The Impact of Regional Educational Development Policies on Poverty Reduction in North Sumatra Province

Although the education budget allocation in North Sumatra is larger compared to other provinces on Sumatra Island, its positive impact on poverty reduction has not yet been fully optimized [1]. This issue may be influenced by the quality of education policy implementation, which depends not only on the size of the budget but also on the effectiveness of fund management. For example, some studies show that the quality of education and the effectiveness of education fund management play important roles in influencing educational outcomes and their impact on poverty reduction [2, 3]. Therefore, despite an increase in the education budget, without improvements in teaching quality and better management, the impact on poverty in North Sumatra may not be optimal [4].

Additionally, the uneven distribution of the education budget across districts and cities in North Sumatra is an important factor influencing the policy's impact. Some districts and cities may receive larger budget allocations compared to others, but without a fair distribution strategy and improvements in education quality across all regions, the impact on poverty reduction will be limited. For example, based on data from the Central Bureau of Statistics [5], several areas in North Sumatra, such as Medan City and Deli Serdang Regency, received higher education budget allocations compared to other areas. However, the quality of education in isolated regions, such as Labuhanbatu Regency, remains lagging. This underscores the need for equitable budget distribution as well as improvements in education quality across all districts and cities [6, 7]. Research shows that effective education policy must consider the specific needs of each area to ensure that all districts and cities benefit from the allocated budget in a proportional manner [8]. With the right distribution strategy, the education budget allocation can be more effective in reducing poverty and increasing community welfare throughout North Sumatra [9, 10].

1.1 Impact of education budget on regional education development

Measuring the impact of education budget allocation on the REDI is crucial to understanding how educational development in the districts and cities of North Sumatra can be achieved. The REDI consists of several indicators, namely the School Participation Rate Index (SPRI), School Facilities and Infrastructure Index (SFII), and the Regional Education Index (REI). SPRI reflects the percentage of school-age children enrolled in school, indicating the level of access to education [2, 3, 11]. SFII measures the quality and adequacy of available education facilities, which significantly influence the learning environment in schools [7, 8]. On the other hand, REI includes the quality of teaching and student learning outcomes [12, 13]. In this regard, research shows that while an increase in the education budget has the potential to improve the REDI, its impact on enhancing education quality and access still requires further in-depth evaluation to ensure that the budget allocation truly leads to better educational outcomes [2].

1.2 The impact of educational development performance and GRDP percapita on poverty reduction

The impact of educational development performance and Gross Regional Domestic Product (GRDP) per capita on poverty reduction shows that while education budget allocation can contribute to poverty reduction, its impact is relatively small compared to other factors such as economic growth and access to health services [6]. Research by the Central Bureau of Statistics [14] revealed that, although there has been an improvement in education budget allocation, its impact on poverty reduction is not always significant. This is influenced by various other factors, such as the still low quality of education, social inequality, and suboptimal budget management. Similar findings were also reported in previous studies [6, 15], which indicate that ineffective educational development performance slows down the positive impact of education on poverty reduction. Considering these challenges, measuring the impact of educational development on poverty in Indonesia is often hindered by the limited availability of complete and up-to-date data on education quality, as well as other variables influencing poverty reduction, such as socio-economic infrastructure and government policies [16, 17].

1.3 Development of an education index for regional development

The development of the Regional Development Education Index (RDEI) is a strategic step for evaluating the effectiveness of educational development at the district and city level, especially in North Sumatra. The RDEI was developed to provide a more comprehensive picture of how various education indicators contribute to regional development in a holistic manner. This index integrates various aspects such as school participation rates, education quality, learning opportunities, and the impact on poverty, using a geometric mean of the conditional weighted product method (CWPM), which provides more accurate and contextual results [7, 18]. Given the challenges faced by North Sumatra in ensuring equitable education distribution and effective budget allocation across districts and cities, the development of RDEI is highly relevant. Previous research emphasizes that while increased education budgets contribute to educational development, their effectiveness largely depends on the quality of policy implementation and budget management [2, 19]. Therefore, RDEI is expected to become a useful tool for further evaluation, helping to understand how education can function as a main catalyst for poverty reduction and sustainable development in underdeveloped regions. Further research is needed to understand the complex interaction between these factors and to identify more effective strategies for utilizing education as a tool for poverty reduction [19].

This study aims to measure the impact of the realization of the 20% education budget on the REDI, as well as the impact of REDI and GRDP per capita on the Regional Poverty Index (RPI). Secondary data used in this study originate from the Central Statistics Agency (BPS), the North Sumatra Provincial Education Office, and the Ministry of Education and Culture's Basic Data on Students, covering the period from 2019 to 2022. This research is conducted in 25 districts and 8 cities in North Sumatra Province. Data collection took place from December 2023 to February 2024.

