The Hidden Toll: Investigating the influence of Corruption on Persistent Suicide in the Americas

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The recent trends reveal a rise in suicide rates across the Americas, while all other regions show declines over twenty years of time. This study investigates corruption’s effect on suicide in 26 American countries, considering moderating factors of unemployment, inflation, and economic growth. Methods This study analysed latest two decades of available data, using stepwise panel regression method to investigate the effects of corruption and economic variables on suicide across income levels. Data were sourced from Transparency International, World Bank and the World Health Organization. Initially, unit root tests and CUSUM plots were used to ensure the stability and stationarity of the dataset, and model specification were validated through F test, LM test and Hausman test to select the ideal econometric model - POLS, REM, or FEM for the study. Results A strong suicide rate persistence revealed, particularly in high-income countries, where the lagged suicide variable showed a coefficient of 0.8063 (p < 0.001). Corruption significantly impacted suicide rates in upper-middle-income countries (coefficient = -0.0268, p < 0.05), with higher corruption perceptions scores correlating with lower suicide rates. Additionally, unemployment acted as a significant moderator, intensifying the corruption’s adverse impact on suicide with a coefficient of 0.0022 (p < 0.001) in upper-middle income nations. Economic growth demonstrated a minor protective effect, particularly in high-income regions, with an interaction coefficient of -0.0005 (p < 0.1), suggesting slight suicide reduction linked to economic stability. Conclusion This study found that corruption, unemployment, and economic growth significantly influence suicide rates across the Americas. Corruption exacerbates suicide risks in upper-middle-income countries, while unemployment amplifies this effect. Economic growth offers a slight protective effect, particularly in high-income regions, suggesting that economic stability may help mitigate suicide rates. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/13-1427/v3", "name": "The Hidden Toll: Investigating the influence of Corruption on Persistent..." } } ] } Home Browse The Hidden Toll: Investigating the influence of Corruption on Persistent... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Kularathne S and Jayathilaka R. The Hidden Toll: Investigating the influence of Corruption on Persistent Suicide in the Americas [version 3; peer review: 3 approved with reservations] . F1000Research 2025, 13 :1427 ( https://doi.org/10.12688/f1000research.158285.3 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Revised The Hidden Toll: Investigating the influence of Corruption on Persistent Suicide in the Americas [version 3; peer review: 3 approved with reservations] Sherin Kularathne https://orcid.org/0009-0000-7124-6276 1 , Ruwan Jayathilaka https://orcid.org/0000-0002-7679-4164 2 Sherin Kularathne https://orcid.org/0009-0000-7124-6276 1 , Ruwan Jayathilaka https://orcid.org/0000-0002-7679-4164 2 PUBLISHED 08 Dec 2025 Author details Author details 1 SLIIT Faculty of Graduate Studies, Sri Lanka Institute of Information Technology, Malabe, 10115, Sri Lanka 2 SLIIT Business School, Sri Lanka Institute of Information Technology, Malabe, 10115, Sri Lanka Sherin Kularathne Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Ruwan Jayathilaka Roles: Conceptualization, Methodology, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Sociology of Health gateway. Abstract Background Corruption, a multifaceted governance issue, impacts public well-being globally. The recent trends reveal a rise in suicide rates across the Americas, while all other regions show declines over twenty years of time. This study investigates corruption’s effect on suicide in 26 American countries, considering moderating factors of unemployment, inflation, and economic growth. Methods This study analysed latest two decades of available data, using stepwise panel regression method to investigate the effects of corruption and economic variables on suicide across income levels. Data were sourced from Transparency International, World Bank and the World Health Organization. Initially, unit root tests and CUSUM plots were used to ensure the stability and stationarity of the dataset, and model specification were validated through F test, LM test and Hausman test to select the ideal econometric model - POLS, REM, or FEM for the study. Results A strong suicide rate persistence revealed, particularly in high-income countries, where the lagged suicide variable showed a coefficient of 0.8063 (p < 0.001). Corruption significantly impacted suicide rates in upper-middle-income countries (coefficient = -0.0268, p < 0.05), with higher corruption perceptions scores correlating with lower suicide rates. Additionally, unemployment acted as a significant moderator, intensifying the corruption’s adverse impact on suicide with a coefficient of 0.0022 (p < 0.001) in upper-middle income nations. Economic growth demonstrated a minor protective effect, particularly in high-income regions, with an interaction coefficient of -0.0005 (p < 0.1), suggesting slight suicide reduction linked to economic stability. Conclusion This study found that corruption, unemployment, and economic growth significantly influence suicide rates across the Americas. Corruption exacerbates suicide risks in upper-middle-income countries, while unemployment amplifies this effect. Economic growth offers a slight protective effect, particularly in high-income regions, suggesting that economic stability may help mitigate suicide rates. READ ALL READ LESS Keywords Corruption, Suicide, Inflation, Unemployment, Economic growth, Americas, Corruption, Inflation, Economic growth, Latin America, North America, South America Corresponding Author(s) Ruwan Jayathilaka ( [email protected] ) Close Corresponding author: Ruwan Jayathilaka Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Kularathne S and Jayathilaka R. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Kularathne S and Jayathilaka R. The Hidden Toll: Investigating the influence of Corruption on Persistent Suicide in the Americas [version 3; peer review: 3 approved with reservations] . F1000Research 2025, 13 :1427 ( https://doi.org/10.12688/f1000research.158285.3 ) First published: 26 Nov 2024, 13 :1427 ( https://doi.org/10.12688/f1000research.158285.1 ) Latest published: 08 Dec 2025, 13 :1427 ( https://doi.org/10.12688/f1000research.158285.3 ) Revised Amendments from Version 2 We thank the reviewer for the observation regarding data recency. The study uses the most recent data available at the time of submission (up to ~2019), and even in 2025, WHO suicide records extend only to 2021. Although we acknowledge the time gap, WHO and World Bank remain the most reliable global data sources, and the study’s purpose is to analyse long-term links between corruption and suicide over two decades, rather than recent fluctuations. We agree that the original Figure 1 could be misinterpreted. It has been replaced with a Joinpoint Regression graph (Ilic, 2022), which clearly shows that the Americas is the only region with an increasing suicide trend. Descriptions were updated accordingly. To address multicollinearity, stepwise panel regression was used to exclude unstable predictors, and robust standard errors were applied. Interaction terms were included carefully to capture moderating effects. We recognise that suicide is shaped by social and psychological factors. Country fixed effects capture time-invariant cultural differences, and the Limitations section now recommends including mental-health and societal indicators in future research. Endogeneity risks are also acknowledged, with suggestions for future improvements such as instrumental variables and healthcare controls. Fixed-effects modelling was retained based on standard model selection tests, and findings are limited to observed regional patterns, particularly in the Americas. We thank the reviewer for the observation regarding data recency. The study uses the most recent data available at the time of submission (up to ~2019), and even in 2025, WHO suicide records extend only to 2021. Although we acknowledge the time gap, WHO and World Bank remain the most reliable global data sources, and the study’s purpose is to analyse long-term links between corruption and suicide over two decades, rather than recent fluctuations. We agree that the original Figure 1 could be misinterpreted. It has been replaced with a Joinpoint Regression graph (Ilic, 2022), which clearly shows that the Americas is the only region with an increasing suicide trend. Descriptions were updated accordingly. To address multicollinearity, stepwise panel regression was used to exclude unstable predictors, and robust standard errors were applied. Interaction terms were included carefully to capture moderating effects. We recognise that suicide is shaped by social and psychological factors. Country fixed effects capture time-invariant cultural differences, and the Limitations section now recommends including mental-health and societal indicators in future research. Endogeneity risks are also acknowledged, with suggestions for future improvements such as instrumental variables and healthcare controls. Fixed-effects modelling was retained based on standard model selection tests, and findings are limited to observed regional patterns, particularly in the Americas. See the authors' detailed response to the review by Eiji Yamamura See the authors' detailed response to the review by Febby Mutiara Nelson READ REVIEWER RESPONSES Introduction Corruption is more than a mere failure of governance; it erodes the individual’s mental and emotional well-being, resulting in negative consequences, such as growing suicide rates. Previous research has shown that corruption and inadequate institutions such as weak legal systems, insufficient healthcare, and ineffective social services create despair severe enough to lead to suicide ( Glaeser & Saks, 2006 ; Johnston, 1983 ; Stockman, 2013 ). Weak government, characterised by corruption and a lack of transparency, fosters a climate conducive to mental health crises, greatly influencing suicide rates ( Desjarlais, 1995 ; Saad, 2022 ). These findings reveal a darker, often neglected element of corruption: it not only suppresses economic development but also compromises lives by creating a climate of despondency within the nation. Any government’s motive is to foster better living conditions and promote sustainable development, yet this ideal is often unmet ( Yamamura, Andrés, & Katsaiti, 2012 ). Suicide, a complicated worldwide public health issue, differs by gender, age, area, and geopolitical context, and is influenced by a variety of risk factors ( Turecki & Brent, 2016 ). While some studies have revealed that corruption restricts economic growth ( Mauro, 1995 ; Seligson, 2002 ), others have investigated its relationship with well-being and governance ( Fischer & Andres, 2008 ; Helliwell & Huang, 2008 ; Ott, 2011 ). Furthermore, evidence also points to a high link between suicide and subjective well-being ( H. Koivumaa-Honkanen et al., 2004 ). Similarly, studies conducted in the United States demonstrate common determinants of suicide rates and well-being ( Daly & Wilson, 2009 ). However, despite efforts to improve well-being and governance, the persistent link between socio-political factors, economic inequality, and suicide highlights the need for more targeted and effective government interventions. While the negative impacts of corruption on mental health are obvious worldwide, regional trends in suicide rates suggest specific patterns that require further investigation, particularly in the Americas, where suicide mortality has increased between 2000 and 2019, despite global declines shown in other regions. A joinpoint regression analysis revealed that suicide mortality decreased significantly across all World Health Organization (WHO) regions except the Americas, where rates increased in both males and females during 2000-2019 time period ( Ilic & Ilic, 2022 ). The following Figure 1 depicts a visual representation of this finding. Figure 1. Suicide Mortality Trends (WHO regions), 2000-2019; a joinpoint analysis, A: In males; B: In females. APC: Annual Percentage Change. Source: Based on past literature, which employed WHO data ( Ilic & Ilic, 2022 ). Suicide, a globally complex issue, is caused by the combination of several elements, including neurobiology, family history, societal settings, and economic pressures ( Breault, 1986 ; Bunney, Kleinman, Pellmar, & Goldsmith, 2002 ; Quinlan-Davidson, Sanhueza, Espinosa, Escamilla-Cejudo, & Maddaleno, 2014 ; Turecki & Brent, 2016 ). Current suicide preventative measures seem to be insufficient (especially in the Americas) because they frequently ignore deeper sociopolitical issues such as corruption and instability, which aggravate citizens’ despair. While worldwide efforts emphasise diagnosis and response, yet many related factors are there for investigation and governments, and policymakers must work persistently to improve suicide prevention ( Levi-Belz et al., 2019 ). Thus, it is apparent that suicide prevention initiatives must go beyond immediate treatments to address the underlying socio-political and economic causes. The primary goal of this study is to investigate how corruption, paired with economic factors such as unemployment, inflation, and economic growth, affects suicide rates across different income levels in the Americas. By evaluating these variables together, the study expects to uncover the complex, frequently overlooked relationships that influence mental health outcomes. This study contributes to a more holistic picture of how government failures and economic stressors interact to compound public health challenges, including suicide. This study makes numerous essential contributions to the body of current literature and policy-making assistance. First, although earlier studies have investigated the relationship between mental health, macroeconomic variables, and corruption in local, national, global and sub-continent areas, there is a lack of investigation executed for the whole American region. The current study concentrates on the American region. In this critical geographic area, suicide rates have shown a unique upward trend over the past 20 years although worldwide decreases of other regions. There is a lack of recent research in the overall arena, which concentrates exclusively on the Americas. Therefore, this region is underexplored. This study seeks to fill the gap in the literature caused by the inadequate or outdated research that is currently available. Additionally, the study also utilises an income category-wise analysis, which provides a more detailed understanding of how these correlations fluctuate among countries among various economic circumstances. Second, this study uses the most recent data from 2000 to 2019 that is available at present, making it one of the few that covers such a lengthy period with the most recent data, allowing for a more comprehensive and up-to-date understanding of long-term patterns and fluctuations explaining corruption’s impact on suicide. Hence, the study offers a new and contemporary viewpoint on the relationship between governance (corruption) and suicide rates in the American region. income category-wise analysis, with a particular focus on income category. Third, this study stands out in terms of its methodological approach. Few studies in current research, utilise Panel regression techniques that include interaction terms, particularly about moderating economic factors such as inflation, unemployment, and economic growth This study provides novel insights by taking a comprehensive approach to understand how corruption interacts with inflation, unemployment, and economic growth to affect suicide rates. The use of time-series visual analyses of factors incorporated in the study enriches the study by presenting trends and patterns in a clear and understandable manner, allowing for a more accurate data interpretation. These efforts in the s survey enhance the contextual understanding of the results, offering additional layers of insight. Furthermore, although previous studies have primarily evaluated these variables in isolation or within distinct contexts, this study bridges a gap by studying significant macroeconomic indicators and corruption together within the context of the Americas. The study’s findings have important significance for policymakers since they identify the primary causes contributing to growing suicide rates in the region, providing strategic insights for targeted responses. Understanding the macroeconomic and governance-related factors allows governments to develop more effective policies to combat corruption, while mitigating its devastating impact on suicide, which is a critical public health concern. Literature Review Corruption is widespread worldwide, and eliminating it is critical for equitable economic growth ( Olken & Pande, 2012 ). Although its negative impact on nations is widely understood, the precise costs to individuals, notably mental health, are understudied. Suicide is a serious global public health hazard, accounting for about 700,000 deaths annually (2024). Addressing both corruption and suicide is crucial for society’s well-being, since they both risk individual mental health and long-term development. Theoretical Foundation The association between corruption and suicide can be theoretically explained through social disorganization theory strain theory, and Durkheim’s theory of anomie. According to Ruth Cavan’s social disorganization theory ( 1928 ), societal structures that fail to meet communal needs lead to negative outcomes, such as suicide, by undermining social cohesion and support systems. Corruption erodes trust in institutions, creating environments of disorder and instability, marked by social alienation and isolation, which are linked to higher suicide rates in corrupt societies ( Abrutyn, 2023 ). Similarly, Jie Zhang’s strain theory ( 2019 ) explains that psychological strain arises from conflicting values, aspiration-reality gaps, relative deprivation, and ineffective coping strategies. In corrupt settings, individuals often face limited opportunities, heightened inequality, and a sense of injustice, leading to dissatisfaction, stress, and vulnerability to suicidal ideation. Without adequate coping mechanisms or social support, such mental strain may drive individuals toward suicide. According to Durkheim’s theory of anomie, suicide is associated with social instability, which is a state of normlessness brought on by swift social change or eroded norms. People feel alone and aimless as a result of this connection, which weakens social cohesiveness and raises the chance of suicide since they find it difficult to adjust to the absence of social control ( Marks, 1974 ). Thus, the theoretical grounds emphasise that corruption, by generating social disorganisation, psychological stress, and normlessness, produces an environment that magnifies the risk factors for suicide, underscoring the important connection between societal dysfunction and individual vulnerability. Corruption and suicide A victim of corruption can have several negative consequences for one’s mental health, including severe depression ( Bai, Jayachandran, Malesky, & Olken, 2019 ). Previous researchers have explained that in low-income nations, harassment bribes- defined as bribes frequently demanded by officials for services or rights to which people are already entitled, are a sort of regressive income tax that is typically a significant portion of one’s income, particularly for poorer households, and are unpredictable and frequent, causing chronic stress in low-income families ( Adhvaryu, Fenske, & Nyshadham, 2018 ). As a result, such corruption erodes trust in local governance, diminishes social capital, and damages mental health. A leading, study on OECD countries indicated lower suicide rates in nations with less corruption ( Yamamura et al., 2012 ). On the other hand, another study revealed that poor governance causes more stress and despair among citizens, raising the likelihood of suicide ( Sharma, Singhal, & Tarp, 2021 ). This aligns with results that effective governments and strong social trust contribute to life satisfaction ( Bjørnskov, Dreher, & Fischer, 2010 ). Thus, corruption and poor governance not only weaken economic and social institutions but also have severe consequences for mental health, raising the risk of psychological distress and suicide across a wide range of socioeconomic circumstances. Past literature emphasises the undeniable link between corruption and worsening mental health, including higher suicide rates, particularly among poor communities. Corruption weakens economies, destroys institutional trust, and denies access to essential services, instilling hopelessness. Despite its clear public health implications, current research on corruption’s role in driving suicide, particularly in the Americas, is insufficient. The lack of contemporary, focused studies on this pressing issue reveals a critical gap in comprehending how deeply corruption impacts mental health, indicating an urgent need for further exploration. Inflation and Suicide Understanding mental health and crime rates is made more difficult by macroeconomic issues such as inflation. Although there is conflicting evidence, some argue that decreasing inflation lowers crime, including suicides. For instance, growing food inflation in Turkey disproportionately increased male suicide rates, while rising inflation in the United States is associated with increasing criminality ( Şentürk & Erbay, 2022 ). Though their success depends on political and financial support, welfare programs and minimum wage laws have been suggested as suicide prevention techniques ( Stack, 2021 ; Tuttle, 2018 ). Increasing salaries has the potential to decrease suicide, but achieving this will be politically challenging ( Kaufman, Salas-Hernández, Komro, & Livingston, 2020 ). Furthermore, economic crises, such as the 2008 Great Recession, which experienced high inflation, intensify these psychological consequences, with repossessions and family breakdowns contributing to mental health toll ( Agrrawal, Waggle, & Sandweiss, 2017 ; Coope et al., 2014 ; Mucci, Giorgi, Roncaioli, Fiz Perez, & Arcangeli, 2016 ). The broader socioeconomic implications, such as family breakdowns and lower academic accomplishment for children, compound the mental health concerns ( National Council of La Raza, 2009 ). However, the linear assumption that financial crises cause an increase in suicide rates may oversimplify the issue. While financial stress is clearly a factor, it is essential to evaluate how other factors may influence these outcomes. Simply focussing on financial indicators without addressing underlying socioeconomic inequities risks providing an inadequate picture of the suicide-inflation nexus. The literature also contains inconsistent knowledge, with some studies disputing the commonly held idea that inflation constantly raises suicide rates. For example, South African research discovered a negative association, with suicides increasing as inflation dropped, though this relationship became statistically insignificant when province adjustments were applied, particularly among women ( Noh, 2009 ). Adding to the stated findings, similar studies reveal countercyclical suicide trends ( Granados, 2008 ). This contradiction raises serious concerns regarding the universality of the inflation-suicide association. Such findings highlight the need to examine context-specific factors, such as cultural attitudes and thinking patterns of the public towards economic hardship and the availability of government safety nets, rather than assuming equal outcomes across countries. Although there is evidence associating inflation with suicide, this relationship is nuanced and complex to generalise. This demands a more concentrated approach that takes socioeconomic, gender, and regional variables