Evaluating the Universality of the Relation between Unemployment and different Economic Determinants Across Developed and Developing

Unemployment is a global concern for both policy makers and federal governments of different nations. Unemployment is also a social and socioeconomic concern. This is because unemployment impacts economic welfare leads to crime and erosion of human capital, misery and social instability. The present article explored the relation between unemployment rate are inflation rate, the rate of interest of financial institutions, population, GDP (Gross Domestic Product) and Debt/GDP percentage. The results indicated that unemployment rate could not be significantly predicted from inflation rate, the rate of interest of financial institutions, population, GDP (Gross Domestic Product) and Debt/GDP percentage for either developed countries or developing countries. On the other hand, the study clearly indicated that unemployment rate cannot categorize the status of a country as “developing” or “developed.” However, the study implicated certain interesting findings. The relation between the unemployment rate and debt/GDP was negative for developed countries and positive for developing countries. This meant that developed countries might use the acquired debt for addressing unemployment while developing countries might not use the acquired debt for addressing unemployment. Hence, international financial institutions should be more stringent while extending financial stimulus to developing countries. One of the criteria for providing financial stimulus to such countries should be to appraise the rate of unemployment in those countries over the past five years.

Keywords: “Unemployment rate” “Interest rate” “Inflation rate” “Debt/GDP” “Population” “GDP”
Introduction
Background
Unemployment is a global concern for both policy makers and federal governments of different nations. In 2007, almost 34 million people all across the globe lost their employment due to the global financial crisis. Since then, the unemployment rates have steadily increased all over the world, with the exception of few countries. Unemployment refers to the situation where individuals fail to secure employment, in spite of having the necessary skills and knowledge required for the specified job. The unemployment rate is a key determinant of the economic health of any nation. Unemployment is also a social and socioeconomic concern. This is because unemployment impacts economic welfare leads to crime and erosion of human capital, misery and social instability (Kyei &Gyeke, 2011). Moreover, unemployment is also a predisposing risk factor for various psychological and psychosocial problems. Studies have indicated that unemployment leads to hopelessness, depression, anxiety, frustration, hostile behavior and even increases the risk of self-harm. Unemployment leads to increased criminal behavior (Kyei &Gyeke, 2011). Various studies have indicated that different socioeconomic parameters of a country influence the rate of unemployment.
Purpose of the Study
The major socioeconomic determinants of unemployment rate are inflation rate, the rate of interest of financial institutions, population and GDP (Gross Domestic Product). However, no studies to date have explored the universality of the relation of these variables to the unemployment rate. Hence, the present article explored the relation between unemployment rate are inflation rate, the rate of interest of financial institutions, population, GDP (Gross Domestic Product) and Debt/GDP percentage. The study explored the requisite socioeconomic variables of developing and developed countries, for appraising the universality of the relation between the specified determinants.
Review of Literature
Search Strategy
The literature review for the present study was based on key word search strategy. Different keywords were connected by Boolean connectors for selecting the required articles. The keywords and Boolean connectors used for the present study include “Unemployment rate” or “Jobless rate” AND “inflation rate” AND “rate of interest of financial institutions” AND “population” AND “GDP (Gross Domestic Product)” AND “Debt/GDP percentage” The websites that were visited include Google Scholar, Research Gate, Trading Economics and OVID online.
Systematic Review
Cheema & Atta (2014) reported a negative relationship between economic growth and unemployment. However, the author also reported that unemployment rate is positively correlated with GDP and manufacturing/production growth rate. Gillani, Rehman & Gill (2009) reported that unemployment rate is positively correlated with the rate of inflation, crime rates and poverty. Cheema & Atta (2014) also reported that increased inflation rates were positively correlated with the unemployment rate. On the other hand, Adewale et al. (2011) indicated that unemployment rate is negatively correlated with GDP. Adewale et al. (2011) also demonstrated that the Philips curve between inflation rate and rate of unemployment holds true for Nigeria. The author also indicated that population is has a positive correlation with the unemployment rate. Umaru & Zubairu (2012) exhibited a negative relation between inflation rate and the unemployment rate in Nigeria.
Appraisal of Literature and Theoretical Framework for the Present Study
Different studies indicated that although there is a significant relation between the unemployment rate and different socioeconomic variables, the results are often inconclusive. Moreover, no studies have incorporated Debt/GDP as a determinant of the unemployment rate. Debt/GDP ratio is a measure of the debt component of GDP. Although GDP indicates the economic growth of a country, Debt/GDP ratio is the real predictor of economic stability of a country. If a country has a high Debt/GDP ratio, then the country has to share a significant amount of revenue to the international financial institutions for paying-off the debt. Hence, the actual revenue of federal governments would be much lesser for funding and generating employment.
