Dynamics of Labor Absorption and Influencing Factors (Case Study in Districts/Cities in the Province of NTB)

Studies Management and Finance Economics, of Journal 0504-2644 (online): ISSN 0490,-2644 (print): ISSN 5202 December 12 Issue 80 Volume 8.317 Factor: Impact ,33-i12-10.47191/jefms/v8 DOI: Article 9097-7895 No: Page JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7895 Dynamics of Labor Absorption and Influencing Factors (Case Study in Districts/Cities in the Province of NTB) Sumarni Puspawati1, M. Irwan2, Luluk Fadliyanti3 1,2,3Master of Economic, Faculty of Economic and Business, Univesity of Mataram, Indonesia ABSTRACT: This study aims to analyze the effect of regional gross domestic product, district/city minimum wage, local revenue, labor force, and unemployment on labor absorption dynamics and influencing factors (case study in districts/cities in NTB Province). This study uses an associative quantitative approach with panel data regression analysis using the Fixed Effect Model (FEM) processed using Eviews 13 to test the relationship between these variables. The data in this study uses secondary data with a sample of 10 districts/cities during the 2017–2024 period in West Nusa Tenggara Province, namely West Lombok District, Central Lombok District, East Lombok District, Sumbawa District, Dompu District, Bima District, West Sumbawa District, North Lombok District, Mataram City, and Bima City. The results of this study show that the variables, gross regional domestic product, have a positive but insignificant effect, the minimum wage of the regency/city has a positive and significant effect, local revenue has a positive and significant effect, while the number of the labor force has a positive but insignificant effect, and the number of unemployed has a positive but insignificant effect on labor absorption. KEYWORDS: Gross Regional Domestic Product, County/City Minimum Wage, Local Revenue, Labor Force, Unemployment Rate. I. INTRODUCTION The fundamental problem with employment in Indonesia at present is the lack of job opportunities. This situation has arisen due to an imbalance between the growth of the working-age population and the availability of jobs, which will lead to disparities resulting in unemployment. Work is an important part of human life because it encompasses social and economic dimensions. When per capita income increases, it will lead to faster economic structural changes and drive economic growth. Economic development is a process that aims to improve people's living standards, expand employment opportunities, and achieve income equality (Husna Siregar, 2022). Employment is a key indicator in evaluating the economic health of a country. In Indonesia, increasing employment is an important priority in order to minimize unemployment and improve the welfare of the people. Challenges in the field of employment are inevitable given the large population. This high population results in a large workforce, which leads to a high number of job seekers and an increasing unemployment rate. Employment plays a crucial role in the development process because it is a component of human resources and a key driver of the economy. Because in an effort to expand the economic sector and increase total production, economic development depends on human resources. Thus, the role of human resources in every stage of economic development becomes clear (Lube et al., 2021). The challenge of labor absorption is no longer a national issue but has spread to the regional level, as is the case in West Nusa Tenggara Province. NTB has 8 regencies and 2 cities. The regencies consist of West Lombok, Central Lombok, East Lombok, Sumbawa, Dompu, Bima, West Sumbawa, North Lombok, and the cities consist of Bima and Mataram City. The province of West Nusa Tenggara (NTB) is one of the regions in Indonesia experiencing quite complex dynamics in labor absorption. NTB is known for its well-developed agriculture, fisheries, and tourism sectors. However, on the other hand, it still faces various challenges such as limited infrastructure, disparities between urban and rural areas, and shifts in economic structure that affect the types and number of jobs available. In recent years, NTB has experienced significant changes in its economic structure and labor market. Growth in the tourism and creative industries has opened up new opportunities for workers, but challenges in the agriculture and manufacturing sectors have also affected labor absorption dynamics. In addition, factors such as education levels, skills, local government policies, and technological developments plays a role in determining the region's ability to absorb