Poverty Profile and Health Dynamics of Indigenous People

This paper examines the critical aspect of health dynamics in the context of poverty and development of Indigenous people (Agta Isarog & Agta Tabangnon) in Mt. Isarog, Southern Luzon, the Philippines. The datasets were gathered from the Community-Based Monitoring System (CBMS) of Goa Municipality, complemented by IP Censuses of 2018-2019. The core poverty indicators were analyzed in aggregated and disaggregated approaches. The poverty of each locality differs yet a large portion of the entire households and population of indigenous people are living below the poverty and food thresholds. In addition, the poverty incidence, gap, and severity using headcount ratios, gap metrics, squared gap statistics, and Watts indices were evaluated. It has been revealed that the poverty of Indigenous people is moderate to intense and manageable through intervention programs and policy initiatives. It then subsequently characterized the variables of health dynamics which vary per locality, and have been impacting poverty across all barangays. To confirm whether health dynamics predict poverty occurrences, Logistic regression models were estimated in an individual and consolidated manner. Results confirm that health dynamics significantly predict poverty outcomes.


Introduction
Deleterious impacts of poverty on people's health and development have been asserted through important empirical findings. Poverty, according to Aber et al. (1997), leads to higher infant and child mortality rates, a higher risk of injuries from accidents or physical abuse and neglect, a higher risk of asthma, and lower developmental scores in a variety of tests at different ages. As a result, children's health and development deteriorate. Haan et al. (1987) verified that lower socioeconomic statuses and higher mortality on health show significant associations.
Moreover, developing countries have worse health outcomes than developed countries (Wagstaff, 2002). It confirms that poverty is positively linked to ill-health; poverty levels and income inequality have had a significant role in poor health.
Poverty is one of the oldest social problems that has ever occurred in history and continues to exist today. According to Haughton and Khandker (2009), it has a negative association with societal development and economic growth. It's essential to measure poverty since only the results of a poverty analysis can be used to design and implement developmental interventions. Health and nutrition, housing and households, water and sanitation, education, income and hunger, employment, and peace and order are all core indicators of poverty. The community-based monitoring system (CBMS) census was used to identify these indicators and collect data on them. The CBMS is a system of collecting and analyzing data at the local level.
The CBMS' outputs are used in policymaking, local planning, service delivery, and impact evaluation (Reyes, 2014). This database offers crucial information about health dynamics and poverty issues.
Studies showed that poverty incidence is higher in indigenous people (Waxman, 2016;Gordon & White, 2014;Hall & Gandolfo, 2016;United Nations, 2020;International Labour Organization, 2020;Arriagada et al., 2020). For instance, Tindowen (2016) disclosed in a study of 25 families of Aetas in Northern Philippines that they have low access to technology and their socioeconomic characteristics differ from people living in rural and urban areas. As to livelihood, they rely on farming and agriculture yet some families are benefiting from government transfers that have already reached the society. Indigenous peoples are those who follow their traditions and have social, cultural, economic, and political traits that differ from the dominant societies in which they reside (Beteille, 1998). In Camarines Sur, indigenous people known as Agta Isarog and Agta Tabangnon have been living peacefully since the pre-colonial are 12 Agta Communities in the Municipality of Goa (NCIP, 2019, LGU Goa, 2021. The overriding physical characteristics of Agta Tabangnon are short in stature, darkskinned, with kinky hair, large noses, thick lips, and deep-set eyes. Intermarriage with lowlanders and outsiders has resulted in changes to these characteristics (Gerona, 2010). A Tabangnon's cabin is composed of light materials (nipa, sawali, coconut leaves, abaca). A typical hut consists of four robust poles, secondary growth tree rafters, and a thatched roof. A section of the house is raised three feet from the ground, with a bamboo slatted floor that also serves as a sleeping and eating platform. The Agta's average home is modest and fleeting, much like their outlook on life. Balakbak, pineapple fibers, and balete tree bark make up the Agta's traditional clothing. The bahag and tapis were originally worn by Agta men and women. Women began concealing their breasts and wearing longer tapis after becoming Christians (Calleja, 1992;Gerona, 2005;Obias, 2009;Ragragio, 2012).

