Macroeconomic and Social Precursors of Suicide Rates in the Philippines: A Quantitative Analysis (Preprint)

BACKGROUND Suicide is a complex, serious and multifaceted public health issue that poses significant challenges to societies worldwide. In fact, it represents a significant global health crisis wherein approximately 800,000 people die by suicide, and the actual numbers could be higher due to underreporting and misclassification of deaths (Van Harmelen et al., 2019; WHO, 2021). In 2019, suicide ranked as the fourth leading cause of death among individuals aged 15 to 29 worldwide. A substantial majority of global suicides, about 77%, were reported in low- and middle-income countries (WHO, 2021). In the United States alone, more than 47,500 individuals died from suicide in 2019, marking a 33% increase in the suicide rate from 1999 to 2019 (Abdou et al., 2022). In recent years, it has garnered increased attention from policymakers, researchers, and healthcare professionals due to its devastating impact on individuals, families, and communities. The Philippines, like many other countries, has not been immune to the concerning rise in suicide rates. Its suicide rate stood at 2.5 per 100,000 population in 2019, according to data from the Department of Health (2019). According also to the most recent statistics released by the Department of Education, 404 students took their own lives during the 2021-2022 school year, with an astonishing 2,147 students making suicide attempts within the same timeframe (Pineda, 2023). This underscores the vital importance of identifying the root causes and contributing factors to suicide in order to develop effective prevention strategies and raise public consciousness. In this study, the researcher explores the potential link between some macroeconomic variables that are said to be correlated with suicide rates, examining if a significant correlation exists. In recent years, advancements in data analytics and predictive modeling techniques have enabled researchers to explore the relationship between macroeconomic indicators and suicide rates more comprehensively. By leveraging large datasets and sophisticated analytical tools, researchers can identify patterns, trends, and potential risk factors that may contribute to suicidal behaviors. Various macroeconomic variables, such as urban population growth, unemployment rate, and access to technology, reflect the broader socio-economic environment in which individuals live (Smith, 2020). These factors can significantly influence an individual's sense of well-being, financial stability, and social connectedness, all of which are known to impact suicide risk (Jones & Brown, 2018). Rapid population growth often leads to increased urbanization, which can result in higher levels of anonymity, social isolation, and reduced community cohesion. These factors are known to contribute to the risk of mental health issues, including depression and anxiety, which are significant predictors of suicide (Miller, 2021). Moreover, urban settings with fast-growing populations may struggle to provide adequate mental health services, leaving individuals without the support they need during crises. Furthermore, unemployment rates, which are often exacerbated by rapid population growth, are strongly associated with increased suicide rates. The lack of economic stability and the stress associated with unemployment can lead to feelings of hopelessness and despair, which are risk factors for suicide (Chang et al., 2019). This study aims to contribute to the existing literature by conducting a predictive analysis of macroeconomic variables and their association with suicide rates in the Philippines. Specifically, we will examine the influence of urban population growth, unemployment rate, internet usage, and mobile cellular subscriptions on suicide rates over a specified period. Understanding the factors that contribute to suicide is crucial for developing effective prevention strategies and interventions. While individual-level risk factors such as mental illness and substance abuse have been extensively studied, there is growing recognition of the role that macroeconomic conditions play in shaping population-level suicide rates. By gaining insights into the macroeconomic factors that influence suicide risk, this research seeks to inform evidence-based policy initiatives and interventions aimed at preventing suicide and promoting mental well-being in the Philippines. The study aims to enhance our understanding of the relationship between macroeconomic factors and suicide rates in the Philippines and provide valuable insights for informing evidence-based interventions and policies aimed at reducing suicide risk and promoting mental well-being in the population in response also with Sustainable Development Goal (SDG) # 3. Specifically, this study has the following OBJECTIVE 1. To analyze the trends and patterns of suicide rates in the Philippines over a specified period. 