Total Income Inequality Research Articles

Non-timber forest incomes and economic welfare in the South-West region of Cameroon: The incidence of rural income inequality

Abstract. With the aim of answering the question whether or not Non-Timber Forest Products can contribute in reducing rural income inequality in the South-West region of Cameroon, the study used primary data collected from a survey on 408 rural household heads. The Data was collected using a structured questionnaire. We adopted three different methodologies – The Gini Coefficient, The Lorenz curve, and The Income Decomposition by income sources to obtain identical results. The results revealed that incomes from non-timber forest products reduces rural income inequality in the rural parts of the region significantly, and occupy an important position amongst the different income sources which were investigated. We therefore recommend improved value-added for Non-Timber Forest Products through processing. A better management of the forest in general and the forest resources in particular will ensure improved benefits to the community as a whole especially in the areas of total income and income inequality.. Keywords. Economic Welfare, Rural Income Inequality, Non-Timber Forest Incomes, Gini Coefficient Income Decomposition by income sources and Cameroon. JEL. O11, E20, Q13, C30.

The role of public pensions in income inequality among elderly households in China 1988–2013

Using data from the Chinese Household Income Project surveys for 1988, 1995, 2002 and 2013, we investigate the role of public pensions in income inequality among households with elderly members across two decades of pension policy reforms. We examine the distribution and role of public pensions at a national level and analyse the evolution of the contribution of public pensions to national income inequality across a much more extended time period than earlier studies, which have generally focused on regional changes over short periods. Our findings suggest that public pensions have become the most important source of income for households with elderly members on average in China, but the distribution of pension income is highly unequal, with a Gini coefficient of 0.74 in 2013. Public pension income has been the largest source of income inequality for elderly households since 2002 and contributed to more than half of total income inequality in the most recent year of the survey. This finding is robust against variations in the income inequality measures used. Additionally, our analysis suggests unequal distribution of pension benefits is the primary driver of pensioners' income inequality. Among several hypothetical policy changes, ensuring a minimum pension benefit for all existing pensioners seems to be the most fiscally effective option in reducing income inequality, with a 0.8% reduction in the Gini coefficient for a 1% increase in public pension expenditure.

3 Assessing inequality using labour income

This chapter focuses on the implications for inequality of recent ILO estimates of the labour income share and distribution. Using household surveys for 95 countries, mainly from the ILO Harmonized Microdata collection, we estimate the labour income of the self‐employed to produce an internationally comparable labour income share dataset. Furthermore, we use the same methodology to obtain the first‐ever estimates of the labour income distribution. The labour income distribution estimates complement the two main data sources used until now to study inequality: expenditure distribution and total income distribution. Crucially, labour income distribution data have a reasonable coverage for all country income groups, unlike other data sources that are characterized by undercoverage for either lower‐income countries (regarding data on total income) or higher‐income countries (regarding data on expenditure).The estimates show that the global labour income share declined substantially between 2004 and 2017. In high‐income countries, the decline in the labour share is driven largely by decreases in the average labour income of the self‐employed. This is consistent with a scenario in which new forms of work erode the earning power of the self‐employed. Focusing on income inequality, the use of the labour income distribution as a proxy for the total income distribution is found to be a more reliable proxy than the commonly used expenditure distribution data. Moreover, the estimates suggest that the use of expenditure as a proxy of income have led earlier studies to underestimate total income inequality severely in less developed countries. Hence, global income inequality is likely to be much higher than previously assumed.

Regional income and wave energy deployment in Ireland

AbstractAlongside environmental benefits, renewable energy deployment is often evaluated on grounds of regional development. Focusing on wave energy deployment in Ireland, this paper quantifies employment‐related welfare change net of associated subsidy costs. Although the added employment reduces inter‐regional inequality, certain subsidies increase total income inequality by a greater extent. Total inequality increases by 0.25% in the preferred scenario. This pattern of incidence persists under an optimistic scenario where all manufacturing activity is carried out locally. This finding highlights that policies of regional development should consider the spatial distribution of associated subsidy costs.

