Lecture 5, 6, 7, 8-Inequality

Inequality

MDGs and SDGs

• SDG 10. Reduce inequality (in general, not only gender as with the MDGs even though there is a separate objective for that in the SDGs) within and among countries. Targets:
  • 10.1 By 2030, progressively achieve and sustain income growth of the bottom 40 per cent of the population at a rate higher than the national average.
  • 10.3 Ensure equal opportunity and reduce inequalities of outcome, including by eliminating discriminatory laws, policies and practices and promoting appropriate legislation, policies and action in this regard.
  • 10.4 Adopt policies, especially fiscal, wage and social protection policies, and progressively achieve greater equality.
    • Perhaps the same that can fight against poverty.
  • 10.6 Ensure enhanced representation and voice for developing countries in decision-making in global international economic and financial institutions in order to deliver more effective, credible, accountable and legitimate institutions.
    • Non-discrimination politically and legally.
      • Also between countries, in institutions like the UN this isn’t the case→some have greater decision power.
• Means (of implementation) :
  • 10.a Implement the principle of special and differential treatment for developing countries, in particular least developed countries, in accordance with World Trade Organisation agreements.
  • 10.b Encourage official development assistance and financial flows, including foreign direct investment, to States where the need is greatest, in particular least developed countries, African countries, small island developing States and landlocked developing countries, in accordance with their national plans and programmes.
  • 10.c By 2030, reduce to less than 3 per cent the transaction costs of migrant remittances and eliminate remittance corridors with costs higher than 5 per cent.
    • Because the money is sent from abroad, remittances bring foreign currency (foreign exchange) into the receiving country. This increases the country’s external financial inflows, similar to exports, foreign aid, or foreign investment. Transfers from the richest to the poorest.

Income-based

Info stopped in 2020 when going a bit downwards but massively high. Difference of 40x in 2020 and 53x at the max! For the green dots (not a line, doing everyone every time is too hard) we do everyone together, the world is a country. Without any country reference poverty is higher. PPP is taken into account here, but border effects still affect and explain this difference.

• Global inequality (Gini coefficient) ranges from 0.55 to 0.70 depending on the measure used.
  • More difficult to communicate than ratios between income share groups.
• The high level of global inequality reflects sizeable per capita income disparities across countries, which account for around three quarters of global inequality (Milanovic 2013).
• Some measures of global inequality exhibit a declining trend in the last few decades in response to rising incomes for those living in China and India, where hundreds of millions of people have been lifted out of poverty.
• However, other measures of global income inequality— adjusted for top incomes which tend to be underreported in most household surveys—appear to be broadly stable since the early 1990s.
  • Explains differences between global and inter-country despite PPP adjustment.
• What to use? Weighted or not? Individual information would be best but perhaps then we would just be capturing the effects redistribution policies often present in rich countries as each individual is subject to different ones. Rich countries don’t always have them, but poor ones usually dont.
  • Comparing the top 10% to the bottom 50%, so basically measures that do ratios between income shares in a country or between some, don’t reflect the whole distribution of income either. Unclear at first without a specific measure if inequality has risen or not in this specific country. A larger share of the population earns the average income basically, which allows to conclude that there is less inequality. Gini is useful as a measure, confirms this. The MDGs changed things for the better on inequality. Self-explanatory. Income of the poorest increases faster so we can expect a convergence, although this tendency can take a lot of time to complete. But lower-middle class grows slower. The richest ones are those who gain the most in terms of income growth. If there is an interest in reducing inequality this bimodelisation of the distribution should be targeted to be reverted. Also a lot of inequality between regions in income terms.

Evolution summarised globally

• Measures of inequality based on Gini coefficients of gross and net incomes have increased substantially since 1990 in most of the developed world.
• Inequality, on average, has remained stable in DVGs, albeit at a much higher level than that observed in advanced economies.
• However, there are large disparities across DVGs, with Asia and Eastern Europe experiencing marked increases in inequality, and countries in Latin America exhibiting notable declines (although the region remains the most unequal in the world).

