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Jean-Pierre

An Econometric Assessment of Socioeconomic Indicators in

different countries’ Poverty Dynamics!

Jimmy Jean-Pierre

Business Administration, Winthrop University, International Student

This study examines a country’s poverty rate utilizing panel data analysis of World Bank Data

Development Indicators' social variables. The study focuses on energy, education, government

success, and the labor market to see what factors affect poverty in the country. The research will

employ modern economic modeling to identify statistically significant relationships between

poverty rates and these characteristics. The study found that education, especially secondary and

higher education, reduces poverty. We discuss how education spending influences poverty rates,

how good government benefits business, and more. We also examine how joblessness and other

job market characteristics like city population affect poverty levels in different countries. This

study seeks to explain these links by examining the country’s poverty types. The findings

demonstrate that government and education must be targeted to combat poverty. The study aims

to fund socioeconomic growth projects and investigate poverty. Policymakers and voters are

their target. This study will contribute to the discussion on how to eliminate poverty by

suggesting ways to improve people’s lives and boost long-term economic growth

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Introduction and Background of the Issue

This paper will show that poverty is a complex phenomenon that negatively impacts the

global economy and the wellbeing of millions of people. Many debates have been caused by

literature relating to some determinants of poverty including education or GDOP (government

quality and labor market), or some of its causes or impacts (World Bank, 2020).

This paper seeks to establish a grate between poverty levels and several socioeconomic

factors such as education, government effectiveness, unemployment, and urbanization in

different nations. Hence, this analysis provides a literature gap by estimating the combined

influence of these variables on poverty for a panel of countries with the latest data.

Therefore, the findings from this study are expected to help in filling this gap through

improving the understanding of the factors since the different dimensions of socioeconomic

development can be improved with a view of reducing world poverty.

It is hypothesized that

1. with the proper improvements in education, especially at the secondary and the

tertiary levels, poverty rates will reduce.

2. Poverty will decrease through improvement of productivity through accessing reliable

energy.

The analysis also showed that:

3. effective governances will enhance poverty reduction.

4. predictions arising from the demographics of employment and population are that

increased unemployment coupled with high urbanization levels leads to high poverty levels.

Data and Empirical Methodology

Description of Variables

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In this case, the dependent variable and the independent variables are business relevant

economic measures which have theoretical ground to be included in the regression model.

Dependent Variable: Poverty Rate (%): The target for this conceptual model is on the

determinants of the percentage point increase in poverty level, with the level of analysis being

countries. It is an important indicator as to the state of the economy and or people of a particular

country. Eradicating poverty is perhaps one of the major policy priorities in the contemporary

period for both national governments and global organisations, understanding the determinants

of poverty thus is a very important priority (Cavallo et al., 2020).

Independent Variables: Access to Electricity (%): This variable measure represents the

proportion of the population of a given country that has access to electricity. The availability of

electricity is one of the foremost facilitators of economic development and the general

improvement of the human quality of life. Education can enhance electrical usage which can

again have a positive impact on educational accomplishment, boost industrial growth, and

enhance standard of living. The sign expected for this coefficient is negative; this is, as more

people gain access to electricity the poverty rates should reduce by -15.47% the regression model

demonstrates.

Educational Attainment (%): This estimates the proportion of the population with

education to a specified standard. The research shows that education is one of the most effective

ways in which individuals can break the poverty cycle and be empowered with certain skills that

can enable him or her to get a better job and earn better wages. The expected coefficient is

negative, indicating that, as education level rises, poverty levels should decline by -13.80%.

Government Effectiveness: This is an index of the standards of administration in a nation.

Where there is higher government effectiveness, this leads to higher public service, better rule of

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law and reduced corruption, which all influence the economic performance. The coefficient sign

is expected to be negative; this means that the efficiency of government operation impedes

poverty level.

Unemployment (%): This variable indicates the proportion of coefficient of the labor

force that contains the unemployed or unemployment rate. High unemployment usually goes

hand in hand with high poverty because those without jobs forget their ability to purchase

necessities. Thus, as expected, the sign of this coefficient will be positive, because high rates of

unemployment should result in high levels of poverty.

Description of the results of descriptive statistics.

Mean Standard deviation Maximum Minimum Poverty rate (%) 24.73 14.65 82.30 0.00 Access to electricity 81.64 14.27 100.00 0.53 Educational attainment (%) 25.14 15.39 79.02 0.00 Government effectiveness -0.03 1.00 25.14 15.39 Unemployment (%) 8.17 6.06 38.80 0.10

Descritive statistics

Poverty Rate (%): Mean (24.73%): On average, poverty rate is at about 24.73%) thus

there is still much that can be done to improve this figure. Standard Deviation (14.65): Thus, the

variation is highly appreciable that some of the countries fall in the 0% band while others have as

high as 82.30% poverty rate.

Access to Electricity (%): Mean (81.64%): Electricity access is relatively high all over

the world with global average electricity access of 81.64%. Standard Deviation (14.27): Some of

the countries nearly have achieved access while some are lagging far behind with the access

level of only 0.53%.