into consideration. Understanding such a relationship and creating remedies that address the psychological as well as the economic aspects of inflation require thorough, context-sensitive studies. Without careful examination, policy recommendations have the potential of being detrimental or ineffectual, depending on the local or national contexts. Unemployment and suicide Unemployment, especially during economic downturns, is a well-known cause of suicide ( Jung, Lee, Hong, Park, & Jeong, 2024 ; Schapiro & Ahlburg, 1982 ), And lower unemployment rates often improve mental health and lessen the risk of suicide. This fact underlines how important it is to conduct cross-national replication to assess the validity of the relationships between unemployment and societal outcomes, including suicides ( Goulas & Zervoyianni, 2023 ; McKee-Ryan, Song, Wanberg, & Kinicki, 2005 ; Milner, McClure, & De Leo, 2012 ; Park, Lee, & Kim, 2003 ; Paul & Moser, 2009 ; Yang & Lester, 1995 ). The Great Recession demonstrated this fact since growing unemployment was associated with greater suicide rates, especially in areas where supportive measures such as social programs were missing ( Norström & Grönqvist, 2015 ; Shand, Duffy, & Torok, 2021 ). Also, a systematic review found that long-term unemployment increases the risk of suicide while the risk decreases over time, probably due to habituation ( Milner, Page, & LaMontagne, 2013 ). However, contradicting the majority of the past literature, some studies have revealed a negative association between unemployment and suicide in low-income countries ( Barth et al., 2011 ). Unemployment in these regions may not carry the same social stigma or economic challenges, which could explain the lower suicide rates. In contrast, in high-income countries, unemployment tends to increase suicide rates due to increased psychological and societal pressures. This emphasises the significance of income levels in understanding the diverse effects of unemployment on suicide across different socioeconomic circumstances ( Noh, 2009 ). This divergence in findings underscores the importance of considering regional economic structures and cultural contexts when studying the unemployment-suicide relationship. Economic growth and suicide Suicide is a complicated issue driven by economic, social, and psychological factors, although the link between economic growth and suicide rates is still debated. Early studies revealed that economic downturns increase suicide risk by lowering prospects, whereas economic prosperity lowers these risks ( Hamermesh & Soss, 1974 ). Also, it has found a negative association between GDP growth and suicide rates, supporting this viewpoint ( Mattei & Pistoresi, 2019 ; Zhang et al., 2010 ). However, the drop in suicide was often only temporary during periods of economic prosperity, implying that economic growth alone may not be a long-term answer to lowering suicide rates. This emphasises the importance of rigorously examining the long-term psychological benefits of economic growth, which may be underestimated in standard economic theory. More recent research indicated that GDP alone has poor predictive value for changes in suicide rates, calling into doubt the premise that economic prosperity inevitably improves mental health ( Cao, 2024 ). In fact, it has also discovered a positive relationship between economic growth and suicide rates, particularly during periods of fast expansion, attributing these trends to increased workplace stress and cultural changes, notably among older populations ( Fountoulakis et al., 2014 ; Rajagukguk, Samosir, Budiono, & Adirinekso, 2020 ). These conflicting findings imply that economic expansion might occasionally intensify rather than reduce pressures, challenging the myth that growth is always favourable to mental health. The relationship between economic progress and suicide is more nuanced than previous research believed. While expansion may enhance material situations, it can also increase stress, which increases the possibility of suicide. The contradicting findings underscore the need for further research, emphasising the need to include social and mental health factors in economic policies. Focussing primarily on GDP growth risks ignoring significant public health implications, possibly increasing suicide rates. This demonstrates the crucial need for a more comprehensive approach to economic planning. Suicide-related literature search An extensive literature search was done in the Google Scholar database to acquire a basic understanding of the evolution and expansion of the related disciplines. Figure 2 illustrates the search results over decades. Figure 2. Bar Chart of Related Literature Over the Years. Source: Authors’ illustrations based on literature search. This bar chart illustrates the distribution of key research publications over time, focussing on the studies included in this analysis. The figure reveals an interesting trend: while research on topics such as unemployment, economic growth, and inflation influencing suicide has risen over the years, studies on the link between corruption and suicide have only recently gained attention. Despite the growing global relevance of both corruption and suicide, these arenas remain unexplored in comparison to other common variables in this study. This study attempted to emphasise more recent key studies, which showed a rising but still limited focus on corruption as a suicide factor, highlighting the need for further research in the domain of corruption’s influence on suicide. Following Figure 3 illustrates the geographical distribution of major literature cited in the study, emphasising the global aspect of suicide risk factor research spanning on to various regions and countries. Figure 3. Bubble Pie Charts of Key Literature. Source: Authors’ illustrations based on literature search. This map depicts the geographic distribution of relevant literature included in the study, demonstrating that while regions such as Latin America, the Caribbean, and portions of Asia have gotten significant attention, there is a noticeable lack of focus on the broader American region, including North America. Especially, despite the crucial challenges of corruption, and suicide in the American region, research in these areas is still under-explored. The unlabelled icons in this map are country-wise studies. This literature map signals the need for more concentrated research in the American region, where socioeconomic issues and suicide rates are continuously growing. These studies highlight the key literature used in this study, emphasising geographical differences and the need to resolve these gaps in future research. The conceptual base for the current study was built based on the literature study and existing knowledge. The independent variables mentioned have revealed a significant impact on suicide rates. Table 1 divides the supporting key literature into four independent variables relevant to the study of suicide: corruption, inflation, unemployment, and economic growth. Table 1. Variables and key supporting studies. Variables Past studies (+) (-) CORR → SUI Sharma et al. (2021) Bjørnskov et al. (2010) Yamamura et al. (2012) INFL → SUI Şentürk and Erbay (2022) Fountoulakis et al. (2014) Coope et al. (2014) Botha (2012) ; Granados (2008) UNEMP → SUI Jung et al. (2024) Shand et al. (2021) Milner et al. (2012) Barth et al. (2011) Noh (2009) EG → SUI Rajagukguk et al. (2020) Fountoulakis et al. (2014) Mattei and Pistoresi (2019) Zhang et al. (2010) Park et al. (2003) With this conceptual foundation, this study is executed to investigate the impact of corruption on suicide corruption, incorporating moderating macroeconomic indicators and their impact on suicide rates across the American region. This framework aims to provide a comprehensive understanding of how various economic and governance qualities influence the rising suicide rates in the Americas and to reveal the underlying dynamics that contribute to mental health crises in the region. Methods The variables used in this study, the data sources used to collect data, and the statistical methods used to compute the results are illustrated in Figure 4 . It begins with data collection and preparation, followed by stability/stationary testing and the execution of stepwise panel regression models to produce the final results. Figure 4. Overview of the Workflow of the Study. Source: Authors’ illustrations. Data The study used secondary data sources that are presented in Table 2 . The study used a panel dataset of 26 nations, focusing on the American region, across 20 years, starting from 2000. The latest reported data is the latest data available currently. Table 2. Data variable definitions and variables. Variable Code Variable Measurement Unit Source CORR Corruption Control Score Corruption Perception Index (0-100 scale, 100 is very clean and 0 is highly corrupt) Transparency International https://www.transparency.org/en/cpi/2023 SUI Suicide Crude suicide rates (per 100,000 population) World Health Organization https://www.who.int/data/gho/data/indicators/indicator-details/GHO/crude-suicide-rates-(per-100-000-population ) INFL Inflation Inflation, consumer prices (annual %) The World Bank https://data.worldbank.org/indicator/FP.CPI.TOTL.ZG UNEMP Unemployment Unemployment, total (% of total labour force) (modelled ILO estimate) The World Bank https://data.worldbank.org/indicator/SL.UEM.TOTL.ZS EG Economic Growth GDP growth (annual %) The World Bank https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG Even though there are several measures of corruption introduced, this study employs Corruption Perceptions Index (CPI) to represent the corruption control of nations. It is a commonly acknowledged measure of corruption that combines data from 13 independent surveys and expert judgements to provide a comprehensive picture of perceived corruption in the public sector across countries. A country’s inclusion requires data from at least three of these sources, ensuring substantial and multifaceted insights into corruption levels. The approach enables the CPI to maintain consistency, stability, and comparability across countries, making it a useful tool for academic research and policymaking ( Transparency International, 2024 ). The study utilised stepwise panel regression models as analytical methodology. The analyses were executed for the whole Americas and for the three income level categories that can be primarily noticed within the region - high, upper-middle, and lower-middle income nations. First, the stationarity and the stability of the dataset were tested using the unit root tests: Levi-Lin-Chu (LLC) and Fisher Augmented Dicky Fuller (Fisher ADF), and the CUSUM tests. Ensuring stationarity is critical for maintaining stable relationships between variables throughout time ( Kwiatkowski, Phillips, Schmidt, & Shin, 1992 ). The CUSUM plots are recommended for minimal projected movement to measure process stability ( Saniga, McWilliams, Davis, & Lucas, 2006 ). Then, three specification tests were performed to determine the most suitable model: Pooled Ordinary Least Squares (POLS), Random Effect Model (REM), or Fixed Effect Model (FEM). The F test was used to assess the suitable model from POLS and FEM ( X. Yang, Shen, Xu, & Shoptaw, 2007 ). The LM (Lagrange Multiplier) test assessed the best model from POLS and REM ( Breusch & Pagan, 1980 ). Finally, the Hausman test was used to determine the applicability of the RE and FE models ( Chmelarova, 2006 ). Furthermore, the analysis was performed with conventional robust error, which eliminated the issue of heteroscedasticity. Next, to analyse how CORR, with the influence of three selected moderating variables affects SUI with a focus on the American region, below Equation 1 was developed. (1) SU I it = β 0 + β 1 SUI it − 1 + β 2 COR R it + β 3 ( CORR × ∆ INFL ) it + β 4 ( CORR × UNEMP ) it + β 5 ( CORR × EG ) it + ε it The SUI it is the suicide rate, with i as income category and t as time. SUI it −1 represents the lagged suicide rate. This variable was introduced to eliminate the stationary issue of the dataset. CORR it measures corruption control scores, whereas ( CORR × ΔINFL ) it demonstrates the moderating effect of inflation (first-differenced). This ΔINFL was introduced to fix the stability issue of the INFL variable. The terms ( CORR × UNEMP ) it and ( CORR × EG ) it denote the moderating effects of unemployment and economic growth, respectively. The coefficients β 0 , β 1 , β 2 , β 3 , β 4, and β 5 represent the intercept and slopes of the regression line, which describe the impact of the independent variables on the dependent variable SUI it , and the error term is denoted by ε it . Next, the initial stepwise panel regression models were developed to analyse the stepwise effects of SUI it −1 , CORR, CORR × ΔINFL, CORR × UNEMP , and CORR × EG on SUI. Ultimately, the final model was refined by stepwise panel regression, with variables added or eliminated based on statistical significance or discrepancies with previous studies. The model was rerun to obtain the final findings. Panel regression provides several advantages, including the capability to account for unobserved heterogeneity, improve statistical efficiency, model dynamic relationships, and accommodate both fixed and random variables, resulting in more robust and flexible findings ( Hsiao, 2007 ). Furthermore, stepwise regression makes model selection easier by automatically finding relevant predictors, lowering the risk of overfitting, and enhancing model interpretability and generalisability, especially in large datasets. Thus, the technique is suitable for developing socioeconomic models. To derive the results of the study, panel regressions and stationary tests were carried out using STATA whereas RStudio and DATAtab were utilised, to generate the CUSUM plots and violin plots respectively. Results This study contains 520 observations, 160, 280, and 80 across 20 years, associated with high-income, upper-middle-income, and lower-middle-income countries in the American region, respectively. Figure 5 presents the mean values and 95% confidence intervals of key variables across different income groups in the Americas. It illustrates notable variations in corruption control scores across income groups and the Americas region. High-income countries exhibit the lowest corruption levels, with a mean value of 58.69 and a narrow confidence interval (±3.16), reflecting well-established governance structures. In contrast, lower-middle-income countries have the highest corruption levels, with a mean value of 24.66 (±1.14). Upper-middle-income countries and the Americas region fall in between, with means of 34.46 (±0.93) and 40.65 (±1.59), respectively. These differences highlight the relationship between economic development and institutional integrity, with high-income countries achieving better corruption control and lower-income countries struggling with systemic corruption. Figure 5. Mean variable values with 95% Confidence Interval. Source: Authors’ illustrations based on the data from the World Bank (2024) , Transparency International (2024) , and the World Health Organization (2024) . Suicide rates exhibit an inverse pattern compared to corruption, with higher rates observed in wealthier countries. High-income countries report the highest mean suicide rate of 13.08 (±1.50), while lower-middle-income countries report the lowest at 5.83 (±0.61). Upper-middle-income countries and the Americas region exhibit intermediate rates of 7.10 (±0.67) and 8.81 (±0.65), respectively. This trend suggests that economic prosperity does not necessarily translate to better mental health outcomes, with factors like cultural differences, mental health awareness, and reporting practices likely influencing these rates. In lower-income countries, cultural stigmas or underreporting may also play a role in the lower observed rates. In terms of inflation, high-income countries enjoy the lowest inflation rates, with a mean of 3.95% (±0.49%), indicating stable economic conditions. Inflation rises significantly in upper-middle-income and lower-middle-income countries, averaging 7.93% (±1.31%) and 7.17% (±1.06%), respectively. However, given the highly country-specific nature of the inflation situation in Venezuela, its data were excluded from the analysis as an outlier to avoid misinterpretation caused by hyperinflation. The Americas region exhibits a moderate level of inflation rate of 6.54% (±0.73%), positioned between high-income and lower-income groups. This trend underscores the economic volatility often experienced by lower-income countries, where inflationary pressures are more prevalent, and economies are less stable. Unemployment trends are less distinct across income levels but still offer meaningful insights. High-income countries surprisingly report a relatively high unemployment rate of 7.99% (±0.47%), potentially reflecting labour market challenges persist in Americas. Lower-middle-income countries show slightly lower unemployment at 6.78% but with higher variability (±0.98%), signalling economic instability. Upper-middle-income countries and the Americas region exhibit similar rates of 6.70% (±0.38%) and 7.12% (±0.30%), respectively, highlighting regional and global labour market challenges that transcend income levels. Economic growth shows relatively similar mean patterns among different income levels in the Americas. High-income countries experience the lowest growth at 2.94% (±0.53%), typical of mature economies that have reached a plateau in their development. Growth increases in upper-middle-income and lower-middle-income countries, with means of 3.07% (±0.37%) and 3.30% (±0.56%), respectively, reflecting the potential for expansion in emerging markets. The Americas region, at 3.06% (±0.27%), closely mirrors the growth rates of upper-middle-income countries, suggesting a similar stage of economic development. However, the higher variability in lower-middle-income countries highlights the challenges of maintaining consistent growth amidst economic instability. These observations are supported by detailed descriptive statistics which provide more information on the mean, standard deviation, and range for each variable (Available in repository - Appendix 1). Together, the visual and statistical summaries establish the path for a more in-depth examination of the linkages between economic conditions, corruption, and suicides in the subsequent sections of this study. Initial Observations The observations of suicide rates across the American region from 20 years apart depicted in Figure 6 , reveal considerable regional variations. Suicide rates in countries such as Guyana increased dramatically, from 31.35 in 2000 to 40.28 in 2019, while in Brazil, the rate nearly doubled, from 3.98 to 6.89. Conversely, specific countries, such as Argentina and Venezuela, saw their rates fall from 9.11 to 8.37 and 5.44 to 2.05, respectively. The United States had a significant increase, from 11.03 to 16.14. Overall, it shows a complex pattern of gains and falls, while overall, in many countries ‘suicides have risen (An illustration of suicide in the countries of the Americas during the same period (2000-2019) is available in repository - Appendix 2). Over two decades, Guyana had the highest suicide rates, showing significant growth. Canada, while the second highest, maintained consistent levels. Suriname and Uruguay had high rates with minor volatility. Canada, the U.S., and Argentina showed steady trends, while Venezuela, Jamaica, Honduras, and Panama consistently had low suicide rates. Figure 6. Suicide Rates Comparison Maps 2000 Vs 2019. Source: Authors’ compilation based on the World Health Organization data (2024) . The analysis of corruption perception index values from 2000 to 2019 in Figure 7 depicts the Americas and reveals diverse trends across the American area. As key observations, Argentina’s corruption control improved from 35 to 45, indicating a decrease in corruption, whereas Brazil fell from 39 to 35. Canada and the United States both showed reductions, with Canada dropping from 92 to 77 and the United States from 78 to 69, indicating growing corruption. Guyana improved corruption control dramatically from 25 to 40, whereas Venezuela’s grade fell from 27 to 16, indicating worsening corruption. Overall, the region presents a mixed picture, with some governments making headway in tackling corruption and others falling back. Figure 7. Corruption Comparison Maps 2000 Vs 2019. Source: Authors’ compilation based on the Transparency International data (2024) . Furthermore, Venezuela’s corruption control score has dropped significantly, indicating that corruption is on the rise. In contrast, countries such as Canada, the United States, Guatemala, Nicaragua, and Barbados have all seen increased corruption in recent years. In contrast, Argentina, Guyana, Ecuador, Costa Rica, and Panama all improved in corruption control, while Brazil and Barbados demonstrated more variable tendencies. Overall, the data show an increasing burden of corruption across the area, with notable differences between governments that manage corruption well and those that are losing control (An illustration of corruption in the countries of the Americas during the same period (2000-2019) is available in repository - Appendix 3). The following Figure 8 illustrates inflation rates in the Americas between 2000 and 2019. Venezuela encountered an exceptional increase in inflation, rising from 16.29% to 19,906.62%, indicating a period of hyperinflation Argentina similarly experienced a rise in inflation from -0.70% in 2000 to 53.80% in 2019. The fundamental cause of this crisis was the country’s reliance on oil exports, which made the economy sensitive to global oil price fluctuations, known as “Dutch disease.” Additional factors, such as Chavismo’s restrictive state-controlled policies, the growth of black markets, and the authoritarian government under Chávez and Maduro, intensified economic instability and hyperinflation ( Wang, 2022 ). Other countries, including Mexico, Canada, and the United States, kept inflation rates relatively low and consistent during this time. However, the region’s inflationary tendencies are varied, with some nations stabilising and others, such as Argentina and Venezuela, experiencing intense inflationary pressures. Figure 8. Inflation Comparison Maps 2000 Vs 2019. Source: Authors’ compilation based on the World Bank data (2024) . The examination of unemployment rates from 2000 to 2019 yields varied results, as depicted in Figure 9 . Argentina’s unemployment rate fell significantly from 15.00 to 9.84, whilst Brazil’s grew from 10.53 to 12.05. Colombia improved, going from 20.52 to 9.96, while Suriname decreased from 12.74 to 8.03. The United States saw a modest decline from 3.99 to 3.67. In comparison, Guyana’s rate increased marginally, from 11.89 to 13.52. Overall, while some nations made headway in lowering unemployment, others, such as Brazil and Guyana, saw rates rise, reflecting regional labour market patterns. Figure 9. Unemployment Comparison Maps 2000 Vs 2019. Source: Authors’ compilation based on the World Bank data (2024) . The GDP growth comparison maps for the Americas from 2000 to 2019 are depicted in Figure 10 , and they indicate unfavourable economic growth patterns in most Americas countries. Brazil’s growth rate dropped significantly, while Venezuela’s decreased dramatically from 3.69% to -27.67%. Argentina and Ecuador also saw near-zero or negative growth rates, indicating experiences of economically troublesome conditions. However, countries like the Dominican Republic, Bolivia, and Guyana performed well, maintaining or improving their growth rates. Overall, the region saw more economic setbacks than growth outcomes. Figure 10. Economic Growth Comparison Maps 2000 Vs 2019. Source: Authors’ compilation based on the World Bank data (2024) . These maps and line graphs provide insight into patterns of corruption control, suicide rates, and economic indicators such as inflation, unemployment, and GDP growth throughout the Americas from 2000 to 2019. By analysing these trends at the country level, the report identifies significant socioeconomic concerns, setting the foundation