Top PDF Income inequality and poverty in Malaysia.

Income inequality and poverty in Malaysia.

Income inequality and poverty in Malaysia.

During the 1970s, the government only published figures on changes in the mean incomes for different ethnic g r o u p s , as it was concerned about income imbalances between race and strata. It was not until 1981 in the Fourth Malaysia Plan (4MP) (Malaysia 1981) that income distribution was even mentioned. The 4MP claimed that income inequality was higher in the rural than urban areas, but in both cases, inequality was decreasing over time. The Mid-Term Review of the Fourth Malaysia Plan (MTR4MP) (Malaysia 1983) stated 1970-1979 saw the narrowing of the gap between the poor and n o n ­ poor incomes and that overall income inequality had improved. For the most part, the government's target has been to reduce the differences in income between the different groups. The official figures reflect this by only publishing mean income disparity ratios, with the exception of the income share data of 1984 and 1987 in the MTR5MP (Malaysia 1989: 38). The MTR5MP published the income shares of the top 20 percent , middle 40 percent and bottom 40 percent of households for Peninsular Malaysia, Sabah and Sarawak. The data showed an improvement in income inequality with a decrease in the income share of the top 20 percent accompanied by an increase in the income share of the bottom 40 percent. This was found for the case of overall inequality as well as rural and urban areas in Peninsular Malaysia, Sabah and Sarawak.
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Determinant Analysis Of Household Income Inequality And Poverty In Indonesia

Determinant Analysis Of Household Income Inequality And Poverty In Indonesia

would not expect the same results because people have different preferences and values. But we can be confident that these results arise because of differences in people's choices rather than barriers to their ability to use their choices [12]. Inequality is another issue that is often associated with poverty. The close relationship between inequality and poverty is that the inequality is part of poverty. Views the relationship between inequality and poverty as a pragmatic relationship, namely that the inequality causes more severe poverty or inequality is a form of poverty [13]. Stated that there is a positive relationship between poverty and inequality, both spatial and inter-personal [14]. Income inequality is a relative inequality of income between groups of people as measured by Gini Ratio. The imbalance of income distribution, decline in welfare, and poverty attracted the attention of various parties, such as policymakers, politicians, social and economic researchers, and the wider community [15].
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The Effects of Financial Development on Income Inequality and Poverty

The Effects of Financial Development on Income Inequality and Poverty

There are basically two reasons for this conflict. On the one hand, different ethnic groups have different preferences over which type of public good to pro- duce with tax revenues. On the other hand, each ethnic group’s utility level for a given public good is reduced if other groups also use it. Put differently, if tax revenues are collected from one ethnic group and used to provide public goods which also serve other ethnic groups, voters are likely to choose lower levels of public good provision (Alesina et al., 1999). Also, an ethnic elite in power may not want to invest in public goods like human capital, since this could raise other groups’ political voices and enable them to replace the currently ruling elite (Easterly, 2001). Hence, by impeding agreement about the provision of public goods, we expect higher ethnic fractionalization to have an increasing effect on income inequality and poverty.
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CORRUPTION, INCOME INEQUALITY, AND POVERTY IN INDONESIA

CORRUPTION, INCOME INEQUALITY, AND POVERTY IN INDONESIA

The purpose of this study is to find out and analyze the influence of corruption, on income inequality and poverty in Indonesia. The method used is a quantitative method with the use of the Two Stage Least Square (2SLS) econometric technique. The scope of the research is 303 districts and cities in Indonesia. Using data in 2010 (cross section). Empirical findings, that high corruption has a positive and significant influence on the spread of income inequality and increasing poverty in Indonesia. While the profit sharing of natural resources has a negative effect on income inequality and poverty, in other words the abundance of natural resources causes income distribution to be better and poverty rates to decline. Similarly, investment has a negative effect on income inequality. Meanwhile, education has a positive influence on income inequality and has a negative effect on poverty. Per capita income has a positive effect on income inequality. Income inequality has a negative effect on poverty. Social spending has a negative effect on poverty. To reduce income inequality and poverty in Indonesia by reducing the level of corruption in the implementation of the APBD in the city districts, one of them is through increasing aspects of supervision, considering that the distance between the region and the national capital is relatively higher than the area close to the nation's capital.
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The effects of financial development on income inequality and poverty

