Regional Inequality In Indonesia's Land Border Areas:

How to Increase Economic Performance?

Abstract

In order to attain developmental objectives, the government allocates transfer funds, including profit-sharing funds (PSF), general allocation funds (GAF), non-physical special allocation funds (SAF-NP), physical special allocation funds (SAF-P), and village funds (VF). This study seeks to assess the impact of these transfer funds on economic performance in land-border areas and formulate development strategies. Utilising secondary data from 2017 to 2022, the analysis employs Panel Data Regression and the Analytical Network Process (ANP). The chosen locations are provinces in Indonesia that share land borders with neighbouring countries, specifically the Kalimantan corridor and the East Nusa Tenggara-Papua corridor. The findings indicate that in the Kalimantan corridor, some of the transfer funds significantly contribute to the economic performance, but not to regional inequality. In the East Nusa Tenggara-Papua corridor, all transfer funds, except SAF-P, significantly influence economic performance. In terms of strategy, the East Nusa Tenggara-Papua corridor takes precedence for development, followed by the Kalimantan corridor. The overarching developmental goals include improving welfare, enhancing productivity, and reducing inequality. In the allocation of development funding through transfer funds, the priority is PSF, VF, SAF-NP, GAF, and SAF-P, with SAF-P being the last.

Introduction

The goal of development is not only economic improvement and the reduction of disparities, but also the enhancement of society's welfare and the strengthening of the nation's identity. Development is defined as a continuous process aimed at improving the well-being of the community, measured by GDP (Djirimu, Taqwa et al., 2024; Supartoyo, Tatuh et al., 2013). The Indonesian government applies this development concept to border areas. The new paradigm of development in border areas emphasises increasing productivity and fostering connectivity, with the long-term goal of reducing poverty. In general, Indonesia's land border areas are remote and challenging to access, often distant from the economic hubs of their respective districts or provinces. As a result, these regions remain underdeveloped and plagued by high levels of poverty. This situation leads to various problems, including individuals choosing to engage in economic activities in neighbouring countries rather than Indonesia, instances of trafficking, and even separatist activities in border areas (Latifa and Romdiati, 2008). Consequently, the government faces significant challenges and must implement various development policies and programs to address these regional issues (Sofilda, Zilal Hamzah et al., 2023). In realising all developmental objectives, the Government of Indonesia has enacted a range of policies and programmes. In general terms, the government utilises transfer funds (the Profit Sharing Fund, General Allocation Fund, Special Allocation Fund, and Village Fund) to support development across all provinces, including those situated in border regions. However, the most fundamental challenges to development in these border areas remain pronounced territorial disparities, persistently high levels of poverty, and low standards of welfare.

Based on this premise, this study aims to assess the extent of transfer funds' influence on economic performance in land border areas and to propose a development strategy for these areas through the effective utilisation of transfer funds.

Literature Review

Indonesia has 38 (thirty-eight) provinces spatially; some of these provinces border other countries, both on land and sea. Seven provinces and eighteen districts that share direct land borders with neighbouring countries are in Kalimantan, East Nusa Tenggara and Papua. Specifically, in the Kalimantan Corridor, West Kalimantan (five districts), East Kalimantan (two districts), and North Kalimantan (two districts) share borders with Malaysia (Figure 1).

Figure 1. The Kalimantan corridor

In the East Nusa Tenggara Corridor, East Nusa Tenggara (four districts) shares land borders with Timor Leste (Figure 2)

Figure 2. The Nusa Tenggara corridor

Lastly, in the Papua Corridor, Papua (two districts), South Papua (two districts), and Papua Pegunungan (one district) share land borders with Papua New Guinea (Figure 3).

