Analysis of the Effect of the Open Unemployment Rate and Income Distribution Inequality on the Number of Poor People in West Nusa Tenggara Province for the Period 2010–2024

Studies Management and Finance Economics, of Journal 0504-2644 (online): ISSN 0490,-2644 (print): ISSN 5202 berDecem 21 Issue 80 Volume 8.317 Factor: Impact ,02-i12-10.47191/jefms/v8 DOI: Article 7591-7581 No: Page JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7581 Analysis of the Effect of the Open Unemployment Rate and Income Distribution Inequality on the Number of Poor People in West Nusa Tenggara Province for the Period 2010–2024 M. Zulpazlyawan1, St. Maryam2 1,2Faculty of Econimics an Business, University of Mataram ABSTRACT: This study aims to analyze the effect of open unemployment and income inequality on the number of poor people in West Nusa Tenggara Province during the period 2010–2024. The background of this research is based on the persistently high poverty rate, which reflects the suboptimal achievement of regional economic development, particularly in terms of equitable income distribution and labor absorption. This research employs a quantitative approach with an associative method. The data used are secondary time-series data obtained from the Central Bureau of Statistics of West Nusa Tenggara Province. The data were analyzed using multiple linear regression with the Ordinary Least Squares approach to determine the relationship between the independent and dependent variables. The results indicate that open unemployment has a positive and significant effect on the number of poor people in West Nusa Tenggara Province. Similarly, income inequality, represented by the Gini Ratio, has a positive and significant effect on poverty. Simultaneously, both variables significantly influence the poverty rate in West Nusa Tenggara. These findings imply that poverty reduction efforts should focus on improving human resource quality, creating productive employment opportunities, and promoting equitable income distribution. KEYWORDS: Poverty, Open Unemployment, Income Inequality. I. INTRODUCTION Poverty remains one of the most persistent socio-economic challenges in developing countries, including Indonesia. It reflects not only the insufficiency of income but also the limited access to productive resources, education, health services, and economic opportunities. In the context of regional development, poverty is a multidimensional phenomenon that requires comprehensive policy interventions. West Nusa Tenggara (NTB) Province, despite showing economic progress over the last decade, continues to face a relatively high level of poverty compared to the national average. Understanding the factors influencing poverty dynamics in this region is therefore crucial for achieving inclusive and sustainable development. From a macroeconomic perspective, open unemployment is one of the main determinants of poverty. According to Keynesian theory, unemployment arises when aggregate demand falls short of productive capacity, leading to lower household income and purchasing power. High unemployment reduces economic participation and increases dependency ratios, which eventually worsen poverty conditions. Similarly, income inequality remains a crucial issue that reflects the uneven distribution of economic gains among different social groups. The persistence of inequality, even amid economic growth, suggests that development outcomes have not been equitably shared across all segments of the population. The phenomenon of income inequality has long been associated with poverty persistence, particularly in regions with dual economic structures like NTB. The province’s economy is characterized by a mix of traditional agricultural sectors and emerging modern sectors such as tourism and services. According to the Kuznets Hypothesis, inequality tends to increase in the early stages of economic development before declining once per capita income rises. However, in regions where redistributive policies are weak, high inequality can perpetuate poverty by limiting access to education, capital, and productive employment opportunities. Empirical evidence suggests that both open unemployment and income inequality significantly influence poverty levels in Indonesia. Studies such as Rahmadani (2023) and Setya (2024) found that unemployment and the Gini Ratio positively and significantly affect poverty rates across provinces. In NTB, where economic transformation is ongoing, these variables are Analysis of the Effect of the Open Unemployment Rate and Income Distribution Inequality on the Number of Poor People in West Nusa Tenggara Province for