The Correlation Between Minimum Wage and Youth Unemployment. A Cross Country Analysis

 INTRODUCTION 

In this article , it mainly depicts about the correlation between minimum wage regulations and youth unemployment by examining data and sources from 25 different OECD countries in the year 2008. It consider youths between the ages from 15 and 24. The arcticle focuses on the hypothesis that as the minimum wage increases , youth unemployment will increase as well and as a result a positive correlation exists between the two variables. Since the minimum wages exixts as the price floor in the labour market , it skews the signals of demand and supply and thus produce a situation where there is surplus labour and thus it leads to more people willing to work at the minimum wage rate. And thus it is theretically said that , an increase in minimum wage would exacerbate this effect and increases unemployment.

There are several reasons for focusing youth unemployment. Primarily , youth are among the most highly susceptible to minimum wage fluctuations as many of them lack the work experience, and therefore the value, that older more experienced workers have. And also various research analysts states that youth unemployment can forecast greater unemployment in the long run. This relation also gives a clear picture of how minimum wage laws affect the segments of the labour force.

The article mainly focuses on the cross country analysis and by conducting such analysis , we can avoid the situations where the relationships are skewed. Also, countries simply do not change their minimum wage often . so by including multiple countries in the analysis, its helpful to explore a variety of minimum wage data alongside varying youth employment rates.

 

SCOPE OF THE STUDY

 

• In correlation , one of the variable may be affection the other. The two variables may act uponj each other and thus the minimum wage rate and youth unemployment exists.

• Economy could be benefitted with the positive relation between minimum wage rate and youth employment.

• In simple regression model , it assumes or hypothesis is that there is a positive relation between the minimum wage rate and the youth unemployment.

• In correlation , a solution will be prepared for both uncertainity and risk estimation and in turn , model setups , results and impacts will be shown.

• Increased income provides labour productivity and thus results in production of more employment opportunities.

• This relation also gives a clear picture of how minimum wage laws affect the segments of the labour force.

• The article focuses only on the cross country analysis inorder to prevent the situations where the variables gets skewed.

 

RESEARCH OBJECTIVES 

 

This particular article focuses  the correlation of minimum wage with youth unemployment across 25 OECD countries.

The article mainly focuses on the youth unemployment rather than overall unemployment due to many reasons which are mentioned in“ introduction page”.

The article focuses exclusively on the relationship between the current minimum wage rate and current levels of youth unemployment to determine what effect the relative level of the minimum wage, rather than the effect of a change in the

minimum wage, has on youth unemployment. For this purpose , two models have been used.

• The first is a simple linear regression model;

However, because of low statistical significance in this model, it used to     explore the correlation using a ;

• multiple linear regression model :With this model, we found the relationship between the minimum wage and youth unemployment is positive and has a coefficient of 0.0176.

 

METHODOLOGY OF THE STUDY

 

To explore the correlation between minimum wage and youth unemployment, they developed two models. 

The first is a simple linear regression model and the second is a multiple linear regression model.

i. The simple linear regression model utilizes the youth unemployment rate as the dependent variable and the real minimum wage measured in U.S. dollars as the independent variable. The youth unemployment rate is comprised of all persons between the ages of 15 and 24 who are actively seeking employment, but who are still unemployed. In regressing this model, we used the log of the youth unemployment rate to analyze the percent change on youth unemployment as the result of a change in the minimum wage.

           The simple regression model form :

 

RESULTS : 

 

TABLE : Simple linear regression model results

This model shows a slight negative correlation between the minimum wage and the youth unemployment rate. The coefficient of the minimum wage value

can be interpreted as a one-value increase in the minimum wage will lead the youth unemployment rate to drop by approximately 1.74%. Using a one-tailed t-test of statistical significance, the minimum wage variable is not significant at any level. Also, the 𝑅2 value is only 0.0367 meaning that this model only accounts for about 3.6% of variation in the youthunemployment rate. This simple linear regression model shows that minimum wage is not a strong or statistically significant variable to explain youth unemployment on its own.