III. Methodology
Selection of Sample and Study Design
The study would be conducted based on secondary data. Secondary data for the present study was obtained from www.tradingeconomics.com. The study was conducted as a retrospective, randomized, open-ended trial. Twenty countries were selected for the present study. The countries were randomly selected based on their socioeconomic status. Hence, ten developed and ten developing countries were finally included in the study. The socioeconomic data that was selected for the study include the unemployment rate, an interest rate of financial institutions, inflation rate and GDP. Purposive sampling was used as a process of randomization. Description of the sample data is represented in Table 1. This method of sampling reduced the chances of elemental bias. The detailed inclusion and exclusion criteria for the present study include:
Inclusion Criteria
Developing or developed countries were included in the study.
All socioeconomic data considered for the trial should be available.
The socioeconomic data should be retrieved from past two years only.
Exclusion Criteria
Underdeveloped countries would not be included in the study.
Countries for which the relevant socioeconomic data would not be included in the study.
The socioeconomic data beyond past two years would not be included in the study.
Procedure
Secondary data was used for exploring the research questions considered for the study. Statistical analysis was conducted on socioeconomic data for answering the research questions framed for the trial. Different statistical tests of inference were used to find the search questions. After retrieving the relevant secondary data from wwww.tradingeconomics.com, STATA program was used to conduct the statistical analysis.
Research Questions and Hypothesis Testing
The present study explored the following research questions based on rejection or acceptance of a framed hypothesis. The hypothesis testing was based on acceptance or rejection of the null and alternative hypothesis. The research questions and its associated hypothesis were as follows:
Research Question 1: Whether unemployment rate could be significantly predicted from an interest rate of financial institutions, inflation rate, GDP and Debt/GDP?
Hypothesis testing for Research Question 1: H0 (null hypothesis) =contended that unemployment rate could not be significantly predicted from the interest rate of financial institutions, inflation rate, GDP and Debt/GDP of a nation. Any observed prediction would be attributed to chance factors of random sampling. The null hypothesis will be accepted if the p-value for the regression analysis is greater than 0.05 (p>0.05).
Ha (alternative hypothesis) = contended that unemployment rate could be significantly predicted from the interest rate of financial institutions, inflation rate, GDP and Debt/GDP of a nation. Any observed prediction will not be attributed to chance factors of random sampling. The alternative hypothesis will be accepted if the p-value for the regression analysis is less than 0.05 (p <0.05).
The logic behind the research question: To find out the direction and magnitude of the relation between the unemployment rate and the other socioeconomic variables considered for the study. The equation could be used to predict/forecast unemployment rate from the econometric variables.
Research Question 2: Whether unemployment rate significantly differs between developed and underdeveloped countries?
Hypothesis testing for Research question 2: H0 (null hypothesis) =contended that unemployment rate does not significantly differ between developed and underdeveloped countries. Any observed difference would be attributed to chance factors of random sampling. The null hypothesis will be accepted if the p-value for the unpaired t-test is greater than 0.05 (p>0.05).
Ha (alternative hypothesis) = contended that unemployment rate significantly differs between developed and underdeveloped countries. Any observed difference would not be attributed to chance factors of random sampling. The alternative hypothesis will be accepted if the p-value for the unpaired t-test is less than 0.05 (p <0.05).
The logic behind the research question: This question would explore whether unemployment rate could suffice the categorization of a developed or a developing country. If the alternative hypothesis is accepted, it would certainly contend that difference in the unemployment rate could alone signify the socioeconomic status of a country.
Research Question 3: Whether the different socioeconomic variables (unemployment rate, the interest rate of financial institutions, inflation rate and GDP and Debt/GDP of a nation) are significantly correlated with each other?
Hypothesis testing for Research question 3: H0 (null hypothesis) =contended that different socioeconomic variables (unemployment rate, the interest rate of financial institutions, inflation rate and GDP of a nation) are not significantly correlated with each other. Any observed correlation would be attributed to chance factors of random sampling. The null hypothesis will be accepted if the p-value for the Pearson’s correlation coefficient is greater than 0.05 (p>0.05).
Ha (alternative hypothesis) = contended that different socioeconomic variables (unemployment rate, the interest rate of financial institutions, inflation rate, GDP and Debt/GDP of a nation) are significantly correlated with each other. Any observed correlation would be attributed to chance factors of random sampling. The alternative hypothesis will be accepted if the p-value for the Pearson’s correlation coefficient is less than 0.05 (p <0.05).
