Abstract
In this study, we will explore how the wage gap between men and women affect the economy.
Despite that the women today women make-up approximately half the nations workforce, they continue to earn less than men on average in every occupation. According to the Institute for Women’s Policy Research in the article The Impact of Equal Pay and Poverty and the Economy, “In 2015 women working full-time, year round earned just 80 percent for every dollar that men earned (Hegwisch and DuMontheir 2016b).” Progress toward equal pay has “slowed down and may take until 2059 for women to finally reach pay equality. In addition, the cross-sectionality between being a women and being a women of black or hispanic background may have to wait even longer. The article suggests they may have to wait until the year of 2248 to reach equal pay. Furthermore, the data, simple and multiple linear regression models were formed to determine the relationship between the two variables. The study found position relationships between income inequality and economic growth.
Introduction
According to the International Monetary Fund’s October 2017 World Economic Seeking Sustainable Growth suggests that the global upswing in economic activity is getting stronger with he global growth projecting at a 3.6 percent in 2017 and 3.7 percent in 2018. In both advanced and developing economies, more than offset downward revisions for the US and the United Kingdom (IMF, 2017). However, there are factors that play into this goal that, if not successfully addressed, could derail economic growth. One of these key elements is wealth inequality, and as global economic interdependence and interconnectedness continues to increase, it becomes vital to analyze the relation between economic growth and inequality. As mentioned in an except “Cause and Consequences of Income Inequality A Global Perspective,” states that in advanced economies, the gap between the rich and poor is at its highest level in decades. Inequality trends have been more mixed in emerging markets and developing countries (EMDCs), with some countries experiencing declining inequality, but pervasive inequities in access to education, health care, and finance remain. Not surprisingly then, the extent of inequality, its drivers, and what to do about it have become some of the most hotly debated issues by policymakers and researchers alike.
Today the topic of sex inequality is a topic that is very important since women are no longer fulfilling the roles of a “stay at home mom” and are climbing the corporate ladder. However, according to the Business Insider article “Americans' wages are growing — but inequality keeps increasing” states that although we see a strong economic growth this years due to the “near zeroing inflation between 2014 and 2015, families in the top 5th of income distribution are faster… while the 80 percent of families saw slower growth.” Income inequality impacts the poor and underprivileged the most, because the cost of living and the quality of life people live depends largely on their level of income. Since income level and quality of life are so interrelated, income inequality impacts the poor in several negative ways. The primary effect of income inequality is that it prevents capital accumulation (both human and physical) (Mo, 2000; Kaldor, 1956; Aghion, Caroli and Garcia-Penelosa, 1999). Secondly, inequality can generate socio-political instability that undermines incentives to save and invest, and would generate pressure on government (Mo, 2000). Finally, inequality has a detrimental effect on social mobility: countries with higher levels of inequality show a dependence of child’s future earning capacity on the current earning capacity of their parents (Corak, 2013). Inequality, which currently shows no signs of undergoing income redistribution, is indisputably a problem that disrupts the balance within a society.
Economic growth has been a popular topic since the financial crisis of 2008. In fact, since the Great Recession, economic growth has been regarded as a sign of advancement, development, and recovery. During a period of economic growth, poverty and unemployment is reduced, the standard of living of the population rises, it incentivizes the young, and the country’s currency appreciates against that of other countries’, giving it more international trade power. Ultimately, all countries desire to experience economic growth in order to progress. According to a German proverb on the subject of growth, “Stagnant water starts to stink at some point” (New York Department of Health, 1909). Economies cannot stand still; either they can go up for they go down–and everyone wants to go up.
Without a doubt, economic growth is instrumental in poverty reduction in a country, but is economic growth related positively or negatively in regards to income inequality?
This paper declares a positive relationship between income inequality and economic growth, and we shall test this prediction empirically with regression analysis. Using cross-country data obtained from World Bank for the year 2011, we conducted regression analysis of economic growth on income inequality. Existing studies determined there to be a positive relation between income inequality and economic growth. This research contributes to the statement and proposes that with higher inequality, economic growth will continue to accelerate.