Figure 3. The Papua Corridor

One of the ways the government addresses the underdevelopment of border areas is by utilising transfer funds for development (Rahim, D. A., Kasikoen et al., 2024; Suhyanto, Juanda et al., 2021; Yushkov, 2015). Transfer funds are defined as funds derived from state revenues and distributed to regions for management by local governments. These funds are intended to support the execution of government activities, which are delegated to regional authorities and utilised to stimulate fiscal activities for regional development (Ginting, A. M., Hamzah et al., 2019; Tilahun Mengistu, 2022). Essentially, transfer funds serve varied functions depending on the needs of each area, yet all such funds share complementary aims. The allocation of transfer funds also serves as evidence and commitment from the central government's policy in implementing decentralisation, thus making regional autonomy effective. For that, the government provides various transfer funds, including the Profit Sharing Fund (PSF), General Allocation Fund (GAF), Special Allocation Fund (SAF), and Village Fund (VF). In practice, the Profit Sharing Fund (PSF) aims to enhance vertical balance between the central and regional governments by considering the potential for resource production within each area. Generally, the Profit Sharing Fund (PSF) is derived from natural resources in specific regions and is used for local development purposes. On the other hand, the General Allocation Fund (GAF) seeks to equalise the financial capacity of different regions to ensure that each region has a relatively equal ability to manage government affairs and provide public services. The General Allocation Fund (GAF) funds are typically allocated for regional team member expenses and public planning operations. While the Special Allocation Fund (SAF) is a fund allocated for specific regions to finance special activities that align with national priorities. The Special Allocation Fund (SAF) itself is further divided into two categories: physical (SAF-P) and non-physical (SAF-NP). On the other hand, the Village Fund (VF) is a fund designated for enhancing community service facilities by addressing basic needs, fortifying village institutions, and undertaking other activities determined through village deliberations (Ginting, A. H., Widianingsih et al., 2024; Indartuti, 2022). In the end, it is seen that transferring funds has acrucial instrument of the central government’s policy, designed to strengthen regional autonomy and stimulate development in underdeveloped areas by addressing diverse local needs through the specific function of funds.

Empirically, from 2017 to 2022, transfer funds in the land border areas of the Kalimantan corridor exhibited fluctuations. However, it is noteworthy that the most substantial allocation of development funds was in the GAF. On average, the PSF experienced a growth rate of 0.37%, while the GAF saw a growth rate of 0.26%. The SAF-P decreased by 0.05%, while SAF-NP and the VF increased by 0.1% and 0.05%, respectively (Figure 4). The condition shows that, in the Kalimantan Corridor, GAF consistently represented the largest share of developing financing, underscoring its central role in equalising regional fiscal capacity.

Figure 4. Transfer funds allocated to the Kalimantan corridor between 2017 and 2022

In the same year, the government also allocated significant funds to the land border area of the East Nusa Tenggara corridor. Notably, GAF received a substantial portion in 2018, although its growth decreased by 0.01%. On average, PSF increased by 0.06%, SAF-NP by 0.21%, and VF by 0.08%, while SAF-P decreased by 0.02% (Figure 5).

Figure 5. Transfer funds allocated to the East Nusa Tenggara corridor between 2017 and 2022

Furthermore, there is little similarity between the two corridors mentioned above and the land border area of the Papua corridor. Notably, in 2018, GAF Papua received the most substantial portion of government transfer funds. Overall, transfer fund growth in the Papua corridor has shown a consistent increase year by year. From 2017 to 2022, there was an average increase of approximately 0.23% for the PSF, 0.74% for GAF, 0.40% for SAF-P, 0.31% for SAF-NP, and 0.09% for VF (Figure 6). In fact, the Papua Corridor stands out as the most consistent and robust beneficiary of transfer fund growth in 2017 – 2022, unlike East Nusa Tenggara, which experienced fluctuations and slower growth.