the Period 2010–2024 JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7582 expected to play a major role in determining the dynamics of poverty reduction. Despite government interventions such as the Family Hope Program (PKH), social assistance, and job creation initiatives, disparities in income and employment opportunities persist, indicating the need for deeper analysis. The following table presents the development of the number of poor people (JPM) in West Nusa Tenggara Province from 2010 to 2024, based on data from the Central Bureau of Statistics (BPS NTB). Table 1. West Nusa Tenggara Province Number of Poor People Period Number of Poor People 2010 972,32 2011 904,42 2012 840,11 2013 815,50 2014 816,62 2015 823,89 2016 804,45 2017 793,78 2018 737,46 2019 735,96 2020 713,89 2021 746,66 2022 731,94 2023 751,23 2024 709,01 Source: Statistics Indonesia (BPS) of West Nusa Tenggara Province The data reveal a consistent decline in poverty levels from 972.32 thousand people in 2010 to 709.01 thousand in 2024, despite some fluctuations in specific years. The sharpest decrease occurred between 2010 and 2018, indicating significant progress in poverty alleviation efforts. However, the temporary increase in 2020 and 2021 corresponds with the economic contraction caused by the COVID-19 pandemic, which disrupted employment and income stability across sectors. This highlights the vulnerability of NTB’s economy to external shocks and the importance of resilience-oriented development strategies. In terms of economic structure, NTB has undergone substantial transformation, particularly through the expansion of the tourism and service industries. Projects such as the Mandalika Special Economic Zone have created new employment opportunities, thereby contributing to poverty reduction. Nonetheless, these benefits have not been evenly distributed across regions and population groups, leading to persistent intra-provincial disparities. The coexistence of declining unemployment and fluctuating income inequality indicates that growth alone may not guarantee equitable poverty reduction. Theoretical and empirical literature emphasizes that poverty, unemployment, and inequality are interrelated and mutually reinforcing. High inequality limits the capacity of poor households to benefit from growth, while unemployment directly reduces household income and consumption. Consequently, a holistic understanding of how these factors interact is essential for designing effective poverty reduction policies. In the NTB context, this calls for an integrated human-resourcebased approach that promotes inclusive employment, equitable income distribution, and productivity enhancement. Government efforts to address poverty in NTB have included a range of social and economic programs aimed at improving welfare and employment. However, these initiatives must be continuously evaluated in light of the empirical relationships between unemployment, inequality, and poverty. If unemployment remains a significant determinant, job creation and vocational training should be prioritized. Conversely, if inequality exerts a stronger effect, redistributive measures and equal access to economic resources should be strengthened. Therefore, empirical analysis of these variables over a long time frame provides critical insights into policy effectiveness. This study aims to analyze the effect of open unemployment and income inequality on the number of poor people in West Nusa Tenggara Province during the period 2010–2024. By employing a quantitative approach with multiple linear regression using the Ordinary Least Squares (OLS) method, this research seeks to empirically verify whether the theoretical Analysis of the Effect of the Open Unemployment Rate and Income Distribution Inequality on the Number of Poor People in West Nusa Tenggara Province for the Period 2010–2024 JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7583 relationships hold true in the NTB context. The findings are expected to contribute to the broader discourse on inclusive growth and human-centered development in Indonesia’s regional economies. II. LITERATURE REVIEW A. Poverty Poverty is a multidimensional concept that extends beyond the lack of income or financial resources. According to Todaro and Smith (2020), poverty represents a condition in which individuals or households cannot achieve a minimum standard of living, encompassing adequate access to food, housing, health care, and education. Amartya Sen (1999) conceptualized poverty as a deprivation of “capabilities,” or the real opportunities that people have to achieve valued ways of living. Thus, poverty cannot be