In Section II draws literary support and analyzes existing sources to reinforce and elaborate on the research and hypothesis tests we conducted. Section III introduces the data and explains the techniques used to conduct our study. Section IV interprets the results from the data and analysis methods employed, and Section V concludes the findings of this research
Literature Review
Throughout the vast number of articles and research made to fin the relationship between these variables. I found that there is not clear answer on whether there is a positive or negative connection between the two variables. There are many theories about the variable that date back since the 1950s.
Inequality and Economic Growth: The Perspective of the New Growth Theories
There is a consensus among many authors of literature that there is a negative relationship between the average rate of economic growth and the measure of inequality. (Aghion, Caroli, Garcia-Penalosa, 1999). Aghion, Caroli, and Garcia-Penalosa (1999) examined case studies of South Korea and the Philippines. According to their research, the ratio of the income share of the top 20% of the bottom 40% of the population in Philippines was almost twice as large as in South Korea. Despite their differences in degree of income inequality. these two countries demonstrated similar levels of macroeconomic health (through GDP per capita, investment per capita, average saving rates, etc) at the beginning of the study. Over the course of 30 years, however, Aghion, Caroli, and Garcia-Penalosa (1999) found a marked difference in the rate of growth between the two countries (Aghion, Caroli, Garcia-Penalosa 1999). They ascertained that South Korea’s output level underwent a five-fold increase, while that of the Philippines barely doubled, demonstrating that the country with a higher level of income inequality grew at a slower rate. After they determined these results in a case study, they conducted research on redistribution to find whether redistribution fosters or hinders growth. Aghion, Caroli, and Garcia-Penalosa (1999) found that income inequality was found to be positively correlated with volatility, and through a series of cross-country regressions found that greater volatility reduces the average rate of growth during a set period. Their findings were bolstered with results declaring that redistribution has stimulating effect on economic growth, therefore determining that inequality has a negative impact of economic growth. These results coincide with other literature declaring a negative relationship between income inequality and economic growth.
A Non-Parametric Measure of Poverty Elasticity
In a study that yielded similar results, Chambers and Dhongde (2011) pursued a non-parametric approach to examine an extensive and up-to-date dataset from the World Bank, inclusive of 1977 through 2007, representing more than 96% of the population of the developing world. Rather than GDP, Chambers and Dhongde(2011) measured the growth elasticity of poverty (GEP) and found that countries with higher levels of inequality had lower GEP, and countries with lower inequality had higher GEP. Through more extensive research (and their non-parametric approach), they studied the typical linear model to measure the relationship between poverty, mean income, and the Gini index and found evidence that the relationship between income inequality and growth is best described as non-linear. Chambers and Dhongde (2011), by analyzing a model which considers the nonlinearity of the growth-poverty-inequality neux, found that poverty declines rapidly with higher mean income, but slowly with lower values of the Gini index. In short, their results were obtained using data that was much more comprehensive and methods that were more robust than those of most studies. Their findings reflect those of Aghion, Caroli, and Garcia-Penalosa (1999) as well as many others that have also found a negative relationship between economic growth and income inequality.
Income Inequality is Not Harmful for Growth: Theory and Evidence
While there seems to be insurmountable evidence in favor of a negative relationship between income inequality and economic growth, there are numerous studies that yielded a positive connection between the two variables. In an analysis conducted by Li and Zou (1998), the results stated that empirical evidence revealed through a regression of GDP growth rate on the Gini coefficient that income inequality is positively associated with economic growth. Following in previous literature’s footsteps, Li and Zou (1998) followed Alesnia and Rodrik (1994) and Barro (1990) to find income inequality’s relationship with economic growth by dividing government spending into production services and consumption services. However, in contrast with Alesnia and Rodrik (1994) and Barro (1990) according to their results, income inequality can lead to fast economic growth when government spending is wholly driven by public consumption. In fact, by using this extension of government spending, Li and Zou (1998) found that since government spending is all for consumption, individuals will try to allocate resources between public and private consumption. Therefore Li and Zou (1998) state that income inequality can generate high savings rates and growth rates if the rich have a larger share of income, or if income is more unequally distributed in the economy.
