Introduction
According to economic theory, GDP per capita of a country or a state can be increased in many ways. Education here plays a very vital role in the increase of GDP per capita. In general, the higher the education level, the higher the productivity and the higher the income. Affects the scale of the magnified state. The following applies: The higher the education level of a country, the better the economic outlook for that country. Education is an important variable, but other factors such as gender discrimination, quality of education, religion, and even the area in which the person lives play an important role in a person’s income. Therefore, further interpretation is needed. It can be expressed in many scales, including: number of people with a bachelor’s degree or higher / population ratio, enrollment rate in secondary education, etc.
Our dissertation is based on per capita income at various levels of education, as well as the other factors mentioned above. If we look closely, bachelor’s and master’s degrees, including higher education, have an impact on our income. In this paper we mainly focused on how GDP per capita in all the states of USA is affected by higher level of education.
In the United States 79 percent adults are educated in the year 2022. Following that 21 percent adult population of United States are illiterate in the same year. There are 54 percent adult whose level of education is below sixth grade. US have to bear the cost of lowlevel literacy up to 2.2 trillion dollars every fiscal year.
Literature review
For economists and politicians’ educational economy impact has been a controversial topic. In the configuration of this paper, various journals and publications are reviewed as a reference.
Among them one that explores the economic impact of universities that was published by Valerio and Van Reenen (2018). Authors of this paper pointed out the hyped trend of an increasing number of universities and the increasing number of universities per million people and mean growth in GDP per capita. A fluctuating plot that plots the number of universities per million people and the mean growth of GDP per capita income and is utilized to represent the trend. They represented the hypothesized relationship that has positive and strong correlation between GDPs per capita and universities Further results suggested that if in a region there is a 10% increase in the number of universities then GDP per person increases by 0.4 percent.
Mamit Deme and Ali M. A Mahmud (2020) stated that statistically primary and secondary education positively impact per capita real GDP. But the correlation between economic growth and education is not robust and weak. They conclude to a result that policy makers aim both the quality and quantity of education to achieve growth in real GDP per capita and primaryeducation for everyone needs to be ensured.
Another study that contributes to this field of study is Aghionet. Al. (2009) research is written by Aghion et. (“The Causal Impact of Education on Economic Growth”, 2009) This paper focuses on the impact of all education sectors, not universities. Introduced effects such as the transition to a model of skilled workers built a complex model explaining the impact of investment in education on GDP and introduced impacts such as the movement of skilled workers into the model.
Interesting insights into Odit et. al. (2010) The driving force of economic growth is what they see as an educational qualification that contributes to the quality of human capital. Harvest on scale. They used a constant CobbDouglas production function. Here, the human capital augmentation growth model uses human capital as an independent element of production. In conclusion, the results of the calculations suggest that education is productive and convincing, not “a way for individuals to inform their employers of their skill level.”
Methodology:
The purpose of our paper is to detect how real GDP per capita of the states of USA is associated with its level of education which is measured with population percentage that hold a bachelor’s degree or more than that. In our paper data we used here for GDP per capita is calculated in chained 2012 dollars. The advantage it has is that it is inflation adjusted. On the other hand, for the level of education the measure is population percentage of states who are 25 years old or older and who has bachelor’s degree or more than that. 2019 is the year both of the data sets are obtained from. Both data are taken from BEA (U.S. Bureau of Economic Analysis). There are some other variables that affects GDP per capita of sates across the country. These variables are total labour force, urban population, unemployment, and labour participation rate. Total number of sates of United states with Washington D.C is the sample size of the data set which is 51. The data of unemployment rate is taken from FRED. Urban population is taken from U.S Census Bureau. Total labour force and labour participation rate both are taken from U.S Bureau of labour statistics.