3.1 Study approach

This study uses panel data analysis, which allows for the merging of dimensions over time and across regions. This method is useful for exploring the dynamic relationships between variables such as education, the economy, and poverty in a more in-depth manner. A panel data regression model is used to evaluate the relationship between education spending, REDI, GRDP per capita, and RPI, while considering the regional characteristics. Model assumption tests, such as multicollinearity, heteroscedasticity, and autocorrelation, were carried out to ensure the validity of the results.

3.2 Study variables

The variables used in this research include the REDI, GRDP per capita, the Regional Poverty Index (RPI), and dummy variables for districts/cities. REDI consists of three main components: the School Facilities and Infrastructure Index (SFII), the School Participation Rate Index (SPRI), and the Regional Education Index (REI), which together describe the quality and access to education in the region. GRDP per capita is chosen because it reflects the average income of communities in the district/city, which is relevant for explaining the economic capacity of the public to access education and reduce poverty. RPI measures the level of public welfare based on three main indicators: the Percentage of the Poor Population (PPP), the Poverty Depth Index (PDI), and the Poverty Severity Index (PSI).

The dummy variables for district/city are included to account for significant structural differences between districts and cities. In general, cities tend to have better infrastructure and more resources to support educational development compared to districts. This variable is important for controlling the differences that arise from administrative and geographical distinctions between districts and cities, which can influence development outcomes. For example, cities may have better access to educational facilities and more qualified human resources compared to more remote districts with limited infrastructure. This explanation clarifies why this dummy variable is necessary to describe the differences in educational development and poverty reduction between these two types of regions.

3.2.1 Regression models and testing assumptions

The regression model used for the first objective is:

$\begin{aligned} { REDI }=\beta_0+\beta_1 20 & \% { Education \,Spending } +\beta_2 { District/City \, Dummy }+\epsilon\end{aligned}$      (1)

where, REDI is the District/City REDI; $\beta_0$ is a constant; $\beta_1$ is the $20 \%$ education spending coefficient, and $\beta_2$ is a dummy variable differentiating districts (0) from cities (1). The error term is denoted by $\epsilon$. To test multicollinearity, we use the Pearson correlation between the $20 \%$ education budget and the district/city dummy, with the following formula:

$r x_1 x_2=\frac{\sum\left(X_1-\overline{X_1}\right)\left(X_2-\overline{X_2}\right)}{\sqrt{\sum\left(X_1-\overline{X_1}\right)^2\left(X_2-\overline{X}_2\right)^2}}$   (2)

where, $X_1$ is the $20 \%$ education spending value, and $X_2$ is the district/city dummy. The average $20 \%$ Education Budget is denoted as $X_1$, and $X_2$ represents the average of the district/city dummy. If $r x_1 x_2$ approaches 1 or -1 , there is a strong correlation, indicating multicollinearity. If it approaches 0 , there is no significant correlation, meaning multicollinearity does not occur. Heteroscedasticity testing is performed using the White test, which tests variables in quadratic form. Finally, autocorrelation testing is done using the Durbin-Watson test, where values approaching 2 indicate no autocorrelation.

3.2.2 Regression model for measuring poverty impact

The second objective is to measure the impact of REDI, GRDP per capita, and district/city dummies on regional poverty reduction. The regression model used is:

$\begin{aligned} &  { RPI }=\beta_0+\beta_1 { REDI }+\beta_2 { GRDP \,  percapita }+\beta_3 { District/City }\, {Dummy }+\epsilon\end{aligned}$   (3)

where, $R P I$ is the Regional Poverty Index; $\beta_0$ is a constant; $\beta_1$ is the regression coefficient for REDI (including its three components: SFII, SPRI, REI); $\beta_2$ is the regression coefficient for GRDP per capita, and $\epsilon$ is the error term. Multicollinearity, heteroscedasticity, and autocorrelation tests are carried out for the second objective in the same manner as previously.

3.2.3 Calculation of the RDEI

The third objective is to measure the RDEI, which is calculated using the geometric mean formula of the Conditional Weighted Product Method (CWPM):

$R D E I_{i t}=\sqrt{X_{i t} \cdot Y_{i t}}$   (4)

where, $X_{i t}$ is the normalized REDI value, and $Y_{i t}$ is the normalized RPI value. The geometric mean is calculated for each district/city based on the normalized REDI and RPI values. This approach maintains a balance between the two indicators, avoiding dominance by one over the other [28].

The use of the CWPM method for calculating RDEI offers advantages in integrating more comprehensive educational indicators related to extreme poverty. Unlike IREA, which is more limited, HDI combines broader and more comprehensive educational dimensions, focusing on accessibility to education and its impact on poverty.