 

 

ii. The multiple linear regression :

Because the simple linear regression model had a low 𝑅2 value, and therefore did not account for a large portion of the variation in youth unemployment, and because the minimum wage variable is not statistically significant in this model, we deemed it necessary to explore the correlation using additional explanatory variables. So this model added five additional explanatory variables

 

The dependent variable lyunemp is the log of the youth unemployment rate. The

variable minwage is the same real minimum wage value used in the simple linear regression model. The variable labeled emplratio is the youth employment to overall youth population ratio and uses the same age group as the youth unemployment rate (AGE : 15-24). The article also included this variable as a control of the general participation of youth in the labor market. The variable unemp represents the overall unemployment rate per country, and GNIpercap is gross national income per capita. We included the unemployment rate and the gross national income percapita to control for differences in economic conditions between countries. Finally, ypop is the total youth population per country. The data sources used to acquire these variables include the World Bank, OECD Statistics, and the United Nations Statistics Division.This general model typically includes a minimum wagevariable, business cycle variables, and a few other potentially exogenous variables. Like this model, we include a minimum wage variable, business cycle variables such as unemployment and GNI per capita, as well as other variables we believe to be exogenously related.

 

TABLE : multiple linear regression models.

 

The 𝑹𝟐 value in this model is much higher than in the simple linear regression model and accounts for about 95.38% of variation in youth unemployment. Unlike in the simple linear regression model, this model shows a positive relationship between the minimum wage andyouth unemployment, meaning as the minimum wage increases, youth unemployment also increases. The youth employment to population ratio, the poverty rate, and the GNI per capita variables all show a negative correlation with youth unemployment. Intuitively, a negative

coefficient for GNI per capita and the youth employment to population ratio makes sense. As gross national income increases, one would expect youth unemployment to decrease as growth in income typically represents economic growth, which signifies lower unemployment as the economy is expanding. Similarly, increases in the ratio of youths who are employed relative to

the youth population would always mean that the youth unemployment rate was decreasing unless for some reason the overall labor force dramatically increased without an equivalent increase in the number of youth who were employed.

The coefficient for minimum wage is only slightly higher than the coefficient for

minimum wage in the simple linear regression model. Specifically, the impact of minimum wage on youth unemployment increases from 1.74% to 1.76%. Using a one-tailed t-test of statistical significance, the minimum wage and youth population variables are statistically significant at 10%. The youth employment to population ratio variable is significant at 2.5%, and the unemployment variable is significant at 0.5%. The GNI per capita variable and the poverty rate variable are not statistically significant. The low significance of both GNI per capita and the

poverty rate could have occurred because multicollinearity exists between these two variables.

 

CONCLUSION

 

There has been various researches on the topics related to the relationship between minimum wage rates and youth unemployment as well as with overall unemployment. They depicts mostly a positive relation between the two variables. And here , in this article , it did not include a lag variable or regress data from a time series analysis, but rather used a cross-section of relationships between the minimum wage and youthunemployment from one year. According to our simple linear regression model, our hypothesis that there will be a positive correlation between the minimum wage and the youth unemployment rate is false. However, we found that in this model, the minimum wage variable was not statistically significant and the overall 𝑅2 value was low. But in case of Multiple regression model , the  coefficient on the minimum wage variable changed signs and became statistically significant at 10%. Also, the 𝑅2 value increased dramatically from 0.0367 to 0.9538, an almost 25% increase. According to the multiple linear regression model, its hypothesis is correct in predicting a positive correlation between minimum wage and youth unemployment; however, a change in minimum wage will lead to only a 1.76% increase in youth unemployment, which is not adramatic change. And thus with the help of simple regression model and then followed by multiple regression model , a correlation between the minimum wage rate and the youth unemployment is determined.