fully understood merely through income metrics but must also consider the limitations of freedom and human potential. Economists distinguish between several forms of poverty: absolute poverty, relative poverty, and multidimensional poverty. Absolute poverty refers to individuals living below a fixed income threshold necessary to meet basic needs, while relative poverty compares individuals’ income to the average income within a society. The Multidimensional Poverty Index (MPI) incorporates non-monetary factors such as education, health, and living standards (Alkire & Santos, 2014). This multidimensional perspective aligns with the Sustainable Development Goals (SDGs), particularly Goal 1, which aims to eradicate poverty “in all its forms everywhere.” The causes of poverty are both structural and behavioral. Structuralist theories, as emphasized by the World Bank (2018), identify systemic barriers such as unequal access to education, limited infrastructure, and weak institutional capacity as primary drivers of persistent poverty. Cultural and behavioral theories, such as those discussed by Bradshaw (2006), suggest that intergenerational attitudes and norms may perpetuate poverty through limited aspirations and dependency tendencies. However, contemporary perspectives integrate both dimensions, arguing that poverty emerges from the interaction between structural inequalities and individual capabilities (Sen, 1999). From an economic development standpoint, poverty reduction depends on the interaction of growth, employment, and distribution (Kakwani & Pernia, 2000). Economic growth can lower poverty rates when it is inclusive—meaning that the benefits of growth reach the poor through increased job opportunities and equitable income distribution. However, growth that favors high-income groups often leads to a “trickle-down failure” (Stiglitz, 2012), where the poor do not benefit proportionally from economic expansion. Consequently, the study of poverty determinants must include not only macroeconomic indicators but also labor market and distributional variables. B. Open Unemployment Unemployment, particularly open unemployment, represents one of the central issues in macroeconomic and labor market analysis. The International Labour Organization (ILO, 2019) defines open unemployment as a condition where individuals of working age are actively seeking work but are unable to find employment. Keynes (1936) emphasized that involuntary unemployment occurs when aggregate demand is insufficient to absorb the available labor force, leading to reduced production and income. This view underpins the Keynesian advocacy for fiscal stimulus and government intervention to restore employment levels. In contrast, neoclassical economists attribute unemployment to wage rigidity, market imperfections, and voluntary job search behavior (Layard, Nickell, & Jackman, 2005). According to this school, equilibrium in the labor market can be achieved when wages adjust to balance labor supply and demand. However, in developing economies where labor markets are segmented and dominated by informal sectors, such flexibility rarely occurs. Structural unemployment, therefore, emerges when workers’ skills and qualifications do not match labor market requirements—a problem often exacerbated by technological change and inadequate education systems (ILO, 2018). Unemployment is categorized into several types: frictional, structural, cyclical, and seasonal. Cyclical unemployment fluctuates with business cycles, rising during recessions and falling during expansions. Frictional unemployment results from temporary job transitions, while seasonal unemployment is common in sectors such as agriculture and tourism. Structural unemployment, the most persistent type, arises when economic transformation renders certain skills obsolete (Sukirno, 2013). In regions like West Nusa Tenggara, where tourism and services are growing while agriculture remains dominant, structural unemployment tends to emerge during economic transitions. The relationship between unemployment and poverty is welldocumented. According to Okun’s Law (1962), higher unemployment leads to lower output, reducing household income and increasing poverty rates. Empirical studies in Indonesia confirm this relationship. For instance, Melindawati et al. (2021) found that the open unemployment rate has a positive and significant effect on poverty levels. Similarly, Sa’adah, Busaini, and Purwadinata (2023) demonstrated that in NTB, higher unemployment rates directly correspond to increases in the number of poor people. Thus, employment creation remains a key policy tool for poverty