2. To identify and examine the key macroeconomic variables that may influence suicide rates in the Philippines, including urban population growth, unemployment rate, and internet usage. 3. To assess the correlation between urban population growth, unemployment rate, internet usage, and suicide rates in the Philippines. 4. To evaluate the implications of the study findings for policymakers, public health professionals, and other stakeholders involved in suicide prevention efforts in the Philippines. Method Used This study employed a quantitative research design to examine the correlation between suicide rates, urban population growth, internet usage, and the unemployment rate using secondary data. The study analyzed trends over multiple years to determine statistical relationships among these variables. The following outlines the methodological framework for the study: Data Collection. The data were collected from World Bank Open Data, ensuring that all variables were derived from a consistent and reliable source. Suicide rates, measured as the number of suicides per 100,000 people, served as the dependent variable. The independent variables included urban population growth (annual percentage increase in the urban population), internet usage (percentage of the population with internet access), and the unemployment rate (percentage of the labor force without employment). The data covered multiple years, depending on availability, to capture long-term trends and patterns. Data Preprocessing. Before analysis, the collected data underwent preprocessing, which involved handling missing values, ensuring consistency in measurement units, and standardizing the dataset for statistical analysis. Descriptive statistics were used to summarize the distribution and trends of each variable. For inferential analysis, Pearson’s correlation coefficient was used to measure the strength and direction of relationships between the variables. A multiple regression analysis was conducted to assess the combined effect of urban population growth, internet usage, and the unemployment rate on suicide rates. Scatterplots and correlation matrices were generated to visualize the relationships. Interpretation and Implications. The findings of the study will be interpreted in the context of existing literature and theoretical frameworks related to suicide and macroeconomics. The implications of the study findings for policymakers, public health professionals, and other stakeholders will be discussed, highlighting potential policy interventions and recommendations for suicide prevention efforts in the Philippines. The limitations of the study, such as data constraints, methodological assumptions, and potential biases, will be acknowledged and discussed. Ethical considerations related to data privacy, confidentiality, and responsible dissemination of findings will be addressed in accordance with ethical guidelines and protocols. Sources of Data This study utilized secondary data from World Bank Open Data, a publicly accessible database that compiles standardized global economic and social indicators from national governments, international organizations, and research institutions. The dataset included suicide rates (number of suicides per 100,000 people) sourced from global health reports, particularly the World Health Organization (WHO). Urban population growth, measured as the annual percentage increase in urban population, was derived from national censuses and demographic surveys. Internet usage, represented by the percentage of individuals using the internet, was obtained from reports by the International Telecommunication Union (ITU) and national telecommunications agencies. Unemployment rate, defined as the percentage of the labor force actively seeking but unable to find employment, was gathered from labor force surveys conducted by national statistical offices. The dataset covered multiple years to facilitate trend analysis, with only countries and years containing complete data included to ensure accuracy. While the use of World Bank Open Data provided broad geographic coverage and methodological consistency, limitations such as variations in data collection methods and potential underreporting of suicide cases were acknowledged. Despite these challenges, the dataset offered a reliable foundation for examining the statistical relationships among suicide rates, urban population growth, internet usage, and unemployment rates. Data Gathering Instrument This study utilized secondary data extraction as the primary data-gathering METHODS The data were obtained from World Bank Open Data, a publicly accessible database that compiles economic and social indicators from national governments, international organizations, and research institutions. No primary data collection instruments, such as surveys or interviews, were used, as all data were pre-recorded and standardized. A data extraction sheet was developed