Figure 6. Transfer funds allocated to the Papua corridor between 2017 and 2022

Nevertheless, the condition of these land border areas still raises concerns. The government still has a lot of homework to do for the border area to develop. When considering the economic performance of a region, including an increase in GDP per capita, an increase in HDI, a decrease in regional inequality (measured by the Gini ratio), and a decrease in poverty rates (Hung and Thanh, 2022). The land border area of the Kalimantan corridor stands out with the highest GDP per capita, increasing by 7.1% annually. However, this situation sharply contrasts with the land border areas of the Papua corridor and the East Nusa Tenggara corridor. Both the Papua corridor and the East Nusa Tenggara corridor fall into the low-income category, with GDP per capita growth rates of approximately 3% and only 0.2%, respectively (Figure 7a). In alignment with these findings, the East Nusa Tenggara corridor also exhibits the highest poverty rates, which appear to be on the rise. Over the period from 2017 to 2022, the average poverty rate in this region stood at approximately 48.5%. In contrast, the land border area of the Papua corridor reports an average poverty rate of 23.6%, with the lowest rate observed in the Kalimantan corridor at 20.3% (Figure 7b).

Figure 7. (a) GDP per capita; (b) Poverty in border areas

The aforementioned conditions certainly prompt essential questions. On one hand, the government is making concerted efforts by implementing various programs and allocating funds to enhance regional economic performance. However, some regional performance indicators, such as GDP per capita, remain relatively low, and poverty rates continue to be high in these areas.

Methods

This study utilised secondary data spanning from 2017 to 2022. It was a time when the development of border areas was carried out by the government massively through the Nawacita program. The research area encompasses a district situated along the Indonesian land border, encompassing both the Kalimantan corridor and the East Nusa Tenggara-Papua corridor. Kalimantan itself has several districts that directly border neighbouring countries (Malaysia). The amalgamation of Kalimantan into a corridor not only stems from being situated on the same island but also from sharing similar regional characteristics (Figure 1 and Table 1). The grouping of the land border areas of East Nusa Tenggara and Papua into a single corridor is based on the region's shared characteristics (Table 1). In terms of how they are categorised, the data available are classified into two types: cross-sectional and time series. Cross-sectional data suggests the existence of several entities (individuals), whereas time series data indicates each individual has multiple observations over different periods of time. So, the analysis in this study employs the Panel Data Regression Method, chosen for its ability to yield more accurate estimation results (Shin, 2007; Suparman and Muzakir, 2023). It occurs because an increased number of observations automatically leads to greater degrees of freedom and reduces the likelihood of variable omission errors (Agusalim, Karim et al., 2019; Hsiao, 2014).

Table 1. Border regions classified by corridor

No	Province	Districts	Description
Kalimantan Corridor
1	West Kalimantan	Sambas	All districts are located on a single island, namely Kalimantan, sharing the same regional economic characteristics and a common border with Malaysia (Rahim, D. and Adiatmojo, 2020; Rahim, D. A., 2023)
		Bengkayang
		Sanggau
		Sintang
		Kapuas Hulu
2	East Kalimantan	Berau
		Mahakam Ulu
3	North Kalimantan	Malinau
		Nunukan
Corridor East Nusa Tenggara - Papua
4	East Nusa Tenggara	Kupang	Both East Nusa Tenggara and Papua share similar regional characteristics: high poverty rates and limited human resources (Hill, 2021)
		Timor Tengah Utara
		Belu
		Malaka
5	Papua	Jayapura
		Keerom
6	Papua Pegunungan	Pegunungan Bintang
7	South Papua	Boven Digoel
		Merauke

The study will measure the extent to which central government transfer funds influence regional economic performance, using GDP per capita, HDI, the Gini ratio, and poverty as dependent variables (Table 2).

Table 2. Operational variables

Variable	Unit	Description
Independent Variables
Profit Sharing Fund (PSF) (X1)	IDR	Funds allocated by the government to border areas through profit-sharing schemes.
General Allocation Fund (GAF) (X2)	IDR	Funds allocated by the government to border areas through a general allocation scheme.
Special Allocation Fund – Non-Physical (SAF-NP) (X3)	IDR	Funds allocated by the government to border areas through non-physical development.
Special Allocation Fund – Physical (SAF-P) (X4)	IDR	Funds allocated by the government to border areas through physical development
Village Fund (VF) (X5)	IDR	Funds allocated by the government for each village
Dependent Variables
GDP Per capita (GDP)	IDR	The value of per capita income in border areas.
Human Development Index (HDI)	Point	Human Development Index figures in border areas
Gini Ratio (GR)	Point	The number of inequalities that occur in border areas
Poverty (Pov)	Percent	Poverty levels in border areas

