Poverty hasbeen linked to a variety of negative outcomes, including poor health, a low level of education or skills, an inability or unwillingness to work, a high proportion of disruptive or disorderly behavior, and impoverishment. This research concern is the low level of education that keeps people in poverty trap. From the 17 Goals of Sustainable Development Goals that has been created as a common goal from countries all over the world, there is goal 1 that said “No Poverty” which is about ensuring social protection for all children and other vulnerable groups, and it is critical to reduce poverty. The SDGs goal 1 is subdivided again into seven targets that varies from eradicate extreme poverty to make strong policy framework that can accelerate investment in poverty eradication (Source Title, 2022). This research aims to develop human capital in Indonesia to support accelerated investment in poverty eradication actions through literacy-based cognitive ability for the purpose of high quality of education.

Many countries, both developing and developed countries, continue to strive achieving universal basic education. However, in a higher level of education, if it is not accompanied by an improvement in the quality of education, it cannot function effectively as a tool to stem the transmission of poverty from one generation to the next generation (Santos, 2011). According to the National Research Council, one of the skills that need to be improved in the 21st century is cognitive skills (problem-solving, critical thinking, and systematic thinking). The educational process is not only concerned about subject matter, but also it must cultivate thinking skills and develop literacy.

Literacy represents one of the most influential cultures as a tool for the development of a global society. However, the low literacy rate in Indonesia obstructs the quality improvement of human resources in education. Indonesia has a target that they will reach proportion of 9th graders who reach the minimum reading proficiency standard by 50%, and minimum numeric proficiency by 38% in 2030 (Source Title, 2019). However, it is still challenged by poor achievement nowadays. Based on 2019 Program for International Student Assessment (PISA) data, Indonesia ranks 62nd in the literacy level out of 70 countries. The learning outcome which is not aligned with a number of resources that have been spent by the government in the education sector indicated that there is a need to allocate resources more effectively (Idem).

Indonesia as a developing country still have to face the poverty problem which is one of the greatest threat for human in modern times.

Based on data from the Central Bureau of Statistics (BPS), in March 2021 the poverty rate in Indonesia reached 10.14%, or 27.54 million Indonesians were poor. The poverty rate in Indonesia showed decreasing trend until 2019. However, in 2020 when Covid-19 pandemic arose which required people to work and study from home, the poverty rate started to increase.

Poverty reduction aims to reduce unemployment, cultivates self-sufficient youth, and makes positive contributions to society. In order to reduce poverty, it is necessary to improve the quality of human capital which can be done through fixed investment in education (Olopade et al., 2019). The question that arises on this paper is whether development of literacy-based cognitive ability could reduce poverty or could not. Previous studies have found about poverty eradication strategies through education investment as one of the early childhood development interventions (Richter et al., 2017). However, previous studies were limited to the level of education and years of schooling. This study tries to fill these gaps by using literacy as a measurement of education.

Literacy can be defined as the ability to identify, understand, interpret, create, communicate, and compute, using printed and written materials associated with varying contexts (Montoya, 2018). Comprehensibly, reading or writing words are not enough to assess literacy. People need to be able to understand and use the literacy ability for practical purposes or tasks that related to the real world experiences. This broader definition of literacy is called functional literacy which refers to the practical skill set needed to read, write, and do math for real-life purposes, so people can function effectively in their community (U.N.E.S.C.O., 2020). Functional literacy also can be defined as the ability to operate a saving account and access the internet for functional purposes. Functional literacy relates more to the levels of skills that individuals or populations needed to complete some specified real-life experiences (Goodman & Goodman, 2013). For the example, literacy ability is essential to achieve the outcomes in the job, transportation, economics, and digital world. Therefore, people who have literacy ability that can use internet or people who have literacy ability that are financially literate can be called as having functional literacy. In a recent study, after considering for a range of socioeconomic characteristics and the invested amount, they found that financial literacy has a significant relationship with households’ individual returns on savings accounts: a one- standard deviation in a higher advanced financial literacy is associated with approximately 29 basis points higher interest rate, which represents an increase of 12% compared to the median interest rate (Deuflhard et al., 2019).

