Effect of Inflation and Economic Growth on The Rate of Unemployment: Empirical Study on Province in Indonesia

© 2021 The Author(s): This is an open-access article distributed under the terms of the Creative Commons Attribution ShareAlike (CC BYSA 4.0) Abstract This study aims to determine the effect of inflation and economic growth on the unemployment rate in Indonesia. This research is a quantitative research. Data analysis in this study used mult iple linear regression with a random effect model, which was processed using the EViews 10 application. The population in this study was all provinces in Indonesia in 2016-2018. The sample in this study was 34 provinces in Indonesia. The data used in this study is secondary data obtained from the Central Statistics Agency. The results of this study indicate that inflation and economic growth have a negative and insignificant effect on the unemployment rate in Indonesia. Based on the F test and t test, inflation and economic growth variables have no significant effect on the unemployment rate in Indonesia, either partially or simultaneously.


Pendahuluan
Job opportunities or unemployment are extremely difficult for a country or area to avoid, since they might result in societal problems such as criminal activities and economic difficulties. This state has the potential to erode the people's standard of living and buying power. Therefore, the lower the unemployment rate in a country, the better off its residents are, and vice versa. The success or inability of a country to address its own economic issues may be 610 determined by its macroeconomic performance. Macroeconomics is the examination of activities that concern a country's economy (Kalsum, 2017).
Inflation is one of the economic indicators used to assess/measure a country's financial stability. Inflation is a monetary phenomen on that occurs in a country where the rise and fall of inflation tends to create economic upheaval. Inflation has both beneficial and harmful consequences, depending on its degree. When inflation is low, it has a beneficial influence on the economy, raising nation al income and motivating individuals to work, save, and invest. On the other side, when inflation spirals out of control during periods of high inflation , the economy becomes chaotic and sluggish. This is one of the primary reasons for avoiding inflation difficulties (Windra et al., 2016).
Inflation is one of the economic in dicators used to determine the financial stability of a country. Inflation is a monetary phenomenon in countries where the rise and fall of inflation frequently results in economic instability. Inflation may h ave both favorable and detrimental effects, depending on its magnitude. When inflation is low, it benefits the economy by increasing national income and encouraging people to work, save, and invest. On the other hand, when inflation spirals out of control, the economy becomes chaotic and sluggish. This is a fundamental basis for avoiding inflationary problems (Johan et al., 2016).
The current problem with unemployment is that if the population continues to grow each year, it will become unbalanced in the world of work. Because the existing population is not proportional to employment, this will affect Gross Domestic Product (GDP), or more commonly referred to as gross domestic product (GDP). GDP is a measure of a country's national output as realized by its factors of production, and economic growth, with a growing GDP, is expected to be able to absorb workers, or vice versa, with a decreasing GDP indicating widespread unemployment (Albab et al., 2020).
In Indonesia, unemployment is not just due to population increase, but may also be impacted by other variables such as inflation and economic growth (Joh an et al., 2016). This is what motivates scholars to conduct empirical studies on the influence of inflation and economic growth on the unemployment rate in Indonesian provinces.

Methodology
Th is is a quantitative study. The population in this research is comprised of statistics on Indonesia's inflation, economic growth, and unemployment rates. While the study's sample is based on data on inflation, economic growth, and unemployment rates in 34 Indonesian provinces from 2016 to 2018. The standard panel model or the common constant model (poled model) is used in this study, along with a Linear Model perspective of panel data processed using Eviews 10 data processing software.
The data for this study came from secondary sources such as the Central Statistics Agency. This study uses panel data (Pooled Data). According to Gujarat, (2006), panel data does not require testing for classical assumptions because it combines cross-section and time-series data.

Dependent Variable (Y)
The dependent variable is referred to as the one that is impacted by other factors. The dependent variable in this study is the degree of unemployment.

Independent Variable (X)
Independent variables are also called independent variables that affect other variables. In this study the independent variables are inflation (X1) and economic growth (X2).

Data analysis technique
The investigation of characteristics, pattern correlations, and consequences that are frequently observed in a phenomenon or symptom that has occurred, is occurring, or will occur is referred to as data analysis. To investigate the variables impacting Indonesia's provincial unemployment rates, including inflation and economic growth. The researchers will use traditional panel data analysis techniques, such as the common constant model or the linear panel data model (multiple linear regression), to determine the suitability of the regression model using the common effect, fixed effect, and random effect methods, as well as hypothesis testing using the F test, t test, and coefficient of determination test.

