In this research paper I’m going to provide a comparative analyzing and effectiveness assessment of various models that can be applied to measure poverty and inequality. First of all, the methods and model that will be covered are used to measure relative poverty. While absolute poverty is measured with the help of poverty line which is the minimum amount of income to acquire a basket of basic needs, the methods to assess relative poverty are more complex and sophisticated.
Functional and size distribution of income are often used to measure poverty. Functional distribution (sometimes referred to as factor share distribution of income) indicates distribution of income amongst factors of production without regard to the ownership of the factors. Economists point out three factors of production – land, labour and capital. Size distribution shows the proportions of income by aggregate proportions of households. These indicators are inter-connected, since the size of distribution relies on who owns the factors of production and what is the importance of each factor in production.
The next method is the Gini Ratio. This indicator is used to measure inequality rather than poverty. This indicator is measured by dividing the area between the Lorenz Curve and 45° line with the total area under the 45° line. The Gini Ratio lies within the range from 0 to 1, where 0 means perfect equality and 1 stands for perfect inequality. Therefore, the higher the value of the coefficient, the higher the inequality of income distribution; the lower it is, the more equitable the distribution of income. Yet this method of measuring inequality has come in for criticism. It can happen that different curves describing different distribution of income have identical Gini Ratio. This method also has problems with describing the insensitivity of gaps and changes in income distribution.
However, the strong side of this method is that it can be used to measure how the distribution of income varies between sectors of the population (e.g. urban and rural). It can also be useful while measuring the change in the distribution of income over time. The formula is as follows:
The Kuznets’s inverted-U hypothesis describes the relations of income per capita and inequality of income distribution. As per capita incomes increase, the distribution of income worsens at the beginning and later improves.
The headcount index is the percentage of the population living below the poverty line considered tolerable by the authorities, i.e. every state establishes this ratio itself. The subjectivity of this method is its major weakness, however, this index can be used to investigate government’s policies on poverty rather than poverty itself.
Poverty gap is defined as the average shortfall of the poor with respect to the poverty line, multiplied by the headcount ratio. The measurement of both poverty line and headcount index were discussed above.
Yet poverty is not merely the result of economic considerations. Poverty is as well related to political, social, security, cultural, and human rights factors. It’s clear that non-economic factors should be also assessed while measuring poverty if the researchers want to see the bigger picture of the situation in the area of investigation. UNDP suggested an alternative approach to measuring poverty. UNDP completes the Human Development Reports, and one of the indexes it uses is Human Poverty Index (HPI). This index describes deprivations of living standard using the variables such as the percent of people expected to die before age 40, adult illiteracy rate, percent of people with no access to healthcare and clean water, and percent of underweight children five years of age.
In my humble opinion, this is the most comprehensive approach to measuring poverty, inequality, and deprivation as social phenomena.
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