to systematically record the following variables: suicide rates (number of suicides per 100,000 people), urban population growth (annual percentage increase in the urban population), internet usage (percentage of individuals using the internet), and unemployment rate (percentage of the labor force actively seeking employment). The extraction sheet included columns for the country, year, and values for each variable to ensure consistency in data recording. To maintain data accuracy, only years with complete datasets for all four variables were included. The extracted data were reviewed for missing values, inconsistencies, and reporting variations before being processed for statistical analysis. The use of World Bank Open Data ensured that the study relied on verified, globally recognized data sources, enhancing the reliability of findings. Sampling Technique This study employed a total population sampling technique, as it analyzed suicide rates and macroeconomic indicators for the entire Philippine population over a specified period (2000–2019). Since the study utilized secondary data from World Bank Open Data, it did not involve direct participant selection. Instead, it included all available data points that met the completeness criteria for suicide rates, urban population growth, internet usage, and unemployment rate within the Philippines. This approach ensured a comprehensive and unbiased analysis of national trends without the limitations of sampling errors or selection bias. Procedure of the Study By following rigorous data gathering procedures, the study derived accurate and reliable insights into the economic determinants of suicide in the Philippines. The following sections detail the steps involved in identifying data sources, accessing public databases, extracting and compiling data, ensuring data quality and integrity, and adhering to ethical considerations throughout the research process. Data Collection. Relevant data on suicide rates, urban population growth, internet usage, and unemployment rates in the Philippines from 2000 to 2019 were extracted from World Bank Open Data. The dataset was reviewed to ensure completeness and consistency. Data Cleaning and Preparation. The extracted data were examined for missing values, inconsistencies, and anomalies. Data points with incomplete records were excluded to maintain analytical accuracy. Values were standardized where necessary to ensure comparability across years. Descriptive Analysis. The study first analyzed trends and patterns for each variable over the 20-year period, identifying key fluctuations and significant changes. Visual representations such as line graphs and summary tables were used to illustrate trends. Correlation Analysis. Pearson’s correlation coefficient was applied to determine the strength and direction of relationships between suicide rates and each macroeconomic variable. The analysis assessed whether urban population growth, internet usage, and unemployment rate had significant positive or negative correlations with suicide rates. Interpretation of Findings. The statistical results were analyzed in the context of economic and social factors influencing mental health. The study examined possible explanations for observed trends, considering external factors such as technological advancements, economic crises, and urbanization pressures. Policy Implications and Recommendations. Based on the findings, the study discussed implications for policymakers, public health professionals, and suicide prevention efforts. Recommendations were made for integrating economic stability, urban planning, and digital mental health resources into national suicide prevention strategies. Statistical Treatment The study derived meaningful insights into the relationship between macroeconomic variables and suicide rates in the Philippines by applying these statistical treatments: Descriptive Statistics. Descriptive statistics was employed to summarize and describe the central tendency, dispersion, and distribution of variables included in the analysis. Measures such as mean, median, standard deviation, and range was calculated to provide insights into the characteristics of the data. Correlation Analysis. Pearson correlation coefficients was calculated to examine the relationships between macroeconomic variables (urban population growth, unemployment rate, internet usage) and suicide rates in the Philippines. Correlation matrix was used to visualize the strength and direction of correlations between variables, allowing for the identification of potential associations and patterns. Urban Population Growth (Annual %) in the Philippines (2000-2019) It can be seen in Table 1 the annual percentage growth of the urban population in the Philippines from the year 2000 to 2019. The growth rates exhibit a general trend with fluctuations that reflect the socio-economic dynamics influencing urbanization over the two decades. Particularly, in 2000, the