ANALYSIS OF HUMAN DEVELOPMENT INDEX, UNEMPLOYMENT AND POVERTY ON ECONOMIC GROWTH IN INDONESIA

The purpose of this study is to see and analyze how much influence the Human Development index, Unemployment and Poverty on Economic Growth in Indonesia. The data used in this study is secondary data obtained from related agencies over a period of 16 years (2006 - 2021). To achieve the results of this study, multiple linear regression analysis was used using the EViews 10 program. The results of this study simultaneously showed that the Human Development Index variable had no effect on Economic Growth, then the Unemployment variable had a negative and significant effect on Economic Growth, and the Poverty variable positive and significant effect on economic growth. Simultaneously, the Human Development Index, Unemployment and Poverty variables have significant and significant impact on economic growth.


INTRODUCTION
The government has a major role in economic development, which is manifested in the form of implementing fiscal policy to achieve the main goal of economic development in the form of high economic growth. In the midst of the 7th Indonesian President's administration, Indonesia's economic condition was under pressure due to global economic turmoil. Apart from Indonesia, the global economic turmoil has also put pressure on the economies of various countries in the world, including in the Asian and regional regions, in the regional area. In a developing country like Indonesia, economic growth has two inhibiting components, namely HR (Human Resources) and capital accumulation. The human resources in question are population growth and workforce growth, while the collection of capital in question is the wages earned by a region. In addition, Indonesia has the fourth largest population in the world. The population in Indonesia continues to increase from year to year. The current population of Indonesia is 271 million people, this has caused Indonesia to be inseparable from existing economic problems, which cause economic growth to fluctuate every year. Economic growth is supported by many sectors, where these sectors are the accumulation of activities carried out by residents of a country. That is, economic growth is highly dependent on the activities of its population. (Kismawadi et al., 2021).
Economic development is the initial instrument in achieving national goals. Economic development is inseparable from economic growth, as well as economic growth facilitating the course of economic development (Handayani, 2016) in (Shari & Abubakar, 2022). Economic development aims to equalize economic development and its results for the whole community, increase the rate of economic growth, expand employment opportunities, and minimize differences in regional capabilities. The success of the development of a region can be seen from its economic growth. Where the higher regional economic growth can be a benchmark and indicator of the high level of development success and regional economic conditions. If the economic growth in a region is lower, it indicates low development in that area and weak economic conditions.

Figure 1
Based on Graph 1, it shows that economic growth in Indonesia from 2016 to 2019 has generally been stagnant, namely at 5% until 2019, then in 2020 it has decreased drastically at -2.1% and in 2021 it has increased again by 3. .69%. Indonesia's economic growth experienced a drastic decline due to the Covid-19 outbreak in early 2020. Due to Covid19, the government implemented social distancing. Social distancing means efforts to reduce close contact with many people or what is also known as physical distancing. These two policies are used to overcome the pressure from the burden on health services, but every day the number of Covid-19 cases in Indonesia is still increasing.
Efforts to control the Covid-19 pandemic carried out by the Government together with all stakeholders, including all Indonesian people, succeeded in boosting national economic growth by 3.69% in 2021. With this growth rate, Indonesia's GDP per capita increased to IDR 62.2 million (or equivalent with US$4,349.5), higher than pre-pandemic GDP per capita which amounted to IDR 59.3 million in 2019. This achievement will also bring Indonesia back into the classification of an upper middle-income country. This position is an excellent initial foundation to promote economic recovery so that we are able to get out of the middle-income trap.
Overall, the pattern of changes in economic growth, HDI, unemployment and poverty can be seen as follows:

