to \u0026minus;\u0026thinsp;0.094 (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:IPWR,\\:p\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eBy contrast, humor and positive thinking, reflecting the capacity to find optimism and humor in difficult situations, and personal trust and responsibility, reflecting autonomy and accountability, are not significantly affected, suggesting that certain psychological resources may remain more resilient to gambling practices.\u003c/p\u003e\n \u003cp\u003eOverall, the convergence of findings across Kernel, NN, and IPWR methods confirms a robust and unfavorable effect of gambling on multiple key components of psychological resilience among youth.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eImpact of gambling on psychological resilience\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKernel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIPWR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerseverance/Commitment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.103*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.124**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.113**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.048)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.050)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.048)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive self-image/Optimism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.091*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.100**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.098**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.048)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.049)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.048)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelationship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.114*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.064)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHumor/Positive thinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.077)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.079)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.079)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmotional regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.105*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.142**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.113**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.055)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpirituality / Faith\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.087*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.118**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.094**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.047)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.046)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonal trust / responsibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.057)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.053)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.055)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.090**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.115***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.099**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.041)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eNote: Robust standard errors in parentheses; *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussions","content":"\u003cp\u003eThis study investigated the determinants of gambling participation and its impact on the psychological resilience of young Senegalese adults. Results highlight the central role of sociodemographic factors in gambling decisions. Gender emerged as a key predictor, with women significantly less likely to gamble than men. This finding is consistent with international and African studies showing that young men are more exposed to gambling environments and more prone to problematic gambling behaviors (Appiah \u0026amp; Awuah, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lee \u0026amp; Lee, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Such patterns may be explained by social and cultural norms that frame gambling as a male activity, while regarding female participation as inappropriate (Abbott et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Risk attitudes also contribute, as women are generally more cautious and risk-averse (D\u0026iacute;az et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Harris \u0026amp; Jenkins, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Media and marketing campaigns, which disproportionately target men, further reinforce this perception (Guillou-Landreat et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kroon, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEducation was positively associated with gambling participation, suggesting that more educated youths are more likely to gamble. This contrasts with studies showing that lower education is linked to higher gambling engagement and expenditures (Salonen, 2018), suggesting that education can both inform and encourage gambling. This may reflect greater exposure to digital and social environments where gambling is visible and accessible (Lind et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although education improves understanding of probabilities and risks, it also enhances exposure and social acceptance of gambling. Educated individuals may also perceive gambling as an opportunity for quick financial gains or a socially valued leisure activity (Tade et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBy contrast, age and occupation were not significant predictors, indicating a relatively homogeneous propensity to gamble among young adults regardless of employment status or income. However, residential areas strongly influenced gambling behavior. Urban youth were significantly more likely to gamble than their rural counterparts, reflecting the greater availability and accessibility of both physical and online gambling opportunities in urban settings (Hamilton-Wright et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBehavioral correlates included tobacco use and sports participation. Tobacco consumption may capture a general predisposition toward risk-taking (Butler et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The link between sports and gambling can be explained by several mechanisms. Sporting environments, such as events, clubs, or leagues, facilitate exposure to sports betting, while active participants tend to monitor competitions and odds closely, encouraging betting involvement (Guillou-Landreat et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, the competitive and risk-taking culture of sports may translate into gambling behavior. Finally, sports provide an effective channel for gambling marketing, which portrays betting as a fun, profitable, and desirable lifestyle choice (Derevensky et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Popular sports such as football and basketball, widely practiced by youth, also correspond to the most common betting formats; in our sample, 50% of sports participants reported playing one of these two, reinforcing familiarity and participation.\u003c/p\u003e\u003cp\u003eBeyond determinants, the study assessed the causal impact of gambling on psychological resilience. Findings reveal a robust negative effect across all matching methods, with overall resilience scores significantly reduced among gamblers. The strongest impacts were observed for perseverance and engagement, optimism and positive self-image, emotional regulation, and spirituality or religious beliefs, indicating measurable weakening of psychological resources essential for coping with adversity.\u003c/p\u003e\u003cp\u003eThese results align with international evidence linking gambling to adverse mental health and reduced resilience. For instance, Hamilton-Wright et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) showed that for vulnerable youth, gambling simultaneously functions as escape and stressor, undermining long-term goals. Similarly, problematic gambling among adolescents has been associated with lower self-efficacy and impaired emotional regulation (Kang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). High rates of problematic gambling have also been linked to depression, anxiety, and stress, which further weaken perseverance and optimism (Lee \u0026amp; Lee, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Tran et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yimam et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e;). In our sample, reported losses (21% of gamblers) and relatively low winnings (69% earning less than 30,000 XOF) exacerbate stress, thereby reducing the capacity to manage frustration and recover from setbacks.