Poverty is the deprivation of basic things like money, food or assets that are essential for a normal life. GDP on the other hand is the goods and services produced within the by a country in a year. It is known that with increase in poverty it leads to reduction in output of a country. This is because essential resources that are vital for production are lacking. It also means that human capital is insufficient and consequently the resources needed to stir growth are used to feed and provide for the poor population.
Poverty and unstable GDP are common phenomena in developing countries. It should not be assumed that there is poverty only in developing countries because poverty in developed countries can strain the functionality and the growth rate.
Poverty rate therefore, is the percentage of people that are below the poverty threshold in a year. According to the UN estimates of 2000-2007, they estimated that poverty rate is the population percentage that lives on $1.25 per day. This amount of money is absolute though it differs in various countries.
Statistics show that there are over 210 million people in Africa who live below the poverty line. In Sub-Saharan Africa 50% of the population live under the poverty line. In the 1990s many African countries underwent deterioration in their GDP per capita and this led to the decline in their living standards. Africa’s macroeconomic indicators do not favor it making development so hard to be achieved .
More than a third of the world’s population lives in poverty in Asian countries. The majority of these live in Southern Asia. Asian countries like China have fast growing populations. The trend in these countries in Asia and Africa is that poverty is transmitted to the next generation because of unstable economic growth and hence very low GDP per capita. The reason for this is because the poor are ignorant and their participation in economic, social, and political activities is worry some.
Data from the Census Bureau shows that in the US population under poverty has remained constant and the programs by the federal government seem to have no impact on the poor population. The population rate therefore has remained 16%. The programs by the federal government to aid its citizens include; food stamps, tax credits, housing aid and subsidized school lunches .
This shows how poverty is a strain to economic development since there is limited production and hence a low GDP growth rate. Higher growth leads to a reduction in poverty. Changes in economic growth are affected by changes in poverty levels by either diminishing or remaining unchanged over a period of time.
A good example is the period of 1980s in the US, changes in economic growth had little effect on poverty levels in the country. In the following decades in the 1960s, 1970s, 1980s, 1990s, economic growth had significant effects on the poverty levels. Through using the appropriate regression method and using of latest software such as Gretl for calculating facts, we will find out the relationship between poverty rate and GDP growth by using a simple economic model and regression. (Adams).
The table below shows the population of countries and the percentage of the population the lives below the poverty line of $1.25v per day and those that live below $2 per day. As per the table only 0.5% of the world’s populations live with an income of over $1.25 per day in Asia and Europe. 36 % of the population south Asia lives below the poverty line of $1.25 while in Sub-Saharan Africa it is 47.1%. When the criterion of $2 is taken, the percentage population in these areas significantly increases. 70.9 % in South Asia are found to be below the poverty line while in Sub-Saharan 66.2% live below the poverty line.
Analysis of poverty in relation to GDP per capita is essential in that, it helps in alleviating poverty and providing an encompassing intervention scheme, not only economic aspects but also social and political issues of poverty.
There is usually a relationship between GDP per capita and poverty rate. It is a common scenario that poverty rates are very high when GDP per capita is low. This is a common scenario in most developing countries. Developed countries like the US, Europe, Korea and Singapore have stable economic growth have low poverty rates and high GDP per capita.
Economic theory on poverty and economic growth shows that, with increase in economic growth it tickles down to the poor in the population. Unlike popular belief that economic growth leads to an overall performance and welfare of society, economists believe that it does not lead directly to welfare and increase in the living standards of all the population. Economists in the 1960s discovered that the effects of economic growth do not tickle down to the poor population in developing countries. Therefore their recommendation to governments to act as a third party and redistribute resources to ensure the overall increase in welfare and living standards.
Countries that experienced overall reduction in poverty and increase in GDP per capita growth rate were due to the redistribution effect of the government. Most literature on this topic has diverse perspectives. Some literature acknowledge that an increase in economic performance; through increase in production of both goods and services, increase in specialization and efficient technologies, it leads to a decrease in poverty since there is increase in gross domestic output .
Other literature stipulates that with increase in economic output, it leads to an increase in inequalities and therefore the problem of poverty increases. This is because economic growth and development favor the rich whereas the poor remain to be poorer. This is a common scenario especially in developing countries that have ineffective institutions. Therefore economic development can impact negatively the poor rather than help them.
Kuznets hypothesis (1955) claims that that growth and inequality have an inverted relationship. The hypothesis claims that inequality and growth have an inverted relationship because in early stages of development income worsens distribution until the countries reach middle income status.
Empirical literature rejects the findings of Kuznets. GDP increased in 1996 to 26% in the developing world, since income inequality tends to be stable for some period of time, economic growth is expected to reduce poverty to some extent (Deininger and Squire)
Ravaillion (2010) did a study of 90 developing countries and contended that that since poor countries have less capacity for redistribution, it leads to further increase in poverty in those developing countries. Other researches contend that benefits of growth reach the rarely reach the poorest of the citizens (Ahluwlia, Carter &Chennery). Dart and Ravillion (2011) examined the effects of economic growth in India after major macroeconomic reforms. They noted that there was increase in inequality, however, the rural poor gained from the major transformations that the urban poor receive.