Economic Activity of Indigenous People in Southern Luzon
The Agta Tabangnons are engaged in various economic activities. Summer is when most hunting and agricultural operations are carried out. Fishing can be done at any time of day or night. Corn, coconut, banana, and upland rice are among the root crops grown by the Tabangnons. Major crops such as abaca, sugarcane, coconut, corn, vegetables, and fruit trees are concentrated in some areas of Mount Isarog. Land preparation, harvesting, abaca-stripping, and construction operations frequently employ men as laborers. Small-scale trading and food processing are carried out by women. A number of them work as farm workers or on haciendas and coconut plantations. The trapichi (sugar grinder), basisi (planting tool), and laya are examples of work instruments (fishing net). When the forest and wali-wali (hunting games) began to diminish, most Tabangnons moved to upland farming. They have a lot of hunting experience. They hunt with a bow and arrow, a sumbiling (harpoon), bitik or lit-ag (traps), and an ayam (dog). They are also engaged in weaving, pottery, and processing wild crops (Calleja, 1992;Gerona, 2005;Obias, 2009;Ragragio, 2012).

Poverty and Economic Development of Indigenous People
In the Philippines, no study has been conducted yet about the quantitative evaluation of poverty among indigenous communities. However, in developed countries such as Australia, an evaluation was made and it has been claimed that Indigenous people have significant constraints in selecting appropriate economic and commercial development options that will benefit their economic and human development within the community (Fuller et al., 2007). Another study focused on four countries: Bolivia, Guatemala, Mexico, and Peru, which account for 81% of the continent's indigenous population. The majority of indigenous people in Latin America live in extreme poverty (Psacharopoulos et al., 1994). Poverty among indigenous peoples varies greatly around the globe, so poverty reduction efforts must be tailored accordingly (Hall et al., 2012).
According to a World Bank study, poverty may be reduced by focusing on human capital, particularly education, which promotes economic development. Policymakers can assist in increasing income, which will help to alleviate poverty (Griffiths, 2005;Davis, 2002).

Health Dynamics of Indigenous People
Indigenous people over the world are marginalized and discriminated and their health is consistently less than that of majority groups (Ijjasz-Vasquez et al., 2017;Lastra-Bravo, 2021;Minority Rights Group International, 2017). In Africa, poor health in the general population is well acknowledged, but Indigenous peoples' continually poorer health and social conditions are usually ignored (Willis et al., 2006). Around 400 million indigenous people across the globe are suffering from poor health status. Poverty, starvation, overcrowding, poor hygiene, environmental degradation, and frequent illnesses are all linked to poor health. This scenario is made worse by insufficient clinical care and health promotion, as well as poor disease prevention programs (Gracey et al., 2009).
Indigenous child mortality is linked to poor living circumstances, malnutrition, and lack of education (Wilk, et al., 2017). However, national interventions have only had a minor impact on mortality disparities (Heaton et al., 2007). Literacy, prenatal screening, hospital births, and economic development all played a role in determining child mortality and life expectancy (Li, 2008). Indigenous women in Mexico and Central America face a higher risk of pregnancy complications, such as maternal death, as a result of poverty and inequality (Schwartz, 2018).
According to Cumming et al. (2014), access to safe drinking water and sanitation are key determinants of human health and well-being, and the international community has lately deemed their human rights. Poor health and lack of access to safe water and sanitation often lead to malnutrition. A low level of income has also been contributing to malnourished children (Ramirez et al., 2014;Dinachandra et al., 2015). In Indonesia, households without access to safe water and sanitation are more likely to suffer from diseases (Patunru, 2015). Moreover, housing characteristics have a significant relationship with health outcomes (Heywood, 2004;Rauh, 2008;Gibson, 2011).

Methodology
The study utilized mixed-method design using secondary data. The secondary data on individual and household levels of indigenous people were obtained from the CBMS of Goa, Camarines Sur. Furthermore, document review was steered to collect extensive data that might be used to compare study findings to existing assertions on a certain issue.
To establish the characteristics of variables that influence a household's income-based poverty status, this paper has adopted the model of Reyes et al. (2011) andSobreviñas (2020).
The dependent variable is the poverty status of households, while independent variables are the health indicators. Various control variables were also incorporated into the model.
, p = probability of being poor of respondent households; α = the intercept or individual effects of health dynamics, which is assumed to be constant; X = vector of independent variables or characteristics of health dynamics; β = vector of coefficients, intercepts, or effects of health characteristics on poverty status; and μ = error term. Logit Regression. It was employed to reveal the link of health dynamics on poverty cases. The Econometric Model was used for logit regression analysis. This is an econometric design concerned with establishing cause and effect between given variables.