Variables  Observations  Mean  Standard Deviation 
gdpp  51  56789.9  20163 
educ  51  32.6  6.6 
urbanp  51  73.8  15.1 
un  51  3.6  0.8 
labf  51  3209000  3590838 
labpar  51  63.8  3.9 
The model in this paper satisfies the assumptions of Gauss Markov. They are:
1) Linear in Parameters:
For a linear regression model, y= _{0}+ _{1}x_{1}+ _{2}x_{2}+………… _{k}x_{k}+ is the basic form. Here, loggdpp is the dependent variable which is represented by y. Then educ is represented by x. However, _{0} here is the intercept. And the coefficients are from _{0} to _{k}. here is the error term.
2) Random Sampling:
51 is the sample size of this data which includes all the states of United States of America. Both low and high GDP per capita of the states and also low and high education level degree rates are included in this data set. And the available data is sampled.
3) No Perfect Collinearity:
When no perfect linear or connection or relationship exists among the independent variables the theory or assumption of no perfect collinearity is satisfied. To test no perfect collinearity R and R studio is used.
loggdpp  educ  un  urbanp  labpar  loglabf  
loggdpp  1.0000  
educ  0.7612  1.0000  
un  0.1408  0.1436  1.0000  
urbanp  0.5324  0.4997  0.1557  1.0000  
labpar  0.6015  0.6321  0.3835  0.2613  1.0000  
loglabf  0.0544  0.0469  0.0588  0.4299  0.1631  1.0000 
4) Zero Conditional Mean:
Zero conditional mean asses that suppose all the values of independent variables are given in that case expected value of which is the error term will be zero. The residual plot can be used to test zero conditional mean.
5) Homoskedasticity:
The assumption of homoskedasticity basically is that the variances of different samples are indifferent. So, the variance of will also be the same for all.
Result:
Model 1:
In the first model a simple linear regression model is conducted. The hypothesized model is:
log(gdpp)= β_{0}+ _{1}log(educ)+
The estimated equation is:
log(gdpp) = 4.298 + 0.0133educ
Number of observations  51  
F (1, 49)  67.49  
Prob > F  0.00  
R squared  0.5801  
Adjusted R squared  0.5709  
Root MSE  .07175  


Source  SS  df  MS  
Model  .347606012  1  .347606012  
Residual  .252285384  49  .005148681  
Total  .599891396  50  .011997828  
loggdpp  Coefficient  Standard error  t value  P > t  [95% Conf. Interval]  
educ  .013254  .0015381  8.22  0.00  .0095469  .0157286 
_cons  4.297786  .051176  84.52  0.00  4.222752  4.428436 
The adjusted Rsquared value of this simple regression model is 0.5801. This means that the association that exist among the twovariable log(gdpp) and log(educ) is moderate. In the table we can see that the coefficient of education is positive. It means that log GDP per capita increases when there is an increase in education. But this hypothesized relationship is not enough or adequate to conclude.
Model 2:
The model 2 is multi regression model which includes all the controlled variables that were omitted in the first simple regression model. The form of the equation is
log(𝑔𝑑𝑝𝑝) = 𝛽_{0} + 𝛽_{1}𝑒𝑑𝑢𝑐 + 𝛽_{2}𝑢𝑛 + 𝛽_{3}𝑢𝑟𝑏𝑎𝑛𝑝 + 𝛽_{4}𝑙𝑎𝑏𝑝𝑎𝑟 – 𝛽_{5}log (𝑙𝑎𝑏𝑓)+
And the equation that is estimated is:
log(𝑔𝑑𝑝𝑝) = 3.7914 + 0.0081𝑒𝑑𝑢𝑐 + 0.0517𝑢𝑛 + 0.0014𝑢𝑟𝑏𝑎𝑛𝑝 + 0.0094𝑙𝑎𝑏𝑝𝑎− 0.0412log (𝑙𝑎𝑏𝑓)
Number of obs  51 
F (5, 45)  25.7 
Probability > F  0.00 
R squared  0.7398 
Adjusted R squared  0.7118 
Root MSE  .0588 
Source  SS  Df  MS 
Model  .444307301  5  .08886146 
Residual  .155584095  45  .003457424 
Total  .599891396  50  .011997828 
loggdpp  Coefficient  Standard error  t value  P > t  [95% Conf. Interval]  
educ  .0081546  .0018195  4.59  0.000  .0046845  .012014 
un  .051692  .0117609  3.63  0.001  .0190303  .0664058 
urbanp  .0014537  .000757  1.92  0.061  .000071  .0029784 
labpar  .0093673  .0031247  3.00  0.004  .0030738  .0156607 
loglabf  .0411538  .022121  1.43  0.160  .0761332  .0129749 
_cons  3.79138  .2552162  14.92  0.000  3.292792  4.320856 
This model adds more variables that has connection with both variables GDP per capita and education. After adding more variables, the value of R squared has increased to 0.7398. This increase is significant. This is an indication that new model is much better in representing the connection than previous model. Except log(labf) all the coefficients found in this regression are positive.