for future research and policy interventions. The primary analysis of this research is the stepwise panel regression. As the first step, the stationarity and stability of the datasets were tested. This step guarantees that the variables maintain stable associations across time. Table 3 below confirms that the datasets are stationarity, utilizing LLC and Fisher ADF tests. Table 3. Stationary unit root tests. Variables LLC Test Fisher ADF CORR it -2.1193** -2.9676*** SUI it−1 -1.1386* -1.9039** UNEMP -4.6994*** -0.6318* EG it -6.5100*** -9.7929*** ΔINFL it -15.7171*** -25.2726*** The structural stability of the study’s datasets is confirmed through CUSUM plots, as depicted in Figure 11 wherein stability is measured under a 95% significance level. Figure 11. CUSUM Plots as Stability Test. Source: Authors’ illustrations using RStudio. The results of the stationarity and stability tests establish the dataset’s dependability for panel regression analysis, guaranteeing that the variables maintain stable associations across time and that the model is appropriate for the next stage of statistical research. Next, the selection of the best model fit for panel data analysis across all Americas is presented in Table 4 , which includes separate income categories. The F-test shows that the POLS model is ineffective, whereas FEM is preferable. The LM test reveals no significant changes between income levels, indicating that the REM does not outperform POLS. The Hausman Test reveals that FEM is the best fit across all income levels (The stepwise results for each category is available in repository - Appendix 4). Therefore, based on the F-test, LM test, and Hausman test, the FEM was selected as the appropriate estimator. Table 4. Specification tests for the final stepwise panel regression model. Income Levels Tests F test F test F test H0: POLS H0: POLS H0: POLS H1: Fixed Effect H1: Fixed Effect H1: Fixed Effect Americas 5017.66 *** 0.00 45.53*** High-Income Level 2236.32*** 0.85 10.65*** Upper-Middle Income Level 3688.55*** 0.00 46.76*** Lower-Middle Income Level 814.22*** 0.00 21.21*** Table 5 shows the results of the final stepwise panel regression models for the Americas and three income categories: high, upper-middle, and lower-middle-income countries (The groundwork of the final stepwise panel regression is available in repository - Appendix 5). In the next step, the base for building the final regression models of the Americas and the different income groups was tested (The fixed and random effect estimates for the final stepwise model is available in repository - Appendix 6). When developing the Americas regression model, the CORR × ΔINFL variable is dropped due to changing coefficient signs across different stepwise models. All income level category regression models successfully included all the chosen variables since they aligned with the coefficient signs in previous literature and were consistent throughout stepwise models. These tests provided necessary information on the applicability and suitability of the various models, allowing to choose the most effective approach for the analysis. Table 5. Final Regression Models. Americas High income Upper middle income Lower middle income Equation SUI = f ( SU I it − 1 CORR CORR × UNEMP CORR × EG ) SUI = f ( SU I it − 1 CORR CORR × ∆ INFL CORR × UNEMP CORR × EG ) SUI = f ( SU I it − 1 CORR CORR × ∆ INFL CORR × UNEMP CORR × EG ) SUI = f ( SU I it − 1 CORR CORR × ∆ INFL CORR × UNEMP CORR × EG ) Variables SUI SUI SUI SUI FEM FEM FEM FEM SUI it − 1 0.8063*** 0.8059*** 0.7416*** 0.3929*** (0.0949446) (0.029358) (0.0376741) (0.111296) CORR -0.0001 -0.0001 -0.0268** -0.0151 (0.0115197) (0.0093758) (0.0105321) (0.0184751) CORR × ∆ INFL -2.82e-07 -2.55e-07 -0.0002 (6.40e-07) (5.09e-07) (0.000498) CORR × UNEMP 0.0006 0.0006* 0.0022*** 0.0040*** (0.0009214) (0.0004467) (0.0006107) (0.0015929) CORR × EG -0.0005* -0.0005* -0.0003 -0.0005 (0.0003641) (0.0002868) (0.0003484) (0.0008034) Constant # 1.557312 1.5663 2.2007 3.3192 No of Countries 26 8 14 4 R 2 Within 0.6490 0.6492 0.6481 0.3916 R 2 Between 0.9995 0.9995 0.9971 0.9709 R 2 Overall 0.9893 0.9892 0.9848 0.9453 # In a FEM, the constant term reflects the average FE after across entities and is included for completeness. It should not be interpreted as a traditional intercept, as time-invariant factors are absorbed into the fixed effects. The lagged suicide variable ( SUI it− 1 ) exhibits consistently positive and significant associations with current suicide rates across all models, with coefficients ranging from 0.3929 in lower middle income countries to 0.8063 in the Americas, all significant at the 1% level. Even so, the coefficient of the lagged suicide variable is smaller than 1, indicating that the suicide rate will converge among countries in the long run. However, these findings suggest a notable persistence of suicide rates over time, with a tendency of historical trends strongly influencing current patterns in the Americas, upper middle and lower middle countries with coefficients 0.8063, 0.8059, and 0.7416, respectively. But the lower middle income countries with a coefficient of 0.3929 point to a comparatively modest persisting nature in suicide and a greater potential for change of pattern over time. The main effects of corruption control score ( CORR ), as the main independent variable, demonstrates a significant negative association with suicide rates in upper middle income countries (-0.0268, p<0.05), indicating that stronger governance is modestly associated with lower suicide rates. This suggests that effective corruption control can foster conditions that reduce stressors contributing to suicide in these countries. In the Americas, high income countries, and lower-middle income countries, the coefficients for corruption control are not statistically significant (-0.0001, -0.0001, and -0.0151, respectively). This implies that, on its own, corruption control may not have a direct measurable and significant impact on suicide rates in these contexts, however, the negative signs of values support the existing literature findings which state that there is a negative association between corruption control and suicide rates. Furthermore, the interaction effects show how the moderating factors of inflation, unemployment, and economic growth shape this relationship. The moderation between CORR and inflation ( CORR × INFL ) reveals insignificant, extremely subtle negative effects in high-income, upper middle and lower middle countries, with coefficients of -2.82×10 −7 , -2.55×10 −7 and -0.0002 respectively. Compared to the unmoderated effect of corruption control on suicide rates, the further minimised moderated values indicate that inflation’s involvement limits the slightly beneficial impact existent of corruption control on suicide. The moderated effect of corruption control mixed with unemployment ( CORR × UNEMP ) shows highly significant positive results. In high-income countries, the coefficient is -0.0006 (p < 0.10), signifying a modest effect on suicide rates. In contrast, upper-middle-income and lower-middle-income countries exhibit relatively higher, albeit still small, coefficients of 0.0022 (p < 0.01) and 0.0040 (p < 0.01), respectively. While the direct effect of corruption control on suicide indicates a slight reduction in suicide rates with higher levels of corruption control, the moderated results reveal a contradictory pattern. Specifically, corruption control, when moderated by unemployment, appears to slightly increase suicide rates. For the moderation between CORR and economic growth ( CORR × EG ), the results show significant effects in several cases, but positive in all income levels. In both Americas and high-income countries, the coefficients are -0.0005 (p < 0.10) and significant, while upper middle and lower middle income countries have insignificant coefficients of -0.0003, and -0.0005 respectively. These results indicate that economic growth mildly strengthens the negative relationship between corruption control and suicide rates compared to the direct effect of corruption on suicide. The overall R 2 levels across all income categories demonstrate a solid fit for the models. For the entire Americas, the overall R 2 indicates the model explains that 98.93% of the variation in suicide rates. Similarly, in high-income countries, the R 2 states 98.92% reflecting a very close level of explanatory power. Upper-middle-income countries’ overall R 2 indicates 98.48% model success, while lower-middle-income countries show a slightly lower but still strong R 2 explaining 94.53% of overall explanatory power. These high R 2 values indicate that the models effectively capture the relationships between suicide rates and the included variables across all income categories. Discussion The complex interplay between corruption control, economic variables, and mental health is visible in the Americas, where the socioeconomic landscape is marked by significant inequality, different levels of governance, and economic instability. The findings, based on a Panel data regression model, show that control of corruption, combined with financial stresses, have an influence on suicide rates across income categories of the American region. One key finding is that suicide rates in the Americas have remained mostly persistent throughout time across all income levels. The lagged suicide rate is a strong predictor, mainly in Americas and high-income countries. Once suicides rise, they tend to stay high for some time, reflecting long-term social and economic challenges. In high-income countries, unmet expectations for personal and financial success can lead to ongoing mental health issues and rising suicide rates, even in stable economies ( Helliwell & Huang, 2008 ; HeliKoivumaa-Honkanen et al., 2001 ). These findings emphasise the need to take proactive actions to address the underlying social and psychological elements that lead to persistent suicide rates. Corruption control plays a crucial role in especially upper-middle-income countries, where it modestly decreases suicide rates. Countries where corruption is widespread often see weakened public services, a lack of trust in institutions, and heightened social inequality, all of which contribute to a sense of hopelessness. The mixed effect of corruption control and unfavourable economic stressors could intensify the psychological burden on individuals, leading to higher suicide rates. Previous studies confirm that corruption erodes public trust and worsens economic disparities, disproportionately affecting vulnerable populations ( Bjørnskov et al., 2010 ). This upper-middle income category’s findings stand with the empirical evidence suggesting that corruption worsens economic and social inequality, thereby contributing to emotions of despair and societal dissatisfaction and demonstrating that corruption undermines the effectiveness of public services and disproportionately affects the public ( Gupta, Davoodi, & Tiongson, 2001 ; Kaufmann, Kraay, & Mastruzzi, 2005 ). However, in the current study, the impact of corruption control is less significant in Americas, high-income and lower-middle-income countries. In high-income countries, more robust governance and better social services may buffer the adverse effects of corruption. In contrast, in lower-middle-income countries, more immediate concerns like unemployment appear to take precedence over corruption in driving suicide rates. Unemployment has a significant role in explaining suicide rates at all income levels, particularly in lower-middle-income countries. When functioning as a moderating variable for corruption control, it amplifies the adverse consequences of weakened corruption control. High unemployment and financial insecurity produce an environment of economic insecurity in low- and middle-income countries. Individuals who are unemployed for an extended period of time may lose hope in their ability to better their situation, which can lead to worsening psychological issues and, in some instances, suicide ( Goulas & Zervoyianni, 2023 ; Skinner, Osgood, Occhipinti, Song, & Hickie, 2023 ). This aligns with the research demonstrating that unemployment is strongly associated with declining mental health, particularly in countries with weak or non-existent social safety nets ( Glaeser & Saks, 2006 ). Furthermore, the interplay between unemployment and corruption may worsen the psychological toll, as economic hardship and systemic governance failures compound one another, creating a vicious cycle of despair. Despite being statistically insignificant in all the final regression models, inflation is still confirmed to be a crucial variable as there is sufficient literature base to support the finding. Earlier research has shown that it might have a direct or indirect impact on mental health. While many of the studies argue that there is a positive association with inflation’s influence on suicide, the current study reflects more of the minority who suggested that inflation has a negative influence on suicide ( Noh, 2009 ). The current finding suggests that unemployment in some countries may not carry the same social stigma or economic hardships; hence, in some countries and instances, inflation lowers suicide rates The absence of statistical significance in this study implies that inflation may not affect highly on suicide, specifically in the American region but rather a contributing factor when combined with other economic and governance failures. In contrast, economic growth serves as a protective factor, lowering the suicide rate, especially in high-income nations. When economic growth is inclusive, it can raise living standards, lessen poverty, and eventually reduce the suicide rate ( Sannapureddy, Shaw, Bashir, & Vijayalakshmi, 2024 ). Even so, the moderating effect of economic growth is not simple to understand. Although economic growth lowers suicide rates in all the income categories, the impact seems to be extremely low. This suggests that the benefits of economic growth do not necessarily reach the most vulnerable in lower- and upper-middle-income nations. If growth only benefits a small portion of the population, its protective effect is reduced and social inequality potentially worsened ( Fischer & Andres, 2008 ). This highlights the need for inclusive economic policies. Moreover, as the dataset employed in this study is non-random and region-specific, the findings should be interpreted within the contextual boundaries of the Americas, serving primarily as foundational evidence on income-group-specific patterns rather than being generalized to other regional contexts. In conclusion, the regression models show how historical trends, economic conditions, and corruption affect suicide rates in the Americas. Unemployment, inflation, and economic growth all serve as moderating or interacting variables that modify the impact of corruption and other pressures on mental health. While suicide rates continue to be a strong predictor, corruption is especially significant in upper-middle-income countries, increasing socioeconomic inequities and contributing to mental health crises. Unemployment is substantial at all income levels, especially in lower-middle-income countries, where it indicates that economic insecurity and poor governance cause significant psychological distress. While inflation has not been shown to have a substantial impact on suicide, it could play an indirect function in increasing financial instability. Policy implications This study’s findings reveal a complex nexus of corruption, economic issues, and rising suicide rates throughout the Americas. To address these linked concerns, focused policy actions are required, providing a way to minimise corruption and its destructive impact on mental health. Enhanced anti-corruption initiatives Corruption has a significant impact on suicide rates, particularly in upper-middle-income countries, where the consequences are most severe. According to the current study, corruption damages public trust, reduces institutional performance, and promotes social inequalities, creating a climate of hopelessness and despair. In response, upper-middle-income countries in the American region can execute anti-corruption measures. The adverse effects of corruption on mental health can be minimised by strengthening governance, boosting transparency, and promoting public sector accountability. A great example is Vietnam’s anti-corruption initiatives, which improved mental health by minimizing the psychosocial effects of corruption, particularly among vulnerable populations like women, through enhanced governance and institutional reforms ( Sharma et al., 2021 ). Similarly, global health sector interventions targeting transparency and accountability not only improved service delivery but also indirectly supported mental health outcomes ( Vian, 2020 ). Comprehensive unemployment support programs Unemployment is another factor that contributes to moderate generating higher suicide rates across all income categories, particularly middle-income nations (upper and lower). Joblessness, economic insecurity, and insufficient social safety nets, add to increased emotional distress. To tackle this, unemployment support programs should be created. To lessen the psychological burden of unemployment, focused job development strategies, vocational training, and entrepreneurial support should be implemented. Furthermore, strengthening social safety nets, such as unemployment benefits and mental health services, is critical in providing economic and emotional stability to unemployed people. In the United States, Higher unemployment insurance (UI) can be stated as a successful program. The UI recipiency rates are linked to decreased suicide rates among high-risk populations, including men, non-Hispanic White Americans, and people aged 45 to 64. Improving UI accessibility and focussing on disadvantaged populations is found to be reducing the psychological burden of unemployment and improve mental health outcomes ( Kaufman et al., 2020 ). Mental health interventions and services Mental health initiatives must be prioritised in communities with high suicide rates, particularly those plagued by corruption and economic instability. It is critical to increase access to community-based mental health services and integrate mental health care into primary health care systems. These services should primarily target vulnerable groups, such as the unemployed and those living in highly corrupt local areas. Such interventions would lessen the psychological impact of economic stress while improving overall mental health outcomes, which can ultimately increase suicides. Zimbabwe’s Friendship Bench program is a renouwned intervention. It trains lay health workers to provide problem-solving therapy, proven to be benefiting over 70,000 people and inspiring adoption in countries such as Kenya and the United States, demonstrating the effectiveness of community-based interventions ( Munetsi, Chibanda, & Dzapasi, 2021 ). Economic growth policies According to the current study, while moderation of economic progress significantly proven to be offering mild protection against suicide in high-income countries, its benefits do not necessarily extend to vulnerable populations in middle-income nations. Policies for inclusive growth are necessary to guarantee that more significant numbers of people benefit from economic benefits. While middle-income countries must concentrate on providing growth advantages to underprivileged domains, high-income countries should try to ensure that economic prosperity is distributed fairly. It is essential to carry out periodic assessments of the effects of economic expansion on mental health and inequality to prevent the increments of disparities or suicide rates. Japan’s experience with regional suicide prevention programs such as the EFECBSC, illustrate how targeted interventions can reduce suicide rates linked to economic and health-related motives. However, their limited effectiveness for women and school-aged populations highlights the need for policies that equitably address the unique needs of vulnerable groups. This emphasizes the importance of designing inclusive strategies to ensure the benefits of economic progress and prevention efforts are widely distributed ( Nakano, Hasegawa, & Okada, 2021 ). Inflation management Even if there was only marginal and no statistically significant moderated influence between inflation and suicide rates, controlling inflation is still essential to maintaining economic stability. As in the case of Venezuela, hyperinflation exacerbates economic instability and may have a secondary impact on mental health. Controlling the volatility of inflation should be a top priority for policymakers, especially in areas with high levels of inflation. Lowering the chances of suicide and the psychological effects of financial instability are two benefits of stabilising the overall economy. Cross-national collaboration Finally, to address the interrelated problems of corruption, unstable economies, and mental health across the Americas, cross-national cooperation should be considered crucial. To exchange resources, best practices, and knowledge in economic policy, mental health interventions, and governance, countries should work together. Cooperation among nations can enhance anti-corruption initiatives, foster regional stability, and lead to better mental health outcomes. Cross-border collaborations, especially between neighbouring nations, will guarantee that these issues are addressed thoroughly and sustainably. Moreover, tackling the intricate associations among corruption, unemployment, economic growth, inflation, and mental health necessitates a multifaceted strategy. Vulnerable populations’ psychological burden can be lessened by implementing targeted anti-corruption measures, increasing unemployment support, encouraging inclusive growth, and improving mental health services. The 66th World Health Assembly endorsed the first-ever mental health action plan in its history in May 2013, proposing a 10% global suicide rate decrease by 2020 ( PAHO, 2024 ). PAHO identified suicide prevention as one of its top priorities and suggested suicide rates as an effect measure in its Regional Strategies for Mental Health publication. Cross-national collaboration is critical to sustaining these efforts and fostering long-term improvements in public health across the region to fight against the increasing trend of suicide in the American region. Conclusion This study investigates how corruption, combined with economic factors such as unemployment, inflation, and economic growth, affects suicide rates across different income levels in the Americas. Examining panel data from 26 nations over 20 years, yields the following significant findings. First, the persistence of suicide rates is a significant result at all socioeconomic levels. The lagged suicide variable consistently demonstrated a significantly positive impact on current suicide rates, suggesting that an initial increase in suicide rates within a country tends to have a ripple effect. This indicates a persistent, yet gradually diminishing, effect that is expected to converge in the long term. The persistent nature of suicide is more prevalent in the high income and upper middle income levels in the Americas, whereas lower-middle-income groups exhibit a comparatively weaker degree of persistence. However, this gradually diminishing yet persistent effect highlights the enduring psychological and social consequences of economic insecurity and societal negativity across all income levels throughout the Americas. The lower middle income category’s comparatively lower persistent nature indicates more variability indicating there may be other important factors affecting suicide. Second, corruption has been identified as a significant driver of suicide rates in upper-middle-income nations, exacerbating inequality and economic suffering. While corruption did not have a statistically significant impact at all income levels, there was a clear link between corruption and suicide in upper-middle-income countries, consistent with previous research linking poor governance and institutional corruption to adverse mental health