The logic behind the research question: To find out which socioeconomic variables correlates significantly with each other. This question will help to evaluate the impact of one socioeconomic variable on the other and vice-versa.
Statistical tests and Data Analysis
Statistical tests
The different statistical tests of inference that were used for the present study included regression analysis, multivariate statistics, comparison analysis by unpaired t-tests and correlation analysis through Pearson’s correlation coefficient.
Regression Analysis: A multiple regression analysis was conducted to evaluate the relation of the unemployment rate on an interest rate of financial institutions, inflation rate and GDP of a nation. The dependant variable for the regression analysis was unemployment rate, while the independent variables for the regression analysis were an interest rate of financial institutions, inflation rate and GDP of a nation.
Comparison Analysis: Student’s unpaired t-test was implemented to compare the inflation rates between developed and developing countries.
Correlation Analysis: Correlation analysis was conducted to evaluate the relation between unemployment rate, an interest rate of financial institutions, inflation rate and GDP of a nation. Pearson’s product moment correlation coefficient was conducted to explore the correlation between the relevant variables.
Data Analysis
The data analysis for the statistical tests was based on a chosen level of significance: The chosen level of significance for the statistical tests of inference was p= 0.05. This convention signified that at least 5 out of 100 observations has happened due to chance factors associated with random sampling and there is no tangible difference between different observations.
Countries GDPInterest rateInflation rateJobless rateDebt/GDPPopulationUnited States179470.75%1.70%4.60%104.17%321.57Euro Area115400.00%0.60%9.80%90.70%338.47China108664.35%2.30%4.04%43.90%1374.62Japan4123-0.10%0.10%3.00%229.20%126.82Germany33560.00%0.80%4.10%71.20%82.18United Kingdom28490.25%1.20%4.80%89.20%64.88France24220.00%0.50%10.00%96.10%66.63India20746.25%3.63%4.90%67.20%1254.02Italy18150.00%0.10%11.60%132.70%60.8Brazil177513.75%6.99%11.80%66.23%204.45Canada15510.50%1.50%6.80%91.50%35.99South Korea13781.25%1.30%3.60%35.12%50.6Australia13401.50%1.30%5.70%36.80%23.94Russia132610.00%5.80%5.40%17.70%146.3Spain11990.00%0.70%18.91%99.20%46.45Mexico11445.75%3.31%3.70%43.20%121.01Indonesia8624.75%3.58%5.61%27.00%255.46Netherlands7530.00%0.60%5.60%65.10%16.9Turkey7188.00%7.00%11.30%32.90%78.74Switzerland665-0.75%-0.30%3.30%34.40%8.24Table 1: Secondary Data (as retrieved from www.tradingeconomics.com)
IV: Results and Analysis
Descriptive Statistics of Developed and Developing Countries
Jobless rate Interest rate Inflation rate Debt/GDP
Mean 5.82% 1.73% 1.58% 87.05%
Median 5.10% 0.38% 1.25% 89.95%
Standard Deviation 2.38% 3.20% 1.62% 57.80%
Maximum 10.00% 10.00% 5.80% 229.20%
Minimum 3.00% -0.10% 0.10% 17.70%
Table 2: Descriptive Statistics of Developed Countries
Jobless rate Interest rate Inflation rate Debt/GDP
Mean 8.03% 3.90% 2.69% 60.31%
Median 5.61% 3.00% 2.31% 54.15%
Standard Deviation 0.05146 0.046877 0.026881 0.338587
Maximum 18.91% 13.75% 7.00% 132.70%
Minimum 3.30% -0.75% -0.30% 27.00%
Table 3: Descriptive Statistics of Developed Countries
Comparison Analysis (Unpaired t-test results)
t-Test: Two-Sample Assuming Unequal Variances 0.05
Equal Sample Sizes
Data1 Data2 diff 95% Confidence Interval
Mean 0.05824 0.08 -0.022 -0.061 0.017
Variance 0.00057 0.003
Observations 10 10
Hypothesized Mean Difference 0
df 12
t Stat -1.231
P(T<=t) one-tail – Difference < Hypothesized Difference 0.121 0.879 Difference > Hypothesized Difference
T Critical one-tail 1.782
P(T<=t) two-tail 0.242 Cannot Reject Null Hypothesis because p > 0.05 (Means are the same)
T Critical Two-tail 2.179
Table 4: Comparison between Unemployment rate between developed and developing countries
As the p-value for the comparison between unemployment rate between developed and developing countries was greater than 0.05 (p>0.05), it was concluded that that unemployment rate did not significantly differ between developed and underdeveloped countries.