Wealth-based

• Income versus wealth:
  • Income: value individuals read on their pay checks (pre-income tax) versus after all income, wealth and consumption taxes are deducted, and after all non-replacement transfers (e.g. healthcare, disability and housing benefits) are added (post-tax income).
  • Household wealth is defined as the sum of financial assets (e.g. deposits, stocks, bonds, equity) and non-financial assets (e.g. housing, business), net of debts, possessed by individuals = measure of current and future economic gains (power). Intertemporally whereas income is immediate!
    • Even if stocks may generate income in the future (dividends) they aren’t counted as income.
• Inequality is more extreme in wealth than income. In both advanced economies and DVGs, income Ginis, on average, are half the size of wealth Ginis.
  • Almost systematic trend observable in most countries.
• In most countries with available data, the share held by the 1 percent wealthiest population is rising at the expense of the bottom 90 percent population. Average is 70 for wealth and 40 for income (%s of 1!). Not 100% up to date but these differences are still observed nowadays. Considering wealth shows more inequality. With respect to the world average, with some exceptions, inequality is greater in wealth. Inside regions wealth and income also show different inequality gaps. More inequality and more people with more than a million. The rich get richer but the others lose. Here we decompose for the top, those that earn more than 1 billion. 3k people own half of the global wealth. Average vs. median. In CH for example we earn more but there is a larger part of the population earning relatively more in Luxembourg in regards to average. Half of global wealth goes to the US and mainland China. Extremely tiny share going to Africa, highest wealth fortunes going to NA, EUR and AP. Self-explanatory graphs. Wealth of richest 62 is increasing relative to bottom 50% btw. For comparison, some people are worth more than certain countries.

Measurement : intro

• Income inequality—the most widely cited measure of inequality of outcomes—is typically measured by the market gross (can include pension and unemployment benefits) or net (after tax and transfers from social insurance programs) Gini, and by tracking changes in the income shares of the population (for example, by decile/quintile).
• Net household wealth: sum of financial assets (e.g. equity or bonds) and non-financial assets (e.g. housing or land) owned by individuals, net of their debts.
• Inequality of opportunities is often measured by tracking health, education and human development outcomes by income group, or by examining access to basic services and opportunities.

Can be done in various ways basically. Relative ones go through normalisation or ratios, between average incomes and wealth groups f.ex. With absolute measures we see absolute differences of income or wealth between different categories. This can change conclusions quite a lot.

Within/between/global

Measurement: Data sources

World Bank: Poverty and Inequality Platform https://pip.worldbank.org/home

• Based on household surveys + net/gross income. UN-WIDER: World Income Inequality Database – WIID https://www.wider.unu.edu/project/world-income-inequality- database-wiid.
• Based on household surveys + net/gross income Funded by public and non-profit institutions: World Inequality Database: https://wid.world/
• Based on household surveys and national accounts + gross income.
• More systematic with more in-depth analysis of inequality and its dynamic with macroeconomic variables.
• It’s a collaboration between many entities like governments. Not necessarily controlled by an OI.
  • WID often reports higher inequality than World Bank data because household surveys tend to underestimate top incomes, while WID adjusts estimates using fiscal and national-account data to better capture the richest groups. There is not necessarily a single “best” database; the key is to understand methodological differences and remain consistent with one dataset when doing historical analysis or policy research.

Gini

• A popular measure of inequality is the Gini coefficient, which ranges from 0 (perfect equality) to 1 (perfect inequality) but is typically in the range of 0.3-0.5 for per capita expenditures.
• The Gini is derived from the Lorenz curve, which sorts the population from poorest to richest, and shows the cumulative proportion of the population on the horizontal axis and the cumulative proportion of expenditure (or income) on the vertical axis.
• While the Gini coefficient has many desirable properties – mean independence, population size independence, symmetry, and Pigou-Dalton Transfer sensitivity – it cannot easily be decomposed to show the sources of inequality.
• Can be used therefore to trace the impact of redistributive policies. Individuals are ranked according to income or wealth from poorest to richest and in the curve we put the share of total income they represent. If everyone earns the same it wouldn’t matter, not like this is the case in reality.

Country Ginis

Household services are not produced each year obviously… again.

International Ginis

• Unweighted inter-country inequality is calculated across GDPs obtained from household surveys of all countries in the world, without population-weighting.
• The population-weighted inter-country inequality takes into account population weights.
• Finally, the global inequality concept focuses on individuals, instead of countries. The calculation is based on household surveys with data on individual incomes or consumption.

Theil’s index

• There are a number of measures of inequality that satisfy criteria allowing for the decomposition of inequality into the part that is due to inequality within areas (e.g. urban, rural) and the part that is due to differences between areas (e.g. the rural- urban income gap).
• The most widely used are the Theil indexes Theil’s T and Theil’s L.
• In most cases at least three-quarters of inequality in a country is due to within-group inequality, and the remaining quarter to between-group differences.
• Can help see what’s behind inequality and its changes. For the first time, in 1980 more or less, tendencies get reversed. There is a convergence between countries in GDP/capita. Less advanced countries have been able to catch up because of the reduction of energy prices and such energy dependency, can be one of the reasons. Also China and India catching up has a big effect.