Livelihood capitals on income inequality among rural households: evidence from China

ABSTRACT: We used the data of the China Labor-force Dynamics Survey 2014 to examine the effects of livelihood capitals which include natural, material, human, financial, and social capitals on total household income, per capita income, agricultural income, wage income, operational income, and property income inequality among rural households in China. Results showed that different kinds of livelihood capitals have different effects on different types of rural households’ income. Specifically; (1) although, the area of cultivated land reduces agricultural income inequality, it increases per capita income inequality. (2) Forest land area enlarges per capita income inequality and total household income inequality. (3) Tractor variable reduces inequality in agricultural income and total household income. (4) While reducing the property income inequality, education variable enlarges the wage income inequality, the per capita income inequality and the total household income inequality. (5) Book variable reduces property income inequality. (6) Loan variable increases inequality in agricultural incomes. (7) Party variable reduces the agricultural income inequality. (8) Although, the internet variable increases agricultural income inequality, and property income inequality, it reduces wage income inequality, operational income inequality, per capita income, and total household income inequality.

Long-Term Evolution of Inequality of Opportunity

The main goal of this paper is to document and analyze the long-term evolution of inequality of opportunity and thus extend the recent empirical literature, which is mainly concerned with its measurement at a specific point in time. Using repeated cross-section surveys for five European countries (France, Germany, Great Britain, Italy, and Switzerland), the evolution of inequality of opportunity is measured for a period of about two decades for the whole populations, as well as for different birth cohorts. Relative inequality of opportunity represents an important portion of total income inequality, with values ranging from 30 to 50 percent according to the standard deviation of logs (and reaching a lower share in case of mean log deviation) and, for all the countries, it shows a stable or declining time trend. When the birth cohorts are followed across time, inequality of opportunity decreases with age: the effect of circumstances seems to weaken over the life cycle. This is a quite different age profile from that of inequality of outcomes (income or consumption), which generally increases with age. A decomposition of the relative inequality of opportunity allows highlighting some key drivers of its time evolution. In all the countries, there has been a clear enhancement of equality of educational opportunity (as captured by a downward trending intergenerational education persistence) and a reduction of the returns to education. However, for some countries, notably Italy, these trends have failed to translate into decreasing inequality of opportunity in the income distribution because of the increasing role of parental networking (an additional channel through which parental background affects the incomes of offspring).

Long-Term Evolution of Inequality of Opportunity

The main goal of this paper is to document and analyze the long-term evolution of inequality of opportunity (IOp) in the four largest European economies (France, Germany, Great Britain and Italy). Relative IOp represents an important portion of total income inequality, with values ranging from 30 to 50 percent according to the standard deviation of logs. For all the countries, relative IOp shows a stable or declining time trend. In addition to these descriptive findings, the paper proposes a theoretical framework identifying channels of transmission which may affect IOp. Using this framework, a decomposition focuses on the role of three variables: a) intergenerational persistence in educational attainment, b) return of education, and c) networking activity of parents. While the first two variables exhibit a declining trend in all countries, which as predicted by the model should produce a decline in IOp, the third one appears to be rising in some countries, counteracting the effects of the first twoKeywords: Inequality of Opportunity, Decomposition methods, Education mobility, Returns to Education, Family Networking, Cohort Analysis

EU‐Wide Impacts of the 2013 CAP Direct Payments Reform: A Farm‐Level Analysis

AbstractThis paper analyzes the microeconomic effects of the 2013 reform of the EU's Common Agricultural Policy (CAP). This is done using the EU‐wide individual farm model (IFM‐CAP). Simulation results show that although the reform succeeded to partially harmonize direct payments (DP) among farms and Member States, relatively strong differences in the distribution still remain in place. Around 62% of the farms increase their income, whereas the remaining 38% lose from the reform. The reform benefits small farms, while large farms lose out. As measured by the Gini coefficient, the 2013‐CAP reform only partially reduces the disparity in the distribution of DP and farm income among farms. The Gini decomposition shows that subsidies (in particular decoupled payments) contribute to a decrease in the inequality of total farm income. The future CAP reform needs to have a stronger overhaul of the DP system in order to achieve a substantial reduction in inequality in the distribution of payments among farms and regions in EU.

Mapping Stratification: The Industry-Occupation Space Reveals the Network Structure of Inequality

Social stratification is determined not only by income, education, race, and gender, but also by an individual’s job characteristics and their position in the industrial structure. Utilizing a dataset of 76.6 million Brazilian workers and methods from network science, we map the Brazilian Industry-Occupation Space (BIOS). The BIOS measures the extent to which 600 occupations co-appear in 585 industries, resulting in a complex network that shows how industrial-occupational communities provide important information on the network segmentation of society. Gender, race, education, and income are concentrated unevenly across the core-periphery structure of the BIOS. Moreover, we identify 28 industrial occupational communities from the BIOS network structure and report their contribution to total income inequality in Brazil. Finally, we quantify the relative poverty within these communities. In sum, the BIOS reveals how the coupling of industries and occupations contributes to mapping social stratification and the network structure of inequality.

