Dataset for the Project¶
For my project, I get all of my data from World Development Indicators databank from the World Bank website. The database provides extensive data for hundreds of variables on development indicators for all the countries. However, we face the problem of lots of missing data, especially for the datapoints on developming countries. It is the common plight of field of development economics. Here, I first briefly discuss some of the most important variables that are important on the study first and expalin what I am planning to do with the missing data. All the definitions of the variables are the official World bank definitions.
Variables
Gini Index: My primary dependent variable of interest is the GINI Index, the most popular measure of income inequality. The Gini index measures the extent to which the distribution of income or consumption among individuals or households within an economy deviates from a perfectly equal distribution. A Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality (Definition from the World bank).
GDP per capita: GDP per capita is the sum of gross value added by all resident producers in the economy plus any product taxes (less subsidies) not included in the valuation of output, divided by mid-year population.
Inflation: Inflation is the measure of annual percentage change in the cost to the average consumer of acquiring a basket of goods and services in a given year.
Unemployment rate: Unemployment refers to the share of the labor force that is without work but available for and seeking employment in the given year.
Life Expectancy: Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life. This is the proxy for health in our model as it is the most widely available data worldwide.
Population Growth Rate: Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage.
Income Level: Low Income if GNI per capita is greater than USD 1085, Lower-Middle Income if GNI per capita is gbetween USD 1086 and USD 4225, Upper-Middle Income if GNI per capita is between USD 4225 and USD 13205 High Income if GNI per capita is greater than USD 13205. This is the classification used by the World Bank.
The dataset I am using has a panel data on 73 countries for the time period 1991-2019. The unit of observation is a country year. As mentioned earlier, the daset suffers heavily from missing data. gini Idnex is the variable with moss missing data. I filtered the 73 countries that have the most datapoints available. Some countries did not have a single datapoint for any of the years. I drop all the country years that have missing value for any of the major variables listed above except for Gini Index. For Gini Index, i use the linear extrapolation method to generate missing values. This is appropriate for two reasosn. First, it suffers the most from missing values and I want to make the most out of the availabel data. Second, the value of Gini Index doesn't fluctuate heavily in successive years. This means the generated data is meaningful in our analysis.