Figure 2
Based on graph 2, it can be seen that in general, the HDI value shows a positive direction where there has been an increase from 2016 to 2019, the increase in HDI is supported by a decrease in the poverty rate, but in this case, even though the HDI has increased and the poverty rate has decreased, the current economic growth rate stagnant, this is because the unemployment rate decline is still small. From this graph, what is interesting is that from 2019 to 2020, economic growth has decreased dramatically, this is indicated by the poverty rate which has increased again from 2019 to 2020, and this is also due to the unemployment rate which has also increased, so that the HDI has stagnated. , this is the government doing restoration against the danger of covid19. Changes in data in graph 2 poverty in 2018 to 2019, and from 2020 to 2021 this is not in accordance with existing theories, in 2018 to 2019 even though the poverty rate has decreased this is not in accordance with the course of economic growth, previous research stated that between economic growth and poverty has a negative relationship, in fact in that year the poverty rate and economic growth did not have a negative relationship, quite the contrary. In 2019 economic growth declined to 5% but the poverty rate in that year also decreased to 9.4%. Then in the following year there is a positive relationship between economic growth and poverty, in 2021 economic growth will increase to 3.7% but poverty will actually increase to 10.1%.
The conclusion that can be drawn is that there are two phenomena that are inconsistent between data changes and the theory put forward by previous research, the first phenomenon is from the theory of economic growth and HDI which has a positive relationship, but from the graph above the relationship between the two variables is not in accordance with theory, the second phenomenon is from the theory of economic growth and poverty has a negative relationship but what happens to data changes, the relationship of these variables is just the opposite. So from the phenomena that have been described, the author is interested in researching "The Analysis of the Index of Human Development, Unemployment, and Poverty on Economic Growth in Indonesia".

LITERATURE REVIEWS
Economic growth According to Wijono (2005), economic growth in a nutshell is a process of increasing output per capita in the long term, this understanding emphasizes three things, namely process, output per capita and long term. The process describes economic developments from time to time that are more dynamic in nature, output per capita relates aspects of total output (GDP) and aspects of population, so that the long term shows the tendency of economic changes in a certain period driven by internal economic processes (self-generating). Economic growth is also interpreted simply as an increase in total output (GDP) in the long term regardless of whether the increase is smaller or greater than the rate of population growth and whether or not followed by growth in economic structure Airus (2012). Economic growth according to Simon Kuznet (2008) is the ability of a country to provide more and more types of economic goods to its population. This ability grows according to economic progress, institutional and ideological adjustments needed. The definition above has three components of understanding: First, the economic growth of a nation can be seen from the continuous increase in the supply of goods. Second, advanced technology is a major factor in economic growth that determines the degree of growth in the supply of various goods to the population. Third, the widespread and efficient use of technology requires adjustments in the fields of institutions and ideologies. The innovations produced by human science can be used appropriately. So, economic growth is an increase in the prosperity of a country from an increase in overall income (Juliansyah et al., 2022)

Measuring Tool for Economic Growth Gross Domestic Product (GDP)
Gross Domestic Product (GDP), or at the regional level it is called Gross Regional Domestic Product (GDP), is the amount and final services produced by an economy in one year and is expressed in market prices. Both GDP and GRDP are measures that are global in nature, and are not an appropriate measure of economic growth because they cannot yet reflect the actual welfare of the population. In fact, every resident in the country or region concerned must enjoy welfare

Per Capita Gross Domestic Product (GDP)/Per Capita Income
Per Capita Gross Domestic Product or Per Capita Gross Regional Domestic Product on a regional scale can be used as a measure of economic growth that more accurately reflects the welfare of the population of a country or region than the value of GDP or GRDP alone. The indicator used to measure economic growth is the GDP growth rate.

Human Development Index
According to the UNDP (United Nations Development Programme) Human development is a process of enlarging people's choices, and the three main choices that are considered the most important, namely among them are longevity and health (longevity), education/knowledge ), and access to resources that can meet a decent standard of living (living standard) (Hidayat & Paidi, 2015).

Measurement of Human Development
The categories for grouping human development achievements, namely HDI with a value of less than 60 are included in the low HDI category, regions with HDI values between 60 and 70 are included in the medium HDI category, for regions with HDI values between 70 and 80 are included in the high HDI category, and for regions that have an HDI value of more than 80 are in the very high HDI category (Mongan 2019).