The equations in this research are:

1.    The effect of independent variables on GDP

$LnGR=\alpha +{\beta }_1{LnX}_{1it}+{\beta }_{2}Ln{X}_{2it}+{\beta }_3{LnX}_{3it}+{\beta }_4{LnX}_{4it}+{\beta }_5{LnX}_{5it}+{\epsilon }_{it}$ (1)

1. 2.   The effect of independent variables on HDI

$LnGR=\alpha +{\beta }_1{LnX}_{1it}+{\beta }_{2}Ln{X}_{2it}+{\beta }_3{LnX}_{3it}+{\beta }_4{LnX}_{4it}+{\beta }_5{LnX}_{5it}+{\epsilon }_{it}$ (2)

1. 3.   The effect of independent variables on poverty

$LnGR=\alpha +{\beta }_1{LnX}_{1it}+{\beta }_{2}Ln{X}_{2it}+{\beta }_3{LnX}_{3it}+{\beta }_4{LnX}_{4it}+{\beta }_5{LnX}_{5it}+{\epsilon }_{it}$ (3)

1. 4.   The effect of independent variables on poverty

$LnGR=\alpha +{\beta }_1{LnX}_{1it}+{\beta }_{2}Ln{X}_{2it}+{\beta }_3{LnX}_{3it}+{\beta }_4{LnX}_{4it}+{\beta }_5{LnX}_{5it}+{\epsilon }_{it}$ (4)

In the strategy for developing land border areas using transfer funds, the Analytical Network Process (ANP) analysis method is employed. The Analytical Network Process (ANP) is a process for identifying linkages between criteria to make optimal decisions in area development (Khan and Ali, 2020; Zou, Ma et al., 2022). An advantage of the Analytical Network Process (ANP) method lies in its capacity to incorporate different options and associated criteria. The relationship at play involves: the first being the connection existing within one element of its internal dependence, while the second pertains to the element's external dependence or its association with different elements. The results of this analysis are often used as a reference to find out the development strategy. In ANP, the Consistency Index (CI) is a measure used to assess the consistency of respondents' answers to various questions, ranging from 0 to 0.1. If the value exceeds this range, the respondent's response is considered unacceptable and must be confirmed again (Ergu, Kou et al., 2011; Kou, Ergu et al., 2013). The respondents in this study are academics and planners specialising in border area development. Furthermore, there is a rater agreement measure indicating the level of consensus or approval among answers. The rater agreement value is determined by Kendall's Coefficient of Concordance, which falls within the range of 0 to 1. Closer to 1 indicates stronger agreement among raters. The CI equation is as follows:

$W_i={\sum }_{i=1}^n{a}_{ij}/n$ (5)

Then calculate the EigenValue and the maximum eigenvalue.

${\lambda }_1={\sum }_{i=1}^n{a}_{ij}/W_i$ (6)

${\lambda }_{max}={\sum }_{i=1}^n\left(a_{ij}/W_i\right)/n$ (7)

$CI=\frac{{\lambda }_{max}-n}{n-1}$ (8)

Where :

W_i = Weighting

a_ij / n = Line normalization matrix

n = number of respondents

λ₁ = Eigen Value

λ_max = Eigen Value Max

CI = Consistency Index

While getting Kendall's Coefficient of Concordance using the equation

$R_i={\sum }_{i=1}^n{r}_{ij}$ (9)

Then

$R=\frac{m(n+1)}{2}$ (10)

And

$S={\sum }_{1=1}^n(R_i-{R)}^2$ (11)

$W=\frac{12{\sum }_{i=0}^n{d}_i^2}{m^{2}n(n^2-1)}$ (12)

Where :