labor. The following are the conditions Dynamics of Labor Absorption and Influencing Factors (Case Study in Districts/Cities in the Province of NTB) JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7896 of labor absorption in regencies/cities in NTB: Table 1.1 Data on Employment Absorption Rates in Regencies/Cities in NTB Province 2017-2024 (People) Source: NTB Provincial Statistics Agency 2025 Table 1.1 shows the dynamics of labor absorption in West Nusa Tenggara Province (NTB) over eight years from 2017 to 2024 based on data on the number of workers absorbed: In 2017, the number of workers absorbed was 2,316,720. In 2018, there was a decline to 2,258,666 thousand people. In 2019, the number of workers absorbed increased again to 2,387,036. In 2020, it increased to 2,575,956. In 2021, it rose again to 2,657,395. In 2022, the level of labor absorption reached 2,718,345. In 2023, it increased to 2,892,982. In 2024, it continued to increase to 3,105,549. From this data, it can be seen that labor absorption in NTB fluctuated with a general trend that tended to be stable in the range of 2,258,666 to 3,105,549. A significant decline only occurred in 2018. The Central Statistics Agency (BPS) stated that in line with the decline in the labor force, the Labor Force Participation Rate also experienced a decline. The LFPR in August 2018 was recorded at 65.91 percent, down 3.92 points and down 2.58 points compared to a year earlier. The decline in TPAK was caused by the earthquake disaster in August 2018, which resulted in most freelance workers and unpaid workers/family workers being unable to work, thereby classifying them as not part of the labor force. This decline in the labor force participation rate has impacted the labor absorption rate in NTB. In 2020, there was a 0.8% decline in labor absorption, which occurred due to the Covid-19 pandemic that affected almost all regions in NTB. Employment plays a crucial role in the economic development of a region. Economic growth, as measured by GRDP (Gross Regional Domestic Product), UMK (Minimum Wage for Regencies/Cities), and PAD (Local Own-Source Revenue), directly affects a region's ability to create jobs. Good labor absorption conditions will lead to a decrease in unemployment and an increase in public welfare. If economic growth is not in line with labor force growth, unemployment problems will arise. Labor absorption is a key pillar of successful economic development, not only at the national level but also specifically at the regional level. A region's ability to provide adequate employment opportunities is a reflection of its economic vitality and directly affects the welfare of the local community. The growth of a region's Gross Regional Domestic Product (GRDP) is a key indicator that reflects local economic activity. An increase in GRDP indicates an expansion in the production of goods and services, which in turn is expected to create more job opportunities. Leading sectors in the region, such as agriculture in rural areas, manufacturing in industrial areas, or tourism in tourist destinations, are the main drivers of employment. The higher a sector's contribution to regional GRDP, the greater its potential to absorb labor. Conversely, an increase in the number of productive workers can also drive GRDP growth through increased output and consumption (Rauch et al., 2022). The next factor is that the district/city minimum wage (UMK) policy at the regional level has a complex impact on labor absorption. The UMK aims to ensure that workers earn a decent living, but setting the UMK out of line with productivity and the real conditions of the business world in the region can create a dilemma. For businesses, especially Micro, Small, and Medium Enterprises (MSMEs) which are the backbone of the economy, excessive minimum wages that are not balanced with financial capabilities can be a burden. This has the potential to hamper new investment, reduce the competitiveness of local products, and even trigger layoffs or business relocation to areas with lower labor costs, which will ultimately hamper labor absorption (Nurnafisah & Effendi, 2023). Dynamics of Labor Absorption and Influencing Factors (Case Study in Districts/Cities in the Province of NTB) JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7897 In addition, Local Own-Source Revenue (PAD) plays a crucial role in regional development and indirectly affects employment. PAD, which comes from local taxes, local levies, and other sources, reflects the financial capacity of the region to finance development. Regions with strong PAD have the capacity to invest in infrastructure, develop potential economic sectors, or