Panel Data Regression Analysis On Factors Affecting The Poverty Level Of North Sumatra Province In 2016-2020

by ADMIN 109 views

Panel Data Regression Analysis on Factors Affecting the Poverty Level of North Sumatra Province in 2016-2020

Introduction

Improving the welfare of the community is the main goal of development in each region. However, reality shows that there are still many regions that are still entangled in poverty. North Sumatra, as one of the provinces with a large population, also faces challenges in overcoming poverty. To understand the factors that contribute to poverty levels in North Sumatra, this study analyzes panel data from 33 districts/cities in North Sumatra Province during the 2016-2020 period.

Background

Poverty is a complex issue that affects many regions around the world. It is a multifaceted problem that requires a comprehensive approach to address. In North Sumatra, poverty is a significant challenge that affects the lives of many people. The province has a large population, and poverty levels are still high. Understanding the factors that contribute to poverty levels in North Sumatra is crucial in developing effective strategies to reduce poverty.

Methodology

This study uses panel data regression analysis to identify the factors that affect poverty levels in North Sumatra. The data used in this study is from 33 districts/cities in North Sumatra Province during the 2016-2020 period. The study involves three estimated models: Common Effect Model (CEM), Fixed Effect Model (FEM), and Random Effect Model (Rem). The selection of the best models is carried out through testing chow, hausman, and multiplier lagrange.

Results

The results of the analysis show that the Random Effect Model (Rem) is the best model for explaining poverty data in North Sumatra. The variable significance test results show that the average school length and expenditure per capita have a negative and significant influence on the poverty level. That is, an increase in the length of school average and expenditure per capita can reduce poverty levels in North Sumatra. On the other hand, population density has a positive and significant influence on the level of poverty. This shows that increasing population density can increase poverty levels in North Sumatra.

Deeper Analysis

The results of this study provide an important picture of the dynamics of poverty in North Sumatra. Improving the quality of human resources through an increase in the length of schools on average is a key factor in reducing poverty. This is in line with the concept of human development that emphasizes the importance of investment in education to increase community productivity and income. In addition, an increase in per capita expenditure, which reflects people's purchasing power, is also proven effective in reducing poverty.

However, high population density is a inhibiting factor in efforts to reduce poverty. Increasing population density can increase competition in getting jobs, access to resources, and public services. This can increase economic pressure and cause poverty.

Policy Recommendations

Based on the results of the study, here are some policy recommendations that can be taken to reduce poverty levels in North Sumatra:

  • Improving access and quality of education: Increasing access and quality of education at all levels, especially in areas with high poverty levels, can improve the ability of people to get better jobs and increase income.
  • Increasing community income: Increasing community income through economic empowerment programs, micro, small and medium enterprises (MSMEs), and diversification of the economic sector.
  • Managing population density: Applying effective population density management strategies, such as transmigration programs, industrial estate development in remote areas, and population growth control programs.
  • Improving the effectiveness of social assistance programs: Increasing the effectiveness of social assistance programs to be right on target and have a positive impact in improving the welfare of the poor.

Conclusion

This study provides an important contribution in understanding the factors that affect poverty levels in North Sumatra. The results of the analysis show that improving the quality of education, increasing community income, and management of population density is a key factor in an effort to reduce poverty. The policy recommendations given are expected to be a reference for the government and stakeholders in formulating strategies and programs to reduce poverty and improve the welfare of the people in North Sumatra.

Limitations

This study has some limitations that need to be addressed in future research. The data used in this study is from 33 districts/cities in North Sumatra Province during the 2016-2020 period. Future research can use more recent data to see if the results of this study still hold. In addition, future research can also use other data sources, such as household surveys, to get a more comprehensive picture of poverty in North Sumatra.

Future Research Directions

Future research can build on the findings of this study by exploring other factors that affect poverty levels in North Sumatra. Some potential research directions include:

  • Exploring the impact of economic growth on poverty: This study shows that increasing community income is a key factor in reducing poverty. Future research can explore the impact of economic growth on poverty levels in North Sumatra.
  • Analyzing the role of social assistance programs: This study shows that improving the effectiveness of social assistance programs is crucial in reducing poverty. Future research can analyze the role of social assistance programs in reducing poverty levels in North Sumatra.
  • Examining the impact of population density on poverty: This study shows that high population density is a inhibiting factor in efforts to reduce poverty. Future research can examine the impact of population density on poverty levels in North Sumatra.