Net vs. gross income

Smallest one for the USA, inefficient or weak transfer programs could be represented here or a mix of the two, France and Belgium among the biggest in terms of the impact of this effect.

Limitations

• One problem with measuring income inequality with the Theil index or Gini coefficient, or with relative income per capita growth between poor and rich individuals/countries is that it summarises changes in the entire income distribution into one number.
  • For example, a decline in income inequality can be perfectly consistent with poor individuals becoming poorer if there is more equality between high and middle-income individuals.
  • Or, a country may experience both a Gini-reducing decrease in poverty and a rise in the share of income going to the top 10%, which increases the Gini; if these effects offset each other, the overall Gini can remain constant, creating the impression that the distribution of income is not changing—while in fact the middle class is being squeezed out.
  • Transfer policy assessment or policy assessment in general can be impacted by this. Niches can be created for the rich, the poorest, etc.
• The value of the Gini index is not necessarily easy to interpret: above which value inequality can be seen as non-acceptable? →Moral judgement
• It’s like GDP/capita which doesn’t give distribution info, here we get closer but this is far from perfect.

Other indicators

An alternative way of looking at this is to explore changes along the entire income distribution:

• Average income/wealth per quantile group.
• Growth rates per quantile groups.
  • Indicator 10.1.1 of the SDGs: Growth rates of household expenditure or income per capita among the bottom 40 per cent of the population and the total population.
• Share of national income captured by each quantile group.
• Quantiles ratio
  • The Palma ratio focuses on the differences between those in the top and bottom income brackets: the ratio takes the richest 10% of the population’s share of gross national income (GNI) and divides it by the poorest 40% of the population’s share.
• The simplest measurement of inequality sorts the population from poorest to richest and shows the percentage of expenditure (or income) attributable to each fifth (quintile) or tenth (decile) of the population.
• The poorest quintile typically accounts for 6-10% of all expenditure, the top quintile for 35-50%.
• Decile dispersion ratio: ratio of the average income of the richest 10 percent of the population divided by the average income of the bottom 10 percent.

Atkinson’s Index

Absolute vs. Relative

C02

Ratios are prob the same but in absolute values the differences are big. The thing is being careful about what dimension and how to analyse it in inequality. Conclusions might slightly differ. Comparing income distributions is more neutral, and from it absolute and relative measures can be extracted also.

Is inequality (un)desirable?

This hasn’t been observed in the development path of countries, it’s just across different countries at one point in time. There is more redistribution in rich countries also, it’s not an automatic thing. Inequality traps can also be a thing or there can be a lack of institutions to redistribute. It’s not necessarily that simple basically.

Outcomes and opportunities

• The discourse on inequality makes a distinction between inequality of outcomes (as measured by income, wealth, or expenditure) and inequality of opportunities―attributed to differences in circumstances beyond the individual’s control, such as gender, ethnicity, location of birth, or family background.
• Inequality of outcomes arises from a combination of differences in opportunities and individual’s efforts and talent.
• It is not easy to separate effort from opportunity, especially in an intergenerational context (income or whatever already accumulated by ancestors).
• For instance, parental income, resulting from their own effort, determines the opportunity of their children to obtain an education.
• It is in this spirit that Rawls (1971) argued that the distribution of opportunities and of outcomes are equally important and informative to understand the nature and extent of inequality around the world.
• Traditional economic theory (marginal productivity theory) suggests that incomes reflect individuals’ contributions to society.
  • Not very helpful considering real inequality mechanics, however,
  • Some degree of inequality may not be a problem insofar as it provides the incentives for people to excel, compete, save, and invest to move ahead in life.
  • For example, returns to education and differentiation in labor earnings can spur human capital accumulation and economic growth, despite being associated with higher income inequality.

But

• However, marginal productivity theory (optimisation is in the margin, we have to pay attention to it) assumes no market failure. With market failures outcomes may not be efficient anymore. We can’t see everything from the marginal productivity theory standpoint.
• Inequality may stem from rent-seeking (i.e. earning income not through productivity but by extracting wealth from others.
• Together with some institutional and political factors rent-seeking may help explain the rise in inequality observed since the 1980s (e.g. Stiglitz 2016).