Source: Data processed, 2021
The descriptive statistical test in Table 1 above indicates that from 2016 and 2018, the unemployment rate ranged between 1.4 and 9.29. Unemployment is estimated to be 4,965 on average, with a standard deviation of 1,81526. This translates to an average unemployment rate of 4.965 percent across Indonesia's provinces from 2016 to 2018, or 4,965 or rounded up to 5 jobless individuals per 100 employees in Indonesia. This illustrates that Indonesia's provinces are inextricably linked to the country's unemployment crisis.
As measured by GRDP statistics, inflation and economic growth have a minimum value of -0.05 and -5.61, respectively, and a high value of 7.78 and 18.8. Inflation and economic growth have an average value of 3.266176 and 3.726961, respectively, with a standard deviation of 1.246228 and 2.459412. Thus, the average inflation rate in Indonesia's 34 provinces between 2016 and 2018 is 3.26 percent, whereas the average population growth rate is 3.73 percent.

Multiple Linear Regression
Conducting a regression test in a standard panel model, we must determine the applicability of the regression model utilized, determining if the common effects, fixed effects, or random effects approaches are more appropriate than the multiple regression method.

a) Chow Test
According to Widarjono & Agus (2009), the Chow test is used to compare the common effect model against the fixed effect model. The common effect model is a model that mixes cross sectional and time series data and estimates the panel data model using the OLS technique (Ordinary Least Squares). Gujarati (2012) emphasized that because the fixed effect model's assumption of a constant intercept for each individual and period is seen less realistic, a model capable of capturing these differences is required. A dummy variable or Least Squares Dummy Variable (LSDV) approach is used to estimate the Fixed Effects model with varied intercepts amongst individuals. In this study, the Chow test is conducted using the Eviews software and the following hypothesis:

Ha: Fixed Effect Model
If the P-value is less than 5% or 0.05, then Ha is accepted and Ho is rejected. On the other hand, if the P-value is greater than 5% or 0.05, then Ho is accepted (Widarjono & Agus, 2009).

Source: Data processed, 2021 b) Hausman Test
According to Gujarati (2012), the Hausman test is used to assess fixed effect and random effect models in order to determine which model should be utilized for panel data regression. Effects of chance is a procedure for estimating panel data in which the residual variable is believed to have a connection with both time and subjects.
The Hausman test uses a program similar to the Chow test, namely the Eviews program. The hypothesis formed in the Hausman test is as follows: Ho: Random Effect Model

Ha: Fixed Effect Model
If the P-value is less than 5% or 0.05, then Ha is accepted and Ho is rejected. On the other hand, if the P-value is greater than 5% or 0.05, then Ho is accepted (Widarjono & Agus, 2009).

Source: Data processed, 2021
According to the Hausman test findings shown in Table 3, the random crosssection probability value is 0.2749, which is larger than 5% or 0.05. This indicates that Ha is rejected but Ho is accepted, or that the Random Effect Model is accepted.

c) Lagrange Multiplier Test
Than determine if the Random Effects model is superior to the common effects model, the Bruesch-Pagan Lagrange Multiplier (LM) test can be utilized. Additionally, the Lagrange Multipliertest makes use of a software comparable to the Chow and Hausman tests, the Eviews program. The Hausman test generates the following hypothesis:

Ha: Random Effect Model
If the P-value is less than 5% or 0.05, then Ha is accepted and Ho is rejected. On the other hand, if the P-value is greater than 5% or 0.05, then Ho is accepted (Widarjono & Agus, 2009).

Source: Data processed, 2021
According to the Lagrange mu ltiplier test findings in Table 4, the Prob Both Breusch-Pagan value is 0.000, which is less than 5% or 0.05. That is, Ho is rejected while Ha is accepted, or the Random Effect Model is approved.
The Chow test, Hausman test, and Lagrange multipier test findings indicated that the Random effect model was utilized to determine th is investigation's multiple linear regression model.

Source: Data processed, 2021
Based on the table above, the estimation model of the random effect regression equation is as follows: Y = 5.147879 -0.037063 X1 -0.016589 X2 The interpretation of the above equ ation is as follows: 1. The constant figure is 5.147879, indicating that assuming inflation and economic growth remain constant (zero), Indonesia's unemployment rate is 5.15 percent.
2. The inflation variable's regression coefficient is -0.037063, which indicates that for every 1% increase in inflation, Indonesia's unemployment rate decreases by 0.04 percent.
3. The economic growth variable has a regression coefficient of -0.016589, which indicates that for every 1% rise in economic growth, unemployment in Indonesia will reduce by 0.02 percent.

d) Simultaneous Test (F Test)
The F test is used to determine if the independent factors have an effect on the dependent variable concurrently. According to table 5, the regression test resulted in a Prob F statistic value of 0.584532, which is more than 5% or 0.05. This demonstrates that in Indonesia, the variables inflation and economic growth have no concurrent influence on the variable unemployment rate.