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Educational Attainment (%): Mean (25.14%): Overall, proportions of population with

certain educational level are at 25.14%. Standard Deviation (15.39): Variation of 0% to 79.02

means a highly varying education level.

Government Effectiveness: Mean (-0.03): From the above information, one is left with

the impression of a lot of contradictories. Usually, anything higher will be more desirable/ better

in the performance of governance in relation to poverty and the economy.

Unemployment (%): Mean (8.17%): The average employment rate of unemployed people

is 17.08%. Standard Deviation (6.06): There are countries with zero unemployment while others

are as high as 38.80% unemployment rate as seen in this simple graph.

0 10 20 30 40 50 60 70 80 90 0

5

10

15

20

25

30

35

f(x) = 0.0523229928794255 x + 5.92526397670121

Unemployment vs Poverty rate

Poverty rate

U ne

m pl

oy m

en t

From these figures we get to see how there are gaps in poverty, education, electricity, and

unemployment rates between countries, which are fundamental to poverty and economic growth.

Data Sources

The information used in this regression equation was obtained from various authentic

sources. The following socio-economic indicators may have been obtained from the world bank,

UNDP, or other international organisations or from country specific national bureau of statistics

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and other authentic sources: Poverty, Electricity Consumption per capita, Education Facility,

Effectiveness of Government, and Unemployment. These databases provide a quick, up-to-date

snapshot of the major variables of social, economic, and industrial development for various

countries. The data was collected for 217 observations, most probably from different countries

and year over time.

Regression Model and Equation

The present study uses an Ordinary Least Squares (OLS) regression technique to model

poverty rate as the dependent variable while the independent variables include availability of

electricity, education standards, government efficiency, and unemployment rate. The output of

the regression model is presented.

0 10 20 30 40 50 60 70 80 90

-2.5 -2

-1.5 -1

-0.5 0

0.5 1

1.5 2

2.5

f(x) = − 0.0291027134020297 x + 0.604341481254337

Government effectiveness vs poverty rates

Poverty rates

G ov

er nm

en t e

ff ec

tiv en

es s

This model identifies the value of the coefficients associated with minimum squared

residuals of the dependent variable.

The general form of the regression equation is:

Poverty Rate = β0 + β1(Electricity Access) + β2(Education Attainment)

+β3(Government Effectiveness) + β4(Unemployment)+ϵ

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Where:

• β0 is the intercept (constant),

• β1, β2, β3, β4 are the coefficients for each independent variable, and

• ϵ error term.

Outcomes and Analysis of Slopes

Intercept (44.90): This value is the poverty rate forecast when all independent variables

are set at zero, is the intercept. In this regard, the poverty rate of 44.90 has been forecasted

provided electricity access, education attainment, government effectiveness and unemployment

where at their base line. They attribute this high intercept maybe to the countries or regions,

which have little development.

Electricity Access (-0.34): Periodic access to electricity was statistically significant, and

the coefficient estimate was negative (-12.98); t = -8.65, p < 0.001. This means that for every 1%

improvement in the proportion of Africans with access to electricity, poverty reduced roughly by

0.34%. This result provides evidence in support of the hypothesis that access to electricity has an

inverse relationship with poverty (Bin, 2021).

Educational Attainment (0.0039): Interestingly, the coefficient for education attainment is

found to be positive but insignificant (t = 0.05), (p = 957). This implies that, in this model,

education has no impact on poverty rates quite a lot. This may stem from two reasons, including

data constraints and low quality of compiled data, or the use of incorrect variables to quantify

educational achievement.

Government Effectiveness (-5.22): The coefficient for government effectiveness is, as

expected, negative and statistically significant (t = -3.99), (p < 0.001). This means that if

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government effectiveness increases by one unit, poverty headcount reduces by 5.22 percentage

points. This result, therefore, reinforces the role of governance in poverty elimination processes.

Unemployment (0.49): The coefficient for unemployment is, however, positive and

statistically significant t = 3.14, (p < 0.01). This means that in as much as the unemployment rate

moves up by 1%, poverty rate also increases by 0.49%. This is in harmony with the economic

theory hence high employment will lead to low level of poverty because of high levels of income

in the households as seen in this figure.

0 10 20 30 40 50 60 70 80 90 0

5

10

15

20

25

30

35

f(x) = 0.0523229928794255 x + 5.92526397670121

Unemployment vs Poverty rate

Poverty rate

U ne

m pl

oy m

en t

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Work Cited

Cavallo, E. A., Powell, A., & Serebrisky, T. (2020). From Structures to Services: The path to

better infrastructure in Latin America and the Caribbean. Inter-American Development

Bank.

Washington, S., Karlaftis, M. G., Mannering, F., & Anastasopoulos, P. (2020). Statistical and

econometric methods for transportation data analysis. Chapman and Hall/CRC.

World Bank. (2020). Poverty and shared prosperity 2020: Reversals of fortune. The World

Bank.