\u003c/p\u003e\u003cp\u003eThe cultural and moral dimensions are equally significant. Although gambling is embedded in youth practices, it often leads to financial and emotional losses that foster feelings of failure (Appiah \u0026amp; Awuah, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tade et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The negative effect on spirituality and religious beliefs may reflect the perceived incompatibility of gambling with dominant moral and religious norms. In a highly religious context\u0026mdash;97% of respondents identified as Muslim\u0026mdash;gambling may induce guilt or cognitive dissonance, eroding spiritual anchoring and the perception of a meaningful life. This finding confirms previous studies that reporting negative associations between religiosity and gambling (Billah et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mutti-Packer et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, the cross-sectional nature of the data prevents establishing definitive causal relationships or examining the evolution of gambling effects over time. Second, all measures of gambling behavior, weekly gains or losses, and psychological resilience are self-reported, which may introduce recall bias, social desirability bias, or underreporting, particularly for sensitive behaviors. Third, while several socio-demographic and behavioral factors were included, other relevant determinants such as family stress, peer influence, access to online gambling platforms, or pre-existing mental health conditions, were not considered. Fourth, the psychological resilience indicators rely on self-reported scales, which, although validated, may not fully capture all facets of resilience, particularly behavioral responses and situational adaptability in real-life contexts. Finally, the low participation of women and the lack of distinction between gambling types limit the generalizability of the findings. Future research should combine longitudinal data, objective measures, and disaggregation by gambling type to better understand the mechanisms linking gambling to psychological resilience.\u003c/p\u003e"},{"header":"5. Conclusions and Policy Implications","content":"\u003cp\u003eThis is among the first comprehensive analyses of gambling behavior and its impact on the psychological resilience of young people in Senegal. It highlights several significant factors influencing participation in gambling. Gambling is highly prevalent among young men and is positively associated with educational attainment, urban residence, and, to a lesser extent, sports participation and tobacco use. The findings also indicate that most gamblers experience limited financial gains, with 21% reporting net losses and 69% earning less than 30,000 XOF per week.\u003c/p\u003e\u003cp\u003eCausal analysis reveals that gambling significantly reduces psychological resilience, particularly affecting perseverance and engagement, self-esteem and optimism, emotional regulation, as well as spirituality and faith. These results suggest that gambling is not merely a leisure activity but a serious issue that undermines individuals\u0026rsquo; adaptive capacities in the face of life challenges. By compromising key dimensions of psychological well-being, it increases both social and mental vulnerability, especially among young people and already at-risk populations.\u003c/p\u003e\u003cp\u003eSeveral policy implications emerge from this study. First, there is a pressing need to strengthen education and awareness regarding the risks of gambling. The strong link between educational level and gambling behavior underscores the importance of prevention strategies targeting not only less-educated youth but also those with higher educational resources, promoting critical understanding of gambling-related risks. This could include school-based programs and targeted public awareness campaigns.\u003c/p\u003e\u003cp\u003eSecond, gambling should be integrated into public health policies. Given its negative effects on key well-being dimensions (optimism, emotional regulation, spirituality), gambling should be treated as a mental and social health issue. Institutional monitoring, stronger regulatory frameworks, and psychological support services are necessary, particularly for most young people willing to stop gambling (67%, including 20% immediately and 47% soon). Specific programs should also be developed for addicted youths not intending to quit in the near term (18%, including 8% not at all).\u003c/p\u003e\u003cp\u003eThird, promoting healthy, accessible leisure alternatives can enhance youth resilience without resorting to gambling. This could involve developing cultural and digital spaces for young people, especially in rural areas and among better-educated youth.\u003c/p\u003e\u003cp\u003eFinally, limiting aggressive gambling advertising across traditional and social media is essential. Gambling companies should be encouraged to adopt responsible marketing practices, minimize exposure of vulnerable populations, and promote safe and responsible gambling behaviors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and informed consent The survey protocol was approved by the Technical Committee on Statistical Programs (clearance No. 0001A2023). Informed consent was obtained from all participants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbadie A, Imbens GW (2006) Large sample properties of matching estimators for average treatment effects. econometrica 74(1):235\u0026ndash;267\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAbbott M, Binde P, Clark L, Hodgins D, Johnson M, Manitowabi D, Williams R (2018) Conceptual framework of harmful gambling: An international collaboration. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.11575/PRISM/43603\u003c/span\u003e\u003cspan address=\"10.11575/PRISM/43603\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Gambling Research Exchange Ontario\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAdebisi T, Alabi O, Arisukwu O, Asamu F (2021) Gambling in transition: assessing youth narratives of gambling in Nigeria. 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Afr J Social Behav Sci, \u003cem\u003e14\u003c/em\u003e(3)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Consortium pour la recherche économique et sociale","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gambling, Young people, Sports betting, Psychological resilience, Senegal","lastPublishedDoi":"10.21203/rs.3.rs-7437944/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7437944/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOver the past decade, gambling has expanded rapidly in Senegal, affecting both adults and youth. This study aims to identify the factors influencing gambling behavior and its impact on psychological resilience among young people in Senegal. Based on a representative survey of 1,560 youths aged 18\u0026ndash;35, we performed probit regressions and propensity score matching (PSM) to assess both the determinants of gambling and its causal effect on psychological resilience. The results indicate that males are significantly more likely to gamble than females, post-secondary educated youth exhibit higher gambling propensity than those without formal education, and urban residents are more likely to gamble than rural peers. Tobacco use and sports participation show weaker positive associations, while age and labor market participation are not significant predictors. PSM analysis further reveals that gambling substantially reduces psychological resilience, particularly in perseverance and engagement, positive self-image, emotional regulation, and spirituality, with an overall decline of approximately 12%. These findings underscore the need for targeted interventions, including responsible gambling education, mental health support, and programs offering alternative recreational opportunities, to enhance resilience and mitigate the risks associated with gambling among Senegalese youth.\u003c/p\u003e","manuscriptTitle":"Youth and Gambling: Determinants and Impact on Psychological Resilience in Senegal","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 10:28:47","doi":"10.21203/rs.3.rs-7437944/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3ff326cb-86c3-449e-95eb-0c9b511c6db9","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53748792,"name":"Behavioral Economics"}],"tags":[],"updatedAt":"2025-09-01T10:28:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 10:28:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7437944","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7437944","identity":"rs-7437944","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}