Inequality of Opportunity in Indian Women

Inequality of opportunity (IOp) in any society is defined as that part of overall inequality which arises from factors beyond the control of an individual (circumstances) such as parental education, caste, gender, religion etc. and is thus considered unfair and is against the meritocratic values of a society. Hence, it needs to be controlled and compensated. We estimate the IOp in economic outcomes among Indian women by using the nationally representative India Human Development Survey 2011–2012. We include parental education, caste, religion and region of birth as circumstances. The overall IOp in income ranges from 18–25% and 16–21% (of total income inequality) in urban and rural areas, respectively. The corresponding figures for consumption expenditure are 16–22% and 20–23% in urban and rural areas, respectively. We also estimate the partial contributions of the circumstances to the overall IOp. We find that the parental education is the most significant contributor to IOp in urban areas, whereas, region of birth is the most significant contributor to IOp in rural areas. Fortunately, findings imply that socially and culturally imbedded factors like caste and religion which are more persistent do contribute to the IOp, but, the largest contribution is due to factors like parental education and region which can be relatively easily tackled and addressed with policy interventions.

Investor sophistication and capital income inequality

Capital income inequality is large and growing fast, accounting for a significant portion of total income inequality. We study its growth in a general equilibrium portfolio choice model with endogenous information acquisition and heterogeneity across household sophistication and asset riskiness. The model implies capital income inequality that grows with aggregate information technology. Investors differentially adjust both the size and the composition of their portfolios, as unsophisticated investors retrench from trading risky securities and shift their portfolios to safer assets. Technological progress also reduces aggregate returns and increases the volume of transactions, features that are consistent with recent U.S. data.

Inequality in Chile before the first globalization: an approach derived from agricultural market income, 1830s-1850s

This article assesses agricultural market income inequality by examining three untapped comprehensive agricultural censuses of all of Chile, undertaken in 1834, 1838 and 1852. Since there had been no Chilean income inequality measurements prior to 1860, this is a novel contribution. Given Chile’s great dependence on the agricultural sector during the pre-industrial period of the 1830s to 1850s, measures of agricultural market income inequality can safely be taken as a proxy for total income inequality. This study found that agricultural market income inequality was extremely high during the first decades after Chilean independence. Gini coefficients for agricultural market income among landowners were 0.75, 0.75 and 0.79 for 1834, 1838 and 1852 respectively, while the figures for the entire rural Chilean population, including the landless, were 0.79, 0.87, and 0.89. Around 85% of the population did not own any land and for an unskilled labourer to rent a plot of 1,500 hectares in 1834 cost 3.3 years of wages, and annual wages of 11.3 in 1838. In a conclusion that is at odds with previous historiographical findings, our data suggest that inequality in Chile was very high and had begun to increase decades before the first globalization.

Estimating the Contribution of Circumstances that Reflect Inequality of Opportunities

Until recently, researchers have typically followed an indirect approach to decomposing income inequality into its “fair” and “unfair” components, by examining income mobility. This study contributes to the existing literature by demonstrating the advantages of employing a direct approach, through measuring the contribution of various sources of inequality of opportunities. Based on recent Australian data, we estimate that at least 8 per cent of total income inequality before government transfers and taxes and at least 6 per cent of total income inequality after government transfers and taxes is attributable to factors outside of people's control.

Shifting livelihood strategies in northern Nigeria - extensified production and livelihood diversification amongst Fulani pastoralists

This paper presents an in-depth investigation of the livelihood strategies of Fulani pastoralists in north central Nigeria. Results show a diversified crop-livestock system aimed at spreading risk and reducing cattle offtake, adapted to natural resource competition and insecurity by extensification, with further diversification into off-farm activities to spread risk, increase livelihood security and capture opportunities. However, significant costs were associated with extensification, and integration of crop and livestock enterprises was limited. Mean total income per capita in the study area was $554 or $1.52/person/day with 42% of households earning less than 1.25/person/day. Income levels were positively correlated with income diversity and price received per animal sold, rather than herd size.The outcomes of this livelihood strategy were favourable across the whole community, but when individual households are considered, there was evidence of moderate economic inequality in total income, cash income and herd size (Gini coefficient 0.32, 0.35 and 0.43 respectively). The poorest households were quite vulnerable, with low assets, income and income diversity. Implications for sustainability are discussed given the likelihood that the negative trends of reduced access to natural resources and insecurity will continue.

Inequality of Opportunity and Economic Growth: How Much Can Cross‐Country Regressions Really Tell Us?