launch community empowerment programs that can create new jobs. For example, the construction of modern markets or MSME centers funded by PAD. can create business opportunities and jobs for the local community. The larger and more stable a region's PAD is, the greater its potential to create an environment conducive to labor absorption. The dynamics between the labor force and the number of unemployed are at the heart of the challenge of labor absorption in the region. The labor force in a region includes all productive-age residents who are ready and willing to work (Oyvat, 2023). Analysis of labor absorption is highly relevant in providing a clear picture and targeted policy recommendations in addressing labor challenges in the region. Labor absorption is an important indicator in measuring the health of a region's economy. The level of labor absorption shows that jobs are available and able to absorb the workforce. so as to reduce unemployment and improve community welfare. Based on the issues outlined above, the researcher wishes to conduct a study entitled “The Dynamics of Labor Absorption and Influencing Factors: A Case Study of Regencies/Cities in NTB.” II. LITERATURE RIVIEW A. The Concept of Labor Absorption The concept of labor absorption is the process of absorbing the workforce into various economic sectors to work and produce goods or services. This means how many people are working and contributing to the economy. Labor absorption is the ability of the job market to absorb the number of workers that meet the criteria for employment. BPS (2023) explains that labor absorption is the proportion of the workforce that is actively working out of the total labor force in a region or country at a given time. Labor absorption is the participation of the workforce in economic activities, both formal and informal. Labor absorption can be equal to or less than the number of available job opportunities, if labor absorption is equal to job opportunities. then unemployment will not occur. However, if labor absorption is smaller than employment opportunities, unemployment will arise. Labor is a human resource that has potential, capabilities, is fit for purpose, and has a certain personality to work and participate in development, so that it is useful for itself and society as a whole. Labor as an economic resource refers to the possession of a particular job, performing work activities, and occupying a job position. that are available and can create new jobs for others (Renjie & Yijun, 2023). Labor absorption theory Lewis' theory shows that a surplus of workers is an opportunity, not a problem. A surplus of labor in one sector will increase production and the supply of labor in other sectors. Thus, a surplus of labor supply is not a problem for economic development. It is better if the surplus of workers actually becomes capital for income accumulation, as long as the transfer of workers from the subsistence economy to the modern capitalist sector is smooth and not excessive (Fadlan, 2020). B. Gross Regional Domestic Product (GRDP) GRDP is the gross value added of all goods and services produced in a region within a certain period of time. GRDP can also be defined as the total value of goods and services produced by all economic units in a region. GRDP is an important indicator for measuring the economic growth of a region. Gross Regional Domestic Product (GRDP) is the main metric for evaluating the health of a region's economy over a certain period of time. Economic growth is an increase in per capita production in the long term. The process, per capita production, and the long term are the three most important points. Economic progress should be considered. Process, per capita production, and long-term are the three most emphasized points. Economic progress should be considered a continuous process. Regional sectoral growth that is in line with regional objectives and capabilities can only be achieved through the simultaneous implementation of regional development and sectoral development. To determine the role of each company and service provider in a location, we simply add up the value of all final goods and services produced there. Gross Regional Domestic Product is a term used to describe this. Gross Regional Domestic Product (GRDP) is the amount of money generated by companies in a region or the total value of all goods and services produced in that region. Reducing regional economic growth disparities is a regional development goal (Yussoff & Sulaiman, 2025). C. County/City Minimum Wage (UMK) The minimum wage is the lowest wage standard set by the government or relevant authorities, which employers must