Ri = the aggregated ranking of the i criterion

R = the mean of the Ri values

r_ij = the rank given to the i criterion by the evaluator group j

m = the number of rater groups rating n factors

S = a sum-of-squares statistic deviation over the row sums of ranking Ri

W = Kendall’s Coefficient of Concordance; 0≤ W≤ 1

Table 3 Interpretation of Kendall's Coefficient of Concordance

W	Interpretation
0	No agreement
0.1	Weak agreement
0.3	Moderate agreement
0.6	Strong agreement
1	Perfect agreement

Result and Discussion

Kalimantan Corridor

As an area bordering Malaysia, several observations are related to the Kalimantan corridor land border area (Table 3). Firstly, PSF and GAF have a positive and significant impact on increasing GDP per capita in the Kalimantan corridor, in contrast to SAF-NP, which exhibits the opposite trend. Every 1% increase in PSF and GAF corresponds to a 50.0% and 30.0% increase in GDP per capita, respectively, with a coefficient of determination (R²) of 78.9%. PSF and GAF, for the record, are crucial elements in raising a community's income, particularly in areas with an abundance of natural resources. In particular, the Kalimantan Corridor has a wealth of natural resources, such as petroleum and forest products. The abundant wealth increases the transfer funds through the PSF mechanism, and the resulting rise in transfer funds automatically contributes to GDP growth. Referring to the experience of European countries, transferring funds has also been shown to effectively increase people's GDP per capita (Checherita-Westphal, Nickel et al., 2009; Frick and Rodríguez-Pose, 2025; Rodríguez‐Pose and Krøijer, 2009; Siliverstovs and Thiessen, 2015). However, SAF-NP is seen to have a negative and significant influence on GDP per capita. This suggests that SAF-NP in the Kalimantan corridor's land border area is less flexible in terms of utilisation, making it less relevant to GDP per capita (Martati and Asniwaty, 2021). Simultaneously, it can be observed that SAF-P and VF have failed to exert a tangible influence on increasing GDP per capita in the land border areas of the Kalimantan corridor.

Secondly, much like GDP per capita, the PSF and GAF variables also have a positive and significant impact on HDI. Every 1% increase in PSF can lead to a 2.9% increase in HDI, while every 1% increase in GAF can result in a 2.3% increase in HDI. One of the reasons behind the ability of PSF and GAF to enhance HDI in the border areas of the Kalimantan corridor is that both are formulated to calculate potential revenue and fiscal requirements for the region, and they are utilised for regional interests as well (Bird and Tarasov, 2004). Upon closer examination, it becomes evident that from 2017 to 2022, the government disbursed the highest amount of GAF in the land border area of the Kalimantan corridor in comparison to other funds (Figure 1), making it logically plausible for GAF to have a significant influence. Furthermore, the Kalimantan Corridor is also renowned for its abundance of natural resources, which can make a substantial contribution to the border areas through PSF for regional development (Voss, 1985) . Similar to GDP per capita, SAF-NP negatively and significantly affects HDI, with a coefficient of determination (R²) of 54%.

Thirdly, regional inequality, as indicated by the Gini ratio, represents an imbalance in spatial structure either within or between regions (Azmi, 2023; Khoeriyah, Juawita et al., 2025; Yang, Partridge et al., 2022). The consequences of regional inequality encompass social envy, regional insecurity, disintegration, and the exacerbation of economic disparities (Cartone, Panzera et al., 2022; Lipps and Schraff, 2021). In terms of the Gini ratio, this study demonstrates that transfer funds have not been able to significantly impact the Gini ratio. In other words, inequality in the border areas of the Kalimantan Corridor has not been reduced through the use of transfer funds. It is suspected that the high fiscal dependence of the border region on the central government is hindering the exploration of taxation and local revenue sources. Moreover, the failure to explore taxation and local revenues contributes to the escalation of regional inequality (Aye and Odhiambo, 2022).