Income inequality and Economic Growth (Shin)
While some literature declare a positive relationship and others support a negative one, there are some studies in which no position is taken and both sides of the debate are examined and analyzed (Shin, 2012). Shin (2012) chose not to pursue a particular stance on the topic but rather chose to examine reasons why this disparity exists. According to Shin (2012), there is a correlation between the positive/negative relationship between inequality and economic growth and whether or not the country is developed or not. Shin (2012) performed a case study of East Asian and South American countries, which are developing countries. The findings revealed a negative relationship between income inequality and economic growth in those countries. Conversely, in a case study of the United States and France, which are developed countries, a positive relationship between income inequality and econ growth was found. In an agreement with Barro (2000), Shin (2012) declared that the effect of income inequality on economic growth was contingent on the state of economic development. Specifically, Shin (2012) found that income inequality in poor countries retards economic growth; that is, in countries with GDP per capita below 2070, the effect of income inequality is negative. According to Shin (2012), this is caused by a lack of opportunity to invest by the population of a developed country. This in turn would lead to political and social instability, which contributes towards economic growth decline. Therefore income inequality reduces economic growth. In contrast, income inequality in rich countries encourages growth; that is, in countries with GDP per capita over 2070, the effect is positive. Income redistribution from the rich to the poor reduces the saving rate of the economy which would lower the incentive for the rich to work hard. So, income equality would reduce economic growth. It can be inferred from this paper that the result of income inequality on economic growth varies depending on whether the country is developed or not
As we stated before, there is a large divide in literature as to if income inequality and economic growth are related through a positive or negative relationship. The purpose of this paper is to evaluate the effect of income inequality on economic growth and to contribute relevant findings to the discussion by examining extensive datasets from the World Bank ranging from 1981 to 2014, which enables us to do a long-term comparison case study. The world has been undergoing constant economic change,, and global interconnectedness and interdependence grows and changes each year. To better analyze our data, we incorporate some other important variables that may have an impact (helpful or detrimental) on the relationship between economic growth and inequality
Data
We chose the Gini coefficient (pre-tax) for the explanatory variable (x) in our simple regression line. The Gini coefficient was chosen for this model because it is a common measure of income inequality across many countries that represents the income distribution of a country’s residents, where 0 represents perfect equality and 100 represents max inequality, and is recognized and used in much of the literature. Annual growth percentage of gross domestic product (GDP) was the dependent variable (y). The Gini coefficient and GDP growth datasets in this paper were obtained from the World Bank’s Development Research Group (World Bank, 2011). We chose to regress GDP growth on the Gini coefficient because most of the literature we referenced found income inequality to have a more marked effect on GDP growth than GDP growth on income inequality. Our ultimate objective was to find the relationship between income inequality and economic growth. However, there are numerous variables that may affect economic growth, including urbanization ratio, population growth rate, financial development (M2/GDP), openness (export/GDP), etc (Li and Zou, 1998). In order to better understand and analyze the effect of income inequality on GDP growth, we controlled for other factors that had the most significant impacts on economic growth in an economy. These variables were gross savings, unemployment rate, education (mean school years), and fertility rate. Gross savings (World Bank, 2011) is one of the most common indicators of the growth of a country because it reflects the country’s ability to consume and save. Fertility rate was included because research has shown that lower fertility rates lead to economic growth. Unemployment rate (World Bank, 2011) represents the long term unemployment rate, or natural rate of unemployment, in a country. Unemployment rate is an obvious indicator of a country’s economic well-being. The mean school years are also expected to have an impact on economic growth. The more educated a country, the more growth is to be expected because of the capacity for high-skilled laborers. Finally, a dummy variable was used to measure if the level of development of a country would affect their economic growth. These two categories (developed and developing) were classified according to the World Bank classification system.
A summary of the variables is provided in Table 1 below.
Table 1: Variable Descriptions
Summary Statistics
Table 2 shows the summary statistics for the data. This study was conducted using 225 countries. Because a country’s economy can regress, the fact that the minimum of grgdp is a negative number is not a huge concern.
Gauss Markov Assumptions
This section tests whether the data meets the Gauss Markov Assumptions. For the sake of accuracy and effectiveness, the data and models were required to fit the Gauss-Markov assumptions so that it is ensured that the Ordinary Least Squares (OLS) estimates are accurate, linear, and unbiased. This way, we can see if our data is justifies our multiple linear regression models.
MLR 1: The model is linear in parameters. Y = β0 + β1X1 + … + βkXk + u, thus our model meets assumption one.