urban population growth rate was 2.03%. There is a noticeable downward trend in growth rates from 2000 to 2009, with a brief increase in 2010. By 2010, the growth rate slightly increased to 1.72%. Moreover, there was a significant spike in 2011, where the growth rate jumped to 2.20%. This spike was followed by a gradual decline until 2014, but another rise in 2015 to 2.08%. From 2016 onwards, the growth rate maintained relatively higher values, fluctuating around the 2.18% to 2.21% range. The data ends with a growth rate of 2.17% in 2019. Table 1. Urban Population Growth (Annual %) in the Philippines (2000-2019) YEAR Urban Population Growth 2000 2.032722259 2001 1.932456962 2002 1.877924114 2003 1.833219464 2004 1.801687765 2005 1.749972532 2006 1.69789219 2007 1.683873839 2008 1.683608804 2009 1.655072885 2010 1.715649639 2011 2.199913067 2012 2.160037149 2013 2.103918131 2014 2.029903104 2015 2.083353285 2016 2.18564514 2017 2.205434066 2018 2.181074695 2019 2.169771157 The overall trend from 2000 to 2019 shows a decrease in the urban population growth rate with notable fluctuations. Early 2000s show a steady decline in the growth rate until a brief recovery in 2010. The significant spike in 2011 indicates a sudden surge in urban population growth which could be due to economic factors, migration patterns, or government policies promoting urban development. Post-2011, there are slight variations, but the growth rate tends to hover around the 2% mark, suggesting a stabilization in urban growth dynamics. The recent years from 2016 to 2019 indicate a more stable urban growth rate, fluctuating slightly but remaining around 2.18% to 2.21%. Figure 1 shows a visual presentation of the urban population growth (annual %) in the Philippines from 2000 to 2019. The plot illustrates the general trends and fluctuations in the growth rate over the two decades, highlighting key periods of increase and stabilization. Figure 1. Urban Population Growth (Annual %) in the Philippines (2000-2019) Economic growth and employment opportunities in urban areas likely drive higher urban population growth. Economic downturns or slower economic growth periods might correlate with the lower urban growth rates observed. Policies aimed at rural development or controlling urban sprawl could influence the urban population growth rates. Urbanization policies, infrastructure development, and housing initiatives could explain the spikes and stability in certain periods. Internal migration from rural to urban areas for better employment, education, and living standards plays a significant role. External factors such as natural disasters or agricultural downturns in rural areas might push people towards urban centers. Internet Usage in the Philippines (2000-2019) Table 2 presents the percentage of the population using the internet in the Philippines from 2000 to 2019. It can be seen that from 2000 to 2005, internet usage grew steadily from 1.98% to 5.40%. The growth was relatively slow but consistent, reflecting the early stages of internet adoption in the Philippines. During this period, infrastructure development, affordability, and awareness likely played crucial roles in driving adoption. From 2006 to 2009, internet usage increased from 5.74% to 9.00%. The rise became more noticeable, indicating a growing acceptance and integration of the internet into daily life. In 2010, internet usage jumped significantly to 25%. This substantial increase can be attributed to major improvements in internet infrastructure, the widespread availability of mobile internet, and possibly significant government or private sector initiatives aimed at increasing internet penetration. From 2011 to 2015, the percentage of internet users continued to rise steadily, reaching 36.90% by 2015. This period marked the expansion phase, where more Filipinos started using the internet for various purposes, including social media, communication, education, and business. The proliferation of smartphones and mobile data services played a critical role during this time, making the internet more accessible to a larger portion of the population. Internet usage continued to grow, reaching 44.10% in 2018 before slightly declining to 43.03% in 2019. The continued increase indicates a deepening of internet penetration across various demographics and regions. However, there was a slight drop in 2019 could be due to market saturation or other socio-economic factors impacting internet usage. Table 2. Internet Usage in the Philippines (2000-2019) YEAR Individuals using internet 2000 1.982253196 2001 2.52400566 2002 4.332275746 2003 4.857672267 2004 5.243628452 2005 5.397636329 2006 5.740586325 2007 5.97 2008 6.22 2009 9 2010 25 2011 29 2012 30.8 2013 32.7 2014 34.7 2015 36.9 2016 39.2 2017 41.6 2018 44.1 2019 43.02661187 Figure 2 shows a visual presentation of the trend in internet usage in the Philippines from 2000 to 2019. The line graph illustrates the significant growth in the