Squire (1993) regressed the amount of poverty reduction against the rate of economic growth. He found that a percentage increase in economic growth rate decreases poverty rate, that is, a reduction in headcount of poverty by 24%. Bruno and Ravillion (1998) did the same study for 20 developing countries in the period of 1984-1993. They regressed the population change rate for people living on less than $1 per day. They got a regression coefficient of -2.11. This result implies that percentage increase in growth by 10% will lead to a decrease of proportion of people living in poverty, less than $1per day, by 21.2% .
The research paper written by Sinnathurai, scrutinizes the relationship between poverty economic growth, employment and dependency ratio. He used cross country data of 41 countries. These countries were selected from three continents, that is, Asia, Sub-Saharan Africa and Latin America. The choice was from those countries that are developing. He used simple regression methods, OLS method, correlation and econometric methodologies. He found that 71% of variation of age dependency ratio was affected by variables like; employment, economic growth and poverty. His model was fit for analyzing and forecasting of such analysis.
Therefore in his paper he found that, poverty impacts on dependency ratio. As poverty increases in a family so does the dependency ratio.
Therefore, from the empirical literature it is shown that growth is a necessary but not sufficient condition to alleviate poverty. The pattern of growth is important also. Previous s literature has critically emphasized how economic growth with other factors has impacted on Poverty levels with the focus on developing countries. This study will solely measure the impact of GDP on poverty rates in developed country US.
This paper uses an econometric multivariate model. An econometric model is used to evaluate the impact of various variables on a macroeconomic problem. The sample size is a period of ten years of various variables that affect poverty rates. The variables that are going to be used in this analysis are; income, GDP per capita and population. These are factors that mostly cause poverty.
The expected results are that level of education can cause poverty. Most illiterate people do not have access to information especially in a nation that has increased knowledge and sophistication. There is an expected inverse relationship. The same inverse relationship is expected in increase in GDP per capita and increase in income .
Data used in this paper is cross-sectional data in a period of ten years, and with this data we expect there will be impure multi co linearity. To curb this sample size has been increased to 20. The data will be easier to run and it covers the time period during a recession and years that follow. The cyclical nature of macroeconomic issues is taken into consideration when choosing the sample size.
If multi co linearity still persists in our data analysis by dropping a redundant variable. Multi co linearity is a violation to the assumptions of ordinary least squares method. It makes the estimators to be sensitive to small changes. In the extreme it may lead to difficulties in making estimations or making wrong conclusions.
In the case of correlation it will be remedied by use of generalized least squares. Heteroscedasticity will be remedied by use of Breusch Pagan test. The consequences of these are that they render the ordinary least squares method void and consequently the estimators will not be best linear unbiased estimators.
The aim of the methodology used here is to find the relationship that exists between poverty rates as our dependent variable and GDP per capita, income, and population growth levels. The data has been gathered from various reports, including Measuring worths, US Bureau of Economic Analysis and US census. Analysis will be carried STATA software. For this analysis where we determine if there is a relationship among the variables, our model therefore is;
Yi=β1+β2G+β3P+ β4I+ u1 (1)
Our null hypothesis is, there is no significant relationship between poverty rate and GDP growth. Consequently the alternative hypothesis is, there is significant relationship between poverty and GDP growth .
Which is in the basis of Yi=ƒ (GDP per capita, income, population). In this equation Yi refers to poverty rate, G is GDP per capita, I, is the income, P is the population, and u1 is the stochastic disturbance term. It includes all the relevant variables that are not included in the model.
This analysis is to find the magnitude and the direction the variables affect poverty rates. As previously stated, it is expected that GDP and income are negatively related while population is positively related to poverty rates. We use US data collected by the US Bureau on its country over a range of 15 years.
Data used is from US bureau of Economic Analysis. The sample data is for a period of 20 years from 1993-2012.The data on population is in millions. Real GDP per capita is in million dollars, while poverty rate is in percentage and income is per million households per year, here we will use inflation adjusted data. Inflation adjusted data makes it easier to not to measure the effects of cyclical macroeconomic impacts during analysis .
US statistical data table on its population, real GDP per capita and actual poverty rate for 1993 to 2012.
US nominal income and number of households’ income from 1993 to 2012.
Source: U.S. Census Bureau
Model 2: OLS, using observations 1-20
Dependent variable: Y
The observed R-square is 0.862496 and the p-values are more than 0.8 and 0.05 significant level respectively. This shows that there is multi co linearity. This shows that population is the only explanatory variable that well explains the dependent variable, that is, poverty rates. To remedy this we drop income as our redundant variable. The analysis is displayed in the model 3 below. This can be explained in that, poverty is highly correlated to poverty rates and therefore its coefficient estimates are biased.
Model 3: OLS, using observations 1-20
Dependent variable: Y
After dropping the redundant variable our R-squared is 0.8 which shows that it is a good correlation coefficient. Therefore we do not reject the null hypothesis meaning that there is no significant relationship between GDP growth and poverty rates.
This paper analyzed the relationship between GDP growth and poverty rates in the US for a period of 20 years. This was done by use of multivariate econometric model. From the analysis it is observed that there is no significant relationship. From the coefficients of the explanatory variables, keeping all the variables constant there is a 9% decrease in poverty. Keeping GDP growth constant an increase in the population leads to 15% increase in poverty. An increase in GDP growth in millions will lead to a 54 % reduction in poverty rates.
The relationship found in the analysis confirmed what was anticipated by regressing variables on the dependent variable. However, there is an unexpected result in the relationship between poverty and income. This leaves room for further research to be done to determine why this is the case.
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