Unemployment
Samuelson (1992) provides a definition of unemployment (unemployed) is a person who does not work but is actively looking for work or is being called back to work in his company. In other words, a person is said to be unemployed if he is not working and (a) has been trying to find a job for the last four weeks, (b) has just been laid off from work and is waiting to be recalled or (c) is preparing a job application for the next month ( Haryanto 2014). Unemployment is a situation where someone belonging to the labor force wants to get a job but they have not been able to get the job. The unemployment rate is the percentage of the number of unemployed to the total labor force. Residents who are looking for work but do not currently have a job are called unemployed  Poverty The definition of poverty according to Michael Parkin in 2008, poverty is a situation where household income is too low to be able to meet their basic needs. They struggle to be able to buy the food, housing and clothing they need every day. This situation is driven by the unequality of income, or in other words this poverty occurs because it is driven by income inequality that occurs in a country (Ginting & Dewi 2013). Poverty according to Sudewi & Wirathi, (2013) in (Mudawari et al., 2022)defines as a lack of basic measures of use, lodging, and clothing that are expected to be self-sustaining in perpetuity. According to BPS (2012), poverty is seen as financial powerlessness to meet basic food and non-food needs which are estimated from the consumption side. The idea of poverty from Bappenas is to regard poverty as a condition in which an individual or an association, people, are unable to fulfill their fundamental freedom to follow and lead a respectable life. .

RESEARCH METHODS Object of research
The objects in this study are the Human Development Index, Unemployment and Poverty in Indonesia as independent variables, Economic Growth as the dependent variable. The location in this research is in Indonesia

Data Types and Sources
This study uses a quantitative method because the research data is in the form of numerical data. This study uses secondary data with the type of time series data taken from the 2006-2021 period. Secondary data is distinguished in three types of nature, namely secondary data that is private, secondary data that is public, and secondary data in the field of law. And what is used in this study is secondary data that is public. Data obtained through library research in the form of journals, articles and from various results of previous studies related to the discussion in this thesis. As for this study, the authors took data from the Central Bureau of Statistics (BPS).

Method of collecting data
To obtain the data, the researcher knows the data collection method used, because the data collection method is the most strategic step in obtaining data. Therefore, in this study researchers used data collection methods by collecting data provided by the Central Statistics Agency (BPS) needed by researchers, including the Human Development Index, Unemployment, Poverty, and Economic Growth for sixteen years (2006). until 2021)

Data analysis method
The analytical model used in this study is a multiple linear regression analysis model (Multiple Regression Model). Multiple Linear Regression is a linear regression model that includes more than one independent variable or predictor. Multiple Linear Regression is a multiple regression model if the dependent variable is an interval or ratio data scale (quantitative or numeric). While the independent variables are interval or ratio data scales. But there is also a linear regression where the independent variables use a nominal or ordinal data scale, which is more properly referred to as dummy data, so such linear regression is called linear regression with dummy variables. The model used in this study is as follows: The multiple linear regression analysis model for this study uses the Eviews 10 program with the Ordinary Least Squares (OLS) method procedure to determine the magnitude of the influence of one independent variable on the dependent variable.

Multicollinearity Test
The existence of multicollinearity in the regression equation model used will result in unclear estimates, then lead to conclusions to accept the null hypothesis. Multicollinearity aims to test whether there is a high or perfect correlation between the independent variables in the regression model. A good regression model should not have a correlation between the independent variables. Multicollinearity can be seen from the Variance Inflation Factors (VIF). VIF tries to see how the variance of an estimator increases if there is multicollinearity in an empirical model. If VIF tries to see how the variance of an empirical model is. If the VIF of a variable exceeds 10, then a variable is said to be highly correlated .