Fourthly, poverty reduction, as one of the indicators of successful economic performance in a region, is shown to be influenced by PSF, SAF-NP, and VF. These three variables exert a positive and significant influence. Each 1% increase in PSF can lead to a 2% reduction in poverty, 1% increase in SAF-NP reduces poverty by 37%, and 1% increase in VF contributes to a 4% poverty reduction. The coefficient of determination (R²) is 49.6%. The effectiveness of transferring funds in reducing poverty has also been demonstrated in Iran and Indonesia. Iran has proven in reducing poverty through the use of transfer funds (Arham, Muh Amir and Naue, 2015; Arham, Muhammad Amir and Payu, 2019; Salehi-Isfahani and Mostafavi-Dehzooei, 2018).

Table 4. The effect of transferring funds on economic performance in the land border areas of the Kalimantan corridor

Independent Variable	GDP per capita		HDI		Gini Ratio		Poverty
	Coeff	Prob	Coeff	Prob	Coeff	Prob	Coef	Prob
C	-6.253	0.178	3.316	0	0.956	0.821	7.026	0.022
lnPSF	0.501	0**	0.0294	0**	0.008	0.866	-0.138	0.002**
lnGAF	0.305	0**	0.023	0**	0.0107	0.817	-0.057	0.088
lnSAF-NP	-0.206	0.021*	0.023	0.007*	0.0845	0.292	-0.376	0**
lnSAF-P	0.045	0.723	0.018	0.135	-0.065	0.571	-0.032	0.693
lnVF	0.280	0.055	0.023	0.093	-0.120	0.362	-0.400	0**
R Square	0.789		0.5469		0.0402		0.496
Prob (F-Stat)	0		0		0.0084		0.000003
DW-Stat	1.6230		2.8702		2.9344		9.456

* : α = 0.05, ** : α = 0.01

East Nusa Tenggara – Papua Corridor

As an area situated in eastern Indonesia, the land border area of the East Nusa Tenggara corridor shares a border with Timor Leste, while the Papua Corridor is bordered by land with Papua New Guinea. These two regions exhibit similar typologies and regional characteristics, specifically high poverty rates (Purwono, Tamtelahitu et al., 2020). High poverty ultimately diminishes community welfare and exacerbates regional inequality (Halkos and Aslanidis, 2023; Hill, 2021). Furthermore, both regions share similar geographical features, specifically their considerable distance from each other and the challenges associated with reaching them. Papua faces intricate security issues concerning its border regions. Certain separatist activities in this area demand particular attention from the government.

From the results of this study (Table 4), there are four key points to note. Firstly, the contribution of PSF is substantial in the context of increasing GDP per capita. Every 1% increase in PSF can lead to a 20% increase in GDP per capita. The coefficient of determination (R²) is 50.2%. PSF is designed to enhance the vertical balance between the central and regional levels by focusing on the potential of production areas. In this corridor, the success of PSF in improving community welfare is evident.

Secondly, PSF, SAF-NP, and VF exert a significant influence on the increase in HDI in the land border area of the East Nusa Tenggara-Papua corridor. The coefficient of determination (R²) was recorded at 39.4%. An increase in HDI also signifies an improvement in education, living standards, and health. Every 1% increase in PSF can elevate HDI by 3.6%. Similarly, every 1% increase in SAF-NP has the potential to raise HDI by 14.2%. VF's contribution to enhancing HDI is also noteworthy. This success underscores that all three components of the transfer fund have been allocated to advance the development of land border areas in this corridor.

The third result finding is that only PSF has a significant impact on reducing inequality in the land border area of the East Nusa Tenggara-Papua corridor. Each 1% increase in PSF reduced inequality by 6%. The coefficient of determination (R²) was recorded at 34.9%. Alya also demonstrated in her research that the existence of one of these transfer funds was effective in reducing regional inequality (Rachmawati, Wulandari et al., 2021), resulting in a decline in the Gini ratio. One of the advantages of PSF in this region is that its distribution is based on the principle of allocating a larger portion to producing areas compared to non-producing regions, taking into account actual revenue.