number of internet users over the years, with notable periods of rapid increase, especially between 2009 and 2010. The slight decrease in 2019 is also visible, indicating a possible market saturation or other factors affecting internet adoption. Figure 2. Trend in internet usage in the Philippines from 2000 to 2019 The data reflects significant advancements in internet infrastructure, such as the expansion of broadband services, the introduction and expansion of mobile internet, and the deployment of fiber optics. Policies and programs aimed at increasing digital literacy, affordability of internet services, and investment in infrastructure likely contributed to the growth in internet usage. The rise in internet users supports the growth of the digital economy, including e-commerce, online banking, digital marketing, and other internet-based services. This contributes to economic development, job creation, and innovation. Increased internet access facilitates better educational opportunities through e-learning platforms, online courses, and access to vast information resources. This can enhance the educational landscape and promote lifelong learning. The internet has transformed how people communicate, stay in touch, and participate in social networks. Social media platforms have become integral to daily life, influencing social dynamics and connectivity. Unemployment rate (% of total labor force) in the Philippines Table 3 presents the unemployment rate in the Philippines from 2000 to 2019 expressed as % of the total labor force. As can be seen, the unemployment rate in the Philippines experienced various fluctuations, reflecting changes in the country's economic conditions, labor market policies, and external factors. The early 2000s saw a generally downward trend in the unemployment rate, starting from 3.768% in 2000 and decreasing to 3.55% by 2004. This period marked a phase of stabilization in the labor market, with minor fluctuations indicating a relatively stable economic environment. The steady decline suggests improvements in job creation and economic activities during these years. The mid-2000s were marked by more significant fluctuations in the unemployment rate. In 2005, the unemployment rate increased to 3.8%, and in 2006, it reached a peak of 4.05%. This peak could be attributed to various economic challenges, possibly including global economic conditions or internal policy changes that temporarily impacted the labor market. However, by 2007, the unemployment rate had decreased significantly to 3.43%, indicating a recovery. This recovery was short-lived, as the rate increased again to 3.72% in 2008 and further to 3.86% in 2009. The global financial crisis of 2008 likely played a role in this increase, affecting employment rates worldwide, including in the Philippines. The early 2010s were characterized by a relatively stable unemployment rate, with minor improvements observed. The rate decreased from 3.61% in 2010 to 3.59% in 2011 and continued to drop slightly to 3.5% in 2012 and 2013. This stability reflects a period of gradual economic recovery and consolidation following the global financial crisis. By 2014, the unemployment rate had increased slightly to 3.6%, indicating a period of minor economic adjustments. The late 2010s marked a significant and sustained improvement in the unemployment rate in the Philippines. Starting from 3.07% in 2015, the rate consistently decreased each year, reaching 2.7% in 2016, 2.55% in 2017, 2.34% in 2018, and finally 2.24% in 2019. This period of consistent decline suggests substantial improvements in the labor market conditions, possibly due to robust economic growth, effective labor market policies, and increased job creation. The steady decrease in the unemployment rate indicates that more Filipinos were able to find employment, contributing to the overall economic health of the country. Figure 3 shows the visual presentation of the unemployment rate trend in the Philippines from 2000 to 2019. The line graph illustrates the fluctuations and overall trends in the unemployment rate, with key periods of increase and decrease clearly visible. The steady decline in the unemployment rate from 2015 to 2019, reaching its lowest point in 2019, is particularly notable. Figure 3. Unemployment Rate (% of total labor force) in the Philippines Number of Cases of Suicide (per 100,000 population) in the Philippines Table 4 shows the number of cases of suicide in the Philippines per 100,000 population. It can be seen that the lowest recorded rate in this period was 1.5 in 2001. On the other hand, the highest recorded rate was 2.7 in 2011. From 2005 to 2011, the rate of suicides showed a consistent increase. Post-2011, the suicide rate appears to stabilize around the mid-2s (2.2 to 2.4). While there was a slight decrease from 1.8 in 2000 to 1.5 in 2001, from 2002 onwards, there is a general increasing trend in the number of suicide cases, peaking at 2.7 in 