Autocorrelation Test
The autocorrelation test aims to test whether in a linear regression model there are confounding errors in period t with errors in period t-1 (previously). If there is a correlation, then there is called an autocorrelation problem. The existence of symptoms of autocorrelation in regression causes the resulting model cannot be used for the value of the dependent variable of certain independent variables. A good regression model is a regression that is free from autocorrelation In general, many cases of autocorrelation occur in time series data. Autocorrelation can be seen from the results of the Breusch-Godfrey (BG) test or commonly known as the Larange Multiplier test. The BG test is a test recommended by (Gujarati, 2009) to test autocorrelation in a model. The criterion for detecting whether there is an autocorrelation problem is if the Obs*Rsquared probability value is > (5%), there is no autocorrelation. If the probability value is Obs*R-squared, (5%), it means that there is autocorrelation.

Heteroscedasticity Test
The existence of heteroscedasticity causes the estimation of the regression coefficients to be inefficient. A good regression model is free from heteroscedasticity symptoms. Testing the heteroscedasticity problem was carried out using the Glejser Test .

Significance Test Simultaneous (Test f)
The f test aims to determine whether all the independent variables simultaneously influence the dependent variable (Kuncoro, 2009). If F-hit>F-table with a simultaneous level of 5%, it can be concluded that jointly the independent variables have a significant to the dependent variable.

Partial Test (t test)
The t test ascertains how far the influence of one explanatory variable individually explains the variation in the dependent variable (Kuncoro, 2009). Decision making based on if the p-value <5% significance level then there is a significant influence of each independent variable on the dependent variable

Determination Coefficient Test (R 2 )
The coefficient of determination (R 2 ) aims to measure how far the model's ability to explain the variation of the dependent variable is the coefficient of determination between zero and one.

RESULTS AND DISCUSSION Regression Model Selection
The analytical model used in this study is a multiple linear regression analysis model (Multiple Regression Model). Multiple linear regression analysis is used to determine the relationship between two or more explanatory variables and response variables. The multiple linear regression analysis model in this study used the Eviews 10 program with the Ordinary Least Squares (OLS) method procedure to determine the influence of one independent variable on the dependent variable. To calculate the multiple linear regression equation through the Ordinary Least Square (OLS) model, the data must meet the basic assumptions, namely the normality test and the classical assumption test (multicollinearity test, heteroscedasticity test, autocorrelation test).

Normality test
The normality test aims to test whether the confounding or residual variables in the regression model have a normal distribution or not. The basis for decision making is based on probability > 0.05 then the population is normally distributed and if the probability < 0.05 then the population is not normally distributed.
Source: (Data processed, 2022) Figure 1 Based on the normality test using the Jarque-Bera method (JB-Test). And the test results produce a probability > 0.05 (0.800919 > 0.05). Based on these results, it can be concluded that the regression model meets the assumptions of normality.

Classic assumption test Multicollinearity Test
Multicollinearity aims to test whether there is a high or perfect correlation between the independent variables in the regression model. A good regression model should not have a correlation between the independent variables. Multicollinearity can be seen from the Variance Inflation Factors (VIF). VIF looks at how the variance of an estimator increases if there is multicollinearity in an empirical model. If the VIF of a variable exceeds 10, then a variable is said to be very highly correlated (Damodar N Gujarati, 2012). 1. If the coefficient of Variance is > 0.80, then the data has multicollinearity. 2. If the coefficient of variance is <0.80, then the data does not have multicollinearity. Based on the table above, it can be seen that none of the independent variables has a VIF value of more than 10, and the coefficient of variance is less than 0.80. So it can be concluded that this study is free from multicollinearity.

Autocorrelation Test
The autocorrelation test aims to test whether in a linear regression model there are confounding errors in period t with errors in period t-1 (previously). If there is a correlation, then there is called an autocorrelation problem. According to  if the Obs*R-Square value is < X2(chi-square), then there is no autocorrelation. Furthermore, the results of the autocorrelation test can also be seen by comparing the Chi-Squared probability and a significant value of 5% if the Chi-Squared Prob value is > 5%, then there is autocorrelation. A good regression model is a regression that is free from autocorrelation. It can be seen that the p value is indicated by the Prob value. Chi-Square has a value greater than a significance level of 5% or 0.05 (0.1534 > 0.05). This shows that in the regression model used there is no heteroscedasticity problem.