Model 3:
log(𝑔𝑑𝑝𝑝) = 𝛽_{0} + 𝛽_{1}𝑒𝑑𝑢𝑐 + 𝛽_{2}𝑢𝑛 + 𝛽_{4}𝑙𝑎𝑏𝑝𝑎𝑟+
Number of observations  51 
F (3, 47)  39.81 
Prob > F  0.00 
R squared  0.7201 
Adj Rsquared  0.6996 
Root MSE  .06004 
Source  SS  Df  MS 
Model  .430481883  3  .143493961 
Residual  .169409513  47  .003604458 
Total  .599891396  50  .011997828 
loggdpp  Coefficient  Standard Error  T value  P > t  [95% Conf. Interval]  
educ  .0095253  .0016768  5.59  0.000  .006152  .0128986 
un  .0497983  .0114319  4.36  0.000  .0268004  .0727963 
labpar  .0107416  .0030555  3.52  0.001  .0045946  .0168885 
_cons  3.564069  .186281  19.13  0.000  3.18932  3.938818 
The estimated
This model removes the two variables urbanp and log(labf). These two variables are relatively insignificant comparing to the other variables. This is the reason these two variables are omitted in the third new model. The R square value of this model is also much lower. The value is 0.7201 Heteroskedasticity is not detected in the data set used for this paper.
Conclusion:
This paper studied on the fact what relationship GDP per capita has with level of education. And the paper detects a positive connection between the two variables which means that a state which has more population of bachelor’s degree or higher than that has higher GDP per capita. The coefficient of the variable education here is positive. Other factors or variables that effect the GDP per capita of a state are countless and very hard to mode.
In conclusion, the impact of education on the economy is positive, whether in terms of innovation and research and development, or labour productivity. It suggests that a person’s standard of living depends on how higher education affects overall economic outcomes. This paper aims to represent this existing correlation among GDP per capita and higher levels of education.
Reference:
1) The Causal Impact of Education on Economic Growth. (2009). Retrieved 8 May 2022, from https://scholar.harvard.edu/files/aghion/files/causal_impact_of_education.pdf
2) Odit, M. P., Dookhan, K., & Fauzel, S. (2010). ‘The impact of education on economic growth: The case of mauritius. International Business & Economics Research Journal’
3) Mamit Deme and Ali M. A Mahmud (27 July, 2020) ‘Effect of quantity and quality of education on per capita realGDP growth: evidence from low and middleincome African countries’
4) Elizabeth Appiah ‘The Effect of Education Expenditure on Per Capita GDP in Developing Countries’
https://ccsenet.org/journal/index.php/ijef/article/view/69541
5) Łukasz Goczek and Ewa Witkowska ‘How Does Education Quality Affect Economic Growth?’
6) Laura MarquezRamos ‘Education and economic growth: an empirical analysis of nonlinearities’
https://www.emerald.com/insight/content/doi/10.1108/AEA0620190005/full/html
Note: This article was submitted to Professor Farzana Munshi of Brac University of Bangladesh as a part of the writer’s course study.
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Ashhab Khaled Lazim , an young economist, has done his bachelor degree in Economics from Brac University, covering business, finance, and the economy. He has logged thousands of hours interviewing experts, analyzing data, and writing articles to help readers understand economic forces. Currently he has been working as a freelance contributor to the InsuranceNews.