Long-and short-term analysis on the Human Development Index in West Nusa Tenggara

Purpose — With the low human development index in West Nusa Tenggara, this study is intended to analyze important factors in increasing the Human Development Index in an area. Method — This research combined cross-sectional data consisting of 10 regencies and cities in West Nusa Tenggara and time-series data from 2016 to 2020. In addition, a series of model tests were carried out. This research employed the Arellano-bond estimator for the dynamic panel estimation, which used first-difference (FDGMM) with robust standard error. Result — We found that the previous year's Human Development Index, poverty rate, and GDRB significantly increased the human development index in West Nusa Tenggara, especially in the short term. Meanwhile, in the long run, all variables do not affect the human development index in West Nusa Tenggara. In addition, this study revealed that the previous year's HDI, poverty rate, and GRDP only affected the short term. Moreover, long-term policies are needed to increase the HDI in West Nusa Tenggara, such as increasing community capacity, health assistance, price stabilization, and creating new jobs. Contribution — This study clarifies in practice the need for differentiating poverty reduction strategies according to their duration. This is because short-term interventions have little long-term impact on reducing poverty.


INTRODUCTION
The goal of economic development is to improve public welfare. Specifically, the main target of economic development is the improvement of the quality of life of the community. This goal can be achieved by reducing the number of people living below the poverty line. For example, increasing job opportunities can stimulate effective economic activities and reduce the poverty rate in a country. It is important as poverty has become one of the economic challenges faced by many countries that need to be reduced or solved entirely. However, many countries usually run into conflicts, such as social and economic inequalities, towards a modern economic system that result in poverty. Poverty causes the inability of the community to improve their standard of living to meet basic human needs, such as clothing, food, shelter, education, and healthcare. Gweshengwe & Hassan (2020) suggests that poverty is a state where people are unable to meet the minimum standard of living. It is calculated based on certain parameters, such as consumption basis or basic needs which are related to low income, unfit living conditions, poor healthcare quality, and low education levels that promote the low quality of human resources. These poverty parameters also increase the number of unemployed and low-income people. In addition, according to The Economist (2008), poverty is a closed condition where people are isolated from physical and non-physical life needs. The Economist (2008) also mentions that poverty is the inequality of opportunities in formulating social controls in the form of assets, finances, social organizations, politics, social networks, goods or services, knowledge, skills, and information.     Figure 3, it can be seen that West Nusa Tenggara's HDI is still below the average Indonesian HDI where Indonesia's HDI is at 72 percent and West Nusa Tenggara's at 69 percent. In other words, there is a difference of 3 points between the West Nusa Tenggara Human Development Index and Indonesia.
Research on the human development index is no stranger to the world of economics as evidenced by several studies such as Shah (2016), in his research it was stated that GDP, life expectancy, and literacy rates had an effect on increasing HDI in Indonesia. While other independent variables such as the Gini index, fertility rate, CO2 emission level, and inflation show negative constants. Furthermore, research conducted by Franciari & Sugiyanto (2013)   These various studies failed to distinguish the short-and long-term effects of each variable considering that the time variable will give different results to a variable. Thus, this study tries to analyze the effect of the previous year's HDI variables, poverty, Banking capital credit for MSMEs, and GRDP on the latest year's HDI based on the short and long term.

METHOD
To examine the research hypothesis, this study employed dynamic panel data analysis. The research had been conducted from 2016 to 2020 and employed 4 parameters involving independent and dependent variables. Also, dynamic panel data regression is considered superior compared to cross-section or timeseries data, especially in two aspects. First, the data panel makes the number of observations (n) larger. Such an advantage can improve the degree of freedom parameter, test for collinearity between explanatory variables, and econometric estimation efficiency. Second, the dynamic panel data can analyze economic statements that cannot be justified by cross-section or time-series data (Burlig et al., 2020; Keum, 2010 The autoregressive dynamic panel model is a model with a lagged dependent variable as its independent variable. The following is the autoregressive dynamic model equation:  : Lagged dependent variable which also becomes its independent variable (explanatory endogenous variable) In the dynamic model, the coefficients 1, 2, 3, 4 become the short-term effects of the changes in t, and ( (1− )) is the long-term effect of the changes in t. Where: The dynamic panel model of this research is as follows: The estimated independent variables are the poverty rate (Log Poverty), credit for MSMEs (Log MSME Credit), GDRB, and the lagged dependent variable (HDI(-1)). Also, the research considered the last year's human quality development as a proxy or representative of the evidence of any successful effect of human quality development in the year before.
The lagged dependent variable in the equation model above caused problems with the correlation between the lagged dependent variable and the residue. To overcome this inconsistency problem, Generalized Method of Moments (GMM) estimation was utilized to control endogeneity by using the lagged dependent variable and other related variables as instrumental variables.