outcomes. Third, unemployment was revealed as a significant moderator at all income levels, with a comparatively higher impact in middle income nations than high income countries and the whole Americas region. The combination of high unemployment and failed corruption control initiates an environment of economic uncertainty, which could contribute to psychological stress and raises suicide rates. This implies that lowering unemployment is crucial in regions with substantial economic vulnerability. Fourth, while inflation did not demonstrate consistent statistical significance in our study, its impact on worsening financial stress, particularly in hyperinflationary regions such as Venezuela, must be considered. Inflation indirectly contributes to economic uncertainty, which, combined with other variables such as corruption, may have a long-term impact on suicide rates. Finally, the study discovered that when economic development is evenly distributed, suicide rates tend to mildly fall across all income categories. However, in areas with high corruption, the advantages of financial success are less likely to reach poor populations, limiting its protective impact on mental health. In conclusion, this study emphasises the significance of a multifaceted approach to suicide prevention in the Americas, addressing not only corruption unemployment and inclusive economic growth Policy measures should prioritise strengthening governance, lowering economic inequities, and improving social safety, to reduce the psychological impact of corruption and financial instability. Future research should focus on these complicated relationships, particularly in low-income nations where the effects of economic factors may differ from those shown in this study. By tackling these crucial socioeconomic causes, authorities can more effectively counteract rising suicide rates and improve mental health outcomes throughout the American region. Limitations This study, while providing valuable insights into the association between corruption and suicide rates in the Americas, has several limitations. First, the data included in the investigation spans a 20-year period, during which many social and political developments happened. These shifts may have altered the outcomes in ways the panel regression model does not entirely account for. Furthermore, the analysis is based on secondary data from trustworthy sources such as the World Bank and Transparency International, which may contain gaps or inconsistencies in reporting. Another limitation is that the current research only addresses governance and economic factors and, cultural and psychological characteristics, which are known to have a significant impact on suicide rates, outside the scope of this study. Also, while country fixed effects account for time-invariant cultural and institutional differences, we acknowledge that time-varying societal factors - such as mental-health service accessibility or social cohesion, were not included due to data limitations. Subsequently, though fixed effects control for unobserved, time-invariant heterogeneity, endogeneity due to reverse causality and omitted time-varying, social and cultural factors may still remain; future work could employ instrumental variable (IV) approaches or incorporate measures of mental-health service availability to reduce this risk. Finally, The Corruption Perceptions Index (which used to measure corruption control) employed in this study faces unique limitations, including unequal effects and causal interrelations among its data sources, which challenge its assumptions of equal weighting and independence. Additionally, corruption perceptions vary across countries, reflecting cultural and economic differences that the CPI may not fully capture. Therefore, triangulating data sources with different indexes can be an effective potential tactic to overcome this limitation in future exploration. Ethics and consent Ethics and consent are not required for the performed study. Data availability statement Underlying data Figshare: (The Hidden Toll-Dataset), https://doi.org/10.6084/m9.figshare.27637182.v1 ( Kularathne & Jayathilaka, 2024 ). Extended data This study contains the following extended data: Figshare: (Appendix 1), https://doi.org/10.6084/m9.figshare.27637434.v3 ( Kularathne & Jayathilaka, 2024a ) Figshare: (Appendix 2), https://doi.org/10.6084/m9.figshare.27637479.v1 ( Kularathne & Jayathilaka, 2024b ) Figshare: (Appendix 3), https://doi.org/10.6084/m9.figshare.27637557.v1 ( Kularathne & Jayathilaka, 2024c ) Figshare: (Appendix 4), https://doi.org/10.6084/m9.figshare.27637614.v1 ( Kularathne & Jayathilaka, 2024d ) Figshare: (Appendix 5), https://doi.org/10.6084/m9.figshare.27637647.v1 ( Kularathne & Jayathilaka, 2024e ) Figshare: (Appendix 6), https://doi.org/10.6084/m9.figshare.27637725.v1 ( Kularathne & Jayathilaka, 2024f ) Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). 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PubMed Abstract | Publisher Full Text Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 26 Nov 2024 ADD YOUR COMMENT Comment Author details Author details 1 SLIIT Faculty of Graduate Studies, Sri Lanka Institute of Information Technology, Malabe, 10115, Sri Lanka 2 SLIIT Business School, Sri Lanka Institute of Information Technology, Malabe, 10115, Sri Lanka Sherin Kularathne Roles: Conceptualization, Data Curation, Formal Analysis, Methodology, Software, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Ruwan Jayathilaka Roles: Conceptualization, Methodology, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (3) version 3 Revised Published: 08 Dec 2025, 13:1427 https://doi.org/10.12688/f1000research.158285.3 version 2 Revised Published: 28 Jan 2025, 13:1427 https://doi.org/10.12688/f1000research.158285.2 version 1 Published: 26 Nov 2024, 13:1427 https://doi.org/10.12688/f1000research.158285.1 Copyright © 2025 Kularathne S and Jayathilaka R. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Kularathne S and Jayathilaka R. The Hidden Toll: Investigating the influence of Corruption on Persistent Suicide in the Americas [version 3; peer review: 3 approved with reservations] . F1000Research 2025, 13 :1427 ( https://doi.org/10.12688/f1000research.158285.3 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 2 VERSION 2 PUBLISHED 28 Jan 2025 Revised Views 0 Cite How to cite this report: Ramoni-Perazzi J. Reviewer Report For: The Hidden Toll: Investigating the influence of Corruption on Persistent Suicide in the Americas [version 3; peer review: 3 approved with reservations] . F1000Research 2025, 13 :1427 ( https://doi.org/10.5256/f1000research.177046.r418328 ) The direct URL for this report is: https://f1000research.com/articles/13-1427/v2#referee-response-418328 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 03 Nov 2025 Josefa Ramoni-Perazzi , Universidad Industrial de Santander, Colombia, USA Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.177046.r418328 I find the study very interesting, particularly in light of the growing concern over high suicide rates, especially among young people. Unfortunately, much of this trend has emerged after the period covered by the study. In relation ... Continue reading READ ALL I find the study very interesting, particularly in light of the growing concern over high suicide rates, especially among young people. Unfortunately, much of this trend has emerged after the period covered by the study. In relation to this, the study begins with a figure showing trends in suicide rates across continents. While most regions exhibit a declining trend, the Americas are presented as an exception with a reportedly increasing trend. However, this upward trend is barely perceptible in the graph. Furthermore, the document refers to differences in suicide rate behavior among subgroups of countries within the Americas, yet no figure is provided to support this claim. Including a disaggregated visualization is necessary to offer a clearer perspective on the evolution of the variable under study, particularly given the limited variation observed at the aggregate level throughout the analysis period. The model formulation requires further review. The explanatory variables —unemployment, inflation, economic growth, and corruption— are likely to be interrelated, potentially leading to multicollinearity issues. While fixed-effects panel data models help to mitigate certain biases, collinearity within the matrix of regressors may still affect the precision of the estimates and should be thoroughly assessed. The model implicitly assumes that suicide rates can be explained solely through economic variables, under the hypothesis that poverty —driven by corruption, which in turn hampers growth and limits access to the labor market— is the main determinant. Although suicide may indeed stem from despair linked to poor economic performance and lack of opportunities, other factors such as social dynamics, cultural aspects, and access to mental health care may play an equally or even more important role. These are not currently addressed in the model. Another critical issue is the absence of any strategy to address potential endogeneity. Factors such as culture, religion, social cohesion, and access to mental health services may influence both suicide rates and levels of corruption. Failing to control for these variables risks introducing bias into the estimates and undermines the causal validity of the results. Since the sample is not random and is limited to countries in the Americas, the use of random effects models is not methodologically appropriate. Consequently, the findings cannot be generalized to other countries with similar income levels outside the region. The results are valid only within the continental context analyzed and should not be extrapolated globally without caution. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Economics, econometrics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Ramoni-Perazzi J. Reviewer Report For: The Hidden Toll: Investigating the influence of Corruption on Persistent Suicide in the Americas [version 3; peer review: 3 approved with reservations] . F1000Research 2025, 13 :1427 ( https://doi.org/10.5256/f1000research.177046.r418328 ) The direct URL for this report is: https://f1000research.com/articles/13-1427/v2#referee-response-418328 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 26 Nov 2024 Views 0 Cite How to cite this report: Nelson FM. Reviewer Report For: The Hidden Toll: Investigating the influence of Corruption on Persistent Suicide in the Americas [version 3; peer review: 3 approved with reservations] . F1000Research 2025, 13 :1427 ( https://doi.org/10.5256/f1000research.173856.r347489 ) The direct URL for this report is: https://f1000research.com/articles/13-1427/v1#referee-response-347489 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 08 Jan 2025 Febby Mutiara Nelson , University of Indonesia, Depok, West Java, Indonesia Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.173856.r347489 Review Strenghts The study addresses a critical and understudied intersection of corruption and suicide rates in the Americas. By filling a gap in literature that lacks comprehensive regional analyses, it offers ... Continue reading READ ALL Review Strenghts The study addresses a critical and understudied intersection of corruption and suicide rates in the Americas. By filling a gap in literature that lacks comprehensive regional analyses, it offers significant value for public health and governance discourse. The research employs a robust framework to explore moderating variables like unemployment, inflation, and economic growth, effectively contributing to a nuanced understanding of the socio-economic determinants of suicide. Areas for Improvement : Clarification of causal direction : The article implies a causal link between corruption and suicide but provides limited discussion of reverse causality or potential bidirectional influences. Policy Context : Although the article discusses policy implications, it could better integrate real-world examples or case studies where similar strategies have succeeded or failed. Methodological Review Variable Definitions and Justification : The rationale for using specific metrics like the Corruption Perceptions Index (CPI) needs more explicit justification regarding its relevance and limitations in capturing nuanced governance issues. Modeling of Interaction Terms : The interaction effects involving corruption, unemployment, and inflation need more theoretical grounding to explain why specific relationships are anticipated. Theoritical Review The literature review connects corruption to mental health through trust erosion and institutional failures, aligning well with existing governance theories Areas for Improvement : Theoretical Integration : The theoretical framework could be enriched by incorporating more psychological theories linking economic stressors directly to mental health outcomes. References to Durkheim’s theory of anomie or contemporary socio-political models would add depth. Conceptual Clarity : The term "persistence" of suicide rates is used frequently but could be defined more clearly, perhaps linking it to theories of path dependency in social outcomes. Overall Recommendations Strengthen the theoretical foundation by integrating more detailed explanations for the mechanisms connecting corruption, economic conditions, and mental health. Expand on policy recommendations by providing examples or scenarios demonstrating the impact of governance reforms on suicide rates. Consider alternative data sources or indices to triangulate findings on corruption and governance quality. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Criminal law, Corruption Eradication, Economic crimes. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Nelson FM. Reviewer Report For: The Hidden Toll: Investigating the influence of Corruption on Persistent Suicide in the Americas [version 3; peer review: 3 approved with reservations] . F1000Research 2025, 13 :1427 ( https://doi.org/10.5256/f1000research.173856.r347489 ) The direct URL for this report is: https://f1000research.com/articles/13-1427/v1#referee-response-347489 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 28 Jan 2025 Ruwan Jayathilaka , SLIIT Business School, Sri Lanka Institute of Information Technology, Malabe, 10115, Sri Lanka 28 Jan 2025 Author Response Authors Response to Reviewer 2 Dear Reviewer 2, We greatly appreciate the time invested in reading our manuscript and providing the necessary feedback to improve the overall quality ... Continue reading Authors Response to Reviewer 2 Dear Reviewer 2, We greatly appreciate the time invested in reading our manuscript and providing the necessary feedback to improve the overall quality of our work. All the mentioned comments have been considered and the revisions made accordingly. Thank you once again for your valuable feedback. Reviewer 1 comment 1: Clarification of causal direction: The article implies a causal link between corruption and suicide but provides limited discussion of reverse causality or potential bidirectional influences. Authors’ Response to Reviewer 1 comment 1: We sincerely appreciate the reviewer’s insightful comment. However, we would like to kindly clarify that this study aimed to investigate the impact of corruption on suicide rates in the Americas, particularly how corruption interacts with economic moderators such as unemployment and economic growth. The study does not aim to analyze the potential bidirectional relationship between corruption and suicide rates, as this is beyond the scope of the current research. While the potential bidirectional relationship between corruption and suicide rates is indeed an important and interesting area of inquiry, it falls outside the scope of the current research. We hope this clarification aligns with the intended objectives of our study. Reviewer 1 comment 2: Policy Context: Although the article discusses policy implications, it could better integrate real-world examples or case studies where similar strategies have succeeded or failed. Authors’ Response to Reviewer 1 comment As per the reviewer’s valuable suggestion, we included real-world examples with proper references in the study. Reviewer 1 comment 3: Variable Definitions and Justification: The rationale for using specific metrics like the Corruption Perceptions Index (CPI) needs more explicit justification regarding its relevance and limitations in capturing nuanced governance issues. Authors’ Response to Reviewer 1 comment 3: We are grateful for this thoughtful feedback. In light of the reviewer’s suggestion, we have updated the data section to provide a clearer rationale for the use of the Corruption Perceptions Index (CPI). The limitations section also updated to acknowledge the nuances it may not fully capture. Reviewer 1 comment 4: Modeling of Interaction Terms: The interaction effects involving corruption, unemployment, and inflation need more theoretical grounding to explain why specific relationships are anticipated. Authors’ Response to Reviewer 1 comment 4: Thank you for the suggestion. Kindly note that in Table 1, conceptual linkages are shown, as grounding work to the current study. The moderate effects in the current study will be introduced as a novel approach, to this specific study. Reviewer 1 comment 5: Theoretical Integration: The theoretical framework could be enriched by incorporating more psychological theories linking economic stressors directly to mental health outcomes. References to Durkheim’s theory of anomie or contemporary socio-political models would add depth. Authors’ Response to Reviewer 1 comment 5: Thank you for this insightful suggestion. In response, we have enriched the theoretical framework by incorporating additional psychological theories, including references to Durkheim’s theory of anomie and contemporary socio-political models, to provide a more comprehensive foundation for the study. Reviewer 1 comment 6: Conceptual Clarity: The term "persistence" of suicide rates is used frequently but could be defined more clearly, perhaps linking it to theories of path dependency in social outcomes. Authors’ Response to Reviewer 1 comment 6: We appreciate for requesting more clarification on this to improve the clarity of the manuscript. In this study, “Persistence” means the continuing nature of suicides or the tendency of past suicide rates affecting future suicide. Evidence for this is mainly generated through results. As per the reviewer’s concern, theis is clearly mentioned in this revised version. Reviewer 1 comment 7: Consider alternative data sources or indices to triangulate findings on corruption and governance quality. Authors’ Response to Reviewer 1 comment 7: Thank you for this valuable suggestion. We appreciate the importance of triangulating findings to enhance the robustness of the study. For this research, we employed the Corruption Perceptions Index (CPI) from Transparency International due to its widespread acceptance, reliability, and comprehensive coverage across countries and time. The CPI is particularly well-suited for cross-country comparisons, which aligns with our study’s objective of analyzing corruption across the Americas. We acknowledge that exploring alternative data sources or indices, such as the Worldwide Governance Indicators (WGI) or Varieties of Democracy (V-Dem) dataset, could provide additional insights and strengthen our conclusions. While these datasets were considered during our initial planning, limitations such as shorter temporal coverage or lack of comparability for the specific variables of interest influenced our decision to focus on the CPI. To address your suggestion, we propose incorporating these alternative indices in future research to assess the robustness of our findings. This would enable a more nuanced understanding of how different measures of corruption and governance quality interact with suicide rates. We also included a discussion in the limitations section of the manuscript, acknowledging the potential benefits of triangulating data sources and suggesting this as an area for future exploration. Authors Response to Reviewer 2 Dear Reviewer 2, We greatly appreciate the time invested in reading our manuscript and providing the necessary feedback to improve the overall quality of our work. All the mentioned comments have been considered and the revisions made accordingly. Thank you once again for your valuable feedback. Reviewer 1 comment 1: Clarification of causal direction: The article implies a causal link between corruption and suicide but provides limited discussion of reverse causality or potential bidirectional influences. Authors’ Response to Reviewer 1 comment 1: We sincerely appreciate the reviewer’s insightful comment. However, we would like to kindly clarify that this study aimed to investigate the impact of corruption on suicide rates in the Americas, particularly how corruption interacts with economic moderators such as unemployment and economic growth. The study does not aim to analyze the potential bidirectional relationship between corruption and suicide rates, as this is beyond the scope of the current research. While the potential bidirectional relationship between corruption and suicide rates is indeed an important and interesting area of inquiry, it falls outside the scope of the current research. We hope this clarification aligns with the intended objectives of our study. Reviewer 1 comment 2: Policy Context: Although the article discusses policy implications, it could better integrate real-world examples or case studies where similar strategies have succeeded or failed. Authors’ Response to Reviewer 1 comment As per the reviewer’s valuable suggestion, we included real-world examples with proper references in the study. Reviewer 1 comment 3: Variable Definitions and Justification: The rationale for using specific metrics like the Corruption Perceptions Index (CPI) needs more explicit justification regarding its relevance and limitations in capturing nuanced governance issues. Authors’ Response to Reviewer 1 comment 3: We are grateful for this thoughtful feedback. In light of the reviewer’s suggestion, we have updated the data section to provide a clearer rationale for the use of the Corruption Perceptions Index (CPI). The limitations section also updated to acknowledge the nuances it may not fully capture. Reviewer 1 comment 4: Modeling of Interaction Terms: The interaction effects involving corruption, unemployment, and inflation need more theoretical grounding to explain why specific relationships are anticipated. Authors’ Response to Reviewer 1 comment 4: Thank you for the suggestion. Kindly note that in Table 1, conceptual linkages are shown, as grounding work to the current study. The moderate effects in the current study will be introduced as a novel approach, to this specific study. Reviewer 1 comment 5: Theoretical Integration: The theoretical framework could be enriched by incorporating more psychological theories linking economic stressors directly to mental health outcomes. References to Durkheim’s theory of anomie or contemporary socio-political models would add depth. Authors’ Response to Reviewer 1 comment 5: Thank you for this insightful suggestion. In response, we have enriched the theoretical framework by incorporating additional psychological theories, including references to Durkheim’s theory of anomie and contemporary socio-political models, to provide a more comprehensive foundation for the study. Reviewer 1 comment 6: Conceptual Clarity: The term "persistence" of suicide rates is used frequently but could be defined more clearly, perhaps linking it to theories of path dependency in social outcomes. Authors’ Response to Reviewer 1 comment 6: We appreciate for requesting more clarification on this to improve the clarity of the manuscript. In this study, “Persistence” means the continuing nature of suicides or the tendency of past suicide rates affecting future suicide. Evidence for this is mainly generated through results. As per the reviewer’s concern, theis is clearly mentioned in this revised version. Reviewer 1 comment 7: Consider alternative data sources or indices to triangulate findings on corruption and governance quality. Authors’ Response to Reviewer 1 comment 7: Thank you for this valuable suggestion. We appreciate the importance of triangulating findings to enhance the robustness of the study. For this research, we employed the Corruption Perceptions Index (CPI) from Transparency International due to its widespread acceptance, reliability, and comprehensive coverage across countries and time. The CPI is particularly well-suited for cross-country comparisons, which aligns with our study’s objective of analyzing corruption across the Americas. We acknowledge that exploring alternative data sources or indices, such as the Worldwide Governance Indicators (WGI) or Varieties of Democracy (V-Dem) dataset, could provide additional insights and strengthen our conclusions. While these datasets were considered during our initial planning, limitations such as shorter temporal coverage or lack of comparability for the specific variables of interest influenced our decision to focus on the CPI. To address your suggestion, we propose incorporating these alternative indices in future research to assess the robustness of our findings. This would enable a more nuanced understanding of how different measures of corruption and governance quality interact with suicide rates. We also included a discussion in the limitations section of the manuscript, acknowledging the potential benefits of triangulating data sources and suggesting this as an area for future exploration. