2015•AFRICAN HUMAN MOBILITY REVIEWOpen access

The Impact of Remittance on Poverty: Evidence from the South African National Income Dynamics Study (NIDS)

Seyfe Wurku, Joyce Marangu

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Abstract

The study uses two approaches to assess the impact of remittance on poverty in South Africa. The first approach compares the level of poverty between remittance receiving households and non-receiving households using the FGT index. The second approach uses the logit regression model to estimate how remittance determines the probability of falling into a state of poverty. The findings show that remittance non-receiving households have a higher head count ratio compared to remittance receiving households. The probability of remittance non-receiving households being in a state of poverty is also higher than remittance receiving households. Whereas the poverty gap is higher for remittance receiving households compared to remittance non-receiving households, both groups of households have the same poverty severity level.

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What this paper is about

The study uses two approaches to assess the impact of remittance on poverty in South Africa. The first approach compares the level of poverty between remittance receiving households and non-receiving households using the FGT index. The second approach uses the logit regression model to estimate how remittance determines the probability of falling into a state of poverty. The findings show that remittance non-receiving households have a higher head count ratio compared to remittance receiving households. The probability of remittance non-receiving households being in a state of poverty is also higher than remittance receiving households. Whereas the poverty gap is higher for remittance receiving households compared to remittance non-receiving households, both groups of households have the same poverty severity level.

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Available abstract

The study uses two approaches to assess the impact of remittance on poverty in South Africa. The first approach compares the level of poverty between remittance receiving households and non-receiving households using the FGT index. The second approach uses the logit regression model to estimate how remittance determines the probability of falling into a state of poverty. The findings show that remittance non-receiving households have a higher head count ratio compared to remittance receiving households. The probability of remittance non-receiving households being in a state of poverty is also higher than remittance receiving households. Whereas the poverty gap is higher for remittance receiving households compared to remittance non-receiving households, both groups of households have the same poverty severity level.

Key concepts: Remittance, Poverty, Logistic regression, Economics, Demographic economics, Socioeconomics, Economic growth, Statistics

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