Fig 1: Shows the graphical representation of the comparison between unemployment rates of developed (Group 1) and developing countries (Group 2).
Although Fig 1 apparently indicated that the unemployment rates of developed countries are less than the unemployment rates of developing countries, the results were statistically significant (p>0.05).
Regression Analysis: Multivariate Statistics
Holistic Regression analysis with developed and developing countries
SUMMARY OUTPUT Force Constant to Zero
FALSE
Regression Statistics
Multiple R 0.350
R Square 0.123 Goodness of Fit < 0.80
Adjusted R Square -0.190
Standard Error 0.044
Observations 20

ANOVA
df SS MS F P-value
Regression 5 0.0039 8E-04 0.392 0.846
Residual 14 0.0275 0.002
Total 19 0.0314       Confidence Level
0.95 0.99
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 99% Upper 99%
Intercept 0.04749 0.0294 1.614 0.129 -0.016 0.11 -0 0.14
GDP -9E-07 3E-06 -0.33 0.746 -7E-06 0 -0 0
Interest rate -0.2629 0.9387 -0.28 0.783 -2.276 1.75 -3.1 2.53
Inflation rate 0.94989 1.6849 0.564 0.582 -2.664 4.56 -4.1 5.97
Debt/GDP 0.02173 0.0247 0.878 0.395 -0.031 0.07 -0.1 0.1
Population -2E-05 3E-05 -0.52 0.614 -9E-05 0 -0 0
Table 5: represents the regression statistics of the Unemployment rate on the inflation rate, the rate of interest of financial institutions, population, GDP (Gross Domestic Product) and Debt/GDP percentage.
As the p-value for the regression analysis was greater than 0.05 (p=0.84), it was concluded that unemployment rate could not be significantly predicted from the inflation rate, the rate of interest of financial institutions, population, GDP (Gross Domestic Product) and Debt/GDP percentage. On the other hand, the Y intercept was also not significantly related with (p=0.129) with the unemployment rate. However, the regression model has constructed as per the regression statistics as:
Unemployment rate = 0.047 0*GDP -0.263*Interest rate +0.95*Inflation rate +0.022*Debt/GDP 0*Population
An interesting observation was noted from the regression model where Debt/GDP seemed to increase the unemployment rate. However, lack of statistical significance could not support the observation.
Regression analysis with data of Developed Countries
SUMMARY OUTPUT Force Constant to Zero
FALSE
Regression Statistics
Multiple R 0.530
R Square 0.281 Goodness of Fit < 0.80
Adjusted R Square -0.619
Standard Error 0.030
Observations 10

ANOVA
df SS MS F P-value
Regression 5 0.0014326 0 0.31188 0.883
Residual 4 0.0036746 0
Total 9 0.0051072       Confidence Level
0.95 0.99
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 99% Upper 99%
Intercept 0.105 0.0451154 2.33 0.080 -0.0202 0.2303 -0.1 0.31
GDP 1E-06 2.778E-06 0.54 0.620 -6E-06 9E-06 -0 0
Interest rate 0.9397 1.8469704 0.51 0.638 -4.1883 6.0677 -7.56 9.44
Inflation rate -2.4391 3.639315 -0.67 0.539 -12.543 7.6653 -19.2 14.3
Debt/GDP -0.027 0.0247392 -1.09 0.336 -0.0957 0.0417 -0.14 0.09
Population -4E-05 4.595E-05 -0.8 0.468 -0.0002 9E-05 -0 0
Table 6: represents the regression statistics of the Unemployment rate on the inflation rate, the rate of interest of financial institutions, population, GDP (Gross Domestic Product) and Debt/GDP percentage for developed countries.
As the p-value for the regression analysis was greater than 0.05 (p=0.88), it was concluded that unemployment rate could not be significantly predicted from the inflation rate, the rate of interest of financial institutions, population, GDP (Gross Domestic Product) and Debt/GDP percentage for developed countries. Once again, the Y intercept was also not significantly related with (p=0.080) with the unemployment rate. However, it was close to being near significance. This indicated that there could be legitimate variables that could have included in the regression model for developed countries as a predictor of the unemployment rate. However, a regression model was constructed for developed countries also as:
Unemployment rate= 0.105 +0*GDP +0.94*Interest rate -2.439*Inflation rate -0.027*Debt/GDP 0*Population
Once again, an interesting observation was noted from the regression model where Debt/GDP seemed to be negatively correlated with the unemployment rate. However, lack of statistical significance could not support the observation. Such observation was opposite to what was observed for the holistic regression equation. This means that it might be possible that the debt which is acquired by developed countries is certainly used to address unemployment rates.