2011. After 2011, the rate seems to stabilize around 2.2 to 2.4, with no significant upward or downward trend. YEAR Suicide Rate 2000 1.8 2001 1.5 2002 1.7 2003 1.8 2004 1.8 2005 1.9 2006 2 2007 1.9 2008 2 2009 2.3 2010 2.4 2011 2.7 2012 2.4 2013 2.4 2014 2.3 2015 2.3 2016 2.2 2017 2.2 2018 2.2 2019 2.2 Based on the data, the average suicide rate over the two decades is 2.1 per 100,000 population. This suggests that, on average, about 2.1 people per 100,000 in the Philippines committed suicide each year. The median value of 2.2 indicates that half of the years had suicide rates below this value and half above. This is consistent with the data showing more stable rates around this value in the latter years. A standard deviation of 0.29 indicates a relatively low variability around the mean. The rates do not deviate much from the average, suggesting a relatively stable trend despite the observed increase and stabilization phases. Figure 4 shows a visual presentation of suicide rates in the Philippines from 2000-2019 depicting its overall trend. Figure 4. Suicide rates in the Philippines (2000-2019) (every 100,000 population) The analysis of suicide rates in the Philippines from 2000 to 2019 reveals a concerning trend. Initially, the rate fluctuated between 1.5 to 1.8 per 100,000 population, with a significant increase observed from 2005 onwards, peaking at 2.7 in 2011. After this peak, the rate stabilized around the mid-2s, averaging 2.1 per 100,000 population over the two decades. The relative stability in rates post-2011, as evidenced by the low standard deviation of 0.29, indicates that while the rates have not fluctuated wildly, they have consistently remained higher than in the early 2000s. Recent studies further underscore the gravity of the situation. The COVID-19 pandemic exacerbated mental health issues, leading to a sharp increase in suicide rates. The Philippines saw a rise in reported suicide cases from 2,810 in 2019 to 4,420 in 2020. Additionally, the number of suicide-related calls to the National Center for Mental Health (NCMH) surged, indicating a growing mental health crisis. Experts have even described the situation as a "mental health pandemic" within the country, as more individuals, especially the youth, struggle with mental health challenges without adequate support (Gatchalian, 2023; Nelson, 2023). These findings suggest that while the suicide rate may have stabilized in the years leading up to 2019, the underlying factors contributing to suicide, particularly mental health issues, have intensified. This calls for a more robust response to mental health care, especially in the wake of the pandemic, to prevent further increases in suicide rates. Significant Relationships of Internet Usage, Urban Growth Development, Unemployment Rate, and Suicide Rates Correlations Individuals using internet Unemployment Rate Urban Population Growth Suicide Rate Individuals using internet Pearson Correlation 1 -.762** .335 .720** Sig. (2-tailed) .000 .081 .000 N 28 28 28 20 Unemployment Rate Pearson Correlation -.762** 1 -.323 -.245 Sig. (2-tailed) .000 .072 .299 N 28 33 32 20 Urban Population Growth Pearson Correlation .335 -.323 1 .433 Sig. (2-tailed) .081 .072 .056 N 28 32 32 20 Suicide Rate Pearson Correlation .720** -.245 .433 1 Sig. (2-tailed) .000 .299 .056 N 20 20 20 20 **. Correlation is significant at the 0.01 level (2-tailed). Table 5 presents correlation coefficients between four variables: Rate of Internet Usage, Unemployment Rate, Urban Population Growth, and Suicide Rate. The values reflect the strength and direction of relationships between these variables. With an r-value of -0.762 and p-value of .000, percentage of internet usage and unemployment rate reveals a astrong, significant negative correlation. This suggests that as internet usage increases, the unemployment rate tends to decrease. This could indicate that access to the internet may contribute to employment opportunities, potentially through online job search platforms, remote work, or skill development opportunities. The high significance (p < .01) underscores the reliability of this RESULTS Meanwhile, the correlation between internet usage and urban population growth is positive but not statistically significant (p > .05) with an r-value of 0.335. This implies a potential but weak relationship where higher internet usage might be associated with faster urban population growth, but the data does not strongly support this connection. Urban areas typically have better access to digital infrastructure, but this does not appear to be a decisive factor in population growth based on this dataset. On the other hand, there is a strong, significant positive correlation between internet usage and the suicide rate, with an r-value of 0.720 and p-value of .000. This suggests that higher internet usage is associated with higher suicide rates. While internet access can bring benefits, this correlation may indicate that excessive internet