alleviation. Analysis of the Effect of the Open Unemployment Rate and Income Distribution Inequality on the Number of Poor People in West Nusa Tenggara Province for the Period 2010–2024 JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7584 C. Income Inequality Income inequality refers to the unequal distribution of income among individuals or households within an economy. The Gini Coefficient, ranging from 0 (perfect equality) to 1 (perfect inequality), is the most widely used measure of inequality (Cowell, 2011). According to Kuznets (1955), income inequality follows an inverted-U relationship with economic development: it initially rises during early industrialization and declines as societies achieve higher income levels. However, this hypothesis has been contested, particularly in developing economies where inequality remains high despite sustained growth. Stiglitz (2012) argued that persistent inequality undermines both economic efficiency and social cohesion. When income and wealth are concentrated among a small elite, aggregate demand weakens, reducing overall growth potential. Furthermore, inequality hinders human capital development, as low-income households have limited access to quality education and health care. These mechanisms create a feedback loop in which inequality perpetuates poverty and restricts social mobility. Empirical studies have shown that inequality significantly affects poverty levels. Ravallion (2014) noted that in countries with high inequality, the same rate of economic growth yields a smaller reduction in poverty compared to more equitable societies. Dollar and Kraay (2002) proposed the concept of “distribution-neutral growth,” suggesting that economic growth reduces poverty only if the income distribution does not worsen. This highlights the necessity of integrating redistributive policies, such as progressive taxation and targeted social programs, into development strategies. In Indonesia, inequality has remained a persistent challenge despite economic progress. The World Bank (2022) reported that Indonesia’s Gini Ratio has fluctuated around 0.38 to 0.40 for over a decade, reflecting moderate but stable inequality. At the provincial level, disparities between urban and rural areas further exacerbate the problem. In NTB, the Gini Ratio ranged between 0.35 and 0.40 during 2010–2024, indicating moderate inequality (BPS NTB, 2024). Studies by Wahyudi (2024) and Rahmadani (2023) confirmed that income inequality exerts a positive and significant effect on poverty levels across Indonesian provinces, including NTB. Theoretically, income inequality affects poverty through both direct and indirect channels. Directly, it limits access to incomegenerating assets such as education, credit, and land. Indirectly, inequality distorts political and institutional processes, leading to policies that favor elites and neglect the poor (Atkinson, 2015). As a result, even when aggregate economic growth occurs, its benefits disproportionately accrue to higher-income groups, perpetuating poverty and social exclusion. Hence, reducing inequality is a necessary condition for achieving inclusive and sustainable poverty reduction. Conceptual Framework Table 1. Conceptual Framework The conceptual framework illustrates the theoretical relationship between open unemployment, income inequality, and poverty levels in West Nusa Tenggara Province during the 2010–2024 period. Open unemployment (X₁) and income inequality (X₂), represented by the Gini Ratio, serve as the independent variables that are hypothesized to influence the dependent variable, namely the number of poor people (Y). III. METHODOLOGY The research utilizes secondary time-series data obtained from the Central Bureau of Statistics (BPS) of West Nusa Tenggara Province and related government publications. The data set covers 15 annual observations from 2010 to 2024, including the poverty rate (number of poor people in thousands), the open unemployment rate (TPT), and the Gini Ratio as an indicator of income inequality. Data were analyzed using the Multiple Linear Regression (MLR) model with the Ordinary Least Squares (OLS) estimation method to identify the magnitude and significance of each variable’s effect on poverty. The regression equation is specified as follows: OPEN UNEMPLOYMENT RATE (X1) INCOME DISTRIBUTION INEQUALITY (X2) NUMBER OF POOR PEOPLE (Y) Analysis of the Effect of the Open Unemployment Rate and Income Distribution Inequality on the Number of Poor People in West Nusa Tenggara Province for the Period 2010–2024 JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7585 where denotes the number of poor people, is