Regression analysis with data of Developing Countries
SUMMARY OUTPUT Force Constant to Zero
FALSE
Regression Statistics
Multiple R 0.848
R Square 0.720 Goodness of Fit < 0.80
Adjusted R Square 0.369
Standard Error 0.041
Observations 10

ANOVA
df SS MS F P-value
Regression 5 0.017151 0.0034 2.053 0.253
Residual 4 0.006682 0.0017
Total 9 0.023833       Confidence Level
0.95 0.99
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 99% Upper 99%
Intercept -0.00817 0.054399 -0.15 0.888 -0.159 0.1429 -0.259 0.242
GDP -2.6E-05 6.26E-05 -0.422 0.695 -2E-04 0.0001 -3E-04 3E-04
Interest rate -0.63772 1.271807 -0.501 0.642 -4.169 2.8934 -6.493 5.218
Inflation rate 2.14986 2.063493 1.0419 0.356 -3.579 7.879 -7.351 11.65
Debt/GDP 0.15434 0.066796 2.3107 0.082 -0.031 0.3398 -0.153 0.462
Population -2.3E-05 5.46E-05 -0.423 0.694 -2E-04 0.0001 -3E-04 2E-04
Table 7: represents the regression statistics of the Unemployment rate on the inflation rate, the rate of interest of financial institutions, population, GDP (Gross Domestic Product) and Debt/GDP percentage for developing countries.
As the p-value for the regression analysis was greater than 0.05 (p=0.253), it was concluded that unemployment rate could not be significantly predicted from the inflation rate, the rate of interest of financial institutions, population, GDP (Gross Domestic Product) and Debt/GDP percentage for developing countries also. Once again, the Y intercept was also not significantly related with (p=0.88) with the unemployment rate. However, a regression model was constructed for developed countries also as:
Unemployment rate= -0.008 0*GDP -0.638*Interest rate +2.15*Inflation rate +0.154*Debt/GDP 0*Population
Once again, an interesting observation was noted from the regression model where Debt/GDP seemed to be positively correlated with the unemployment rate. However, lack of statistical significance could not support the observation. Such observation cleared the dilemma of the holistic regression equation. This means that it might be possible that the debt which is acquired by developing countries is not used to address unemployment rates.
Correlation analysis between different socioeconomic variables
CORREL Jobless rate GDP Interest rate Inflation rate Debt/GDP Population
Jobless rate 1.000 -0.143 0.100 0.136 0.120 -0.203
GDP -0.143 1.000 -0.181 -0.165 0.234 0.403
Interest rate 0.100 -0.181 1.000 0.956 -0.433 0.265
Inflation rate 0.136 -0.165 0.956 1.000 -0.469 0.185
Debt/GDP 0.120 0.234 -0.433 -0.469 1.000 -0.108
Population -0.203 0.403 0.265 0.185 -0.108 1.000
p Values Jobless rate GDP Interest rate Inflation rate Debt/GDP Population
GDP 0.546 0.444 0.486 0.321 0.078
Interest rate 0.675 0.444 0.000 0.056 0.258
Inflation rate 0.566 0.486 0.000 0.037 0.434
Debt/GDP 0.614 0.321 0.056 0.037 0.649
Population 0.392 0.078 0.258 0.434 0.649
Table 8: Represents correlation coefficients between different socioeconomic variables
Table 8 indicated that unemployment rate is not significantly correlated with interest rate of financial institutions, inflation rate, GDP and Debt/GDP of a nation. However, the traditional positive correlation between inflation rate and rate of interest hold true for this study too.
V. Discussion and Conclusion
The present study supported the inconsistency between the unemployment rate and different socioeconomic determinants. On the other hand, the study clearly indicated that unemployment rate cannot categorize the status of a country as “developing” or “developed.” However, the study implicated certain interesting findings. The relation between the unemployment rate and debt/GDP was negative for developed countries. This meant that it might be possible that the debt which is acquired by developed countries is certainly used to address unemployment rates. Hence, debt/GDP might be seen as a positive stimulus for economic growth of developed countries. On the other hand, the relation between the unemployment rate and debt/GDP was positive for developing countries. This meant that it might be possible that the debt which is acquired by developing countries is not used to address unemployment rates. Hence, international financial institutions should be more stringent while extending financial stimulus to developing countries. One of the criteria for providing financial stimulus to such countries should be to appraise the rate of unemployment over the past five years.
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