Logit Model
POVOUTCOCC = β0 + β1CDEATH5 + β2WDEATHPC + β3CMALNO5 + β4MSHDWELL + β5SQUATH + β6WATACCESS + β7STFACCESS + β8TNOHHM + μ  The headcount ratio (HCR) is the percentage of the population that falls below the poverty line. The i is an indicator function that returns 1 if the bracketed expression is true and 0 if it is not. So, if the household's income (yi) is less than the poverty threshold (z), the i equals 1 and the household is considered poor. The headcount index's main strengths are its ease of construction and comprehension. However, one of the drawbacks of the head count ratio is that it ignores the depth of poverty; as the poor get poorer, the headcount index stays the same (Haughton et. al., 2009).

B. Poverty Gap Metrics
The poverty gap index is a metric for determining the intensity of poverty. It is defined as The poverty gap index is connected to the squared poverty gap index, also known as the poverty severity index. It's calculated by taking the square of the poverty gap ratio and averaging it. The measure gives more weight to a poor person's observed income as it goes below the poverty line by squaring each poverty gap statistic. The squared poverty gap index is a type of weighted sum of poverty gaps in which the weight is proportional to the size of the gap. It also takes inequality among the poor (Foster et. al.,1984).

D. Watts Index
N individuals in the population are indexed in ascending order of income (or expenditure), and the sum is taken over q individuals whose income yi falls below the poverty threshold z. The index is calculated by dividing the poverty line by income, taking logs, summing the poor, and then dividing by the total population. This is one of the first poverty measurements that is sensitive to distribution (Haughton et al., 2009).

General Profile of Goa
The locale of the study was the Municipality of Goa, Camarines Sur, which is considered Goa is one of the six municipalities and a city that has territorial jurisdiction over Southern Luzon's highest forested peak, Mt. Isarog. These municipalities and city are Calabanga, Tinambac, Ocampo, Tigaon, Goa, Pili, and Naga City. Mt. Isarog is a stratovolcano and is 1,966 meters above sea level (DENR, 2020). In 2002, Proclamation No. 214 was signed by Pres.
Arroyo declaring the Mt. Isarog Natural Park. Such area contains different endemic and endangered flora and fauna and is home to Indigenous People (or Aetas) of the Southern Luzon locally known as Agta Isarog, Agta Tabangnon, or Inagta Partido. Goa is consisting 34

Poverty Profile of Goa Municipality and Indigenous People Locality
The poverty in the Municipality of Goa and 12 localities was analyzed through the core poverty indicators based on the CBMS. The data were examined in a holistic approach and then disaggregated to determine each locality's poverty profile. In Goa, 63.70% of the total households are living below the poverty threshold and 55.80% of the total population are living below the food threshold. The mortality rate of children and pregnant women is low, while  Moreover, 1/5 of the entire population has no access to safe water and 3/25 of the entire population has no access to a sanitary toilet. Concerning basic education, poverty is evident due to the high proportion of out-of-school children ranging from 25%-75% of the total households with children aged 6-17 years old. Food shortage is not rampant and the unemployment rate is low. However, 84 households experienced crime. In the entirety, the core poverty indicators suggest that the incidence of poverty in the municipality of Goa is evident in income and livelihood, basic education, and water and sanitation.

Characterization of Health Dynamics
Health dynamics were characterized through child mortality, maternal mortality, children malnutrition, type of housing, type of settlement, access to safe water, and access to a sanitary toilet facility.

Extent of Poverty
Poverty is multidimensional and cannot be captured easily by a single indicator. Thus, various indices were generated to determine the incidence, gap, severity, and extent of poverty. The headcount ratio was estimated as follows 0 = 1 ∑ ( < ) in ascending order of income (or expenditure), and the sum is taken over q individuals whose income (or expenditure) yi falls below the poverty line z. The index is computed by dividing the poverty line by income, taking logs, and taking the sum over the poor, then dividing it by the entire population. The results show that poverty is extensive in Abucayan, Balaynan, Digdigon, Payatan, and Tabgon while less extensive in Salog, Hiwacloy, San Pedro Aroro, and Cagaycay. is the probability of being poor, poverty incidence, or poverty outcomes.