The effects of financial development on income inequality and poverty

There are basically two reasons for this conflict. On the one hand, different ethnic groups have different preferences over which type of public good to pro- duce with tax revenues. On the other hand, each ethnic group’s utility level for a given public good is reduced if other groups also use it. Put differently, if tax revenues are collected from one ethnic group and used to provide public goods which also serve other ethnic groups, voters are likely to choose lower levels of public good provision (Alesina et al., 1999). Also, an ethnic elite in power may not want to invest in public goods like human capital, since this could raise other groups’ political voices and enable them to replace the currently ruling elite (Easterly, 2001). Hence, by impeding agreement about the provision of public goods, we expect higher ethnic fractionalization to have an increasing effect on income inequality and poverty.
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The Impact of Income Inequality and Poverty on the Economic Growth in Nigeria

The Impact of Income Inequality and Poverty on the Economic Growth in Nigeria

This study focused on investigating the comparative analysis of the impact of income inequality and poverty on Nigeria economic growth between 1986 and 2015. The source of data for this study is secondary; the main tool of analysis is the error correction model and the co-integration method for this research study. The result of the study showed clearly that a very high level of unemployment and low level per capital income of the populace significantly impact the economy negatively; these are consistent with the few studies that have investigated on the impact of income inequality and poverty on economic growth. This study thus concludes that since inequality and poverty are two major macroeconomic problems that are eating up the country and are inter woven and the indirect channel of unemployment contributing to the problem, policy measure toward the combat of one should not neglect the other as the efficacy of the policy measures is related to the other problem. Employment has been identified as an important outcome of any welfare intervention. It recommended that employment should be one of the major tools to be considered in the fight against poverty and inequality in Nigeria. This should not be left for the government alone, the private sectors are also encouraged to be actively involved in this as well as individuals through imbibing the spirit of entrepreneurship.
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Poverty and income inequality in the Republic of Macedonia

Poverty and income inequality in the Republic of Macedonia

According to the State Statistical Office of the R. Macedonia, poverty is refers to persons, families, and groups of persons whose resources (material, cultural and social) are so limited that it excludes them from the minimum acceptable way of life in society. As such, the basis for poverty calculations is income, and the poverty threshold used in this case to define the adequate minimum is 60% of the median equivalent income. Wheras income inequality refers to the distribution of income in society. The latter is thus a broader concept than poverty in that it is defined over the entire population, and does not only focus on the poor. Income inequality is measured using Gini Coefficient and the S80/S20 indicator. The latter shows the ratio of total income received by the 20% of the population with the highest income to that received by the 20% of the population with the lowest income. Thus, the larger this relationship is, the greater the inequality is. The Gini coefficient, on the other hand, is a statistical measure that takes into account the overall income distribution in the country, which measures inequality in the distribution of income and wealth. If there were to be perfect equality, meanining that everyone in the society has the same income, then the coefficient would be 0 (0%). If all national income were in the hands of one person, the coefficient would be 1 (100%). Hence, the greater the Gini coefficient is, the greater is the income inequality the country. According to Petrevska and Uzunov (2015), the extreme values 0 and 1 are only theoretical cases and in practice the value of the Gini index ranges from about 0.25 to 0.7.
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Poverty and Income Inequality in Nigeria: Any Causality?

Poverty and Income Inequality in Nigeria: Any Causality?

Our empirical result shows uni-directional causality between unemployment and inequality with unemployment causing inequality and a uni-directional causality between life expectancy rate (LEXP) and inequality (INEQ) with LEXP causing inequality at 5% level of significance. This is in line with studies carried out by Leite et al. (2006) for South Africa on earning inequality and unemployment. It is true that inequality index used in this study represents income inequality, but inequality cuts across various sectors of the economy. There could be assets inequality and access inequality. However, all these other forms of inequality are closely linked to income inequality. For instance, usually there is inequality in access to employment opportunities (maybe due to variations in academic qualifications), but such variation in academic attainment might have been brought about by income inequality. Thus the inequality in employment leads to high rate of unemployment which further leads to inequality in income and rising poverty profile in the country. The result of the relationship between LEXP and INEQ is however not strange. A low life expectancy rate portrays low health status, this leads to low employment opportunity as no employer will like to employ an unhealthy person, low level of income sets in and in relative to others, income inequality is unavoidable.
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Financial Inclusion, Poverty, and Income Inequality in Developing Asia