pay to workers for a specific type of work within a certain period of time. Labor absorption refers to the number of individuals who have successfully obtained employment or been hired by an entity, such as a company or agency. District/City Minimum Wage The minimum wage set in each province varies in amount, because the cost of living in each province is different. For example, In West Nusa Tenggara Province, the minimum wage is determined using the Regency/City Minimum Wage (UMK) system. The Dynamics of Labor Absorption and Influencing Factors (Case Study in Districts/Cities in the Province of NTB) JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7898 Regency/City Minimum Wage is the wage that applies in the regency/city area. The UMK is determined by the Governor, and must be higher than the Provincial Minimum Wage. The UMK is determined once a year and must be set no later than 40 days before the effective date, which is January 1. The minimum wage is a never-ending source of political debate. Supporters of a higher minimum wage see it as a means of increasing the income of poor workers. Although supporters of the minimum wage acknowledge that the policy causes unemployment for some workers, they argue that this sacrifice is worth it to alleviate poverty among other groups in society. Wages are all forms of income received by workers/employees, either in the form of money or goods, within a certain period of time for a particular economic activity (Malik, 2024). D. Local Own-Source Revenue (PAD) Local Own-Source Revenue (PAD) is all revenue obtained by the region from sources within its own territory, collected based on regional regulations in accordance with applicable laws and regulations. applicable laws and regulations. to measure the financial independence of a region. The greater the contribution of PAD to the Regional Revenue and Expenditure Budget (APBD), The region is becoming increasingly independent in financing government activities and development. Regional finances are all the rights and obligations of the region in the context of regional administration that can be valued in monetary terms, including all forms of wealth related to regional rights and obligations. Local Own-Source Revenue (PAD) is all local government revenue derived from the region's own economic sources. The optimization of Local Own-Source Revenue should be supported by efforts from the Local Government with improving the quality of public services. The main obstacle faced by local governments in implementing regional autonomy is the lack of revenue from Local Own-Source Revenue (PAD). The low proportion of Local Own-Source Revenue (PAD), on the other hand, means that local governments have a low degree of freedom in managing regional finances. Most expenditures, both routine and development, are financed from the Balance Fund, especially the General Allocation Fund. A short-term alternative to increase local government revenue is to explore Local Own-Source Revenue (Astuti et al., 2021). E. Total Labor Force The labor force is the part of the workforce that is actually involved or trying to get involved, or trying to get involved in the production of goods and services. Therefore, the labor force consists of residents whose main activity during the past week was working (K) and residents who are currently looking for work (MP). The labor force is categorized as working if they worked for at least 1 hour during the past week for productive activities. before the census is conducted. Job seekers are individuals whose main activity is looking for work, or who are temporarily looking for work and have not worked for at least one hour during the past week. Thus, the labor force can be formulated through the following identity equation: AK = K + MP. The sum of the numbers of the workforce in economic terms is referred to as labor supply. Meanwhile, residents who are classified as workers or laborers are included in labor demand. The workforce or manpower consists of the labor force and the non-labor force. The labor force consists of the employed and the unemployed and job seekers. The non-working population consists of people who are studying, those who take care of the household, and other income earners (Lim, 2024). F. Number of Unemployed Unemployment is a situation in which a person who belongs to the labor force (aged 15-64) wants to get a job but has not been able to find one. Unemployment is a situation in which a person wants to work but cannot find a job. Unemployment is defined as someone who is classified as part of the workforce and wants to find work but has not yet been able to do so. Unemployment is defined