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 28 Jan 2025 Ruwan Jayathilaka , SLIIT Business School, Sri Lanka Institute of Information Technology, Malabe, 10115, Sri Lanka 28 Jan 2025 Author Response Authors Response to Reviewer 2 Dear Reviewer 2, We greatly appreciate the time invested in reading our manuscript and providing the necessary feedback to improve the overall quality ... Continue reading Authors Response to Reviewer 2 Dear Reviewer 2, We greatly appreciate the time invested in reading our manuscript and providing the necessary feedback to improve the overall quality of our work. All the mentioned comments have been considered and the revisions made accordingly. Thank you once again for your valuable feedback. Reviewer 1 comment 1: Clarification of causal direction: The article implies a causal link between corruption and suicide but provides limited discussion of reverse causality or potential bidirectional influences. Authors’ Response to Reviewer 1 comment 1: We sincerely appreciate the reviewer’s insightful comment. However, we would like to kindly clarify that this study aimed to investigate the impact of corruption on suicide rates in the Americas, particularly how corruption interacts with economic moderators such as unemployment and economic growth. The study does not aim to analyze the potential bidirectional relationship between corruption and suicide rates, as this is beyond the scope of the current research. While the potential bidirectional relationship between corruption and suicide rates is indeed an important and interesting area of inquiry, it falls outside the scope of the current research. We hope this clarification aligns with the intended objectives of our study. Reviewer 1 comment 2: Policy Context: Although the article discusses policy implications, it could better integrate real-world examples or case studies where similar strategies have succeeded or failed. Authors’ Response to Reviewer 1 comment As per the reviewer’s valuable suggestion, we included real-world examples with proper references in the study. Reviewer 1 comment 3: Variable Definitions and Justification: The rationale for using specific metrics like the Corruption Perceptions Index (CPI) needs more explicit justification regarding its relevance and limitations in capturing nuanced governance issues. Authors’ Response to Reviewer 1 comment 3: We are grateful for this thoughtful feedback. In light of the reviewer’s suggestion, we have updated the data section to provide a clearer rationale for the use of the Corruption Perceptions Index (CPI). The limitations section also updated to acknowledge the nuances it may not fully capture. Reviewer 1 comment 4: Modeling of Interaction Terms: The interaction effects involving corruption, unemployment, and inflation need more theoretical grounding to explain why specific relationships are anticipated. Authors’ Response to Reviewer 1 comment 4: Thank you for the suggestion. Kindly note that in Table 1, conceptual linkages are shown, as grounding work to the current study. The moderate effects in the current study will be introduced as a novel approach, to this specific study. Reviewer 1 comment 5: Theoretical Integration: The theoretical framework could be enriched by incorporating more psychological theories linking economic stressors directly to mental health outcomes. References to Durkheim’s theory of anomie or contemporary socio-political models would add depth. Authors’ Response to Reviewer 1 comment 5: Thank you for this insightful suggestion. In response, we have enriched the theoretical framework by incorporating additional psychological theories, including references to Durkheim’s theory of anomie and contemporary socio-political models, to provide a more comprehensive foundation for the study. Reviewer 1 comment 6: Conceptual Clarity: The term "persistence" of suicide rates is used frequently but could be defined more clearly, perhaps linking it to theories of path dependency in social outcomes. Authors’ Response to Reviewer 1 comment 6: We appreciate for requesting more clarification on this to improve the clarity of the manuscript. In this study, “Persistence” means the continuing nature of suicides or the tendency of past suicide rates affecting future suicide. Evidence for this is mainly generated through results. As per the reviewer’s concern, theis is clearly mentioned in this revised version. Reviewer 1 comment 7: Consider alternative data sources or indices to triangulate findings on corruption and governance quality. Authors’ Response to Reviewer 1 comment 7: Thank you for this valuable suggestion. We appreciate the importance of triangulating findings to enhance the robustness of the study. For this research, we employed the Corruption Perceptions Index (CPI) from Transparency International due to its widespread acceptance, reliability, and comprehensive coverage across countries and time. The CPI is particularly well-suited for cross-country comparisons, which aligns with our study’s objective of analyzing corruption across the Americas. We acknowledge that exploring alternative data sources or indices, such as the Worldwide Governance Indicators (WGI) or Varieties of Democracy (V-Dem) dataset, could provide additional insights and strengthen our conclusions. While these datasets were considered during our initial planning, limitations such as shorter temporal coverage or lack of comparability for the specific variables of interest influenced our decision to focus on the CPI. To address your suggestion, we propose incorporating these alternative indices in future research to assess the robustness of our findings. This would enable a more nuanced understanding of how different measures of corruption and governance quality interact with suicide rates. We also included a discussion in the limitations section of the manuscript, acknowledging the potential benefits of triangulating data sources and suggesting this as an area for future exploration. Authors Response to Reviewer 2 Dear Reviewer 2, We greatly appreciate the time invested in reading our manuscript and providing the necessary feedback to improve the overall quality of our work. All the mentioned comments have been considered and the revisions made accordingly. Thank you once again for your valuable feedback. Reviewer 1 comment 1: Clarification of causal direction: The article implies a causal link between corruption and suicide but provides limited discussion of reverse causality or potential bidirectional influences. Authors’ Response to Reviewer 1 comment 1: We sincerely appreciate the reviewer’s insightful comment. However, we would like to kindly clarify that this study aimed to investigate the impact of corruption on suicide rates in the Americas, particularly how corruption interacts with economic moderators such as unemployment and economic growth. The study does not aim to analyze the potential bidirectional relationship between corruption and suicide rates, as this is beyond the scope of the current research. While the potential bidirectional relationship between corruption and suicide rates is indeed an important and interesting area of inquiry, it falls outside the scope of the current research. We hope this clarification aligns with the intended objectives of our study. Reviewer 1 comment 2: Policy Context: Although the article discusses policy implications, it could better integrate real-world examples or case studies where similar strategies have succeeded or failed. Authors’ Response to Reviewer 1 comment As per the reviewer’s valuable suggestion, we included real-world examples with proper references in the study. Reviewer 1 comment 3: Variable Definitions and Justification: The rationale for using specific metrics like the Corruption Perceptions Index (CPI) needs more explicit justification regarding its relevance and limitations in capturing nuanced governance issues. Authors’ Response to Reviewer 1 comment 3: We are grateful for this thoughtful feedback. In light of the reviewer’s suggestion, we have updated the data section to provide a clearer rationale for the use of the Corruption Perceptions Index (CPI). The limitations section also updated to acknowledge the nuances it may not fully capture. Reviewer 1 comment 4: Modeling of Interaction Terms: The interaction effects involving corruption, unemployment, and inflation need more theoretical grounding to explain why specific relationships are anticipated. Authors’ Response to Reviewer 1 comment 4: Thank you for the suggestion. Kindly note that in Table 1, conceptual linkages are shown, as grounding work to the current study. The moderate effects in the current study will be introduced as a novel approach, to this specific study. Reviewer 1 comment 5: Theoretical Integration: The theoretical framework could be enriched by incorporating more psychological theories linking economic stressors directly to mental health outcomes. References to Durkheim’s theory of anomie or contemporary socio-political models would add depth. Authors’ Response to Reviewer 1 comment 5: Thank you for this insightful suggestion. In response, we have enriched the theoretical framework by incorporating additional psychological theories, including references to Durkheim’s theory of anomie and contemporary socio-political models, to provide a more comprehensive foundation for the study. Reviewer 1 comment 6: Conceptual Clarity: The term "persistence" of suicide rates is used frequently but could be defined more clearly, perhaps linking it to theories of path dependency in social outcomes. Authors’ Response to Reviewer 1 comment 6: We appreciate for requesting more clarification on this to improve the clarity of the manuscript. In this study, “Persistence” means the continuing nature of suicides or the tendency of past suicide rates affecting future suicide. Evidence for this is mainly generated through results. As per the reviewer’s concern, theis is clearly mentioned in this revised version. Reviewer 1 comment 7: Consider alternative data sources or indices to triangulate findings on corruption and governance quality. Authors’ Response to Reviewer 1 comment 7: Thank you for this valuable suggestion. We appreciate the importance of triangulating findings to enhance the robustness of the study. For this research, we employed the Corruption Perceptions Index (CPI) from Transparency International due to its widespread acceptance, reliability, and comprehensive coverage across countries and time. The CPI is particularly well-suited for cross-country comparisons, which aligns with our study’s objective of analyzing corruption across the Americas. We acknowledge that exploring alternative data sources or indices, such as the Worldwide Governance Indicators (WGI) or Varieties of Democracy (V-Dem) dataset, could provide additional insights and strengthen our conclusions. While these datasets were considered during our initial planning, limitations such as shorter temporal coverage or lack of comparability for the specific variables of interest influenced our decision to focus on the CPI. To address your suggestion, we propose incorporating these alternative indices in future research to assess the robustness of our findings. This would enable a more nuanced understanding of how different measures of corruption and governance quality interact with suicide rates. We also included a discussion in the limitations section of the manuscript, acknowledging the potential benefits of triangulating data sources and suggesting this as an area for future exploration. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Yamamura E. Reviewer Report For: The Hidden Toll: Investigating the influence of Corruption on Persistent Suicide in the Americas [version 3; peer review: 3 approved with reservations] . F1000Research 2025, 13 :1427 ( https://doi.org/10.5256/f1000research.173856.r344324 ) The direct URL for this report is: https://f1000research.com/articles/13-1427/v1#referee-response-344324 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 11 Dec 2024 Eiji Yamamura , Seinan Gakuin University, Fukuoka, Japan Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.173856.r344324 Report on ‘The Hidden Toll: Investigating the influence of Corruption on Persistent Suicide in the Americas The aim of the study is to examine correlation between government corruption and occurrence of suicide in countries in north, ... Continue reading READ ALL Report on ‘The Hidden Toll: Investigating the influence of Corruption on Persistent Suicide in the Americas The aim of the study is to examine correlation between government corruption and occurrence of suicide in countries in north, central and south America by considering economic condition. As widely known, hyper-inflation occurred in Venezuera. The outcome of inflation in countries in America is valuable to be addressed. However, readers would be confused due to puzzling definition of Corruption. Further, in some parts, interpretation of estimation results is not appropriate or not sufficiently provided. Further, estimation biases seem to occur. Authors should consider suggestions to improve the current version of manuscript. Major issues: 1. Corruption index might lead readers to misunderstand estimation results because the definition is opposed to intuition. According to Table 2, "Corruption Perception Index (0-100)" was defined by Transparency International. Ordinary saying, larger the value, the more corrupted the government is. However, in the text, to take an example of interpretation for corruption index, "As key observations, Argentina improved from 35 to 45, indicating a decrease in corruption,"(p.11). This shows that smaller value of “corruption” is more corrupted. This is opposed to reader’s intuition. Probably, authors follow the definition of the data source. However, I strongly request to modify the definition to be suitable to reader’s intuition. Instead of it, in Table 2, authors explained that the definition is changed from that of “Transparency International” for convenience of reader’s interpretation. 2. By definition of variable, values of COR, SUI, and UNEMP must not be negative. I am not familiar with Violin plots in Figure 5. However, sharded area of these values covered negative area. What does it mean? Authors just mechanically illustrated the Figure 5, but was not informative unless authors carefully and appropriately explain importance of Violin plots. Figure 5 of current version is very curious and might lead readers to be confused. In my view, it is enough to just show mean value and 95% CI in Figure. 3. In results Fixed effects model in Table 5, estimation results of constant are shown. Basically, different from OLS, constant is not calculated in Fixed effects model because constant is completely captured by unobservable time invariant factors (Fixed effects). What does constant mean in Table 5? 4. In Table 5, Lagged dependent variable is included as independent variable. In this model, the dynamic panel model is preferred, where Arellano-bond is the basic model although various types of dynamic model have been developed. Especially, time points is relatively long (20 years) and so the dynamic model is appropriated. Most of independent variables seems to be endogenous, that should be mitigated. Fortunately, the Dynamic model mechanically enables authors to deal with the issue of endogenous bias. 5. In conclusion, influence of lagged suicide variables is shown as below, “The lagged suicide variable repeatedly showed an overwhelmingly substantial positive association with current suicide rates, implying that once suicide rates rise in a country, they likely continue to be elevated over time.” (p.20). Their argument is too strong to be supported by the estimation results. In Fixed effects estimation results of Table 5, coefficient of lagged suicide variable is smaller than 1, despite being statistical significant positive sign. Suicide rate will converge among countries in the long run if the value is smaller than 1. That is, their argument is supported only if the value is statistically positive and larger than 1. Authors hardly interpret values of coefficients, which lead to misinterpretation. 6. In table 5, coefficient of CORR is not interpreted as effect of corruption because there are interaction terms between CORR and other variables. Authors should additionally report results including INFL, UNEMP, and EG, but excluding interaction term. 7. Authors only interpret statistical significance of independent variables in Table 5. Authors should more carefully value of coefficients to indicate degree of impact of variables (see also Comment 5). 8. Author’s interpretation about results of Table 5 is ambiguous, and not so informative for readers. For instance, “corruption combined with increased unemployment rates increases suicide rates in middle-income countries.” This is interpretation about “CORR×UNEMP” showing 0.0022 (coef) with 1 % level (***). Actually, authors interpretation above is meaningless. Indicate more clearly and meaningfully interpretation and implication of results. I request authors to show, In the definition of CORR in the paper, large value of CORR show less corrupted government. Hence, in my interpretation, positive value of “CORR×UNEMP” means that less corrupted government reduces the negative effect of unemployment on citizen’s mental health, and so suicide rate. Is it true? To argue as above, negative sign of “UNEMP” should be shown However, actually, authors did not indicate results of “UNEMP” in this estimation. So, authors should indicate not only interaction terms but also independent “UNEMP”. Further, how do authors interpret 0.0022 by considering scale of CRR and Unemployment? Apart from it, authors should interpret estimation results in more detail. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? No Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? No Competing Interests: No competing interests were disclosed. Reviewer Expertise: Empirical economics. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Yamamura E. Reviewer Report For: The Hidden Toll: Investigating the influence of Corruption on Persistent Suicide in the Americas [version 3; peer review: 3 approved with reservations] . F1000Research 2025, 13 :1427 ( https://doi.org/10.5256/f1000research.173856.r344324 ) The direct URL for this report is: https://f1000research.com/articles/13-1427/v1#referee-response-344324 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 28 Jan 2025 Ruwan Jayathilaka , SLIIT Business School, Sri Lanka Institute of Information Technology, Malabe, 10115, Sri Lanka 28 Jan 2025 Author Response Authors Response to Reviewer 1 Dear Reviewer 1, We greatly appreciate the time invested in reading our manuscript and providing the necessary feedback to improve the overall quality of ... Continue reading Authors Response to Reviewer 1 Dear Reviewer 1, We greatly appreciate the time invested in reading our manuscript and providing the necessary feedback to improve the overall quality of our work. All the mentioned comments have been considered and the revisions made accordingly. Thank you once again for your valuable feedback. Reviewer 1 comment 1: Corruption index might lead readers to misunderstand estimation results because the definition is opposed to intuition. According to Table 2, "Corruption Perception Index (0-100)" was defined by Transparency International. Ordinary saying, larger the value, the more corrupted the government is. However, in the text, to take an example of interpretation for corruption index, "As key observations, Argentina improved from 35 to 45, indicating a decrease in corruption,"(p.11). This shows that smaller value of “corruption” is more corrupted. This is opposed to reader’s intuition. Probably, authors follow the definition of the data source. However, I strongly request to modify the definition to be suitable to reader’s intuition. Instead of it, in Table 2, authors explained that the definition is changed from that of “Transparency International” for convenience of reader’s interpretation. Authors’ Response to Reviewer 1 comment 1: Thank you for highlighting this concern. As you fairly pointed out, we understand that the correct definition of the variable - “Corruption Score” (which represented the Corruption Perception Index), may battle with readers’ intuition, thinking the higher corruption score may indicate higher corruption. Therefore, we made changes in “Table 2” to clarify the measurement of Corruption Perception Index (0-100 scale, 100 is very clean and 0 is highly corrupt), and we changed the the variable name from Corruption score to corruption control score for readers clarity. This represents the definition and helps with readers' intuition on the variable’s behavior.) We also made some amendments to the manuscript body to ease readers’ understanding regarding this matter pointed out. Reviewer 1 comment 2: By definition of variable, values of COR, SUI, and UNEMP must not be negative. I am not familiar with Violin plots in Figure 5. However, sharded area of these values covered negative area. What does it mean? Authors just mechanically illustrated the Figure 5, but was not informative unless authors carefully and appropriately explain importance of Violin plots. Figure 5 of current version is very curious and might lead readers to be confused. In my view, it is enough to just show mean value and 95% CI in Figure. Authors’ Response to Reviewer 1 comment 2: As per the reviewer’s suggestion, we have replaced the violin plots figure with a new figure that illustrates mean value and 95% CI. A new detailed description was added to explain the figure (We have included this replaced Figure 5 in TIFF format in the Figures zip file attached herewith.). However, just to clarify the reviewer’s prior concern on Figure 5, the violin plots we illustrated extend into the negative values because they are based on a smoothed estimate of the data's distribution called kernel density estimation (KDE). This smoothing process doesn't strictly stop at the minimum or maximum values of the data - it spreads out to create a smooth curve. Even though there are no negative values in the dataset, the smoothing "overflows" slightly into the negative range because the KDE curve is designed to be continuous and smooth. However, the actual min and max values are in the upper and lower tails of the boxplot within the violin shape. But we understand the mean value and CI provides much more straightforward understanding for the study. Reviewer 1 comment 3: In results Fixed effects model in Table 5, estimation results of constant are shown. Basically, different from OLS, constant is not calculated in Fixed effects model because constant is completely captured by unobservable time invariant factors (Fixed effects). What does constant mean in