Multiple Linear Regression Analysis
To find out the results of this study, it can be seen from the multiple linear regression output using Eviews 10 as an analytical tool in table 4 below. 1. Based on the regression equation, it shows that the constant value is 0.475495, which means that if the HDI, unemployment and poverty variables are constant = (0) or fixed, economic growth will have a constant value of 0.475495%. 2. The coefficient value of the HDI variable is -0.088802 this shows a negative relationship.
meaning that if the HDI increases by 1 percent, economic growth will decrease by 88802% assuming that the variables of unemployment and poverty are considered constant. 3. The coefficient of the variable Unemployment has a value of -4.148744 which shows a negative relationship, meaning that if unemployment increases by 1%, economic growth will decrease by -4.148744% assuming HDI and poverty are considered constant. 4. The coefficient of the Poverty variable has a value of 3.202913 this shows a positive relationship. meaning that if poverty increases by 1% then economic growth will increase by 3.202913% assuming the HDI and unemployment variables are considered constant.

Hypothesis test
Hypothesis testing is sometimes called "data analysis confirmation". Judgments from hypothesis testing are almost always made on the basis of testing the null hypothesis.

Partial Test Results
Testing the hypothesis using the t test, using the level of confidence (level of significance) or α = 0.05 or α = 5% with conditions, where the test is carried out with decision criteria if Tcount > Ttable Free Variables t-statistics t- Based on the partial test results in Table 6 it can be explained that: 1. The t-count value of the Human Development Index is 0.393799 which is smaller than the t-table which is 1.78229 (0.393799 < 1.78229) meaning that the Human Development Index has no effect and is not significant on economic growth in Indonesia. It can be seen from the probability value that is greater than alpha 0.05 (0.7013 > 0.05). 2. The t-count of unemployment is 6.615232 which is greater than the t-table of 1.78229 (6.615232 > 1.78229) meaning that unemployment has a negative and significant effect on economic growth in Indonesia. It can be seen from the probability value which is smaller than alpha 0.05 (0.0000 <0.05). 3. The t-count value of poverty is 3.720911 which is greater than the t-table which is 1.78229 (3.720911 > 1.78229) meaning that poverty has a positive and significant effect on economic growth in Indonesia. It can be seen from the probability value that is smaller than alpha 0.05 (0.0034 > 0.05).

Simultaneous Test Results (Test f)
Testing the hypothesis using the F test, using the level of confidence (level of significance) or α = 0.05 or α = 5% with conditions, where the test used is the decision criterion if F-count > Ftable at α = 5% then hypothesis H1 is rejected and accepts the H2 hypothesis whereas if F-count <F-table at α = 5% then the H2 hypothesis is rejected and accepts the H1 hypothesis. Because the F statistic is greater than the F table, it means that the HDI, unemployment and poverty all have an effect on economic growth.

Determination Coefficient Test (R 2 )
The value of the coefficient of determination is between zero and one. If the Adjusted R-Squared value is closer to zero, it means that the relationship between the independent variable and the dependent variable is very weak. If the Adjusted R-Squared value is closer to one then the relationship between the independent variables on the dependent variable is very strong. From the table above it can be seen that the Adjusted R-Squared value in this study is 0.750684. This means that the relationship between the independent variables and the dependent variable in this study is 75.06%. And 1 -0.750684 = 0.249316 which means that another 24.93% is influenced by other variables outside of this study.

Testing the Correlation Coefficient (R)
The correlation coefficient is a value that indicates the strength or not of a linear relationship between two variables. Correlation coefficient values vary from -1 to +1, r values that are close to -1 or +1 indicate a strong relationship between the two variables, and r values that are close to 0 indicate a weak relationship between the two variables. In this study the correlation value (r) or R-Squared is 0.804109 or 80.41%, so in this study it can be concluded that there is a moderate correlation between the dependent variable and the independent variable.