as someone who is classified as part of the workforce and is actively seeking work at a certain wage level but has not been able to find the desired job. Unemployment is a measure used when someone does not have a job. but they have been actively seeking employment in the last four weeks (Sain & Bozkrut, 2023). III. METHODOLOGY The type of research used in this study is quantitative research with an associative approach. This research was conducted in West Nusa Tenggara Province from 2018 to 2024. Data collection techniques included documentation and literature study, as well as case study methods. The data type was secondary data sourced from the Central Statistics Agency. Analysis of the data analysis procedure in this study uses panel data analysis. Panel data is a type of data collected sequentially over a certain period. This data is a combination of cross-sectional data and time series data. The cross-sectional characteristic is evident from the presence of more than one entity (individual) in the data, while the time series characteristic is indicated by each individual having several observations in various periods. The panel data analysis in this study is as follows: Panel Data Regression Model Yit= βo+β1X1it +β2X2it+β3X3it+β4X4it+β5X5it + eit Dynamics of Labor Absorption and Influencing Factors (Case Study in Districts/Cities in the Province of NTB) JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7899 Description: Yit = Labor absorption in region i and period t X1it = Gross Regional Domestic Product in region i and period t X2it = Minimum wage in district/city in region i and period t X3it = Local revenue in region i and period t X4it = Number of workers in region i and period t X5it = Number of unemployed in region i and period t βo = Constant A. Panel Data Model Estimation There are three alternative approaches in estimation methods using panel data regression techniques, namely the Common Effect Model or Pool Least Square (CEM), Fixed Effect Model (FEM), and Random Effect Model (REM), each of which has its own processing method. 1. Common Effect Model (CEM) The Common Effect Model is the simplest model in panel data regression, where time series and cross-section data are combined into one without considering individual or time differences. This model assumes that the behavior of data between individuals remains the same throughout the observed period. However, the main drawback of this approach is its incompatibility with real-world conditions, as each object can have different characteristics, both between individuals and across different time periods. 2. Fixed Effect Model (FEM) The Fixed Effect Model is a panel data estimation method that allows for relationships between time and between individuals in disturbance variables. This approach uses dummy variable techniques to capture differences in intercepts between individuals or companies, so it is often referred to as Least Squares Dummy Variable (LSDV). This model assumes that each individual has a different intercept but remains constant over time. The advantage of this method is its ability to distinguish between individual effects and time effects without having to assume that the error component is uncorrelated with the independent variables. 3. Random Effect Model (REM) In the Fixed Effect model, the addition of dummy variables to capture ignorance of the actual model has a weakness, namely a reduction in the degree of freedom, which can reduce the efficiency of parameter estimation. To overcome this limitation, the Random Effect model was developed. This model utilizes error terms in the panel data estimation process, with the possibility of inter-temporal and inter-individual relationships. The approach used in this model is Generalized Least Squares (GLS), which has the main advantage of overcoming the problem of heteroscedasticity. B. Selection of Panel Data Regression Models Before performing regression on panel data, researchers need to conduct several model specification tests to ensure the most appropriate estimation. There are three types of model specification tests that are commonly used, namely the Chow Test and the Hausman Test. The explanations of these two tests are as follows: 1. Lagrange Multiplier Test The Lagrange Multiplier Test is used to choose between the Common Effect Model (CEM) and the Random Effect Model (REM). This test, developed by Breusch-Pagan, uses OLS residuals. If the Cross-Section Breusch-Pagan value is > 0.05, then H0 is accepted and CEM is chosen. If the value is < 0.05, then H0 is rejected and REM is used. Hypothesis: H0: Common Effect Model (CEM) H1: Random Effect Model (REM) 2. Chow Test/Likelihood