Many countries currently provide a framework for enhancing financial literacy and financial inclusion, or for focusing on the financially excluded framework for financial education (Atkinson, 2013). In the previous study, (Mhadhbi, 2014) that uses the literacy rate as a proxy for human capital concludes that the literacy-financial development interaction statistically has a significant positive association with economic development. Furthermore, a study from (Sivachithappa, 2013) found that people who are able to operate and create saving account have a significant impact on recipient’s economic activity and decrease the level of poverty because the majority of loaner from economically disadvantaged backgrounds did not know the terms and conditions of bank loans, including interest rate, repayment schedule, and installment amount. Therefore, it is proven that functional literacy can eradicate poverty through an increase of returns on savings accounts due to having financial literacy. When we are measuring the impact of cognitive ability on SDG 1 “No Poverty”, it has been estimated that if adults aged 15+ had just two more years of schooling, nearly 60 million people would be liberated from poverty. Moreover, achieving universal primary and secondary attainment in the adult population would help to liberate more than 420 million people from poverty, thus it could reduce the number of poor people worldwide by more than half (U.I.S., 2017).

Another factor related to poverty is age. The relationship between age and poverty cannot be separated from the retirement phase. According to a recent calculation, if everyone had a high retirement age as the most educated quintile of the population, the costs of decline in production and expenses for the health system and income transfer payments could be reduced by an amount equal to 5.8% of the European Union’s Gross National Product (GNP) (EU) (Diderichsen et al., 2012). Another study, (Abu, 2019), found that whereas age has a positive link with the likelihood of being an older person in poverty, the probability of being an older person in poverty is inversely correlated with educational attainment.

The level of educational represents a major step forward to fulfill one of the primary objectives of SDGs 1, “No Poverty”. The level of education is also a crucial factor in poverty eradication Among the most notable studies on the linkage between the education level and poverty eradication are by (Ataguba et al., 2013). The study indicated that education level is a significant determinant of poverty rate and shall be included while assessing the struggle with poverty. Furthermore, these findings were supported by study from (Bilenkisi et al., 2015). They conclude that as the education level increases, the probability to remain below the poverty line diminishes. These important studies indicate that educational level provides a cognitive advantage and plays an important role in poverty eradication strategy. Another studies that using mincer model show that there is a higher average rate of return on education in a high economic group and urban areas in Indonesia due to the differentiating factors such as the area of the residence, socio-economy, education level, and the absorption rate of workers from the level of education ((Martak & Chotib, 2021); (Psacharopoulos & Patrinos, 2018)).

A study by (Hendajany et al., 2016) found that there is gender inequality problem in the quality of education in Indonesia. Gender inequalities are not only an issue of moral and social, but also an issue of a critical economic challenge. If female population who account for almost half of Indonesia’s population do not achieve their potential, the economy will suffer (Firmansyah & Sihaloho, 2021). Female are disproportionately represented among the poor, the Indonesia’s Central Bureau of Statistics reported a higher rate of female’s poverty rate compared to men in 2020 with the number of 9.96% compared to 9.56% (Statistics, 2021). An OECD report comes to the statement that the better use of female population will reduce poverty and increase economic growth (Thévenon, 2013). Another research suggests a positive impact of female labor market participation on growth, but there is limited indication that sectoral expansion is the primary driver of female’s economic engagement (Gaddis & Klasen, 2014).

Internet access has been considered as the key enabler of development, especially in developing countries. Access to information and communication technologies (ICT) and broadband Internet in particular has the potential to serve as a major development accelerator. Access to affordable Internet is increasingly a development priority and even considered as a basic right. Based on the data from Indonesia’s Central Bureau of Statistics, ICT sector in Indonesia has contributes for 4.25% of Indonesia’s GDP, the Internet user in Indonesia has also grown and reached the number of 196,7 million people (Source Title, 2020).

