Rural poverty remains a critical economic problem in many developing countries. This paper conducts an econometric analysis of data from the 2006 Vietnam Household Expenditure Survey to assess the impact of selected socio-economic factors on the income of Vietnamese households. The data that is used is cross sectional in that it is widely discrete data (such as per capita income) relating to one period or without respect to variance due to time.
Jehovaness Aikaeli, in his research report, “Determinants of Rural Income in Tanzania:An Empirical Approach”, carries out a study from the 2005 Tanzania Rural Investment Climate Survey to assess the impact of selected socio-economic and geographic factors on the income of rural households and communities. What he found out was that improvement in four variables: the level of education of the household head, size of household labor force, acreage of land use and ownership of a non-farm rural enterprise had a significant positive impact on the incomes of rural households (Aikaeli vi). I will use his paper as a guide to my research paper; however, I will not be using household labor force, acreage of land use and ownership of non-farm rural areas as I do not have the data for it. Steve Onyeiwu, in his paper, “Determinants of Income Poverty in Rural Africa: Empirical Evidence from Kenya and Nigeria”, uses panel and cross- sectional regressions, with socio-economic and demographic survey data collected from rural communities of Kenya and Nigeria to explore the determinants of income and poverty in rural Africa. The determinants he looks at are household size, age, female proportion, education, land ownership and non-durable assets. He found out that income was lower in households run by females (Onyeiwu 2). I will use this paper as a guide to my research paper but again I would not be using land ownership and non-durable assets as I do not have data on it. Education plays an important role in determining income. What I would like to examine in this paper is the casual affect of father and mother’s education on household income. However, education is not the only factor that affects household income. There are many other factors that affect household income such as father’s age, mother’s age, total number of kids at home, if the child works, value of durable assets, if the household is from an urban area or rural and if the household belongs to an ethnic minority or not. To see the casual affect of father and mother’s education on household income, I would need to control for all the other variables so they do not get into the error term and cause omitted variables bias. Explaining the Method and Results
I am going to explain some things that are important as I go on and step by step procedure of implementing with the data. Dependent Variable: the response that is measured, variable to be explained in a model. In this case, household income per capita (pcexp2rl). But throughout the paper, I will refer to it as household income. Independent Variables: variables that are used to explain variation in the dependent variable. In my case: father_edu, mother_edu, father_age, mother_age, totkidshome, kid_work, durbus_2, urban06 and ethn. Multiple Linear Regression (MLR) Model:
To examine the relationship between household income and all the other different variables a multiple linear regression model can be used. Multiple linear regression takes into account the relationship between two or more explanatory variables and a response variable by fitting a linear equation to observed data. y = β0+ β1x1 + β2x2+ …+ βkxk+ u where u is the error term, which includes all other factors not mentioned in the MLR that affect y. General to Simple Model:
I used the strategy general to simple model because if I start with a model that is too simple then there will be omitted variables bias so I chose to start with a general model including many variables. The if I find any irrelevant...
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