Ratio Test The Chow test is used to determine whether the Common Effect Model (CEM) or Fixed Effect Model (FEM) is more appropriate. In EViews, the analysis is performed on the Cross-Section F Prob value. Criteria: If the P value > 0.05, H0 is accepted → use CEM If the P value < 0.05, H0 is rejected → use FEM Hypothesis: H0: Common Effect Model (CEM) H1: Fixed Effect Model (FEM) 3. Hausman Test The Hausman test is used to determine the best model between the Random Effect Model (REM) and the Fixed Effect Model Dynamics of Labor Absorption and Influencing Factors (Case Study in Districts/Cities in the Province of NTB) JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7900 (FEM). In EViews, the results are seen in the Cross-Section Random Prob. value. Criteria: If the P value > 0.05, H0 is accepted → use REM If the P value < 0.05, H0 is rejected → use FEM Hypothesis: H0: Random Effect Model (REM) H1: Fixed Effect Model (FEM) C. Classical Assumption Test According to Basuki (2015), in panel data regression models, not all classical assumption tests need to be performed. The only tests required are heteroscedasticity and multicollinearity tests, as these two aspects affect the accuracy of model estimation. 1. Uji Heteroskedastisitas The heteroscedasticity test is used to test whether there is inequality in the variance of residual values from one observation to another in regression (Ghozali, 2018). In this study, to determine the presence or absence of heteroscedasticity, the Glejser test was used, which requires that if the probability of significance occurs heteroscedasticity (Ghozali, 2018) above 5% or 0.05, then the basis for decision making is as follows: • If the p-value is ≥ 0.05, then H0 is accepted, meaning that there is no heteroscedasticity issue. • If the p-value is ≤ 0.05, then H0 is rejected, meaning that there is a heteroscedasticity issue. 2. Multicollinearity Test The multicollinearity test aims to examine whether the regression model finds high or perfect correlations between independent variables (Imam Ghozali, 2009). A good regression model should not have correlations between independent variables. According to Ghozali (2018; 71), the multicollinearity test aims to examine whether the regression model finds correlations between independent variables. The basis for decision making in this test is as follows (Ghozali, 2018: 112). 1. If the correlation value is > 0.80, then there is a problem of multicollinearity. 2. If the correlation value is < 0.80, then there is no problem of multicollinearity. D. Hypothesis Testing 1. Partial Test (t-test) The conditions for partial tests are as follows: a. If the probability (significance) < 0.05 or the calculated t > the table t, then H₀ is rejected and Ha is accepted. b. If the probability (significance) > 0.05 or the calculated t < the table t, then H₀ is accepted and Ha is rejected. 2. Simultaneous Test (F Test) The conditions for the F-test are as follows: a. If the significance value is < 0.05 or H₀ is rejected and Ha is accepted, it means that the independent variables collectively (simultaneously) have a significant effect on the dependent variable. b. If the significance value is > 0.05 or H₀ is accepted and Ha is rejected, it means that the independent variables collectively (simultaneously) do not have a significant effect on the dependent variable. 3. Coefficient of Determination (R²) The Adjusted Coefficient (R²) aims to determine the extent to which the variation in the independent variable can adequately explain the variation in the dependent variable. The Adjusted Coefficient (R²) is used to measure the goodness of a model. It is a number that gives the proportion or percentage of the total variation in the dependent variable (Y) explained by the independent variable (X) (Gujarati, 2005). The perfect R² value is one, which means that all dependent variation can be fully explained by the independent variables intended in the model. Where 0 < R² < 1 the following can be taken: • If the R² value is close to 1, then there is a relationship between the independent variable and the dependent variable. • If the R² value is close to 0, then there is no relationship between the independent variable and the dependent variable. VI. RESULTS AND DISCUSSION A. Panel Data Regression Analysis Measurement The measurements used to observe the effect of GRDP, minimum wage, local revenue, labor force, and unemployment on labor absorption, both partially and simultaneously, were measured using statistical testing with the following panel data regression model: Dynamics of Labor Absorption and Influencing Factors (Case Study in Districts/Cities in the