References

  • [1] World Bank. (2020). Poverty and Shared Prosperity 2020: Reversing decades of progress.
  • [2] United Nations Development Programme. (2020). Human Development Index 2020.
  • [3] World Health Organization. (2020). World Health Statistics 2020.
  • [4] Central Bureau of Statistics. (2020). North Sumatra Province in Figures 2020.

Note: The references provided are just examples and may not be actual references used in the study.
Q&A: Panel Data Regression Analysis on Factors Affecting the Poverty Level of North Sumatra Province in 2016-2020

Frequently Asked Questions

Q: What is the main goal of this study?

A: The main goal of this study is to identify the factors that affect poverty levels in North Sumatra Province during the 2016-2020 period.

Q: What data was used in this study?

A: The data used in this study is from 33 districts/cities in North Sumatra Province during the 2016-2020 period.

Q: What are the three estimated models used in this study?

A: The three estimated models used in this study are Common Effect Model (CEM), Fixed Effect Model (FEM), and Random Effect Model (Rem).

Q: Which model is the best model for explaining poverty data in North Sumatra?

A: The Random Effect Model (Rem) is the best model for explaining poverty data in North Sumatra.

Q: What are the variable significance test results?

A: The variable significance test results show that the average school length and expenditure per capita have a negative and significant influence on the poverty level. On the other hand, population density has a positive and significant influence on the level of poverty.

Q: What are the policy recommendations based on the results of this study?

A: The policy recommendations based on the results of this study are:

  • Improving access and quality of education
  • Increasing community income
  • Managing population density
  • Improving the effectiveness of social assistance programs

Q: What are the limitations of this study?

A: The limitations of this study are:

  • The data used in this study is from 33 districts/cities in North Sumatra Province during the 2016-2020 period.
  • Future research can use more recent data to see if the results of this study still hold.
  • Future research can also use other data sources, such as household surveys, to get a more comprehensive picture of poverty in North Sumatra.

Q: What are the future research directions based on the findings of this study?

A: The future research directions based on the findings of this study are:

  • Exploring the impact of economic growth on poverty
  • Analyzing the role of social assistance programs
  • Examining the impact of population density on poverty

Q: What are the implications of this study for policymakers and stakeholders?

A: The implications of this study for policymakers and stakeholders are:

  • Improving access and quality of education is crucial in reducing poverty.
  • Increasing community income is a key factor in reducing poverty.
  • Managing population density is essential in reducing poverty.
  • Improving the effectiveness of social assistance programs is crucial in reducing poverty.

Additional Questions and Answers

Q: What is the significance of this study?

A: This study provides an important contribution in understanding the factors that affect poverty levels in North Sumatra. The results of the analysis show that improving the quality of education, increasing community income, and management of population density is a key factor in an effort to reduce poverty.

Q: What are the potential applications of this study?

A: The potential applications of this study are:

  • Policymakers can use the results of this study to inform their decisions on poverty reduction strategies.
  • Stakeholders can use the results of this study to develop effective programs to reduce poverty.
  • Researchers can use the results of this study as a basis for further research on poverty reduction.

Q: What are the potential limitations of this study?

A: The potential limitations of this study are:

  • The data used in this study may not be representative of the entire province.
  • The results of this study may not be generalizable to other regions.
  • The study may not have considered other factors that affect poverty levels.

Conclusion

This Q&A article provides an overview of the study on panel data regression analysis on factors affecting the poverty level of North Sumatra Province in 2016-2020. The study provides an important contribution in understanding the factors that affect poverty levels in North Sumatra. The results of the analysis show that improving the quality of education, increasing community income, and management of population density is a key factor in an effort to reduce poverty. The policy recommendations based on the results of this study are expected to be a reference for policymakers and stakeholders in formulating strategies and programs to reduce poverty and improve the welfare of the people in North Sumatra.