This study uses cross sectional data from the 2019 National Socioeconomic Survey. It was a survey conducted by The Indonesian Central Bureau of Statistics (BPS), which aimed to collect data on the socio-economic conditions of the community relating to the fields of education, health/nutrition, housing, socio-cultural activities, consumption/expenditure and household income, travel, and opinion regarding the welfare of their household. The 2019 National Socioeconomic Survey data collected by BPS on March 2019 covered all 34 provinces in Indonesia with 320,000 household sample sizes that scattered on 514 district and representative of the entire population of Indonesia.

For this study the sample is limited only to individuals who aged over 5 years old, and from that 1,018,844 individuals used as usable samples are utilized for the study. Subsequently, a maximum likelihood method using Probit regression model estimated based on this data with the probability of an individual becoming poor as the dependent variable and a set of literacy-based cognitive abilities along with socio-economic factors as explanatory variables. The descriptions of each variable are shown on the Table 2.1 below:

Based on the selection of these variables, the study specifies the econometric model for estimation as such:

Model 1

P (poori = 1|Xi) = Φ (β0 + β1 abletoread & writei + β2 internetaccessi + β3 savingaccounti + β4 literacy#savingaccount

+ β5 usemobilephonei + β6 usecomputeri + β7 schoolcompletedi + β8 agei + β9 agesqi + β10

malei + β11 urbani + εi) (1)

Further analysis is to analyze the effect of literacy-based on cognitive abilities to poverty status in two regions, namely urban and rural areas Model 2 (urban and rural)

P (poori = 1|Xi) = Φ (β0 + β1 abletoread & writei + β2 internetaccessi + β3 savingaccounti + β4 literacy#savingaccount

+ β5 usemobilephonei + β6 usecomputeri + β7 schoolcompletedi + β8 agei + β9 agesqi + β10

malei + εi) (2)

The parameters of these models are estimated by using the maximum likelihood estimation specifically Probit regression. Analysis on Model 1 will utilize all useable data. Analysis on Model 2 will consider each geographical residence area, and the number of observations will be adjusted for the needs of this analysis. All statistical analysis use STATA 14 software.

To deliberate ideas and solve issues, people must be able to comprehend the language and numbers. Indonesia must improve functional literacy, as children with high quality education are more likely to have a better life expectancy, better job opportunities, and an increased political participation to accomplish SDGs 1 goals by 2030. The majority of policy implementation about literacy rules is unenforced and needed to be improve. In the future, language literacy curriculum will create linkages between learning groups. As the next generation, we must build an effective functional literacy approach by using links between diverse learning communities to inspire deeper understanding between children in various regions of Indonesia, especially rural areas. The government should give funds for teacher in order to provide training class, to create reading materials and to design an effective learning approach for students in Indonesia. Consequently, collaborative effort between policymakers, literacy activists, and the public will help children develop life skills. This implies the following objectives: 1) increasing the quality of functional literacy in primary schools (including poor households), and 2) enhancing elementary school graduates’ cognitive capacities as evidenced in the National Assessment of Progress in Education results.

This research has shown that the ability to read and write variable as the representation of literacy-based cognitive ability has a negative correlation with poverty. The internet access variable is negatively correlated with the probability of becoming poor variable. Having Bank’s saving account, operating mobile phones, and accessing computer have a negative impact with the probability of becoming poor variable. In addition to that, there is a negative correlation between education and the probability of becoming poor variable. Besides that, age and age square variables also indicate that an increase in average age will increase the probability of individual becoming poor until they reach 13 years old. After that, an increase in age will decrease the probability of becoming poor. From all the independent variables used on this model, all of them have a significant impact on influencing the dependent variable on this model. There are interesting findings that the ability to read and write, the ability to access internet, and the ability to use mobile phones & computer have higher impact on reducing poverty in rural areas compared to urban areas. Therefore, the policy intervention to improve literacy-based cognitive ability in rural areas will provide more outcome if it is compared to the urban areas. However, further study could provide more insight if there is a data that represent the establishment of functional literacy in the country.