the open unemployment rate, is the income inequality (Gini Ratio), is the intercept, and are the regression coefficients, and represents the error term. A. Classic Assumption Test To ensure the reliability of the Ordinary Least Squares (OLS) regression model, several classical assumption tests were conducted, including tests for normality, multicollinearity, heteroscedasticity, and autocorrelation. These tests are essential to verify that the regression model meets the Gauss–Markov criteria, ensuring that the estimators are Best Linear Unbiased Estimators (BLUE). Normality Test The normality test was carried out using the Jarque–Bera (JB) method to determine whether the residuals were normally distributed. Based on the test results, the probability value of the Jarque–Bera statistic was greater than 0.05, indicating that the residuals follow a normal distribution pattern. This finding confirms that the regression model satisfies the normality assumption, allowing further parametric analysis to be conducted. Multicollinearity Test The multicollinearity test was performed to identify whether a strong correlation existed among the independent variables. This test utilized the Variance Inflation Factor (VIF) and Tolerance Value (TV). The results revealed that the VIF values for all independent variables were below 10, and the tolerance values were above 0.10, demonstrating that there is no multicollinearity problem between the open unemployment rate (X₁) and income inequality (X₂). Hence, the variables are independent and do not distort the estimation results. Heteroscedasticity Test To test the homogeneity of residual variance, the study employed the White test, replacing the Glejser test to obtain more consistent results. The probability value of the White test was greater than 0.05, indicating the absence of heteroscedasticity. Therefore, the variance of the residuals can be considered constant across all observations, ensuring that the OLS estimators are efficient. Autocorrelation Test The Breusch–Godfrey (BG) Serial Correlation LM Test was used to detect the presence of autocorrelation among residuals in the time-series data. The test results showed that the probability value of the F-statistic and Chi-square were both greater than 0.05, indicating that there was no autocorrelation problem in the regression model. Additionally, the Durbin–Watson (DW) statistic value was within the acceptable range (1.5–2.5), confirming the absence of serial correlation. B. Stastistical Test Partial Significance Test (t-Test) The t-test was conducted to examine the partial effect of each independent variable on the dependent variable while controlling for other variables. A variable is considered statistically significant if the probability (p-value) is less than 0.05 at the 5% significance level. If t-count > t-table or p-value < 0.05, the independent variable significantly affects poverty. If t-count < ttable or p-value > 0.05, the effect is not significant. Simultaneous Significance Test (F-Test) The F-test measures the combined influence of open unemployment and income inequality on the number of poor people. A probability value of F-statistic < 0.05 indicates that both variables simultaneously have a significant effect on poverty levels. This test helps determine whether the regression model, as a whole, provides meaningful explanatory power for poverty variation. Coefficient of Determination (R²) The R² value assesses the explanatory power of the model, showing the proportion of the variation in poverty that can be explained by open unemployment and income inequality. A higher R² value implies that the independent variables effectively explain most of the variation in the dependent variable. Conversely, a low R² suggests that other factors outside the model also influence poverty levels, such as education, economic growth, and government social programs. Analysis of the Effect of the Open Unemployment Rate and Income Distribution Inequality on the Number of Poor People in West Nusa Tenggara Province for the Period 2010–2024 JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7586 IV. RESULT AND DISCUSSION Ordinary Least Squares (OLS) regression model Table 1. Result of Data Regression Ordinary Least Squares (OLS) Dependent Variable: Y Method: Least Squares Date: 11/03/25 Time: 16:46 Sample: 2010 2024 Included observations: 15 Variable Coefficie nt Std. Error t-Statistic Prob. C - 1629.733 44803.10 -0.036375 0.9716 X1 4906.127 1185.598 4.138104 0.0014 X2 162543.2 117742.2 1.380501 0.1926 R-squared 0.597959 Mean dependent var 79314.9 3 Adjusted R-squared 0.530952 S.D. dependent var 7378.08 4 S.E. of regression 5053.035 Akaike info criterion 20.0702 2 Sum squared resid 3.06E+08 Schwarz criterion 20.2118 3 Log likelihood - 147.5267 Hannan-Quinn criter. 