5.1 Impact of 20% education spending realization on regional education development

The impact of the realization of education spending of 20% and the expansion of regional autonomy on education development in districts/cities throughout North Sumatra shows complex results. Although there is a positive relationship between the realization of education spending and the REDI, with an R-squared value of 0.6703 indicating that 67.03% of the variation in REDI can be explained by this model, as well as a significant F-Statistic value, the impact remains limited. The coefficient for the variable "20% Spending Realization" is small (0.0000), and the p-value of 0.2907 indicates that although there is a positive impact, the influence is not significant at the conventional level.

Several factors explain why this impact is limited, one of which is the uneven distribution of the budget that occurs in various regions. As stated by Saputra and Mahmudi [35], uneven budget distribution, combined with corrupt practices and weak supervision, can reduce the effectiveness of education spending, reflected in cases of misappropriation of School Operational Assistance (BOS) funds or procurement of school infrastructure that does not meet needs. On the other hand, although regional expansion increases the education budget, fund management remains a significant challenge in new regions.

Research by Buheji [36] shows that in India, regional expansion increases the education budget, but managing these funds remains a major challenge. The realization of 20% of education spending aims to improve the quality of education, but its impact on REDI in North Sumatra remains limited, due to inefficient fund management, uneven distribution of education infrastructure, and the quality of educators which is still a problem. Although the allocation of the education budget has reached 20%, gaps in fund management remain. The inability to allocate and use funds optimally causes most of the funds to be used to meet basic needs such as facilities and infrastructure, rather than programs that focus on improving the quality of teaching or teacher training, so that even though the education budget is large, the quality of education reflected in REDI remains low.

The uneven distribution of the budget for the development of educational infrastructure also affects the low impact of education spending. In remote or rural areas, educational infrastructure is still very limited, with many schools lacking adequate basic facilities. This is in accordance with the findings of Filmer and Pritchett [37], which show that the quality of educational infrastructure has a significant effect on educational outcomes. The quality of teaching and the competence of educators in North Sumatra are also important factors that affect the impact of education budget allocation. Many areas still have difficulty providing competent teachers, especially in remote areas, and without continuous training for teachers, the quality of teaching will not improve, which in turn affects the low REDI.

Poor teaching quality can reduce the effectiveness of the use of the education budget. In addition, long and non-transparent bureaucratic processes often hinder the effective use of the education budget. Several regions experience the practice of politicization of education, where more funds are allocated to projects that are more related to political interests than to improving the quality of education. This is further exacerbated by weak supervision of budget use, which allows for waste and misuse of funds. Limited community participation in planning and monitoring the use of the education budget also reduces the effectiveness of fund allocation. Without active community involvement, management of the education budget tends not to reflect real needs in the field and has the potential to not provide maximum results, because budget use is often not on target.

5.2 The Impact of regional education development performance and percapita GRDP on regional poverty reduction

Educational development is widely recognized as a critical instrument in reducing poverty. However, the implementation of this policy at the district/city level in North Sumatra reveals that the impact of educational development remains limited. Recent research by Hanushek and Woessmann [2] highlights that the quality of education, particularly the quality of teaching, has a greater influence on economic growth and poverty reduction than merely increasing the education budget allocation. The findings of this study indicate that despite the implementation of the 20% education budget allocation, the quality of human resources produced is still suboptimal. This suggests that increasing the budget alone is insufficient to address poverty if the quality of education remains inadequate.

Moreover, the relationship between GRDP per capita and poverty reduction also yielded statistically insignificant results. This underscores that economic growth does not automatically translate into poverty reduction, particularly when the growth is not derived from labor-intensive sectors that absorb large numbers of low-income workers [38]. Economic growth driven by non-labor-intensive sectors that fail to create sufficient employment opportunities for poor households can hinder poverty alleviation. Conversely, sectors that generate more jobs for skilled workers with quality education tend to have a more substantial impact on poverty reduction. The regression test results of this study confirm that although a positive relationship exists between GRDP per capita and poverty reduction, its impact on poverty alleviation in specific regions, such as South Nias and Padang Lawas, remains minimal.

Another challenge in reducing poverty is the inefficiency in managing the education budget. According to World Bank findings [16], the lack of transparency in education budget management often leads to inefficiencies that undermine efforts to improve educational quality. The practice of budget misappropriation, as documented in the reports of the Audit Board of Indonesia (2024) and the Corruption Eradication Commission (2024), exacerbates the challenges associated with the 20% budget allocation policy for education. For example, the misuse of School Operational Assistance (BOS) funds in North Sumatra has resulted in uneven educational quality and impeded efforts to achieve optimal human resource development.

Research by Psacharopoulos and Patrinos [18] emphasizes the importance of expanding access to education alongside improving teaching quality to alleviate poverty. However, in North Sumatra, disparities in educational quality between developed areas, such as Medan and Binjai, and underdeveloped areas, such as South Nias and Padang Lawas, continue to hinder poverty alleviation efforts. Economic development, which is expected to bring positive changes to poverty levels, must be accompanied by substantial reforms in the education system, particularly in underdeveloped areas.