Relationship of Human Development Index to Economic Growth
Based on the results of the data processing above, it can be seen that the Human Development Index variable has no effect on Economic Growth in Indonesia because the probability value is 0.7013 which is greater than the significant level of 0.05, the Human Development Index coefficient is -0.088802. This is in line with research (Zakaria, 2018), which states that economic growth has no significant effect on HDI because income levels and HDI have a broad correlation. Revenue growth does not automatically increase HDI. Likewise, improvements in health and education that lead to increases in HDI do not always lead to increases in income. This is because the resources generated by economic growth cannot be used to promote improvements in other indicators. Besides that, structures and processes that occur in society cannot benefit the poor. For example, various increases in crop yields only benefit landowners and not labour, so their income will be insufficient to meet their daily needs

The Relationship of Unemployment to Economic Growth
Based on the results of the data processing above, it can be seen that the unemployment variable has a negative and significant effect on economic growth in Indonesia because the probability value is 0.0000 which is smaller than the significant level of 0.05, the unemployment coefficient is -4.148744. This is in line with research conducted by Qomariyah (2013) explained that if the economic growth variable increases, the unemployment variable will decrease. This is in accordance with the statement of Okun's law because if economic growth increases by 2%, the unemployment variable will decrease by more than 1%. With an increase in the rate of economic growth, more output will be produced, thus labor can be absorbed and the unemployment rate can decrease. The economy is said to experience growth when the production of goods and services increases from the previous year. Companies will need more workers when production increases so that job opportunities will also increase and unemployment will be absorbed.

Poverty Relationship Against Economic Growth
Based on the results of the data processing above, it can be seen that the Poverty variable has a positive and significant effect on Economic Growth in Indonesia because the probability value is 0.0034 which is smaller than the significant level of 0.05, the Poverty coefficient is 3.202913. This is in line with research from Wahed (2021) which explains that high economic growth is not necessarily able to reduce the factors that cause poverty, which will lead to structural poverty. Where poverty arises due to increased economic growth, it cannot be felt equally in all parts of society, but only enjoyed by a handful of groups. Prospects for ending poverty depend to a large extent on two factors, namely the rate of economic growth on condition that it is carried out in a sustainable manner and the level of resources allocated to programs for poverty alleviation and the quality of the program itself. Income inequality caused by a person's low education and lack of potential resources causes poverty to always exist.

CONCLUSIONS Conclusion
This research is intended to examine the influence of the Human Development, Unemployment and Poverty Index variables in Indonesia. Based on the results of data analysis and discussion that has been carried out, the following conclusions can be drawn: 1. The results of the t test show that the Human Development Index variable has no effect and is not significant on Economic Growth in Indonesia. This is evident from the results of statistical tests with a value (t-count <t-table) 2. The results of the t test show that the unemployment variable has a negative and significant effect on economic growth in Indonesia. This is evident from the results of statistical tests with values (t-count > t-table) 3. The results of the t test show that the poverty variable has a positive and significant effect on economic growth in Indonesia. This is evident from the results of statistical tests with values (t-count > t-table) 4. The results of the f test show that the variables Human Development Index, Unemployment, and Poverty have a significant effect on Economic Growth in Indonesia. because fstatistik is bigger than f-table.

Suggestion
Based on the results of the discussion and conclusions that have been given, the following suggestions can be given: 1. For the Government to become the driving force for the country, especially in Indonesia by making or emphasizing a policy and taking a role in order to increase economic growth.
Through improving the quality of education not only in cities but also in villages, they must have the same opportunity to take quality education in order to produce superior Human Resources (HR). Indirectly, quality human resources will increase the HDI rate even better, the government is also expected to open up more job opportunities and even information on job opportunities to all people without exception who need jobs, or the government provides training for entrepreneurship communities, so that people are more open to seeking income Opportunity, 2. For academics and researchers, it can be used as reference material for further research interested in examining the Analysis of the Index of Human Development, Unemployment and Poverty on Economic Growth in Indonesia, for the purposes of further discussion. 3. It is hoped that related agencies can provide updated and complete data for easy access to data.