MLR 2: There is a random sampling of regressors. Countries selected at random without a particular reason yield a random sampling. We collected data from random countries in the world according to the World Bank and obtained our sample from whatever data points were available during the year 2011, our year of study. Thus, our model meets assumption two.
MLR 3: There is no perfect collinearity between any of the regressors Table 3 illustrates that there is no perfect collinearity between any of the regressors, therefore our model meets assumption three.
MLR 4: According to the zero conditional mean, the expected value of error given all explanatory values equals 0. Through calculation of the residuals, this was tested and proven. Figure 1 shows the mean of the residuals for the multiple linear regression model tested was about zero.
MLR 5: The error u has the same variance given any value of the explanatory variables. The residual distribution must approximate a normal curve. Our model should reflect the best linear unbiased estimators (B. L. U. E. s). So we conducted several multiple regression models as well as plot the residuals. The residual distribution in Figure 1 approximates a normal curve, so our model fulfills the fifth assumption.
Results
Simple Linear Regression Model
The purpose of the simple linear regression model is to test the relationship between GDP growth and the Gini coefficient. To test this relationship, GDP Growth was only regressed on the Gini coefficient.
The results showed a positive relationship between the Gini coefficient and GDP growth, which can be seen in Figure 2 with a scatterplot of GDP grown (5) on the Gini Coefficient. This indicates that for one unit increase in Gini coefficient, the GDP growth rate increases by 11.06 percent. Since the intercept is negative, this means that with zero inequality (Gini equals zero), there would be negative growth. This is a reasonable inference because perfect inequality, which is what is assumed be no inequality, would allow the assumption of negative growth. The p-value of Gini was 0.001, indicating a very high statistical significance. Also, the R2 found is 0.0981, which means the Gini coefficient only explains 9.8 percent of the GDP growth in the model–a low value. We found this rather unsatisfactory. The reason could be our sample is too diverse or applies for too many different countries since different countries’ situation may vary. For instance, one cannot explain the economic growth of some countries with a universal model. Or, this could indicate a non-linear relationship. In our subsequent research, we will build more models using different sets of datas, hoping to find a theory to explain it.
Multiple Linear Regression Model
We constructed several more multiple regression models to account for other factors or variables with economic significance that may affect economic growth, and to remove any omitted variable bias. These new variables were chosen to control for the Gini coefficient. GDP growth was regressed on the Gini coefficient and 4 new explanatory variables. Table 5 shows the regression estimates for each model and whether they are significant at 10%, 5%, and 1% (*, **, and *** respectively). The additional variables were gross savings, unemployment rate, years of education, and fertility rate. The Gini coefficient was consistently maintaining a positive relationship with GDP growth, as shown in Figure 2. All the variables had positive relationships with GDP growth except for unemployment, which had a negative relationship. While the intercept was consistently negative, this could be due to a strong effect from the Gini coefficient or due to fluctuations in the magnitude of the intercept. The R-squared values did not fluctuate too widely (with the exception of the estimates related to the dummy variable).
Model 2, our first multiple regression model, included the Gini coefficient and the savings rate.
Model 2 in Table 5 shows the regression estimation equation results. In Model 2, both independent variables were positive and significant at the 1% level. The R 2 value was 0.199, which increased from the R2 value of 0.098 for the simple regression model Model 1.
In the following model, Model 3, the variable fertile, for fertility rate, was added to the preexisting variables Gini coefficient and gross savings.
The table yields results that show that fertility rate was also a positive and significant relation to GDP growth. The Gini coefficient and the gross savings rate retained significance in Model 3.The Gini coefficient is now significant at the 5% level, while gross savings and fertility rate were significant at the 1% level. The R2 value increased to 0.385, which means that the variables explain 38.5% of the variation in grgdp. This makes sense because as we control for more variables, the larger R 2 will be.
In Model 4 we added the unemployment rate, which although proved to be significant alongside the other variables, had a negative relationship with GDP growth. The Gini coefficient maintained significance at the 5% level, like the unemployment rate, while gross savings and fertility rate remained significant at the 1% level. The R2 value for this model increased once more to 0.423.