Table 5? Authors’ Response to Reviewer 1 comment 3: Thank you for this valuable observation. We agree with you that in a Fixed Effects Model, the constant term is absorbed into the fixed effects and does not carry the same interpretative meaning as in an OLS model. In Table 5, the constant term represents the average fixed effects across all entities after accounting for deviations. However, its presence might be misleading in the context of FEM. To address this, we will revise Table 5 by adding a footnote clarifying that the constant is there for the completion of results, and reflects average fixed effects rather than a traditional intercept term. This ensures alignment with the theoretical framework of the FEM, and avoids potential misinterpretation. Reviewer 1 comment 4: In Table 5, Lagged dependent variable is included as independent variable. In this model, the dynamic panel model is preferred, where Arellano-bond is the basic model although various types of dynamic model have been developed. Especially, time points is relatively long (20 years) and so the dynamic model is appropriated. Most of independent variables seems to be endogenous, that should be mitigated. Fortunately, the Dynamic model mechanically enables authors to deal with the issue of endogenous bias. Authors’ Response to Reviewer 1 comment 4: Thank you for this thoughtful comment. We agree that addressing endogeneity is crucial, and we appreciate the suggestion to use a dynamic panel model like the Arellano-Bond estimator. However, we would like to clarify our rationale for using the Fixed Effects Model (FEM) with a lagged dependent variable: Purpose and Model Fit: The FEM was chosen to control for unobservable, time-invariant heterogeneity across countries in the Americas. This aligns with the study’s focus on examining variations in suicide rates influenced by country-specific characteristics. Lagged Dependent Variable and Time Period: While including a lagged dependent variable in FEM may introduce bias, this issue is significantly mitigated in panels with a relatively long time dimension. With 20 years of data, the Nickell bias is minimal, as supported by previous econometric literature (e.g., Judson & Owen, 1999). Endogeneity and Robustness: To address potential endogeneity, we employed robust standard errors and conducted specification tests. While dynamic models can mitigate endogeneity, their reliance on strong assumptions about instrument validity may introduce new challenges, especially with a small number of entities. Practical Considerations: The FEM results provide clear and interpretable insights for policymakers and stakeholders. Introducing a dynamic model could add complexity without significantly improving the robustness of our findings. Based on these considerations, we believe the Fixed Effects Model with a lagged dependent variable is an appropriate and reliable approach for our analysis. We are, however, open to further discussing the potential application of dynamic models in future iterations or complementary analyses. Reviewer 1 comment 5: In conclusion, influence of lagged suicide variables is shown as below, “The lagged suicide variable repeatedly showed an overwhelmingly substantial positive association with current suicide rates, implying that once suicide rates rise in a country, they likely continue to be elevated over time.” (p.20). Their argument is too strong to be supported by the estimation results. In Fixed effects estimation results of Table 5, coefficient of lagged suicide variable is smaller than 1, despite being statistical significant positive sign. Suicide rate will converge among countries in the long run if the value is smaller than 1. That is, their argument is supported only if the value is statistically positive and larger than 1. Authors hardly interpret values of coefficients, which lead to misinterpretation. Authors’ Response to Reviewer 1 comment 5: We appreciate the reviewer for pointing this out. As he advised, the argument is adjusted and explained. The coefficient values are explained descriptively. Reviewer 1 comment 6: In table 5, coefficient of CORR is not interpreted as effect of corruption because there are interaction terms between CORR and other variables. Authors should additionally report results including INFL, UNEMP, and EG, but excluding interaction term. Authors’ Response to Reviewer 1 comment 6: We express our gratitude for the thoughtful suggestion provided by the reviewer on including the direct effect of the chosen moderating factors. However, while this can be a valuable extension for our study, the current study’s scope limited to identifying the moderate effects. Therefore, we kindly suggest that measuring the individual effects of the moderating variables (INFL, UNEMP, and EG) may not be essential in this context. This study aims to explain how these moderating factors influence the strength and direction of corruption control’s impact on suicide rates. We made changes to the manuscript to explain this further. Reviewer 1 comment 7: Authors only interpret statistical significance of independent variables in Table 5. Authors should more carefully value of coefficients to indicate degree of impact of variables (see also Comment 5). Authors’ Response to Reviewer 1 comment 7: The manuscript is adjusted to explain the coefficient values more descriptively. Reviewer 1 comment 8: Author’s interpretation about results of Table 5 is ambiguous, and not so informative for readers. For instance, “corruption combined with increased unemployment rates increases suicide rates in middle-income countries.” This is interpretation about “CORR×UNEMP” showing 0.0022 (coef) with 1 % level (***). Actually, authors interpretation above is meaningless. Indicate more clearly and meaningfully interpretation and implication of results. I request authors to show, In the definition of CORR in the paper, large value of CORR show less corrupted government. Hence, in my interpretation, positive value of “CORR×UNEMP” means that less corrupted government reduces the negative effect of unemployment on citizen’s mental health, and so suicide rate. Is it true? To argue as above, negative sign of “UNEMP” should be shown However, actually, authors did not indicate results of “UNEMP” in this estimation. So, authors should indicate not only interaction terms but also independent “UNEMP”. Further, how do authors interpret 0.0022 by considering scale of CRR and Unemployment? Authors’ Response to Reviewer 1 comment 8: We sincerely appreciate the reviewer’s thoughtful request on clarifying his suggested interpretation - “A positive value of CORR × UNEMP means that less corruption reduces the negative effect of unemployment on citizens' mental health, thereby lowering the suicide rate.” However, we kindly note that this interpretation does not align with the focus our study. Our analysis suggests that unemployment moderates the impact of corruption control on suicide rates, rather than corruption control mitigating the negative effects of unemployment. As the study focuses on moderating factors, not mediating factors, we believe it is not necessary to measure the individual effects of the moderating variables (INFL, UNEMP, and EG). Instead, the study aims to explain how these moderating factors influence the strength and direction of corruption control’s impact on suicide rates. However, we have made amendments in the manuscript to highlight this to avoid any confusion to the readers. Authors Response to Reviewer 1 Dear Reviewer 1, We greatly appreciate the time invested in reading our manuscript and providing the necessary feedback to improve the overall quality of our work. All the mentioned comments have been considered and the revisions made accordingly. Thank you once again for your valuable feedback. Reviewer 1 comment 1: Corruption index might lead readers to misunderstand estimation results because the definition is opposed to intuition. According to Table 2, "Corruption Perception Index (0-100)" was defined by Transparency International. Ordinary saying, larger the value, the more corrupted the government is. However, in the text, to take an example of interpretation for corruption index, "As key observations, Argentina improved from 35 to 45, indicating a decrease in corruption,"(p.11). This shows that smaller value of “corruption” is more corrupted. This is opposed to reader’s intuition. Probably, authors follow the definition of the data source. However, I strongly request to modify the definition to be suitable to reader’s intuition. Instead of it, in Table 2, authors explained that the definition is changed from that of “Transparency International” for convenience of reader’s interpretation. Authors’ Response to Reviewer 1 comment 1: Thank you for highlighting this concern. As you fairly pointed out, we understand that the correct definition of the variable - “Corruption Score” (which represented the Corruption Perception Index), may battle with readers’ intuition, thinking the higher corruption score may indicate higher corruption. Therefore, we made changes in “Table 2” to clarify the measurement of Corruption Perception Index (0-100 scale, 100 is very clean and 0 is highly corrupt), and we changed the the variable name from Corruption score to corruption control score for readers clarity. This represents the definition and helps with readers' intuition on the variable’s behavior.) We also made some amendments to the manuscript body to ease readers’ understanding regarding this matter pointed out. Reviewer 1 comment 2: By definition of variable, values of COR, SUI, and UNEMP must not be negative. I am not familiar with Violin plots in Figure 5. However, sharded area of these values covered negative area. What does it mean? Authors just mechanically illustrated the Figure 5, but was not informative unless authors carefully and appropriately explain importance of Violin plots. Figure 5 of current version is very curious and might lead readers to be confused. In my view, it is enough to just show mean value and 95% CI in Figure. Authors’ Response to Reviewer 1 comment 2: As per the reviewer’s suggestion, we have replaced the violin plots figure with a new figure that illustrates mean value and 95% CI. A new detailed description was added to explain the figure (We have included this replaced Figure 5 in TIFF format in the Figures zip file attached herewith.). However, just to clarify the reviewer’s prior concern on Figure 5, the violin plots we illustrated extend into the negative values because they are based on a smoothed estimate of the data's distribution called kernel density estimation (KDE). This smoothing process doesn't strictly stop at the minimum or maximum values of the data - it spreads out to create a smooth curve. Even though there are no negative values in the dataset, the smoothing "overflows" slightly into the negative range because the KDE curve is designed to be continuous and smooth. However, the actual min and max values are in the upper and lower tails of the boxplot within the violin shape. But we understand the mean value and CI provides much more straightforward understanding for the study. Reviewer 1 comment 3: In results Fixed effects model in Table 5, estimation results of constant are shown. Basically, different from OLS, constant is not calculated in Fixed effects model because constant is completely captured by unobservable time invariant factors (Fixed effects). What does constant mean in Table 5? Authors’ Response to Reviewer 1 comment 3: Thank you for this valuable observation. We agree with you that in a Fixed Effects Model, the constant term is absorbed into the fixed effects and does not carry the same interpretative meaning as in an OLS model. In Table 5, the constant term represents the average fixed effects across all entities after accounting for deviations. However, its presence might be misleading in the context of FEM. To address this, we will revise Table 5 by adding a footnote clarifying that the constant is there for the completion of results, and reflects average fixed effects rather than a traditional intercept term. This ensures alignment with the theoretical framework of the FEM, and avoids potential misinterpretation. Reviewer 1 comment 4: In Table 5, Lagged dependent variable is included as independent variable. In this model, the dynamic panel model is preferred, where Arellano-bond is the basic model although various types of dynamic model have been developed. Especially, time points is relatively long (20 years) and so the dynamic model is appropriated. Most of independent variables seems to be endogenous, that should be mitigated. Fortunately, the Dynamic model mechanically enables authors to deal with the issue of endogenous bias. Authors’ Response to Reviewer 1 comment 4: Thank you for this thoughtful comment. We agree that addressing endogeneity is crucial, and we appreciate the suggestion to use a dynamic panel model like the Arellano-Bond estimator. However, we would like to clarify our rationale for using the Fixed Effects Model (FEM) with a lagged dependent variable: Purpose and Model Fit: The FEM was chosen to control for unobservable, time-invariant heterogeneity across countries in the Americas. This aligns with the study’s focus on examining variations in suicide rates influenced by country-specific characteristics. Lagged Dependent Variable and Time Period: While including a lagged dependent variable in FEM may introduce bias, this issue is significantly mitigated in panels with a relatively long time dimension. With 20 years of data, the Nickell bias is minimal, as supported by previous econometric literature (e.g., Judson & Owen, 1999). Endogeneity and Robustness: To address potential endogeneity, we employed robust standard errors and conducted specification tests. While dynamic models can mitigate endogeneity, their reliance on strong assumptions about instrument validity may introduce new challenges, especially with a small number of entities. Practical Considerations: The FEM results provide clear and interpretable insights for policymakers and stakeholders. Introducing a dynamic model could add complexity without significantly improving the robustness of our findings. Based on these considerations, we believe the Fixed Effects Model with a lagged dependent variable is an appropriate and reliable approach for our analysis. We are, however, open to further discussing the potential application of dynamic models in future iterations or complementary analyses. Reviewer 1 comment 5: In conclusion, influence of lagged suicide variables is shown as below, “The lagged suicide variable repeatedly showed an overwhelmingly substantial positive association with current suicide rates, implying that once suicide rates rise in a country, they likely continue to be elevated over time.” (p.20). Their argument is too strong to be supported by the estimation results. In Fixed effects estimation results of Table 5, coefficient of lagged suicide variable is smaller than 1, despite being statistical significant positive sign. Suicide rate will converge among countries in the long run if the value is smaller than 1. That is, their argument is supported only if the value is statistically positive and larger than 1. Authors hardly interpret values of coefficients, which lead to misinterpretation. Authors’ Response to Reviewer 1 comment 5: We appreciate the reviewer for pointing this out. As he advised, the argument is adjusted and explained. The coefficient values are explained descriptively. Reviewer 1 comment 6: In table 5, coefficient of CORR is not interpreted as effect of corruption because there are interaction terms between CORR and other variables. Authors should additionally report results including INFL, UNEMP, and EG, but excluding interaction term. Authors’ Response to Reviewer 1 comment 6: We express our gratitude for the thoughtful suggestion provided by the reviewer on including the direct effect of the chosen moderating factors. However, while this can be a valuable extension for our study, the current study’s scope limited to identifying the moderate effects. Therefore, we kindly suggest that measuring the individual effects of the moderating variables (INFL, UNEMP, and EG) may not be essential in this context. This study aims to explain how these moderating factors influence the strength and direction of corruption control’s impact on suicide rates. We made changes to the manuscript to explain this further. Reviewer 1 comment 7: Authors only interpret statistical significance of independent variables in Table 5. Authors should more carefully value of coefficients to indicate degree of impact of variables (see also Comment 5). Authors’ Response to Reviewer 1 comment 7: The manuscript is adjusted to explain the coefficient values more descriptively. Reviewer 1 comment 8: Author’s interpretation about results of Table 5 is ambiguous, and not so informative for readers. For instance, “corruption combined with increased unemployment rates increases suicide rates in middle-income countries.” This is interpretation about “CORR×UNEMP” showing 0.0022 (coef) with 1 % level (***). Actually, authors interpretation above is meaningless. Indicate more clearly and meaningfully interpretation and implication of results. I request authors to show, In the definition of CORR in the paper, large value of CORR show less corrupted government. Hence, in my interpretation, positive value of “CORR×UNEMP” means that less corrupted government reduces the negative effect of unemployment on citizen’s mental health, and so suicide rate. Is it true? To argue as above, negative sign of “UNEMP” should be shown However, actually, authors did not indicate results of “UNEMP” in this estimation. So, authors should indicate not only interaction terms but also independent “UNEMP”. Further, how do authors interpret 0.0022 by considering scale of CRR and Unemployment? Authors’ Response to Reviewer 1 comment 8: We sincerely appreciate the reviewer’s thoughtful request on clarifying his suggested interpretation - “A positive value of CORR × UNEMP means that less corruption reduces the negative effect of unemployment on citizens' mental health, thereby lowering the suicide rate.” However, we kindly note that this interpretation does not align with the focus our study. Our analysis suggests that unemployment moderates the impact of corruption control on suicide rates, rather than corruption control mitigating the negative effects of unemployment. As the study focuses on moderating factors, not mediating factors, we believe it is not necessary to measure the individual effects of the moderating variables (INFL, UNEMP, and EG). Instead, the study aims to explain how these moderating factors influence the strength and direction of corruption control’s impact on suicide rates. However, we have made amendments in the manuscript to highlight this to avoid any confusion to the readers. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 28 Jan 2025 Ruwan Jayathilaka , SLIIT Business School, Sri Lanka Institute of Information Technology, Malabe, 10115, Sri Lanka 28 Jan 2025 Author Response Authors Response to Reviewer 1 Dear Reviewer 1, We greatly appreciate the time invested in reading our manuscript and providing the necessary feedback to improve the overall quality of ... Continue reading Authors Response to Reviewer 1 Dear Reviewer 1, We greatly appreciate the time invested in reading our manuscript and providing the necessary feedback to improve the overall quality of our work. All the mentioned comments have been considered and the revisions made accordingly. Thank you once again for your valuable feedback. Reviewer 1 comment 1: Corruption index might lead readers to misunderstand estimation results because the definition is opposed to intuition. According to Table 2, "Corruption Perception Index (0-100)" was defined by Transparency International. Ordinary saying, larger the value, the more corrupted the government is. However, in the text, to take an example of interpretation for corruption index, "As key observations, Argentina improved from 35 to 45, indicating a decrease in corruption,"(p.11). This shows that smaller value of “corruption” is more corrupted. This is opposed to reader’s intuition. Probably, authors follow the definition of the data source. However, I strongly request to modify the definition to be suitable to reader’s intuition. Instead of it, in Table 2, authors explained that the definition is changed from that of “Transparency International” for convenience of reader’s interpretation. Authors’ Response to Reviewer 1 comment 1: Thank you for highlighting this concern. As you fairly pointed out, we understand that the correct definition of the variable - “Corruption Score” (which represented the Corruption Perception Index), may battle with readers’ intuition, thinking the higher corruption score may indicate higher corruption. Therefore, we made changes in “Table 2” to clarify the measurement of Corruption Perception Index (0-100 scale, 100 is very clean and 0 is highly corrupt), and we changed the the variable name from Corruption score to corruption control score for readers clarity. This represents the definition and helps with readers' intuition on the variable’s behavior.) We also made some amendments to the manuscript body to ease readers’ understanding regarding this matter pointed out. Reviewer 1 comment 2: By definition of variable, values of COR, SUI, and UNEMP must not be negative. I am not familiar with Violin plots in Figure 5. However, sharded area of these values covered negative area. What does it mean? Authors just mechanically illustrated the Figure 5, but was not informative unless authors carefully and appropriately explain importance of Violin plots. Figure 5 of current version is very curious and might lead readers to be confused. In my view, it is enough to just show mean value and 95% CI in Figure. Authors’ Response to Reviewer 1 comment 2: As per the reviewer’s suggestion, we have replaced the violin plots figure with a new figure that illustrates mean value and 95% CI. A new detailed description was added to explain the figure (We have included this replaced Figure 5 in TIFF format in the Figures zip file attached herewith.). However, just to clarify the reviewer’s prior concern on Figure 5, the violin plots we illustrated extend into the negative values because they are based on a smoothed estimate of the data's distribution called kernel density estimation (KDE). This smoothing process doesn't strictly stop at the minimum or maximum values of the data - it spreads out to create a smooth curve. Even though there are no negative values in the dataset, the smoothing "overflows" slightly into the negative range because the KDE curve is designed to be continuous and smooth. However, the actual min and max values are in the upper and lower tails of the boxplot within the violin shape. But we understand the mean value and CI provides much more straightforward understanding for the study. Reviewer 1 comment 3: In results Fixed effects model in Table 5, estimation results of constant are shown. Basically, different from OLS, constant is not calculated in Fixed effects model because constant is completely captured by unobservable time invariant factors (Fixed effects). What does constant mean in Table 5? Authors’ Response to Reviewer 1 comment 3: Thank you for this valuable observation. We agree with you that in a Fixed Effects Model, the constant term is absorbed into the fixed effects and does not carry the same interpretative meaning as in an OLS model. In Table 5, the constant term represents the average fixed effects across all entities after accounting for deviations. However, its presence might be misleading in the context of FEM. To address this, we will revise Table 5 by adding a footnote clarifying that the constant is there for the completion of results, and reflects average fixed effects rather than a traditional intercept term. This ensures alignment with the theoretical framework of the FEM, and avoids potential misinterpretation. Reviewer 1 comment 4: In Table 5, Lagged dependent variable is included as independent variable. In this model, the dynamic panel model is preferred, where Arellano-bond is the basic model although