Income differences arise from many sources. While some kinds of inequality, caused by differential rewards to effort, might be associated with faster economic growth, other kinds, arising from unequal opportunities for investment, might be detrimental to economic progress. This study uses two new datasets, consisting of 117 income and expenditure household surveys and 134 Demographic and Health Surveys, to revisit the relationship between total inequality and economic growth. In particular, we ask whether inequality of opportunity, driven by circumstances at birth, has a negative effect on subsequent growth. Using the income and expenditure micro dataset, we find that while both total income inequality and inequality of opportunity are negatively associated with growth, the coefficient estimates are insignificant. The evidence is similarly equivocal using the Demographic and Health Surveys data. On balance, the data do not provide support for the hypothesis that inequality of opportunity is bad for growth.

Innocent Bystanders? Monetary policy and inequality

We study the effects of monetary policy shocks on—and their historical contribution to—consumption and income inequality in the United States since 1980 as measured by the Consumer Expenditure Survey. Contractionary monetary policy systematically increases inequality in labor earnings, total income, consumption and total expenditures. Furthermore, monetary policy shocks account for a non-trivial component of the historical cyclical variation in income and consumption inequality. Using detailed micro-level data on income and consumption, we document some of the different channels via which monetary policy shocks affect inequality, as well as how these channels depend on the nature of the change in monetary policy.

The Racial Structure of Economic Inequality in the United States: Understanding Change and Continuity in an Era of “Great Divergence”*

The “great divergence” of America's rich from its middle class and poor has led some observers to see a country increasingly stratified by income and wealth, more so than by race. In this article, the first in a two‐part series, we argue that this conclusion overlooks the persistent importance of the racial “structure” of inequality. A decomposition of income inequality between 1980 and 2010 using the Theil Index shows that inequality between racial groups accounts for a rising share of total income inequality over this period nationally and in most states. We also demonstrate that within‐state trends in the between‐race component of inequality are not fully accounted for by trends in income inequality and racial diversity per se. These findings lay the groundwork for a forthcoming companion piece in Social Science Quarterly that shows that between‐race inequality is strongly linked to welfare policy outcomes in the United States.

Decomposing Global Inequality

This paper provides an intuitive additive decomposition of the global income Gini coefficient with respect to differences within and between countries. In 2005, nearly half the total global income inequality is due to income differences between Europeans and North Americans on the one side and inhabitants of Asia on the other, with the China‐USA income differences alone accounting for six percent of global inequality. Historically, income differences between Asia and Europe have driven a large part of global inequality, but the quantitative importance of within‐Asia income inequality has increased substantially since 1950.

Spatial Income Inequality in India, 1993-2011: A District Level Decomposition

Using nationally representative household survey data, and district and state as two levels of aggregation, we examine role of individual and geographical factors in determining the level and the change in income inequality in India. We find that between-state income differences account for the majority of between-district income inequality in rural India in 2011. However, in urban India within-state income differences explain most of the between- district inequality in 2011. We also find that the between-district component accounts for one-third of the increase in total income inequality in rural India between 1993 and 2011. We find significantly smaller level of inequality but similar trends using the consumption expenditure data.

Distributional effects of non-farm incomes in a Malaysian rice bowl

Purpose – The purpose of this paper is to identify the structure of household income and examine the effects of non-farm incomes on the income distribution of farm households in a relatively developed rural area of the Malaysian rice bowl. Design/methodology/approach – The non-farm incomes were disaggregated into different components to determine the contribution of each income source to total household income and overall inequality. The income distribution and decomposition was examined using the Gini decomposition method. Findings – It was found that almost 71 percent of the households in the sample had at least one source of non-farm income. On average, non-farm incomes contributed about 33 percent to total household income. Non-farm wage employment was the dominant source of non-farm income, accounting for almost 26 percent of overall household income. The farm incomes, especially the paddy incomes were found to be the inequality-decreasing income source. The study also confirmed the proposition that the non-farm incomes were the inequality-increasing income source as they contributed up to 35 percent of the overall income inequality. Originality/value – Previous studies have found that non-farm incomes have different effects on income inequality of rural communities, especially those in the rice granary areas situated in less developed states of Malaysia, where poverty is still a problem. This study is significant because it identifies the effect of certain incomes on the overall income inequality among farm households in the granary areas located in a relatively developed rural area. The studied areas are characterized by an intensive paddy production and a rapid development in business and industrial activities, and hence, providing non-farm employment opportunities to the rural farmers. Therefore, this study shows the income structure and how farm and non-farm incomes affect the overall income distribution of the paddy farmers.