In Model 5 we incorporated the variable education, which represents mean years of education. The mean years of education had a negative relationship with GDP growth. This new variable differed from all the other variables because it was not statistically significant at the 10%, 5%, or 1% levels. The Gini coefficient maintained significance at the 5% and 10% levels, while gsav, fertil, and unemp all retained their statistical significance at the 1% level. In addition, the intercept was not statistically significant at any level in this model, unlike the other models. Therefore, we can conclude from these results that the Gini coefficient, gross savings, fertility rate, and unemployment all have an impact on GDP growth, while no conclusions can be made about education. The R 2 value rose once more to 0.478, which means that 47.8% of the variation can be explained by the model. Model 5 is the model we chose as our restricted model after testing for correlation of variables. A value of positive or negative one would be a perfect correlation while a value of zero is no correlation. These results are shown in Appendix 2 Table A7.
After we had constructed and analyzed these models, we decided to add a dummy variable to show the difference between developed and developing countries. This dummy variable, “dev”, is shown in Model 6’s regression. According to Model 6, compared to the intercept of the developing countries of -0.645, the developed countries had an intercept of -3.995. This model also had the highest R 2 value of 0.632 and the smallest number of observations. This is much larger than the previous values, but expected, as increasing the number of variables always increases the R2 value. These differences in information gathering may be the cause of some of the differences in models. The correlation among variables with the inclusion of the dummy variable can be found in the Appendix 2, Table A8.
The quantities in parentheses are standard errors. *, **, *** denotes significance of coefficients at 10%, 5%, and 1% respectively.
The results yielded from the regressions support our hypothesis that GDP growth and the Gini coefficient are positively related. Depending on the model used, a one point increase in the Gini coefficient can result in about an 11% increase in GDP growth. This may be caused by an unequal distribution of wealth in an economy with income inequality. Essentially, as inequality increases, the majority of the wealth of the economy is concentrated in the hands of the top percentage of the people. This can then increase GDP growth through investment. Unsurprisingly, gross savings and the GDP growth in an economy are positively associated. With a 1% increase in gross savings, there is (depending on the model) a 10-13% increase in GDP growth. These findings support Shin’s (2012) and Malinen’s (2013) research that an increase in the level of saving in an economy will enhance growth. These results also support Aghion, Comin, Howitt and Tecu (2009) which states that increased savings may increase innovation and therefore foreign investment in technology, which in turn would have a positive effect on the economy. Fertility also has a positive impact on the economy. As the population of a country grows, more people are added to the labor force and the country is more productive. In fact, a fertile population of a country signifies health and potential for growth as well. According to our findings, fertility rate is actually one of the more influential variables of an economy’s GDP growth. Unemployment, unsurprisingly, has a negative correlation with GDP growth. An increase in unemployment results in a decrease in a country’s GDP growth, and vice versa. As unemployment rate increases in a country’s economy, there are social and economic implications and repercussions. Generally, unemployment is negatively related to disposable income as well. This results in reduced consumption which will lead to reduced economic growth. Finally, the statistics show that mean years of education does not have statistical significance in these models. Interestingly, the correlation between mean years of education changes from negative to positive when the dummy variable is added. This model including the dummy variable is something that should be further investigated.
Statistical Inferences
Looking at the regression models created, we can see which factors have a positive impact on economic growth and which factors have a negative impact on economic growth. Our models unanimously demonstrated that the Gini coefficient, the gross savings rate, and fertility rate had a positive effect on GDP growth, while unemployment and education had a negative correlation with economic growth (not encompassing the model including the dummy variable). Also, for each regression, two-tailed t-tests were performed on each variable. The null stated that the coefficient of the variable equaled zero, and the alternative hypothesis stated that it did not equal zero. The tests were then examined at the 1%, 5%, and 10% significance level. The t-values and p-values that resulted are in the appendix. In conjunction with the simple regression model, we found that the Gini coefficient was statistically significant at all three levels, and decreased in significance slightly (5% and 1%) when independent variables were added for the construction of Models 2-6. The variables with a positive effect on GDP growth (gross savings and fertility rate) consistently were statistically significant at all three levels, while unemployment, the negatively correlated variable, was consistently significant at the 5% and 10% levels. The significance of the intercept varied widely throughout the tests, and so we cannot conclude much about its statistical significance with our current research results. However, we can conclude that from this model, all variables had an impact on GDP growth.
Looking at the growth rate of developing countries compared to developed countries, on average, developing countries had higher GDP growth rate than developed countries. This could be explained by inequality in those countries. Unequal distribution