use, such as through social media, could contribute to mental health challenges, isolation, or cyberbullying, leading to an increased suicide rate. This relationship requires further exploration, as the internet could have both positive and negative effects depending on usage patterns. Similarly, the negative correlation (r-value = -0.323) between the unemployment rate and urban population growth is not statistically significant (p > .05). This suggests that there may be a weak trend where areas with faster urban population growth experience lower unemployment, potentially due to more job opportunities in urban centers. However, the data does not provide strong evidence to support this. The same is the case to the correlation between unemployment rate and suicide rate. The correlation between unemployment and suicide rates is negative (r-value = -0.245) but not statistically significant (p-value = .299). Although economic hardship is often linked to mental health issues, this data does not show a strong or significant relationship between higher unemployment and suicide rates. More variables may be needed to explain the dynamics between economic factors and suicide rates. On the contrary, there is a moderate positive correlation between urban population growth and suicide rates (r-value = 0.433), approaching significance (p = .056). This suggests that in areas with faster urban growth, suicide rates might increase. Rapid urbanization can lead to social dislocation, increased stress, and reduced social cohesion, which may contribute to higher suicide rates, though further research is required to confirm this link. The most notable relationships are between internet usage and both the unemployment rate (strong negative correlation) and the suicide rate (strong positive correlation), both of which are statistically significant. Other correlations are weaker or not statistically significant, though there are some trends worth further investigation, such as the possible link between urban population growth and the suicide rate. These results suggest that while internet usage may help reduce unemployment, it may also pose challenges for mental health, potentially increasing the suicide rate. The socio-economic impact of urbanization and technological access should be examined in more detail to develop more insights into how these factors interact. Conclusions The data highlights key socio-economic trends in the Philippines over two decades. Urban population growth, after some fluctuations, stabilized in recent years, likely driven by consistent urbanization policies and migration patterns. Internet usage has grown exponentially, reflecting the rapid adoption of digital technologies, although growth has slowed as penetration approaches saturation. The unemployment rate shows the impact of both internal policies and external economic factors, such as the 2008 financial crisis. Meanwhile, the suicide rate increased gradually, peaking in 2011, and later stabilized. Together, these trends suggest the complexity of socio-economic development in the Philippines, with various factors, including technology, migration, and economic cycles, playing significant roles in shaping population and labor dynamics. The analysis further highlights complex relationships where internet usage appears to have a protective effect against suicide, especially when controlling for unemployment. Urban growth, while associated with increased suicide rates, also correlates with higher internet usage and lower unemployment, suggesting that the dynamics of mental health, technology, and socio-economic factors are closely intertwined in urbanizing societies. CONCLUSIONS The data highlights key socio-economic trends in the Philippines over two decades. Urban population growth, after some fluctuations, stabilized in recent years, likely driven by consistent urbanization policies and migration patterns. Internet usage has grown exponentially, reflecting the rapid adoption of digital technologies, although growth has slowed as penetration approaches saturation. The unemployment rate shows the impact of both internal policies and external economic factors, such as the 2008 financial crisis. Meanwhile, the suicide rate increased gradually, peaking in 2011, and later stabilized. Together, these trends suggest the complexity of socio-economic development in the Philippines, with various factors, including technology, migration, and economic cycles, playing significant roles in shaping population and labor dynamics. The analysis further highlights complex relationships where internet usage appears to have a protective effect against suicide, especially when controlling for unemployment. Urban growth, while associated with increased suicide rates, also correlates with higher internet usage and lower unemployment, suggesting that the dynamics of mental health, technology, and socio-economic factors are closely intertwined in urbanizing societies.