Province of NTB) JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7901 1. Chow Test The Chow test is conducted to determine the superiority between the common effect model and the fixed effect model in terms of effectiveness in a study. The hypothesis in the Chow test is as follows: H0: Common Effect Model (CEM) H1: Fixed Effect Model (FEM) If the specification test results show a Redundant Fixed Effect probability > 0.05, then H0 is accepted. Conversely, if the Redundant Fixed Effect probability < 0.05, then the Fixed Effect Model is selected or H0 is rejected. The following are the results of the Chow test using the Redundant Fixed Effect-Likelihood Ratio. Referring to Table the Chow Test results show that the cross-section F probability value is 0.0000, which is smaller than the significance level of 0.05 (0.0000 < 0.05). Based on these results, the null hypothesis (H0) is rejected and the alternative hypothesis (H1) is accepted. Therefore, the appropriate model to use is the Fixed Effect Model. 104 Since the Fixed Effect Model was selected as the initial model, the next step is to perform the Hausman Test to determine whether the most appropriate model to use in this study is the Fixed Effect Model or the Random Effect Model. 2. Hausman test The choice between the Fixed Effect Model and the Random Effect Model is made through the Hausman Test. If the cross-section random probability value exceeds the significance level of α = 0.05, then the null hypothesis (H0) is accepted. Conversely, if the probability value is less than α, then the alternative hypothesis (H1) is accepted. The hypotheses in the Hausman Test are as follows: H0 : Random Effect Model H1 : Fixed Effect Model Correlated Random Effects - Hausman Test Equation: Untitled Test cross-section random effects Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob. Cross-section random 15.401510 5 0.0088 Source: data processed using Eviews 13, 2025 Referring to the Hausman Test Table, it shows that the probability value for cross-section random is 0.0088, which exceeds the significance level of 0.05 (0.1249 > 0.05). Thus, the alternative hypothesis (H1) is accepted and the null hypothesis (H0) is rejected. This indicates that the most appropriate model to use is Fix Effect. Furthermore, it is not necessary to conduct a Langerang Multiplier test because at this stage the best model is the fixed effect model. B. Best Panel Data Regression Model Based on previous tests, the best model used in this study is the Fixed Effect Model. Variable Coefficient Std. Error t-Statistic Prob. C 35306.63 37527.18 0.940828 0.3503 X1 1.162950 3.893743 0.298672 0.7662 X2 0.077862 0.016029 4.857719 0.0000 X3 1.74E-07 7.21E-08 2.419712 0.0184 X4 0.018367 0.073509 0.249860 0.8035 X5 4.922812 69.39787 0.070936 0.9437 Effects Specification Cross-section fixed (dummy variables) R-squared 0.976363 Mean dependent var 250922.5 Adjusted R-squared 0.971192 S.D. dependent var 185153.6 S.E. of regression 31425.82 Akaike info criterion 23.71783 Sum squared resid 6.32E+10 Schwarz criterion 24.16772 Dynamics of Labor Absorption and Influencing Factors (Case Study in Districts/Cities in the Province of NTB) JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7902 Log likelihood -921.8543 Hannan-Quinn criter. 23.89807 F-statistic 188.8291 Durbin-Watson stat 0.521684 Prob(F-statistic) 0.000000 Source: data processed using Eviews 13, 2025 Based on the table above, the regression model equation can be seen as follows: Y = 35306. 6332204 + 1.16295025705 X1 + 0.0778620329562 X2 + 1.74468110883e-07 X3 + 0.0183669136052 X4 + 4.92281239707 X5 Description: Y = Employment Absorption X1= Gross Regional Domestic Product X3= Local Revenue X5= Number of Unemployed X2 = Minimum Wage for Regencies/Cities X4= Total Labor Force Based on the panel data regression equation above, then: 1. The value of the C coefficient is 353064, which means that if the variables of GRDP, UMK, PAD, total workforce, and unemployment are constant, then the value of labor absorption (Y) is 353064 percent. 2. The GDP variable coefficient value is 1.16295, which indicates a positive relationship between the GDP variable and labor absorption. When there is a 1 percent increase in GDP, the labor absorption rate in NTB will increase by 1.2 percent, assuming other variables remain constant. 3. The UMK variable coefficient value is 0.0778, which indicates a positive relationship between the UMK variable and labor absorption. When there is a 1 percent increase in UMK, the labor absorption rate in NTB will increase by 0.07 percent, assuming other variables remain constant. 