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Abstract

The main objective of this research, which is entitled “The Causal Factors Affecting the

Income Distribution of Middle-Income Countries and Their Relevance to the Good Governance

Index: Creating Ways of Bridging the Inequality Gap in Thailand under International Evidence”

was to analyze factors affecting income inequality of populations in middle-income countries
across the world. In this research, two income inequality indicators were involved, which

consisted of the Gini coefficient and income share held by the lowest 10% of the population.
The data for the analysis in this research consisted of secondary data from 101 middle-income

countries between 2 0 0 0 and 2017. Other objectives of this research were to study the
relationship between good governance and income inequality and to develop policy

recommendations on mitigating income inequalities in middle-income countries.

Regarding the Gini coefficient, a balance panel data analysis was carried out based on

five groups of predictive variables. The first group was related to opening up the country to

business as a result of globalization – net foreign direct investment (BoP, current US$) and the

export of goods and services (% of GDP). The second group involved economic growth-related

variables – GNI, PPP (current international $) and general government final consumption

expenditure (% of GDP). The third group was economically-related variables – Inflation, GDP

deflator: linked series (annual %) and total unemployment (% of total labor force). The fourth

group consisted of demographic variables – urban population (% of total population) and

employment in industry (% of total employment). The fifth group involved good governance-

related variables – voice and accountability and the control of corruption. The test

demonstrated that all these variables were stable at Level I (1 ) , and they were appropriate

for the random effect method. The findings were in line with assumptions of this research.

That is, employment of the working-age population (b = 0.437) and the GNI, PPP (current
international $) (b = 9.130) had influence on the Gini coefficient at the statistically significant

level of .05.

As for the income share held by the lowest 10% of the population, the balance panel

data analysis was conducted based on based on three groups of predictive variables. The first