Impact of Health Dynamics on Poverty
log(p/1-p) = -0.0610574 + β1*0.4285075 + β2*-0.1281599 + β3*0 + β4*1.406319 + β5*0.3991358 + β6*0.3199608 + β7*0.4203754+ β8*0.3864454 The relationship between the independent factors and the dependent variable, where the dependent variable is on the logit scale, is described by these estimations. These estimates show how much a 1-unit increase in the predictor would increase the expected log chances of poverty = 1 while keeping all other predictors constant. The coefficients for the non-significant independent variables are not substantially different from 0, which should also be considered when interpreting the results. Because these coefficients are often difficult to interpret because they are in log-odds units, they are frequently transformed into odds ratios. For instance, the coefficient of Households without access to safe water is 0.3991358. This means that for a oneunit increase in Households without access to safe water, an expected 39.91% increase in the log-odds of the dependent variable poverty, holding all other independent variables constant.
Another one, Household Members, for every one-unit increase in the household members, an Regarding the odds ratio, it can be generated by dividing the number of households who are not living below the poverty threshold by the number of households who are living below the poverty thresholds. The same procedure applies to all indicators. Another significant observation, significant variables have a confidence interval at 95% that does not include 1.0, possibly because the lower bound of the 95 percent confidence range is so near to 1, and the p-value is so close to .05. The researcher also utilized various interacting variables which can be seen from the  When compared to formal settlers, informal settlers have a larger likelihood of living below the poverty line as indicated in figure 2. Furthermore, as the number of household members grows, so does the likelihood of being impoverished.  The goodness-of-fit shows Prob > chi2 of 0.9911 which is greater than 0.05 Alpha level. The model's goodness-of-fit test is not significant, however, a test of estat classification is performed.

Conclusion
Within the 12 localities of the Goa municipality, indigenous people known as Agta Isarog or Agta Tabangnon dwell. The CBMS and IP Census datasets were used to assess poverty across barangays, and several policy proposals for economic development were outlined. Because poverty determinants differ and assessment is multifaceted, data were disaggregated to analyze each locality. It makes the following claims: First, the majority of Indigenous Peoples' households and populations live below the poverty and food thresholds. Second, there have been reports of food scarcity and unemployment, although only on a modest scale. Malnutrition, child mortality, and a crime against Indigenous peoples have all been reported, although they are not the primary drivers of poverty in all areas. Third, the majority of indigenous people are poor, and poverty is pervasive, according to headcount indices. Fourth, poverty intensity differs by locality based on poverty gap metrics. However, the overall index indicates that poverty levels are manageable and can be lowered through a variety of policies and efforts. Fifth, the squared poverty gap indices represent the severity of tolerable poverty, which differs by location. Sixth, the Watts indicators are comparable to the severity indices, which reflect the intensity and severity of moderate to severe poverty across barangays. Seventh, health dynamics variables were described in order to gain valuable insights from them and to see if they could predict poverty incidences or occurrences. Every community's health dynamics are distinct from one another. As a result, different policy measures for economic development are required in each community. Furthermore, based on the provided findings of the logistic regression model for individual and consolidated approaches, it can be inferred that health dynamics strongly predict poverty outcomes. Poverty among indigenous peoples in Southern Luzon is mostly caused by a lack of income and livelihood, as well as a lack of access to basic education. Poverty has also been visible in health and nutrition, housing, and access to safe drinking water and sanitation, all 24 | International Review of Social Sciences Research, Volume 2 Issue 1 of which have comprised health dynamics. The null hypothesis should be rejected since it implies that there are no differences or relationships between the data's features. There are significant associations between health dynamics indicators and poverty consequences. In Southern Luzon, the Philippines, health has a substantial impact on poverty classifications and statuses of households and indigenous people.
Cooperation between indigenous people, private institutions, and government agencies is necessary to alleviate poverty, improve the welfare of households, reducing risks and vulnerabilities, and promote socio-economic and community development. The results suggest a strong need for policy mapping in order to establish which aspects of each barangay's vulnerabilities and poverty occurrence should be targeted. The results of policy targeting must be employed to properly allocate resources and achieve economic development objectives, especially for health dynamics. Government initiatives must ensure indigenous people are always included in poverty-reduction plans and impart them with professional skills so that they can become well-rounded individuals who are competent, confident, value-laden, and dependable in a local, national, and global setting.
These statistics and econometrics of indigenous people can be integrated in economic studies and courses and present data quantitatively within Bicol Region. Further studies on crosssectional, repeated cross-sectional, and panel data may be utilized to determine the level of poverty across periods, and the identification of chronic and transient poor households may be made possible when the succeeding CBMS data becomes available.