various types of dynamic model have been developed. Especially, time points is relatively long (20 years) and so the dynamic model is appropriated. Most of independent variables seems to be endogenous, that should be mitigated. Fortunately, the Dynamic model mechanically enables authors to deal with the issue of endogenous bias. Authors’ Response to Reviewer 1 comment 4: Thank you for this thoughtful comment. We agree that addressing endogeneity is crucial, and we appreciate the suggestion to use a dynamic panel model like the Arellano-Bond estimator. However, we would like to clarify our rationale for using the Fixed Effects Model (FEM) with a lagged dependent variable: Purpose and Model Fit: The FEM was chosen to control for unobservable, time-invariant heterogeneity across countries in the Americas. This aligns with the study’s focus on examining variations in suicide rates influenced by country-specific characteristics. Lagged Dependent Variable and Time Period: While including a lagged dependent variable in FEM may introduce bias, this issue is significantly mitigated in panels with a relatively long time dimension. With 20 years of data, the Nickell bias is minimal, as supported by previous econometric literature (e.g., Judson & Owen, 1999). Endogeneity and Robustness: To address potential endogeneity, we employed robust standard errors and conducted specification tests. While dynamic models can mitigate endogeneity, their reliance on strong assumptions about instrument validity may introduce new challenges, especially with a small number of entities. Practical Considerations: The FEM results provide clear and interpretable insights for policymakers and stakeholders. Introducing a dynamic model could add complexity without significantly improving the robustness of our findings. Based on these considerations, we believe the Fixed Effects Model with a lagged dependent variable is an appropriate and reliable approach for our analysis. We are, however, open to further discussing the potential application of dynamic models in future iterations or complementary analyses. Reviewer 1 comment 5: In conclusion, influence of lagged suicide variables is shown as below, “The lagged suicide variable repeatedly showed an overwhelmingly substantial positive association with current suicide rates, implying that once suicide rates rise in a country, they likely continue to be elevated over time.” (p.20). Their argument is too strong to be supported by the estimation results. In Fixed effects estimation results of Table 5, coefficient of lagged suicide variable is smaller than 1, despite being statistical significant positive sign. Suicide rate will converge among countries in the long run if the value is smaller than 1. That is, their argument is supported only if the value is statistically positive and larger than 1. Authors hardly interpret values of coefficients, which lead to misinterpretation. Authors’ Response to Reviewer 1 comment 5: We appreciate the reviewer for pointing this out. As he advised, the argument is adjusted and explained. The coefficient values are explained descriptively. Reviewer 1 comment 6: In table 5, coefficient of CORR is not interpreted as effect of corruption because there are interaction terms between CORR and other variables. Authors should additionally report results including INFL, UNEMP, and EG, but excluding interaction term. Authors’ Response to Reviewer 1 comment 6: We express our gratitude for the thoughtful suggestion provided by the reviewer on including the direct effect of the chosen moderating factors. However, while this can be a valuable extension for our study, the current study’s scope limited to identifying the moderate effects. Therefore, we kindly suggest that measuring the individual effects of the moderating variables (INFL, UNEMP, and EG) may not be essential in this context. This study aims to explain how these moderating factors influence the strength and direction of corruption control’s impact on suicide rates. We made changes to the manuscript to explain this further. Reviewer 1 comment 7: Authors only interpret statistical significance of independent variables in Table 5. Authors should more carefully value of coefficients to indicate degree of impact of variables (see also Comment 5). Authors’ Response to Reviewer 1 comment 7: The manuscript is adjusted to explain the coefficient values more descriptively. Reviewer 1 comment 8: Author’s interpretation about results of Table 5 is ambiguous, and not so informative for readers. For instance, “corruption combined with increased unemployment rates increases suicide rates in middle-income countries.” This is interpretation about “CORR×UNEMP” showing 0.0022 (coef) with 1 % level (***). Actually, authors interpretation above is meaningless. Indicate more clearly and meaningfully interpretation and implication of results. I request authors to show, In the definition of CORR in the paper, large value of CORR show less corrupted government. Hence, in my interpretation, positive value of “CORR×UNEMP” means that less corrupted government reduces the negative effect of unemployment on citizen’s mental health, and so suicide rate. Is it true? To argue as above, negative sign of “UNEMP” should be shown However, actually, authors did not indicate results of “UNEMP” in this estimation. So, authors should indicate not only interaction terms but also independent “UNEMP”. Further, how do authors interpret 0.0022 by considering scale of CRR and Unemployment? Authors’ Response to Reviewer 1 comment 8: We sincerely appreciate the reviewer’s thoughtful request on clarifying his suggested interpretation - “A positive value of CORR × UNEMP means that less corruption reduces the negative effect of unemployment on citizens' mental health, thereby lowering the suicide rate.” However, we kindly note that this interpretation does not align with the focus our study. Our analysis suggests that unemployment moderates the impact of corruption control on suicide rates, rather than corruption control mitigating the negative effects of unemployment. As the study focuses on moderating factors, not mediating factors, we believe it is not necessary to measure the individual effects of the moderating variables (INFL, UNEMP, and EG). Instead, the study aims to explain how these moderating factors influence the strength and direction of corruption control’s impact on suicide rates. However, we have made amendments in the manuscript to highlight this to avoid any confusion to the readers. Authors Response to Reviewer 1 Dear Reviewer 1, We greatly appreciate the time invested in reading our manuscript and providing the necessary feedback to improve the overall quality of our work. All the mentioned comments have been considered and the revisions made accordingly. Thank you once again for your valuable feedback. Reviewer 1 comment 1: Corruption index might lead readers to misunderstand estimation results because the definition is opposed to intuition. According to Table 2, "Corruption Perception Index (0-100)" was defined by Transparency International. Ordinary saying, larger the value, the more corrupted the government is. However, in the text, to take an example of interpretation for corruption index, "As key observations, Argentina improved from 35 to 45, indicating a decrease in corruption,"(p.11). This shows that smaller value of “corruption” is more corrupted. This is opposed to reader’s intuition. Probably, authors follow the definition of the data source. However, I strongly request to modify the definition to be suitable to reader’s intuition. Instead of it, in Table 2, authors explained that the definition is changed from that of “Transparency International” for convenience of reader’s interpretation. Authors’ Response to Reviewer 1 comment 1: Thank you for highlighting this concern. As you fairly pointed out, we understand that the correct definition of the variable - “Corruption Score” (which represented the Corruption Perception Index), may battle with readers’ intuition, thinking the higher corruption score may indicate higher corruption. Therefore, we made changes in “Table 2” to clarify the measurement of Corruption Perception Index (0-100 scale, 100 is very clean and 0 is highly corrupt), and we changed the the variable name from Corruption score to corruption control score for readers clarity. This represents the definition and helps with readers' intuition on the variable’s behavior.) We also made some amendments to the manuscript body to ease readers’ understanding regarding this matter pointed out. Reviewer 1 comment 2: By definition of variable, values of COR, SUI, and UNEMP must not be negative. I am not familiar with Violin plots in Figure 5. However, sharded area of these values covered negative area. What does it mean? Authors just mechanically illustrated the Figure 5, but was not informative unless authors carefully and appropriately explain importance of Violin plots. Figure 5 of current version is very curious and might lead readers to be confused. In my view, it is enough to just show mean value and 95% CI in Figure. Authors’ Response to Reviewer 1 comment 2: As per the reviewer’s suggestion, we have replaced the violin plots figure with a new figure that illustrates mean value and 95% CI. A new detailed description was added to explain the figure (We have included this replaced Figure 5 in TIFF format in the Figures zip file attached herewith.). However, just to clarify the reviewer’s prior concern on Figure 5, the violin plots we illustrated extend into the negative values because they are based on a smoothed estimate of the data's distribution called kernel density estimation (KDE). This smoothing process doesn't strictly stop at the minimum or maximum values of the data - it spreads out to create a smooth curve. Even though there are no negative values in the dataset, the smoothing "overflows" slightly into the negative range because the KDE curve is designed to be continuous and smooth. However, the actual min and max values are in the upper and lower tails of the boxplot within the violin shape. But we understand the mean value and CI provides much more straightforward understanding for the study. Reviewer 1 comment 3: In results Fixed effects model in Table 5, estimation results of constant are shown. Basically, different from OLS, constant is not calculated in Fixed effects model because constant is completely captured by unobservable time invariant factors (Fixed effects). What does constant mean in Table 5? Authors’ Response to Reviewer 1 comment 3: Thank you for this valuable observation. We agree with you that in a Fixed Effects Model, the constant term is absorbed into the fixed effects and does not carry the same interpretative meaning as in an OLS model. In Table 5, the constant term represents the average fixed effects across all entities after accounting for deviations. However, its presence might be misleading in the context of FEM. To address this, we will revise Table 5 by adding a footnote clarifying that the constant is there for the completion of results, and reflects average fixed effects rather than a traditional intercept term. This ensures alignment with the theoretical framework of the FEM, and avoids potential misinterpretation. Reviewer 1 comment 4: In Table 5, Lagged dependent variable is included as independent variable. In this model, the dynamic panel model is preferred, where Arellano-bond is the basic model although various types of dynamic model have been developed. Especially, time points is relatively long (20 years) and so the dynamic model is appropriated. Most of independent variables seems to be endogenous, that should be mitigated. Fortunately, the Dynamic model mechanically enables authors to deal with the issue of endogenous bias. Authors’ Response to Reviewer 1 comment 4: Thank you for this thoughtful comment. We agree that addressing endogeneity is crucial, and we appreciate the suggestion to use a dynamic panel model like the Arellano-Bond estimator. However, we would like to clarify our rationale for using the Fixed Effects Model (FEM) with a lagged dependent variable: Purpose and Model Fit: The FEM was chosen to control for unobservable, time-invariant heterogeneity across countries in the Americas. This aligns with the study’s focus on examining variations in suicide rates influenced by country-specific characteristics. Lagged Dependent Variable and Time Period: While including a lagged dependent variable in FEM may introduce bias, this issue is significantly mitigated in panels with a relatively long time dimension. With 20 years of data, the Nickell bias is minimal, as supported by previous econometric literature (e.g., Judson & Owen, 1999). Endogeneity and Robustness: To address potential endogeneity, we employed robust standard errors and conducted specification tests. While dynamic models can mitigate endogeneity, their reliance on strong assumptions about instrument validity may introduce new challenges, especially with a small number of entities. Practical Considerations: The FEM results provide clear and interpretable insights for policymakers and stakeholders. Introducing a dynamic model could add complexity without significantly improving the robustness of our findings. Based on these considerations, we believe the Fixed Effects Model with a lagged dependent variable is an appropriate and reliable approach for our analysis. We are, however, open to further discussing the potential application of dynamic models in future iterations or complementary analyses. Reviewer 1 comment 5: In conclusion, influence of lagged suicide variables is shown as below, “The lagged suicide variable repeatedly showed an overwhelmingly substantial positive association with current suicide rates, implying that once suicide rates rise in a country, they likely continue to be elevated over time.” (p.20). Their argument is too strong to be supported by the estimation results. In Fixed effects estimation results of Table 5, coefficient of lagged suicide variable is smaller than 1, despite being statistical significant positive sign. Suicide rate will converge among countries in the long run if the value is smaller than 1. That is, their argument is supported only if the value is statistically positive and larger than 1. Authors hardly interpret values of coefficients, which lead to misinterpretation. Authors’ Response to Reviewer 1 comment 5: We appreciate the reviewer for pointing this out. As he advised, the argument is adjusted and explained. The coefficient values are explained descriptively. Reviewer 1 comment 6: In table 5, coefficient of CORR is not interpreted as effect of corruption because there are interaction terms between CORR and other variables. Authors should additionally report results including INFL, UNEMP, and EG, but excluding interaction term. Authors’ Response to Reviewer 1 comment 6: We express our gratitude for the thoughtful suggestion provided by the reviewer on including the direct effect of the chosen moderating factors. However, while this can be a valuable extension for our study, the current study’s scope limited to identifying the moderate effects. Therefore, we kindly suggest that measuring the individual effects of the moderating variables (INFL, UNEMP, and EG) may not be essential in this context. This study aims to explain how these moderating factors influence the strength and direction of corruption control’s impact on suicide rates. We made changes to the manuscript to explain this further. Reviewer 1 comment 7: Authors only interpret statistical significance of independent variables in Table 5. Authors should more carefully value of coefficients to indicate degree of impact of variables (see also Comment 5). Authors’ Response to Reviewer 1 comment 7: The manuscript is adjusted to explain the coefficient values more descriptively. Reviewer 1 comment 8: Author’s interpretation about results of Table 5 is ambiguous, and not so informative for readers. For instance, “corruption combined with increased unemployment rates increases suicide rates in middle-income countries.” This is interpretation about “CORR×UNEMP” showing 0.0022 (coef) with 1 % level (***). Actually, authors interpretation above is meaningless. Indicate more clearly and meaningfully interpretation and implication of results. I request authors to show, In the definition of CORR in the paper, large value of CORR show less corrupted government. Hence, in my interpretation, positive value of “CORR×UNEMP” means that less corrupted government reduces the negative effect of unemployment on citizen’s mental health, and so suicide rate. Is it true? To argue as above, negative sign of “UNEMP” should be shown However, actually, authors did not indicate results of “UNEMP” in this estimation. So, authors should indicate not only interaction terms but also independent “UNEMP”. Further, how do authors interpret 0.0022 by considering scale of CRR and Unemployment? Authors’ Response to Reviewer 1 comment 8: We sincerely appreciate the reviewer’s thoughtful request on clarifying his suggested interpretation - “A positive value of CORR × UNEMP means that less corruption reduces the negative effect of unemployment on citizens' mental health, thereby lowering the suicide rate.” However, we kindly note that this interpretation does not align with the focus our study. Our analysis suggests that unemployment moderates the impact of corruption control on suicide rates, rather than corruption control mitigating the negative effects of unemployment. As the study focuses on moderating factors, not mediating factors, we believe it is not necessary to measure the individual effects of the moderating variables (INFL, UNEMP, and EG). Instead, the study aims to explain how these moderating factors influence the strength and direction of corruption control’s impact on suicide rates. However, we have made amendments in the manuscript to highlight this to avoid any confusion to the readers. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 26 Nov 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 Version 3 (revision) 08 Dec 25 Version 2 (revision) 28 Jan 25 read Version 1 26 Nov 24 read read Eiji Yamamura , Seinan Gakuin University, Fukuoka, Japan Febby Mutiara Nelson , University of Indonesia, Depok, Indonesia Josefa Ramoni-Perazzi , Universidad Industrial de Santander, Colombia, USA Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Ramoni-Perazzi J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 03 Nov 2025 | for Version 2 Josefa Ramoni-Perazzi , Universidad Industrial de Santander, Colombia, USA 0 Views copyright © 2025 Ramoni-Perazzi J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions I find the study very interesting, particularly in light of the growing concern over high suicide rates, especially among young people. Unfortunately, much of this trend has emerged after the period covered by the study. In relation to this, the study begins with a figure showing trends in suicide rates across continents. While most regions exhibit a declining trend, the Americas are presented as an exception with a reportedly increasing trend. However, this upward trend is barely perceptible in the graph. Furthermore, the document refers to differences in suicide rate behavior among subgroups of countries within the Americas, yet no figure is provided to support this claim. Including a disaggregated visualization is necessary to offer a clearer perspective on the evolution of the variable under study, particularly given the limited variation observed at the aggregate level throughout the analysis period. The model formulation requires further review. The explanatory variables —unemployment, inflation, economic growth, and corruption— are likely to be interrelated, potentially leading to multicollinearity issues. While fixed-effects panel data models help to mitigate certain biases, collinearity within the matrix of regressors may still affect the precision of the estimates and should be thoroughly assessed. The model implicitly assumes that suicide rates can be explained solely through economic variables, under the hypothesis that poverty —driven by corruption, which in turn hampers growth and limits access to the labor market— is the main determinant. Although suicide may indeed stem from despair linked to poor economic performance and lack of opportunities, other factors such as social dynamics, cultural aspects, and access to mental health care may play an equally or even more important role. These are not currently addressed in the model. Another critical issue is the absence of any strategy to address potential endogeneity. Factors such as culture, religion, social cohesion, and access to mental health services may influence both suicide rates and levels of corruption. Failing to control for these variables risks introducing bias into the estimates and undermines the causal validity of the results. Since the sample is not random and is limited to countries in the Americas, the use of random effects models is not methodologically appropriate. Consequently, the findings cannot be generalized to other countries with similar income levels outside the region. The results are valid only within the continental context analyzed and should not be extrapolated globally without caution. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Economics, econometrics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Ramoni-Perazzi J. Peer Review Report For: The Hidden Toll: Investigating the influence of Corruption on Persistent Suicide in the Americas [version 3; peer review: 3 approved with reservations] . F1000Research 2025, 13 :1427 ( https://doi.org/10.5256/f1000research.177046.r418328) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1427/v2#referee-response-418328 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Nelson F. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 08 Jan 2025 | for Version 1 Febby Mutiara Nelson , University of Indonesia, Depok, West Java, Indonesia 0 Views copyright © 2025 Nelson F. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Review Strenghts The study addresses a critical and understudied intersection of corruption and suicide rates in the Americas. By filling a gap in literature that lacks comprehensive regional analyses, it offers significant value for public health and governance discourse. The research employs a robust framework to explore moderating variables like unemployment, inflation, and economic growth, effectively contributing to a nuanced understanding of the socio-economic determinants of suicide. Areas for Improvement : Clarification of causal direction : The article implies a causal link between corruption and suicide but provides limited discussion of reverse causality or potential bidirectional influences. Policy Context : Although the article discusses policy implications, it could better integrate real-world examples or case studies where similar strategies have succeeded or failed. Methodological Review Variable Definitions and Justification : The rationale for using specific metrics like the Corruption Perceptions Index (CPI) needs more explicit justification regarding its relevance and limitations in capturing nuanced governance issues. Modeling of Interaction Terms : The interaction effects involving corruption, unemployment, and inflation need more theoretical grounding to explain why specific relationships are anticipated. Theoritical Review The literature review connects corruption to mental health through trust erosion and institutional failures, aligning well with existing governance theories Areas for Improvement : Theoretical Integration : The theoretical framework could be enriched by incorporating more psychological theories linking economic stressors directly to mental health outcomes. References to Durkheim’s theory of anomie or contemporary socio-political models would add depth. Conceptual Clarity : The term "persistence" of suicide rates is used frequently but could be defined more clearly, perhaps linking it to theories of path dependency in social outcomes. Overall Recommendations Strengthen the theoretical foundation by integrating more detailed explanations for the mechanisms connecting corruption, economic conditions, and mental health. Expand on policy recommendations by providing examples or scenarios demonstrating the impact of governance reforms on suicide rates. Consider alternative data sources or indices to triangulate findings on corruption and governance quality. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Criminal law, Corruption Eradication, Economic crimes. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 28 Jan 2025 Ruwan Jayathilaka, SLIIT Business School, Sri Lanka Institute of Information Technology, Malabe, 10115, Sri Lanka Authors Response to Reviewer 2 Dear Reviewer 2, We greatly appreciate the time invested in reading our manuscript and providing the necessary feedback to improve the overall quality of our work. All the mentioned comments have been considered and the revisions made accordingly. Thank you once again for your valuable feedback. Reviewer 1 comment 1: Clarification of causal direction: The article implies a causal link between corruption and suicide but provides limited discussion of reverse causality or potential bidirectional influences. Authors’ Response to Reviewer 1 comment 1: We sincerely appreciate the reviewer’s insightful comment. However, we would like to kindly clarify that this study aimed to investigate the impact of corruption on suicide rates in the Americas, particularly how corruption interacts with economic moderators such as unemployment and economic growth. The study does not aim to analyze the potential bidirectional relationship between corruption and suicide rates, as this is beyond the scope of the current research. While the potential bidirectional relationship between corruption and suicide rates is indeed an important and interesting area of inquiry, it falls outside the scope of the current research. We hope this clarification aligns with the intended objectives of our study. Reviewer 1 comment 2: Policy Context: Although the article discusses policy implications, it could better integrate real-world examples or case studies where similar strategies have succeeded or failed. Authors’ Response to Reviewer 1 comment As per the reviewer’s valuable suggestion, we included real-world examples with proper references in the study. Reviewer 1 comment 3: Variable Definitions and Justification: The rationale for using specific metrics like the Corruption Perceptions Index (CPI) needs more explicit justification regarding its relevance and limitations in capturing nuanced governance issues. Authors’ Response to Reviewer 1 comment 3: We are grateful for this thoughtful feedback. In light of the reviewer’s suggestion, we have updated the data section to provide a clearer rationale for the use of the Corruption Perceptions Index (CPI). The limitations section also updated to acknowledge the nuances it may not fully capture. Reviewer 1 comment 4: Modeling of Interaction Terms: The interaction effects involving corruption, unemployment, and inflation need more theoretical grounding to explain why specific relationships are anticipated. Authors’ Response to Reviewer 1 comment 4: Thank you for the suggestion. Kindly note that in Table 1, conceptual linkages are shown, as grounding work to the current study. The moderate effects in the current study will be introduced as a novel approach, to this specific study. Reviewer 1 comment 5: Theoretical Integration: The theoretical framework could be enriched by incorporating more psychological theories linking economic stressors directly to mental health outcomes. References to Durkheim’s theory of anomie or contemporary socio-political models would add depth. Authors’ Response to Reviewer 1 comment 5: Thank you for this insightful suggestion. In response, we have enriched the theoretical framework by incorporating additional psychological theories, including references to Durkheim’s theory of anomie and contemporary socio-political models, to provide a more comprehensive foundation for the study. Reviewer 1 comment 6: Conceptual Clarity: The term "persistence" of suicide rates is used frequently but could be defined more clearly, perhaps linking it to theories of path dependency in social outcomes. Authors’ Response to Reviewer 1 comment 6: We appreciate for requesting more clarification on this to improve the clarity of the manuscript. In this study, “Persistence” means the continuing nature of suicides or the tendency of past suicide rates affecting future suicide. Evidence for this is mainly generated through results. As per the reviewer’s concern, theis is clearly mentioned in this revised version. Reviewer 1 comment 7: Consider alternative data sources or indices to triangulate findings on corruption and governance quality. Authors’ Response to Reviewer 1 comment 7: Thank you for this valuable suggestion. We appreciate the importance of triangulating findings to enhance the robustness of the study. For this research, we employed the Corruption Perceptions Index (CPI) from Transparency International due to its widespread acceptance, reliability, and comprehensive coverage across countries and time. The CPI is particularly well-suited for cross-country comparisons, which aligns with our study’s objective of analyzing corruption across the Americas. We acknowledge that exploring alternative data sources or indices, such as the Worldwide Governance Indicators (WGI) or Varieties of Democracy (V-Dem) dataset, could provide additional insights and strengthen our conclusions. While these datasets were considered during our initial planning, limitations such as shorter temporal coverage or lack of comparability for the specific variables of interest influenced our decision to focus on the CPI. To address your suggestion, we propose incorporating these alternative indices in future research to assess the robustness of our findings. This would enable a more nuanced understanding of how different measures of corruption and governance quality interact with suicide rates. We also included a discussion in the limitations section of the manuscript, acknowledging the potential benefits of triangulating data sources and suggesting this as an area for future exploration. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Nelson FM. Peer Review Report For: The Hidden Toll: Investigating the influence of Corruption on Persistent Suicide in the Americas [version 3; peer review: 3 approved with reservations] . F1000Research 2025, 13 :1427 ( https://doi.org/10.5256/f1000research.173856.r347489) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1427/v1#referee-response-347489 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Yamamura E. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 11 Dec 2024 | for Version 1 Eiji Yamamura , Seinan Gakuin University, Fukuoka, Japan 0 Views copyright © 2024 Yamamura E. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Report on ‘The Hidden Toll: Investigating the influence of Corruption on Persistent Suicide in the Americas The aim of the study is to examine correlation between government corruption and occurrence of suicide in countries in north, central and south America by considering economic condition. As widely known, hyper-inflation occurred in Venezuera. The outcome of inflation in countries in America is valuable to be addressed. However, readers would be confused due to puzzling definition of Corruption. Further, in some parts, interpretation of estimation results is not appropriate or not sufficiently provided. Further, estimation biases seem to occur. Authors should consider suggestions to improve the current version of manuscript. Major issues: 1. Corruption index might lead readers to misunderstand estimation results because the definition is opposed to intuition. According to Table 2, "Corruption Perception Index (0-100)" was defined by Transparency International. Ordinary saying, larger the value, the more corrupted the government is. However, in the text, to take an example of interpretation for corruption index, "As key observations, Argentina improved from 35 to 45, indicating a decrease in corruption,"(p.11). This shows that smaller value of “corruption” is more corrupted. This is opposed to reader’s intuition. Probably, authors follow the definition of the data source. However, I strongly request to modify the definition to be suitable to reader’s intuition. Instead of it, in Table 2, authors explained that the definition is changed from that of “Transparency International” for convenience of reader’s interpretation. 2. By definition of variable, values of COR, SUI, and UNEMP must not be negative. I am not familiar with Violin plots in Figure 5. However, sharded area of these values covered negative area. What does it mean? Authors just mechanically illustrated the Figure 5, but was not informative unless authors carefully and appropriately explain importance of Violin plots. Figure 5 of current version is very curious and might lead readers to be confused. In my view, it is enough to just show mean value and 95% CI in Figure. 3. In results Fixed effects model in Table 5, estimation results of constant are shown. Basically, different from OLS, constant is not calculated in Fixed effects model because constant is completely captured by unobservable time invariant factors (Fixed effects). What does constant mean in Table 5? 4. In Table 5, Lagged dependent variable is included as independent variable. In this model, the dynamic panel model is preferred, where Arellano-bond is the basic model although various types of dynamic model have been developed. Especially, time points is relatively long (20 years) and so the dynamic model is appropriated. Most of independent variables seems to be endogenous, that should be mitigated. Fortunately, the Dynamic model mechanically enables authors to deal with the issue of endogenous bias. 5. In conclusion, influence of lagged suicide variables is shown as below, “The lagged suicide variable repeatedly showed an overwhelmingly substantial positive association with current suicide rates, implying that once suicide rates rise in a country, they likely continue to be elevated over time.” (p.20). Their argument is too strong to be supported by the estimation results. In Fixed effects estimation results of Table 5, coefficient of lagged suicide variable is smaller than 1, despite being statistical significant positive sign. Suicide rate will converge among countries in the long run if the value is smaller than 1. That is, their argument is supported only if the value is statistically positive and larger than 1. Authors hardly interpret values of coefficients, which lead to misinterpretation. 6. In table 5, coefficient of CORR is not interpreted as effect of corruption because there are interaction terms between CORR and other variables. Authors should additionally report results including INFL, UNEMP, and EG, but excluding interaction term. 7. Authors only interpret statistical significance of independent variables in Table 5. Authors should more carefully value of coefficients to indicate degree of impact of variables (see also Comment 5). 8. Author’s interpretation about results of Table 5 is ambiguous, and not so informative for readers. For instance, “corruption combined with increased unemployment rates increases suicide rates in middle-income countries.” This is interpretation about “CORR×UNEMP” showing 0.0022 (coef) with 1 % level (***). Actually, authors interpretation above is meaningless. Indicate more clearly and meaningfully interpretation and implication of results. I request authors to show, In the definition of CORR in the paper, large value of CORR show less corrupted government. Hence, in my interpretation, positive value of “CORR×UNEMP” means that less corrupted government reduces the negative effect of unemployment on citizen’s mental health, and so suicide rate. Is it true? To argue as above, negative sign of “UNEMP” should be shown However, actually, authors did not indicate results of “UNEMP” in this estimation. So, authors should indicate not only interaction terms but also independent “UNEMP”. Further, how do authors interpret 0.0022 by considering scale of CRR and Unemployment? Apart from it, authors should interpret estimation results in more detail. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? No Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? No Competing Interests No competing interests were disclosed. Reviewer Expertise Empirical economics. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 28 Jan 2025 Ruwan Jayathilaka, SLIIT Business School, Sri Lanka Institute of Information Technology, Malabe, 10115, Sri Lanka Authors Response to Reviewer 1 Dear Reviewer 1, We greatly appreciate the time invested in reading our manuscript and providing the necessary feedback to improve the overall quality of our work. All the mentioned comments have been considered and the revisions made accordingly. Thank you once again for your valuable feedback. Reviewer 1 comment 1: Corruption index might lead readers to misunderstand estimation results because the definition is opposed to intuition. According to Table 2, "Corruption Perception Index (0-100)" was defined by Transparency International. Ordinary saying, larger the value, the more corrupted the government is. However, in the text, to take an example of interpretation for corruption index, "As key observations, Argentina improved from 35 to 45, indicating a decrease in corruption,"(p.11). This shows that smaller value of “corruption” is more corrupted. This is opposed to reader’s intuition. Probably, authors follow the definition of the data source. However, I strongly request to modify the definition to be suitable to reader’s intuition. Instead of it, in Table 2, authors explained that the definition is changed from that of “Transparency International” for convenience of reader’s interpretation. Authors’ Response to Reviewer 1 comment 1: Thank you for highlighting this concern. As you fairly pointed out, we understand that the correct definition of the variable - “Corruption Score” (which represented the Corruption Perception Index), may battle with readers’ intuition, thinking the higher corruption score may indicate higher corruption. Therefore, we made changes in “Table 2” to clarify the measurement of Corruption Perception Index (0-100 scale, 100 is very clean and 0 is highly corrupt), and we changed the the variable name from Corruption score to corruption control score for readers clarity. This represents the definition and helps with readers' intuition on the variable’s behavior.) We also made some amendments to the manuscript body to ease readers’ understanding regarding this matter pointed out. Reviewer 1 comment 2: By definition of variable, values of COR, SUI, and UNEMP must not be negative. I am not familiar with Violin plots in Figure 5. However, sharded area of these values covered negative area. What does it mean? Authors just mechanically illustrated the Figure 5, but was not informative unless authors carefully and appropriately explain importance of Violin plots. Figure 5 of current version is very curious and might lead readers to be confused. In my view, it is enough to just show mean value and 95% CI in Figure. Authors’ Response to Reviewer 1 comment 2: As per the reviewer’s suggestion, we have replaced the violin plots figure with a new figure that illustrates mean value and 95% CI. A new detailed description was added to explain the figure (We have included this replaced Figure 5 in TIFF format in the Figures zip file attached herewith.). However, just to clarify the reviewer’s prior concern on Figure 5, the violin plots we illustrated extend into the negative values because they are based on a smoothed estimate of the data's distribution called kernel density estimation (KDE). This smoothing process doesn't strictly stop at the minimum or maximum values of the data - it spreads out to create a smooth curve. Even though there are no negative values in the dataset, the smoothing "overflows" slightly into the negative range because the KDE curve is designed to be continuous and smooth. However, the actual min and max values are in the upper and lower tails of the boxplot within the violin shape. But we understand the mean value and CI provides much more straightforward understanding for the study. Reviewer 1 comment 3: In results Fixed effects model in Table 5, estimation results of constant are shown. Basically, different from OLS, constant is not calculated in Fixed effects model because constant is completely captured by unobservable time invariant factors (Fixed effects). What does constant mean in Table 5? Authors’ Response to Reviewer 1 comment 3: Thank you for this valuable observation. We agree with you that in a Fixed Effects Model, the constant term is absorbed into the fixed effects and does not carry the same interpretative meaning as in an OLS model. In Table 5, the constant term represents the average fixed effects across all entities after accounting for deviations. However, its presence might be misleading in the context of FEM. To address this, we will revise Table 5 by adding a footnote clarifying that the constant is there for the completion of results, and reflects average fixed effects rather than a traditional intercept term. This ensures alignment with the theoretical framework of the FEM, and avoids potential misinterpretation. Reviewer 1 comment 4: In Table 5, Lagged dependent variable is included as independent variable. In this model, the dynamic panel model is preferred, where Arellano-bond is the basic model although various types of dynamic model have been developed. Especially, time points is relatively long (20 years) and so the dynamic model is appropriated. Most of independent variables seems to be endogenous, that should be mitigated. Fortunately, the Dynamic model mechanically enables authors to deal with the issue of endogenous bias. Authors’ Response to Reviewer 1 comment 4: Thank you for this thoughtful comment. We agree that addressing endogeneity is crucial, and we appreciate the suggestion to use a dynamic panel model like the Arellano-Bond estimator. However, we would like to clarify our rationale for using the Fixed Effects Model (FEM) with a lagged dependent variable: Purpose and Model Fit: The FEM was chosen to control for unobservable, time-invariant heterogeneity across countries in the Americas. This aligns with the study’s focus on examining variations in suicide rates influenced by country-specific characteristics. Lagged Dependent Variable and Time Period: While including a lagged dependent variable in FEM may introduce bias, this issue is significantly mitigated in panels with a relatively long time dimension. With 20 years of data, the Nickell bias is minimal, as supported by previous econometric literature (e.g., Judson & Owen, 1999). Endogeneity and Robustness: To address potential endogeneity, we employed robust standard errors and conducted specification tests. While dynamic models can mitigate endogeneity, their reliance on strong assumptions about instrument validity may introduce new challenges, especially with a small number of entities. Practical Considerations: The FEM results provide clear and interpretable insights for policymakers and stakeholders. Introducing a dynamic model could add complexity without significantly improving the robustness of our findings. Based on these considerations, we believe the Fixed Effects Model with a lagged dependent variable is an appropriate and reliable approach for our analysis. We are, however, open to further discussing the potential application of dynamic models in future iterations or complementary analyses. Reviewer 1 comment 5: In conclusion, influence of lagged suicide variables is shown as below, “The lagged suicide variable repeatedly showed an overwhelmingly substantial positive association with current suicide rates, implying that once suicide rates rise in a country, they likely continue to be elevated over time.” (p.20). Their argument is too strong to be supported by the estimation results. In Fixed effects estimation results of Table 5, coefficient of lagged suicide variable is smaller than 1, despite being statistical significant positive sign. Suicide rate will converge among countries in the long run if the value is smaller than 1. That is, their argument is supported only if the value is statistically positive and larger than 1. Authors hardly interpret values of coefficients, which lead to misinterpretation. Authors’ Response to Reviewer 1 comment 5: We appreciate the reviewer for pointing this out. As he advised, the argument is adjusted and explained. The coefficient values are explained descriptively. Reviewer 1 comment 6: In table 5, coefficient of CORR is not interpreted as effect of corruption because there are interaction terms between CORR and other variables. Authors should additionally report results including INFL, UNEMP, and EG, but excluding interaction term. Authors’ Response to Reviewer 1 comment 6: We express our gratitude for the thoughtful suggestion provided by the reviewer on including the direct effect of the chosen moderating factors. However, while this can be a valuable extension for our study, the current study’s scope limited to identifying the moderate effects. Therefore, we kindly suggest that measuring the individual effects of the moderating variables (INFL, UNEMP, and EG) may not be essential in this context. This study aims to explain how these moderating factors influence the strength and direction of corruption control’s impact on suicide rates. We made changes to the manuscript to explain this further. Reviewer 1 comment 7: Authors only interpret statistical significance of independent variables in Table 5. Authors should more carefully value of coefficients to indicate degree of impact of variables (see also Comment 5). Authors’ Response to Reviewer 1 comment 7: The manuscript is adjusted to explain the coefficient values more descriptively. Reviewer 1 comment 8: Author’s interpretation about results of Table 5 is ambiguous, and not so informative for readers. For instance, “corruption combined with increased unemployment rates increases suicide rates in middle-income countries.” This is interpretation about “CORR×UNEMP” showing 0.0022 (coef) with 1 % level (***). Actually, authors interpretation above is meaningless. Indicate more clearly and meaningfully interpretation and implication of results. I request authors to show, In the definition of CORR in the paper, large value of CORR show less corrupted government. Hence, in my interpretation, positive value of “CORR×UNEMP” means that less corrupted government reduces the negative effect of unemployment on citizen’s mental health, and so suicide rate. Is it true? To argue as above, negative sign of “UNEMP” should be shown However, actually, authors did not indicate results of “UNEMP” in this estimation. So, authors should indicate not only interaction terms but also independent “UNEMP”. Further, how do authors interpret 0.0022 by considering scale of CRR and Unemployment? Authors’ Response to Reviewer 1 comment 8: We sincerely appreciate the reviewer’s thoughtful request on clarifying his suggested interpretation - “A positive value of CORR × UNEMP means that less corruption reduces the negative effect of unemployment on citizens' mental health, thereby lowering the suicide rate.” However, we kindly note that this interpretation does not align with the focus our study. Our analysis suggests that unemployment moderates the impact of corruption control on suicide rates, rather than corruption control mitigating the negative effects of unemployment. As the study focuses on moderating factors, not mediating factors, we believe it is not necessary to measure the individual effects of the moderating variables (INFL, UNEMP, and EG). Instead, the study aims to explain how these moderating factors influence the strength and direction of corruption control’s impact on suicide rates. However, we have made amendments in the manuscript to highlight this to avoid any confusion to the readers. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Yamamura E. Peer Review Report For: The Hidden Toll: Investigating the influence of Corruption on Persistent Suicide in the Americas [version 3; peer review: 3 approved with reservations] . 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