Infrastructure Integration, Poverty, and Inequality in Developing Countries: A Case Study of BRI Transport in the Lao PDR
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This study applied the macro–micro simulation model (i.e., what-if analysis) to investigate the impact of transport related to the Belt and Road Initiative (BRI) on poverty and income inequality in Laos. We selected Laos as a case study of a developing country. We used the standard GTAP model with the GTAP database (version 10) for the macrosimulation, whereas we used the household model with the latest Lao household data from 2019 for the microsimulation. Our findings revealed that the output of the Lao economy was anticipated to increase by up to 0.3%, while the poverty rate was anticipated to decline from 17.0% to 15.7%. However, there would be winners and losers in industries and groups of households in different areas. In particular, rich households with a comparative socioeconomic advantage, such as in education, engagement in nonfarm business, and infrastructure access, would mostly gain benefits; consequently, this would lead to higher inequality in Laos. Therefore, the inequality index (i.e., the Gini coefficient) would increase from 41.2 to 60.1. After a simulation of BRI transport, we also found that some nonpoor households, which are mainly associated with farm activities and lower educational levels, would fall into poverty.
Abstract
This study applied the macro–micro simulation model (i.e., what-if analysis) to investigate the impact of transport related to the Belt and Road Initiative (BRI) on poverty and income inequality in Laos. We selected Laos as a case study of a developing country. We used the standard GTAP model with the GTAP database (version 10) for the macrosimulation, whereas we used the household model with the latest Lao household data from 2019 for the microsimulation. Our findings revealed that the output of the Lao economy was anticipated to increase by up to 0.3%, while the poverty rate was anticipated to decline from 17.0% to 15.7%. However, there would be winners and losers in industries and groups of households in different areas. In particular, rich households with a comparative socioeconomic advantage, such as in education, engagement in nonfarm business, and infrastructure access, would mostly gain benefits; consequently, this would lead to higher inequality in Laos. Therefore, the inequality index (i.e., the Gini coefficient) would increase from 41.2 to 60.1. After a simulation of BRI transport, we also found that some nonpoor households, which are mainly associated with farm activities and lower educational levels, would fall into poverty.
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