Human Development Index
The development in the forms of education and healthcare services

Poverty Rate
Poverty is an economic inability of an individual or family measured by their expenditure to afford basic needs like food and non-food commodities

Micro, Small, Medium Enterprises (MSME) Credit Program
Business capital investment in the current assets BPS NTB

Gross Regional Domestic Product (GRDP)
Total amount of value-added goods or services produced by all economic units including local residents and residents from other areas living in a region within a certain time

RESULT AND DISCUSSION
This research combined cross-sectional data of 10 regencies and cities in West Nusa Tenggara from 2016 to 2020. A series of model tests were carried out to select the best model. This research employed the Arellano-bond estimator for the dynamic panel estimation, which used first-difference (FDGMM) with robust standard error. In addition, the validity test was considered the first test to determine the dynamic panel model test or GMM. In this study, the validity test results were obtained using the Sargan test whose Prob-chi value was 0.0027 or less than 5%; hence, there was a validity problem. The robust standard error method was employed to obtain valid results to overcome this problem. In other words, overidentifying restrictions were valid and could not calculate the Sargan test with vce (robust), and there was no heteroscedasticity. Subsequently, a consistency test was conducted to determine the best model. The estimator's consistency characteristic was obtained using the Arellano-Bond test for mstatistics. The result showed that Prob>z in the 2 nd order was 0.2060 which was greater than 5%, and it meant that it was consistent and had no autocorrelation. According to the results of three Unbiased Estimator tests, the coefficient value of the FDGMM was 0.5457. It was considered unbiased because the value was between the estimator of the fix effect model (FEM) (valued at 0.5120) and the pooled least square (PLS) (valued at 0.9701).  Simultaneous statistical tests are shown with the prob value -Wald Chi2 = 0.000 or less than α = 5%. It means that the variables including X1 (Log Poverty), X2 (Log MSME Credits), and X3 (Log GRDP) collectively affect the HDI in West Nusa Tenggara. Then, the partial statistical test (T-Test) was conducted to investigate whether individual independent variables affected the dependent variable. The result is summarized in the following table: Long-and short-term analysis on the Human Development Index… Journal of Enterprise and Development (JED), Vol. 5, No. 1, 2023

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As shown in Table 3, the lag in the human development index ( HDI(−1)) has a significant positive effect at 0.000 in the current year's human development index (HDI). It shows that the success of human quality development in the previous year would have an impact on the following year. In other words, if the HDI in the previous year was high, the human resources in that area would have an impact on increasing the HDI of other human resources. This can be caused by several things such as an increase in the number of teaching staff, an increase in the number of new business units, etc., so it will impact others. This is consistent with the trickle-down effect theory (Sowell, 2012). Unlike the others, the parameter of banking credit for MSMEs (Log MSME Credit) has no significant effect on the Human Development Index in West Nusa Tenggara. This is because West Nusa Tenggara's MSMEs still only absorb a small portion of capital assistance money, therefore it does not have a significant impact on the local economy. Additionally, MSMEs in West Nusa Tenggara do not need a lot of employees, thus they struggle to find new employees. This has an impact on the insignificance of MSME loans to West Nusa Tenggara's HDI.

DISCUSSION
According to this study, West Nusa Tenggara residents require more suitable long-term policies, such as investing in the health sector, stabilising prices, generating new jobs, and training residents to increase their capacity.
Moreover, we discovered that if the West Nusa Tenggara administration decides on appropriate long-term measures to raise its Human Development Index, then after 2 years the level of inequality between districts in West Nusa Tenggara will be 0 or evenly distributed. In other words, the appropriate approach will abolish inequality in West Nusa Tenggara in less than two years by reducing it by 60% annually. Another finding in this study is that while all districts in West Nusa Tenggara have experienced an increase in the Human Development Index, North Lombok Regency is the only district that has experienced a decrease in HDI. Therefore, further research is required to determine the reasons for the HDI reduction in North Lombok

CONCLUSION
The Human Growth Index shows that the quality of human resources has become one of the measures of regional development (HDI). The HDI level of West Nusa Tenggara is now the fifth lowest among Indonesian provinces, and the proportion of the population living in poverty is higher than the national average. A short-term, long-term, and convergence analysis is required due to the unsatisfactory situation to establish appropriate policies in West Nusa Tenggara moving forward to improve human quality development.
Short-term research revealed that GRDP, poverty, and the HDI from the previous year influenced West Nusa Tenggara's HDI. The HDI is not significantly impacted by MSME financing, though. The HDI in West Nusa Tenggara is not affected by any of the variables over the long term; hence, the study's variables cannot be utilized as a long-term policy reference. More suitable policies are therefore required, such as those that boost community capacity, increase jobs, and stabilize prices while also investing in the health sector. Furthermore, it was discovered that North Lombok Regency was the only district in West Nusa Tenggara to see a fall in HDI. Additionally, if the government of West Nusa Tenggara adopts the proper policies to raise HDI, inequality in West Nusa Tenggara will vanish in less than two years. For further research, it is necessary to examine more deeply the causes of the decline in HDI in the districts.