Proof

1. Corporate Executives: CEO pay has increased exponentially (from 20x the average worker’s wage in 1965 to >300x in 2010s) without a corresponding increase in productivity or firm performance.
2. Financial Sector: Bankers and financiers benefit from implicit government subsidies (e.g., “too big to fail” protections) and anti-competitive practices, leading to excessive pay premiums.
   • E.g.: 2008.
3. Monopoly Power & Intellectual Property Rents: Wealth accumulation increasingly comes from control over assets (e.g., land, patents) rather than productive investment.
   • Innovation should be rewarded but not excessively, tough to tell how much though.
   • Bad platforms that you have to use because of lack of aternatives.
4. Decline of Unions: Weakened labor bargaining power due to anti-union policies (e.g., Reagan’s suppression of strikes).
5. Asymmetric Globalisation: Capital can move freely across borders, but labor cannot, suppressing wages.
6. Tax & Regulatory Policies: Favorable treatment of capital gains, deregulation, and weak antitrust enforcement have amplified inequality.

We can thus end up in inefficient market setups very costly to society.

Positive relationship between inequality and growth

• Unequal Propensity to Save: Richer individuals save more, leading to higher aggregate savings and investment, which fuels growth (Fisher, 1930; Kaldor, 1955).
• Incentives and Equity-Efficiency Trade-off: Inequality motivates effort and investment in education or entrepreneurship, as higher rewards incentivise risk-taking (Mirrlees, 1971; Rebelo, 1991). Redistribution, while reducing inequality, may distort incentives and lower growth.

Negative relationship between inequality and growth

• Political Economy Channel: In democracies, higher inequality may lead to demands for redistribution (via median voter theory), increasing taxes and distorting economic incentives (Alesina and Rodrik, 1994; Persson and Tabellini, 1994).
• Sociopolitical Instability: Inequality can fuel crime, unrest, and conflict, undermining investment and growth (Alesina et al., 1996; Benhabib and Rustichini, 1996). Weak institutions exacerbate these effects.
• Credit Market Imperfections: Poor individuals, lacking collateral, cannot invest in human or physical capital, leading to underinvestment and lower growth (Galor and Zeira, 1993; Banerjee and Newman, 1993).
• Demand-Driven Innovation: Inequality affects innovation by altering market size and price incentives for new products. High inequality may limit demand for mass-market innovations (Foellmi and Zweimüller, 2006).
• In addition to affecting growth drivers, inequality could result in poor public policy choices: It can lead to a backlash against growth-enhancing economic liberalization and fuel protectionist pressures against globalisation and market-oriented reforms.
• Enhanced power by the elite could result in a more limited provision of public goods that boost productivity and growth, and which disproportionately benefit the poor.
• Positive channels often operate in the short run while negative channels manifest over longer periods : empirical investigation needed.

Empirical evidence

Empirical analysis (IMF 2015) found that:

• Income inequality (as measured by the Gini coefficient) negatively affects growth and its sustainability: if the income share of the top 20 percent increases by 1 percentage point, GDP growth is actually 0.08 percentage point lower in the following five years, suggesting that the benefits do not trickle down.
• Instead, a similar increase in the income share of the bottom 20 percent (the poor) is associated with 0.38 percentage point higher growth. However, the empirical literature yields mixed results, influenced by data quality, methodologies, and sample selection.
• Early Cross-Country Studies found a negative relationship between inequality and growth, but these relied on limited data.
  • Doesn’t take into account the time aspect (evolution).
• Panel studies often found short-term positive effects, while cross-sectional studies emphasised long-term negatives.
• Can’t control everything, size of the population f.ex. Data availability doesn’t always allow to conduct analysis on every variable that could change the relationship. The more information=the better the results.

Key findings

• Inequality’s effects vary by development level. Negative effects are more pronounced in poor countries, possibly due to credit constraints.
  • Poorness trap.
• Wealth inequality (e.g., land or human capital) often shows stronger effects than income inequality.
• Indeed we can see that other factors impact this relationship. This could be also more consistent with the Kuznets curve we saw, although this one was as we mentioned only cross-sectional.
• Inequality at the top may boost growth (via savings/incentives), while inequality at the bottom hampers it (Voitchovsky, 2005).
  • So it depends, we want transfers which are best to reduce poverty and inequality but inequality at the top isn’t that bad. We could strangle growth if we hit the rich too hard.
• Short-term growth may benefit from inequality, but long-term growth suffers.
• Recent Studies continue to diverge. Some find negative effects (Berg et al., 2018), others positive (El-Shagi and Shao, 2019), and some none at all (Ferreira et al., 2018).
• Methodological differences drive these disparities.

UNIGE EAIO