Finally, poverty reduction stands as the ultimate goal of development itself (Moyer and Hedden, 2020). Various efforts made by the government are aimed at lowering the poverty rate (De Silva and Sumarto, 2014). Reducing poverty significantly enhances the welfare of individuals. The findings of this study reveal that PSF, SAF-NP, and VF have a significant impact on reducing poverty in the land border area of the East Nusa Tenggara-Papua corridor. The coefficient of determination (R²) is 41.9%. Every 1% increase in PSF resulted in an 11.8% reduction in poverty. The same effect is observed for SAF-NP, where every 1% increase also led to a 24.3% reduction in poverty. Similarly, a 1% increase in VF resulted in a 46.6% reduction in poverty.

Table 5. The Impact of Transfer Funds on Economic Performance in the Land Border Area of the East Nusa Tenggara-Papua Corridor

Independent Variable	GDP per capita		HDI		Gini Ratio		Poverty
	Coeff	Prob	Coeff	Prob	Coeff	Prob	Coef	Prob
C	-64.07	0.050	5.776	0.0001	-3.691	0.073	-0.240	0.944
lnPSF	2.086	0**	0.036	0.024*	-0.060	0.012*	-0.118	0.004**
lnGAF	0.073	0.273	0.002	0.475	-0.003	0.355	-0.0003	0.956
lnSAF-NP	0.763	0.365	0.142	0.0002**	-0.032	0.544	-0.243	0.009**
lnSAF-P	0.613	0.325	0.033	0.2004	0.047	0.234	-0.003	0.957
lnVF	1.080	0.299	0.201	0**	0.0342	0.603	-0.466	0.0001**
R Square	0.5020		0.394		0.3491		0.4193
Prob (F-Stat)	0.000003		0.00019		0.0163		0.0022
DW-Stat	1.7338		1.9604		1.6838		1.8805

* : α = 0.05, ** : α = 0.01

Land border area development strategy

The development of border areas requires a strategic approach. In the Analytical Network Process (ANP), the highest point signifies the priority of the strategy to be pursued. The development goals for land border areas are outlined in three categories: first, enhancing well-being (0.398 points), followed by enhancing community productivity (0.318 points), and finally, reducing inequality (0.283 points). Logically, an increase in well-being tends to boost productivity while simultaneously reducing inequality. Indeed, development in border areas necessitates goodwill from the government and all stakeholders, particularly concerning budgetary matters. At this time, the government is expected to issue policies and welfare enhancement programs, including opening markets for community economic activities, establishing educational and training infrastructure, and implementing agricultural intensification and extension.

In pursuit of these development objectives, primary funding criteria primarily derive from PSF (0.207 points). PSF is often regarded as the lifeblood of a region, given that it represents the largest source of regional development funding. This funding priority aligns with previous studies that demonstrate the influence of PSF on economic performance in both land border corridors. The second criterion is VF (0.174 points), which aims to bolster the economies of rural communities. The third priority is SAF-NP (0.128 points), which contributes to enhancing public service delivery in the region. The fourth position is occupied by GAF (0.115 points), which is geared towards supporting team member expenditures and infrastructure development to elevate the HDI. Lastly, the lowest funding priority is SAF-P (0.093 points), allocated for physical development projects in border areas (Table 5).

Regarding the construction site, the priority lies within the East Nusa Tenggara-Papua corridor (0.645 points), with the Kalimantan corridor following at (0.355 points). Several factors contribute to the East Nusa Tenggara-Papua corridor being the preferred choice, including the high poverty levels and limited human resources in the area . These pressing challenges underscore the urgent need for resolution in the border area of this corridor.

Table 6. Strategies for Border Area Development

	Description	Point
Purpose	Enhancing well-being	0.398
	Enhancing community productivity	0.318
	Reducing inequality	0.283
Funding Criteria	PSF	0.207
	VF	0.174
	SAF-NP	0.128
	GAF	0.115
	SAF-P	0.093
Location	East Nusa Tenggara-Papua corridor	0.645
	Kalimantan corridor	0.355

Expert agreement on the results of this strategy, marked by Kendall's Coefficient of Concordance of 0.787, means strong agreement. As a consequence of this research agreement, experts and academics agreed with the results of this strategy to be realised.