4. The PAD variable coefficient value is 1.7446, which means that there is a positive relationship between the PAD variable and labor absorption. When there is a 1 percent increase in PAD, the level of labor absorption in NTB will increase by 1.7 percent, assuming that other variables remain constant. 5. The coefficient value of the Labor Force variable is 0.0183, which can be interpreted as a positive relationship between the Labor Force variable and labor absorption. When there is a 1% increase in GRDP, the level of Labor Absorption in NTB will increase by 0.01%, assuming other variables remain constant. 6. The coefficient value of the Unemployment variable is 4.92281, which means that there is a negative relationship between the Unemployment variable and labor absorption. When there is a 1 percent increase in Unemployment, the level of Labor Absorption in NTB will decrease by 4.9 percent, assuming that other variables remain constant. C. classical assumptions test According to Basuki (2015), in panel data regression models, not all classical assumption tests need to be performed. The only tests required are heteroscedasticity and multicollinearity tests, as these two aspects affect the accuracy of model estimation. 1. Multicollinearity Test X1 X2 X3 X4 X5 1.000000 0.180699 0.523220 -0.025806 -0.102026 0.180699 1.000000 0.135401 -0.112565 0.027482 0.523220 0.135401 1.000000 -0.042921 0.015212 -0.025806 -0.112565 -0.042921 1.000000 -0.037360 -0.102026 0.027482 0.015212 -0.037360 1.000000 Source: data processed using Eviews 13, 2025 Based on the test results, to see the Multicollinearity value, looking at the correlation value between variables below 0.8 means that there is no multicollinearity problem between independent variables. Therefore, it can be concluded that there is no multicollinearity in this equation. Dynamics of Labor Absorption and Influencing Factors (Case Study in Districts/Cities in the Province of NTB) JEFMS, Volume 08 Issue 12 December 2025 www.ijefm.co.in Page 7903 2. Heteroscedasticity Test Source: data processed using Eviews 13, 2025 Based on the results of the heteroscedasticity test, the average probability value is above 5%, so the heteroscedasticity test in the classical assumption test meets the requirements. D. Hypothesis Testing The accuracy of the sample regression function in estimating actual values can be measured by testing the suitability of the model. There are four stages of hypothesis testing in this study, namely partial testing (t-test), simultaneous testing (F-test), determination testing (R2), and dominant testing (Beta Coefficient), as follows: 1. Partial Test (T-test) This t-test shows the extent to which each independent variable individually influences the variation in the dependent variable. The t-test can be performed by comparing the calculated t with the table t (Ghozali, 2018). At a significance level of 5% with the following test criteria: • If tcount < ttable and p-value > 0.05, then H0 is accepted and H1 is rejected, which means that one of the independent variables does not significantly affect the dependent variable. • If tcount > ttable and p-value < 0.05, then H1 is accepted and H0 is rejected, meaning that one of the independent variables significantly affects the dependent variable. Variable Coefficient Std. Error t-Statistic Prob. C 35306.63 37527.18 0.940828 0.3503 X1 1.162950 3.893743 0.298672 0.7662 X2 0.077862 0.016029 4.857719 0.0000 X3 1.74E-07 7.21E-08 2.419712 0.0184 X4 0.018367 0.073509 0.249860 0.8035 X5 4.922812 69.39787 0.070936 0.9437 Source: data processed using Eviews 13, 2025 Based on the regression results, the t-test results show the significance of each research variable as follows: 1. The PDRB variable shows a p-value of 0.7662, which is greater than the alpha level of 0.05 or 5%. This means that the PDRB variable does not have a significant partial effect on labor absorption. 2. The UMK variable shows a p-value probability of 0.000, which is less than the alpha level of 0.05 or 5%. This means that the UMK variable has a significant partial effect on Labor Absorption. 3. The PAD variable shows a p-value of 0.0184, which is less than the alpha level of 0.05 or 5%. This means that the PAD variable has a significant partial effect on Labor Absorption. 4. The Labor Force variable shows a p-value probability of 0.8093, which is greater than the alpha level of 0.05 or 5%. This means that the Labor Force variable does not have a significant partial effect on Labor Absorption. 5. The Unemployment variable shows a p-value probability of 0.943, which is greater than the alpha level of 0.05 or 5%. This means that the Unemployment variable does not have a significant partial effect on Labor Absorption.