The quality of education is also closely tied to the adequacy of educational infrastructure. The UNESCO report [7] emphasizes that both the quality of teaching and robust educational infrastructure are essential for creating human resources capable of breaking the cycle of poverty. While developed areas in North Sumatra have benefited from better access to educational facilities, significant gaps persist in underdeveloped areas, which must be addressed urgently. This study supports these findings by showing that although variations in the REDI exist across regions, the overall quality of education remains a major barrier to poverty alleviation.

Baker and Letendre [19] caution that an uneven and low-quality education system perpetuates the cycle of poverty. This is particularly relevant in North Sumatra, which not only requires a substantial allocation of the education budget but also necessitates comprehensive reforms in the education system. Without equitable improvements in education quality, economic development efforts through education will struggle to have a significant impact on poverty reduction. Disparities in education quality, both in teaching standards and infrastructure, are key reasons why certain districts/cities in North Sumatra continue to experience high poverty levels despite achieving high GRDP figures.

Additionally, the Central Statistics Agency (2020) report highlights significant disparities in educational quality among districts/cities in North Sumatra. While areas such as Medan and Binjai offer better quality education, regions like Nias and Padang Lawas remain far behind in terms of teaching quality and educational infrastructure. This underscores the need for comprehensive and equitable improvements, with a particular focus on the most disadvantaged areas. Enhancing the transparency and accountability of education budget management, as well as improving access to and the quality of education in underdeveloped regions, are crucial steps toward breaking the cycle of poverty. This study demonstrates that local factors, such as regional policies and resource distribution, play a significant role in influencing poverty reduction. The district/city dummy variable also reveals significant differences in the impact of education budget management and distribution across various regions in North Sumatra, as indicated by its P-value.

5.3 RDEI

The Regional Development Education Index (RDEI) in North Sumatra provides a comprehensive overview of the quality of education across various districts and cities, incorporating factors such as educational infrastructure, teaching quality, and access to basic services. The analysis reveals significant disparities between regions with high and low scores, highlighting the major challenges in achieving equitable education quality throughout the province. Medan City, with the highest RDEI score of 67.22, demonstrates that factors such as superior educational infrastructure and broader access to public services play a crucial role in enhancing education quality. As the provincial capital and primary economic hub, Medan benefits from a larger education sector budget, more extensive educational facilities, and better-trained teachers. These advantages contribute to improved teaching quality and better educational outcomes compared to other regions in North Sumatra.

Conversely, areas with low RDEI scores, such as Sibolga, Pakpak Bharat, and West Nias, face significant challenges, particularly in terms of access to adequate educational facilities. Insufficient infrastructure, such as inadequate classrooms and limited educational equipment, exacerbates the quality of education in these regions. Additionally, the shortage of qualified teaching staff poses a critical obstacle. These deficiencies hinder students in these areas from accessing quality education, which adversely impacts their RDEI performance. Research by Pierskalla and Sacks [39] underscores the importance of infrastructure and economic welfare in determining education quality. This is evident from the observed inequalities, where regions with low scores struggle to provide the basic facilities necessary to enhance education quality. Furthermore, these limitations are closely tied to restricted access to other public services, such as healthcare and transportation, which collectively influence the quality of life and education in these areas.

Although certain districts in North Sumatra achieve favorable RDEI outcomes, the disparities between high- and low-scoring regions underscore the need for greater focus on equitable education development. By improving infrastructure quality, providing enhanced training for teachers, and ensuring a fairer allocation of the education budget, the quality of education in low-scoring areas can be elevated. This, in turn, is expected to improve the overall RDEI across North Sumatra.

This study found that the allocation of the 20% education budget and regional expansion showed a positive relationship with education development in North Sumatra, although its influence was limited. The low but significant coefficient indicates that the effectiveness of the budget depends on proper implementation and management. Challenges such as administrative capacity issues and corruption are inhibiting factors, which have implications for the inequality in education quality between regions.

The analysis shows that increasing access to education must be accompanied by improvements in the quality of services in order to support significant poverty reduction. Areas with strong infrastructure, such as Medan and Deli Serdang, show better educational performance. The same trend was observed when comparing urban and rural areas, with rural areas, especially those that are relatively underdeveloped, such as Nias and Padang Lawas, requiring special attention. This highlights that economic and educational development must go hand in hand to address social disparities and improve the quality of human resources.

The development of the RDEI helps identify gaps between regions and serves as an important reference for formulating education policies in North Sumatra. The main recommendations from this study include reforms for more transparent budget management, increasing administrative capacity, and expanding access to education in disadvantaged areas. With these steps, education development is expected to contribute more significantly to poverty alleviation and inclusive economic development.