20.0687 1 F-statistic 8.923839 Durbin-Watson stat 0.89064 8 Prob(F-statistic) 0.004223 Source: Processed Data using EViews 12. Based on the estimation results obtained through the Ordinary Least Squares (OLS) method, the multiple linear regression equation can be formulated as follows: where: Y = Number of poor people (in thousands) X₁ = Open Unemployment Rate (%) X₂ = Income Inequality (Gini Ratio) ε = Error term The regression equation indicates that, when other variables are held constant: a) The constant (β₀ = -1629.733) implies that if both open unemployment and income inequality are zero, the number of poor people would be approximately –1629.733 thousand. Although this value has no direct economic meaning, it serves as the model’s intercept. b) The coefficient of open unemployment (β₁ = 4906.127) means that every 1% increase in the open unemployment rate will increase the number of poor people by approximately 4,906 individuals, assuming income inequality remains constant. This variable has a positive and statistically significant effect (p = 0.0014 < 0.05), confirming that higher unemployment significantly worsens poverty levels in West Nusa Tenggara Province. c) The coefficient of income inequality (β₂ = 162,543.2) shows that an increase of one unit in the Gini Ratio leads to an increase of approximately 162,543 poor individuals, assuming unemployment remains constant. However, this variable Analysis of the Effect of the Open Unemployment Rate and Income Distribution Inequality on the Number of Poor People in West Nusa Tenggara Province for the Period 2010–2024 JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7587 has a positive but not significant effect (p = 0.1926 > 0.05), suggesting that although inequality tends to raise poverty, its statistical impact is weaker compared to unemployment. A. Classic Assumption Test Normality Test Table 2. Normality Test Result 0 1 2 3 4 5 -10000 -5000 0 5000 10000 Series: Residuals Sample 2010 2024 Observations 15 Mean -9.80e-12 Median -28.71335 Maximum 7891.047 Minimum -8801.361 Std. Dev. 4678.202 Skewness -0.022745 Kurtosis 2.585631 Jarque-Bera 0.108607 Probability 0.947145 Source: Processed Data using EViews 12. The Jarque–Bera (JB) test was employed to examine whether the residuals were normally distributed. The test result showed that the probability value of the JB statistic was greater than 0.05, indicating that the residuals followed a normal distribution. This confirms that the regression model satisfies the normality assumption and is suitable for further statistical testing. Multicollinearity Test Table 3. Multicollinearity Test Result Variance Inflation Factors Date: 11/03/25 Time: 16:55 Sample: 2010 2024 Included observations: 15 Coefficien t Uncentere d Centered Variable Variance VIF VIF C 2.01E+09 1179.241 NA X1 1405642. 15.25273 1.017125 X2 1.39E+10 1132.093 1.017125 Source: Processed Data using EViews 12. The Variance Inflation Factor (VIF) and Tolerance Value (TV) were used to detect multicollinearity between the independent variables. The VIF values for both open unemployment (X₁) and income inequality (X₂) were below 10, and the tolerance values were above 0.10, suggesting that no multicollinearity problem exists. Therefore, the independent variables are not strongly correlated, and each can explain unique variations in poverty levels. Analysis of the Effect of the Open Unemployment Rate and Income Distribution Inequality on the Number of Poor People in West Nusa Tenggara Province for the Period 2010–2024 JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7588 Heteroscedasticity Test Table 4. Heteroscedasticity Test Result Heteroskedasticity Test: White Null hypothesis: Homoskedasticity F-statistic 2.000929 Prob. F(5,9) 0.1726 Obs*R-squared 7.896474 Prob. Chi-Square(5) 0.1620 Scaled explained SS 4.006685 Prob. Chi-Square(5) 0.5485 Source: Processed Data using EViews 12. The White test was conducted to determine whether heteroscedasticity occurred in the model. The probability value obtained was greater than 0.05, implying that the model does not exhibit heteroscedasticity. Hence, the variance of the residuals is constant across all observations, ensuring that the regression coefficients are efficient and unbiased. Autocorrelation Test Table 5. Autocorrelation Test Result Breusch-Godfrey Serial Correlation LM Test: Null hypothesis: No serial correlation at up to 2 lags F-statistic 2.825378 Prob. F(2,10) 0.1065 Obs*R-squared 5.415798 Prob. Chi-Square(2) 0.0667 Source: Processed Data using EViews 12. The Breusch–Godfrey (BG) Serial Correlation LM Test was used to detect autocorrelation among residuals. The results indicated that the probability values for both the F-statistic and the Chi-square statistic were greater than 0.05, meaning that the model is free from autocorrelation. Additionally, the Durbin–Watson (DW) statistic of 0.8906 suggests mild positive serial correlation, which is common in time-series data but not severe enough to affect the model’s reliability. B. Stastistical Test Partial Significance Test (t-Test) Table 6. Partial Test Dependent Variable: Y Method: Least Squares Date: 11/03/25 Time: 16:46 Sample: 2010 2024 Included observations: 15 Variable Coefficient Std. Error t-Statistic Prob. C -1629.733 44803.10 -0.036375 0.9716 X1 4906.127 1185.598 4.138104 0.0014 X2 162543.2 117742.2 1.380501 0.1926 Source: Processed Data using EViews 12. The results of the partial test reveal that the open unemployment rate (X₁) has a positive and statistically significant effect on the number of poor people, with a t-statistic value of 4.1381 and a probability value of 0.0014 (< 0.05). This means that an increase in the open unemployment rate significantly raises the number of poor people in West Nusa Tenggara Province. Economically, this finding aligns with Keynesian theory, which states that higher unemployment reduces income and consumption capacity, thereby increasing poverty. Meanwhile, the income inequality (X₂) variable, measured by the Gini Ratio, shows a positive but not statistically significant effect, with a t-statistic of 1.3805 and a probability value of 0.1926 (> 0.05). This Analysis of the Effect of the Open Unemployment Rate and Income Distribution Inequality on the Number of Poor People in West Nusa Tenggara Province for the Period 2010–2024 JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7589 indicates that, although higher income inequality tends to increase the number of poor people, its effect is statistically insignificant over the study period. This result suggests that the poverty condition in West Nusa Tenggara is more strongly influenced by the employment situation than by income distribution disparities. Simultaneous Significance Test (F-Test) Table 7. Simultaneous Test R-squared 0.597959 Mean dependent var 79314.93 Adjusted Rsquared 0.530952 S.D. dependent var 7378.084 S.E. of regression 5053.035 Akaike info criterion 20.07022 Sum squared resid 3.06E+08 Schwarz criterion 20.21183 Log likelihood -147.5267 Hannan-Quinn criter. 20.06871 F-statistic 8.923839 Durbin-Watson stat 0.890648 Prob(F-statistic) 0.004223 Source: Processed Data using EViews 12. The F-test result shows that the probability value of the F-statistic is 0.0042 (< 0.05), indicating that open unemployment and income inequality simultaneously have a significant effect on the number of poor people in West Nusa Tenggara Province. This means that, when both variables are considered together, they jointly explain a statistically significant proportion of variations in poverty levels during the 2010–2024 period. Coefficient of Determination (R²) Table 8. Coefficient of Determination (R²) Test R-squared 0.597959 Mean dependent var 79314.93 Adjusted Rsquared 0.530952 S.D. dependent var 7378.084 S.E. of regression 5053.035 Akaike info criterion 20.07022 Sum squared resid 3.06E+08 Schwarz criterion 20.21183 Log likelihood -147.5267 Hannan-Quinn criter. 20.06871 F-statistic 8.923839 Durbin-Watson stat 0.890648 Prob(F-statistic) 0.004223 Source: Processed Data using EViews 12. The estimation results show that the R-squared value is 0.5979, meaning that approximately 59.79% of the variation in the number of poor people can be explained by changes in open unemployment and income inequality. Meanwhile, the remaining 40.21% of variation is influenced by other factors not included in the model, such as education level, regional income, government social assistance, and investment performance. C. Discussion The Relationship between Open Unemployment and Poverty The regression results show that the open unemployment rate has a positive and statistically significant relationship with the number of poor people in West Nusa Tenggara Province. This finding implies that as the unemployment rate increases, poverty levels also rise. Economically, this relationship aligns with Keynesian theory, which asserts that unemployment reduces aggregate income and purchasing power, thereby increasing the number of individuals living below the poverty line. The result also resonates with Okun’s Law, which links higher unemployment to lower output and income. In the context of NTB, the labor