e) Partial Test (t Test)
The t test is used to determine if an independent variable has a significant effect on the dependent variable. According to table 5, the regression test findings indicate that inflation and economic growth variables have no statistically significant influence on Indonesia's unemployment rate, since the probability valu e of the t-test is more than 5% or 0.05, which is 0.4143 and 0.6381, respectively.

f) Coefficient of Determination Test (R2)
According to Table 5, the Adjusted R-Square value is -0.009195; this value is negative, indicating that the independent or independent factors, namely inflation and economic growth, have no influence on the unemployment rate. This is consistent with the t and F tests.

Discussion a) Inflation Against Unemployment Tingkat
The findings indicated that inflation had a n egligible influence on In donesia's unemployment rate, with a negative coefficient. Philips articulated this notion in Dornbusch et al. (2008) with reference to Philips law, which argues that inflation and unemployment have a negative connection. When one of them grows, the other shrinks. This Philips hypothesis postulates that inflation grows as aggregate demand increases. The increased demand will result in an increase in the price of products and a decrease in the stock of commodities. Producers will enhance production capacity by hiring additional personnel to fulfill market demand. When there is a greater demand for work, unemployment tends to be lower (Mahzalena et al., 2019).
The inflation variable's insignificant influence on unemployment in In donesia is due to an increase in aggregate demand, which has a multiplier effect on employment, hence lowering the jobless rate (Mankiw & Gregory.N, 2003) (Nanga & Muana, 2001). Additionally, inflation was triggered by a number of variables, including an increase in food supply, an increase in transportation capacity, crop failure owing to irregular weather, and an increase in the cost of products and services. The findings of this study corroborate (Panjawa & Soebagio, 2014), who asserts that inflation has a little influence on the unemployment rate in Medan.
In contradiction to Johan et al. (2016) study, where the findings of multiple linear regression analysis demonstrate that the inflation variable has an influence on unemployment, this is not the case here. One of the reasons contributing to Indonesian inflation is the rise in global oil prices. With the increase in global oil prices, the price of raw materials increases, resulting in an increase in the company's operational costs. With constant increases in operating expenditures, the company's stability is dubious, and the company's profitability would suffer as a result. If inflation continues to rise, it is probable that many businesses may close their doors due to their inability to function and employees would be laid off. The bigger the impact of inflation on the business, the more layoffs the business will make. With a rising number of people being laid off, this will have an effect on the growing number of jobless.

b) Economic Growth Against Unemployment Rate
The results of this study indicate that the variable of economic growth represented by PDRB also has no significant effect on the unemployment rate in Indonesia and the coefficient value is also negative. Okun's legal theory says th ere is a negative relationship between PDRB and unemployment, Okun's law is used by developing countries as a solution to overcome the problem of unemployment. By increasing the PDRB, it will increase the number of jobs, thus absorbing unemployment. If there is an increase in PDRB, the demand for labor will rise and unemployment will fall. On the other hand, if PDRB falls, it will cause producers to reduce production thereby reducing labor which will result in increased unemployment, this means that the increase in PDRB is followed by a decrease in the unemployment rate (Antasari & Soleh, 2012) (Ardiansyah, 2017). The findings of this analysis corroborate those of Nur Fitri Yanti & Adda (2017), who found that from 1993 to 2009, the GRDP had a negative influence on open unemployment in Central Java Province.
This contrasts with the findings of Johan et al. (2016), who found that economic growth has a positive regression coefficient but has no influence on unemployment. This indicates that a rise in economic growth does not necessarily result in a large drop in unemployment. This may be explained in the context of Indonesia by the fact that the increased value of economic expansion benefits ju st a subset of the population, not the entire population. The majority of the rise in Indonesia's GDP is attributed to a small number of persons with non -real sector industries; this non-real sector is defined as a capital-intensive venture. Due to th e sector's capital-intensive nature, it absorbs very few people.

Conclusion
In Indonesia, inflation has a negative but insignificant effect on the unemployment rate. When one of them grows, the other shrinks. Inflation was triggered by an increase in aggregate demand. Demand increases the price of goods and decreases the stock of goods. Producers will increase production capacity by hiring additional workers to meet market demand. On the other hand, economic growth as measured by the GRDP has a negative but insignificant effect on Indonesia's unemployment rate from 2016 to 2018. Increased GDP creates more jobs, resulting in an increase in labor demand and a decrease in unemployment. On the other hand, if GRDP declines, producers will cut production, resulting in a reduction in labor, resulting in an increase in unemployment.