Table 7. Kendall’s coefficient of concordance

Respondent	CI	Kendall’s Coefficient of Concordance	Interpretation
Respondent 1	0.03 – 0.07	0.787	Strong Agreement
Respondent 2	0.02 – 0.09
Respondent 3	0.02 – 0.08
Respondent 4	0.01 – 0.06
Respondent 5	0.03 – 0.09

Conclusion

The land border area of the Kalimantan corridor is relatively more developed when compared to the East Nusa Tenggara-Papua corridor. Various economic performance indicators also indicate that the Kalimantan corridor surpasses the East Nusa Tenggara-Papua corridor. Overall, the Kalimantan corridor also gains advantages from the abundant natural resources within the region.PSF, GAF, and SAF-NP have a significant influence on the increase in both GDP per capita and HDI in this region. When addressing regional inequality, which the government aims to eliminate in the Kalimantan corridor, it's not just about addressing low-income conditions but also improving access and the community's ability to sustain their economic well-being to prevent further impoverishment. Regrettably, transfer funds intended to reduce regional inequality have not shown substantial success so far. Additionally, VF appears to have the potential to reduce poverty when implemented alongside PSF and SAF-NP. Given these results, both the central government as the source of transfer funds and the local government as the fund users should carefully consider the impact of these funds.

In the land border area of the East Nusa Tenggara-Papua corridor, PSF has a significant impact on all aspects of economic performance. PSF contributes to increasing GDP per capita, reducing regional inequality, elevating HDI, and lowering poverty rates. Alongside the influence of PSF, SAF-NP and VF also play crucial roles. This situation underscores that the transfer of funds from the central government, whether through direct allocation or ministerial and institutional expenditures, brings about changes that enhance regional development performance. An intriguing observation in the land border area of the East Nusa Tenggara-Papua corridor is that despite substantial disbursements between 2017 and 2022, GAF has not had a significant impact on the economic performance of this border region.

In the land border area of the East Nusa Tenggara-Papua corridor, PSF has a significant impact on all aspects of economic performance. PSF contributes to increasing GDP per capita, reducing regional inequality, elevating HDI, and lowering poverty rates. Alongside the influence of PSF, SAF-NP and VF also play crucial roles. This situation underscores that the transfer of funds from the central government, whether through direct allocation or ministerial and institutional expenditures, brings about changes that enhance regional development performance. An intriguing observation in the land border area of the East Nusa Tenggara-Papua corridor is that despite substantial disbursements between 2017 and 2022, GAF has not had a significant impact on the economic performance of this border region.

The top-priority location for the border area development strategy is the East Nusa Tenggara-Papua corridor, followed by the Kalimantan corridor. When considering the objectives, the primary focus is on improving people's welfare, increasing productivity, and eliminating societal inequality. Improving prosperity through enhancing community productivity becomes a dynamic strategy. Besides having broad implications in border regions, societal inequalities are also reduced. The role of the central and local governments in ensuring targeted dissemination of the transfer fund is crucial to the success of improving economic performance in the corridors. The most important funding sources for development in this order are PSF, VF, SAF-NP, GAF, and SAF-P. In the end, extensive development in border regions not only has the potential to reduce poverty and regional disparities but also, socially and politically, to enhance the bargaining power of those areas with other countries and boost the nation's self-esteem.

Author Contributions

Conceptualisation, Dian Anggraeny Rahim, Yesi Hendriani Supartoyo, Sigit Setiawan. Methodology, Dian Anggraeny Rahim. Writing—original draft preparation, Yesi Hendriani Supartoyo. Writing—review and editing, Sigit Setiawan. Supervision, Sigit Setawan. All authors have read and agreed to the published version of the manuscript.

Ethics Declaration

The authors declare that they have no conflicts of interest regarding the publication of the paper.

Acknowledgments

The author thanks the National Research and Innovation Agency for providing the Post Doctoral Fellowship 2023-2024 and Darma Persada University Jakarta for their support.

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