Financial Inclusion, Poverty, and Income Inequality in Developing Asia

Poverty and income inequality remain a stubborn challenge in Asia and the Pacific despite the region’s rapid economic expansion in previous decades, which lifted millions out of poverty. Financial inclusion is often considered as a critical element that makes growth inclusive as access to finance can enable economic agents to make longer-term consumption and investment decisions, participate in productive activities, and cope with unexpected short-term shocks. Understanding the link between financial inclusion, poverty, and income inequality at the country level will help policymakers design and implement programs that will broaden access to financial services, leading to reduction of poverty incidence and income equality. This paper extends the existing literature on financial inclusion by focusing on developing Asian economies. We construct our own financial inclusion indicator to assess various macroeconomic and country-specific factors affecting the degree of financial inclusion for 37 selected developing Asian economies. We also test the impact of financial inclusion, along with other control variables, on poverty and income inequality. Our results show that per capita income, rule of law, and demographic characteristics significantly affect financial inclusion in developing Asia. Furthermore, we find that financial inclusion significantly reduces poverty; and there is also evidence that it lowers income inequality. Our findings suggest that the provisions for young and old-age populations, e.g., retirement pensions; and stronger rule of law, including enforcement of financial contracts and financial regulatory oversight, will broaden financial inclusion, thereby contributing to poverty reduction and lower income inequality.
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Crime and Income Inequality: The Case of Malaysia

Crime and Income Inequality: The Case of Malaysia

This paper examines the causality between income inequality and crime in Malaysia for the period 1973-2003. Autoregressive Distributed Lag (ARDL) bounds testing procedure is employed to (1) analyze the impact of income inequality on various categories of criminal activities as well as to (2) analyze the impact of various categories of criminal activities on income inequality. Interestingly our results indicate that income inequality has no meaningful relationship with any of the various categories of crime selected, such as total crime, violent crime, property crime, theft and burglary. Crime exhibits neither long-run nor short run relationships with income inequality and they are not cointegrated. It cannot be denied that there is ambiguity in the empirical studies of crime economics regarding various income variables leading to often mixed and contradicting results, which might be a good explanation of this finding.
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Poverty, income inequality and unemployment in Spain in the 2008's Financial Crises

Poverty, income inequality and unemployment in Spain in the 2008's Financial Crises

Poverty, income inequality and unemployment in Spain in the financial crises Workneh, Migbaru Alamirew University of Insubria.[r]

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Poverty and income inequality in rural Agrarian household of southwestern nigeria: The gender perspective

Poverty and income inequality in rural Agrarian household of southwestern nigeria: The gender perspective

Poverty in Africa has not only grown widespread but in- tense compared to any other region of the world. With this, redistribution towards the poor will definitely not require only increased income but importantly needs an evolution of po- licies, plans and framework that will address the acute income inequality by transforming and improving the sources of livelihood and the space where the poor live. Poverty and inequality have been identified as inseparable evil that are highly related with rural households and with feedback impacts which seem indisputable and must be fought together. In a bid to give empirical evidence of poverty incidence and in- equalities among rural household in Southwest Nigeria, this study has assessed the gender differential in poverty (captured through incidence, depth and severity) and inequality status. This re-examination discovered that poverty incidence was higher among male population than the female population. whereas, income inequality was less acute among the male respondents than their female counterpart.
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Does non-farm income improve the poverty and income inequality among agricultural household in rural Kedah?

Does non-farm income improve the poverty and income inequality among agricultural household in rural Kedah?

This paper used a primary data collected through a surveys among farmers in rural Kedah to examine the effect of non farm income on poverty and income inequality. This paper employed two method, for the first objective which is to examine the impact of non farm income to poverty, we used poverty decomposition techniques- Foster, Greer and Thorbecke (FGT) as has been done by Adams (2004). For the second objective, which is to examine the impact of non farm income to income inequality, we used Gini decomposition techniques. Our result indicate that non farm income can improve the level of poverty or non farm income sources contributed towards poverty reduction among agricultural household. All of the poverty measures show that the inclusion of non-farm income into the agricultural household income reduce the level, depth and severity of poverty. But on the other hand, non farm income increased income inequality among agricultural household in Kedah. As expected agricultural income is the main source of income for rural people in the study area. The policy implication of this study is to encourage non-farm income activities among agricultural households as this would raise their income and hence, reduce poverty among them. However, it should be focused on value-added activities, especially on the lower income group.
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ECONOMIC GROWTH, INCOME INEQUALITY AND POVERTY REDUCTION: A REGIONAL COMPARATIVE ANALYSIS

ECONOMIC GROWTH, INCOME INEQUALITY AND POVERTY REDUCTION: A REGIONAL COMPARATIVE ANALYSIS

Lee and Perera (2013) investigated the contribution of economic growth and institutional qualities to the reduction in poverty in Asia from 1985 to 2009. They argued that, there are many factors behind the persistent poverty problems in developing countries and that economic growth alone cannot account for all the changes in poverty levels. Some of the factors include government stability and rule of law, corruption, and democratic accountability. They found that economic growth significantly reduced poverty levels in the South and East Asia region. Economic growth leaves the income distribution unchanged and therefore results in a higher reduction in poverty levels. On the institutional qualities, they found a negative relationship between government stability, rule of law, and poverty. Thus, improvements in institutional qualities led to a reduction in poverty levels over the years. However, a reduction in corruption, improvements in democratic accountability and bureaucracy have not contributed to reducing poverty and income inequality. This result is interesting since corruption in particular is seen as detrimental to economic development. Moderate rates of corruption may not be harmful to growth initially but in the long run, they argued that corruption will have an adverse effect on economic development and may worsen poverty levels even further. Therefore governments in Asia should adopt policies to mitigate corruption and promote quality institutions.
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The empirical relationship between income poverty and income inequality in rich and middle income countries

The empirical relationship between income poverty and income inequality in rich and middle income countries

Secondly, evidence from cross-country analysis of changes in poverty and inequality suggests that there is strong cross-country correlation between changes in poverty (both anchored and relative) and inequality. Although the correlation between changes in income poverty and changes in income inequality remain strong and statistically significant in terms of most inequality measures (except from the P90:P50 ratio) it is weaker than the one identified by exploiting cross-country variation in the levels of inequality and poverty. The positive correlation between changes in relative poverty risk and changes in income inequality remain strong in a series of OLS regression models which control for the initial level of inequality and the initial level of income and income growth. Results from these models suggest that none of these three variables has any significant effect on the change in the relative poverty risk once we account for inequality growth. This suggests that neither the initial level of inequality nor the initial level of income or indeed the rate of income growth matter for change in relative poverty risk: it is only the change in inequality that matters in driving poverty developments. On the other hand, both the initial levels of inequality and the initial level of income have significant effects on the change in the anchored poverty risk. The coefficients from the anchored poverty risk model that include controls for both these variables imply that anchored poverty risk falls by less in economies with higher levels of initial inequality and with higher levels of initial average household income. However, when controls for income growth are included in the anchored poverty risk equation, the coefficient of the initial level of inequality variable falls and turns statistically insignificant implying a negative correlation between income growth and initial level of inequality (i.e. income grows less in countries with higher level of inequality). The coefficient of the initial level of income variable also falls in magnitude when income growth controls are included in the model but its effect remains statistically significant. Consistent with expectations, the coefficients on the income growth variables suggest that anchored poverty risk falls more when household income growth is higher.
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Multidimensional poverty and income inequality in the EU

Multidimensional poverty and income inequality in the EU

Since we are interested in the relationship between income inequality and our multidimensional measures of poverty, we first discuss the income inequality indicators to be used in the analysis. For these aggregate-level inequality measures, income data is taken from Eurostat rather than calculating the measures over the EQLS micro-level data. This is for the reasons of income data quality given in Section 3.1. Income is defined as household disposable income from employed and self-employed earnings, capital income and public cash transfers in a given year, net of income taxes and social security contributions. This disposable household income is equivalised, allocating among household members and adjusting for economies of scale within the household to reflect different needs for households of different sizes. Inequality is then calculated over the resulting equivalised disposable incomes. It is important to note, therefore, that the income inequality measures already reflect and capture some effect of government policy through taxes and transfers.
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Analysis of Financial Inclusion Toward Poverty and Income Inequality

Analysis of Financial Inclusion Toward Poverty and Income Inequality

The objective of this study is to analyze the relationship and the influence of financial inclusion toward poverty alleviation and income inequality in Indonesia. The analysis methods in this study were Index Inclusion and regression-correlation of panel data. The variable of financial inclusion was obtained from Index of Financial Inclusion (IFI) value measured by dimensions; banking penetration, banking services availability, and the use of banking services. The data was time series from 2014-2016 and cross section from 33 provinces in Indonesia obtained from Bank Indonesia, Financial Service Authority, and Central Bureau Statistics. The results showed; (1) most provinces in Indonesia had moderate financial inclusion level, (2) financial inclusion had a negative and significant relationship and influence toward poverty. (3) financial inclusion had a positive and not significant relationship with income inequality, but it had a negative and significant influence toward income inequality. It means that financial inclusion can reduce poverty, but it has not been able to reduce the economic gap of society well.
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Crime and Income Inequality: The Case of Malaysia

Crime and Income Inequality: The Case of Malaysia

This paper examines the causality between income inequality and crime in Malaysia for the period 1973-2003. Autoregressive Distributed Lag (ARDL) bounds testing procedure is employed to (1) analyze the impact of income inequality on various categories of criminal activities as well as to (2) analyze the impact of various categories of criminal activities on income inequality. Interestingly our results indicate that income inequality has no meaningful relationship with any of the various categories of crime selected, such as total crime, violent crime, property crime, theft and burglary. Crime exhibits neither long-run nor short run relationships with income inequality and they are not cointegrated. It cannot be denied that there is ambiguity in the empirical studies of crime economics regarding various income variables leading to often mixed and contradicting results, which might be a good explanation of this finding.
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Poverty, Income Inequality and Health

Poverty, Income Inequality and Health

In an analysis of Norwegian data, Dahl (1994) concludes that ‘occupation status stands as the most powerful and consistent predictor of ill health among employed individuals’. Winkleby and colleagues (1992) in a study of the employed population found that ‘after adjusting for age and the time of the survey education was the only measure of socioeconomic status that was significantly associated with the risk factors’. In contrast, Hay (1988) found that in an analysis of male earners: ‘of the three socioeconomic measures, income was consistently the best correlate of health status’. Finally, Stronks (1997) in a study of the whole population of working age found that an income proxy resulted in the biggest change in deviance for both chronic condition and perceived general health for men and perceived general health for women. However, Stronks went on to add employment status to her models and found that this substantially reduced the coefficients on the income measures, concluding that ‘the relatively strong association between income and health, relative to that between education/occupation and health, is largely due to the concentration of those with a long-term work disability in the lower income levels’. Stronks, therefore, reanalysed her data to exclude those who had a long-term work disability and found the association between income and health to be similar to that with education or occupation. More recently Benzeval et al (2001) have used British data ‘to develop an understanding of the relative importance of income and other measures of socio-economic status for health’ (p.390). They report that controlling for ethnicity and social roles has little effect on the income/health relationship, but that the association between income and health began to flatten as education and occupation - and more significantly economic status and measures of consumption - are added to the models. Nonetheless, for all of the health measures except recent illness, after adjusting for a broad range of socioeconomic and demographic characteristics, people in the lower income quintiles were still significantly more likely to have poorer health than the richest fifth.
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Greek Farm Households: Income inequality, poverty and distributional impact of farm income

Greek Farm Households: Income inequality, poverty and distributional impact of farm income

According to Sarris and Zografakis (1996) income distribution within farm households is much more skewed compared with income distribution of non-farm households, and this pattern does not seem to change over time. Some other interesting findings indicate that the bulk of inequality in Greece is due to disparities within, rather than between, groups, even when the population is grouped into a large number of small homogeneous groups (Mitrakos and Tsakloglou, 1998). The same conclusion is drawn in the case of farm households, when they are grouped according to various criteria, such as place of residence, age, household size, educational qualifications etc. (Mitrakos and Sarris, 2003). Moreover, employment in the agricultural sector, along with old age, residence in rural areas, low educational qualifications and, to a lesser extent, lack of employment have been identified as closely associated with acute poverty. This conclusion is drawn irrespective of the welfare indicator, the level of the poverty line, or the size of the equivalence scales used in the analysis (Tsakloglou and Panopoulou, 1998)
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