Inequality and Redistribution Flashcards
Case study- Johannesburg
A highly unequal city. South Africa is one of the most unequal countries in the world vying with Brazil. Can see the contrast between neighbourhoods which reflects the legacies of segregation and inequalities in opportunities/outcomes, which fall along racial lines. However high levels of economic inequality persist, generally to a lesser extent, in wealthy countries
Case study- Inequality in OECD countries
Across OECD countries, we see that even at the set of relatively wealthier, advanced democracies, there’s a lot of variation.
Northern European and Eastern European countries tend to be more equal. The US is considered to be an outlier in terms of wealthy, advanced democracies in its high level of economic inequality.
Variation over time. In most OECD countries, the gap between the rich and poor, as reflected by the Gini coefficient, is at its highest level in 30 years. Has been trending upward.
In the late 1970s and early 80s inequality kind of first started to take off in English speaking countries. And the growth has accelerated from the 1980s onwards
Case study- Inequality over time, by type of country
Advanced economies have generally been on an upward trajectory since about 1970.
Emerging market economies have had much more mixed trajectories. Some even appear to be becoming more equal in the last few decades.
Data limitations particularly in non democracies which can keep us from understanding the true extent of economic inequality. E.g. Russia there’s research that official survey based measures vastly underestimate the rise of inequality in Russia- wealth offshore
Why is economic inequality rising
- The rich is getting richer- income and accumulated wealth (assessts prices e.g. houses)
- Role of globlisation/international trade
- Declining power of TU’s
- Growth of the financial sector/ privitisation/deregulation- Jacob Hacker & Paul Pearson argued this is what explains the rise of inequality in the US. Others noted something similar in the UK, beginning with Thatcher.
- Tecnological- rise in automation, AI led to a loss of skilled manual and some non-manual jobs
- Redistribution policies- explains variation between countries predominatley the size of welfare state. There are both cultural & institutional factors that contribute to its size. Welfare state is often operationalised as the proportion of GDP that’s spent on social welfare programs.
Case study- Why does the US look so different to Europe
Why does Eastern Europe have low levels of economic ineuqality pre-tax
Related to political institutions that limit the role of the state. The US has a majoritarian electoral system with extensive checks & balances so we often end up with divided govs which are factors that contribute to keeping the welfare state small
Especially non-EU Eastern Europe, tend to have low levels of economic inequality when measured pre-tax as the legacy of communism, means these countries have strong gov intervention in markets. Though there is an overall trend towards less gov intervention in markets.
How does ethnic heterogeneity impact redistribution policies?
Proposed there are both direct and indirect effects of ethnic heterogeneity
Idea that govs tend to redistribute less when their populations are ethnically diverse, and the US is far more diverse than any European nation.
Some of the wealthy countries that we tend to see, have lower levels of economic inequality, like the Scandinavian countries, tend to be more ethnically homogeneous.
Correlates of economic equality- the individual level of behaviour
Lower generosity and cooperation (Cote et al 2015)- less likely to redistribute the money they’re given
Increases status anxiety and risk-taking (Payne et al 2017)
Suppresses trust in gov (Kuziemko et al 2015)
Increases the belief in the legitimacy of inequality (Trump 2018)- self-reinforcing dynamic to inequality, where in conditions of inequality, people become more accepting of inequality, or they believe that it’s deserved. It’s because some people are hard workers and others aren’t.
Shapes beliefs about inequality and economic opportunity (McCall et al 2017)- reduces people’s optimism about economic opportunities.
Correlates of economic equality- the aggregate level of behaviour
Worse mental and physical health (Pickett & Wilkinson 2015)
Lower perceived social mobility (Buttrick & Oishi 2017)
More crime & vigilantism- (Phillips 2017)- people are more likely to take the law into their own hands in conditions of high economic inequality
Lower self-reported happiness- (Alesina et al 2004)- evidence that happiness depends on your income relative to some reference group typically. Less about absolute levels of wealth/income and more about relative income (how much you make in comparison to friends and family)
Lower levels of economic growth- (De Dominicis 2008)- possible explanation is that inequality tends to lead to underinvestment in education by lower income households then lowers the accumulation of physical and human capital which can lower economic growth.
Limitation to aggregate and individual behaviour
A limitation of this body of research is that it’s really hard to make causal attributions to a big systemic problem like high economic inequality.
We have a lot of correlational evidence about aggregate effects and then evidence that’s based on sort of relatively artificial or superficial interventions, like when we just tell people that conditions are unequal or we create inequality in a lab.
Measuring economic inequality- Gini
Designed to capture income/wealth inequality.
Useful as the distribution of wealth/income in a population is now a single number that we can use for making comparisons.
The larger the Gini coefficient is, the more unequal the distribution is. A Gini coefficient of zero means perfect equality, where everyone has the same income or the same wealth.
Going to depend on what is being measured- wages, before or after tax wages, property wealth.
Easier to measure income than their wealth as it can be illiquid. In settings where we don’t have good information, we don’t have reliable information on people’s income. We might calculate it as an index of goods that people have.
Other ways of measuring economic inequality
Can use percentile ratios, most common being the 80:20 ratio or 90:10 ratio.The 90:10 ratio would measure the income level of individuals at the top 10% of the income distribution relative to those in the bottom 90% of the distribution. Also the Palmer ratio.
Income diversity ratio is meant to capture the variability & distribution of income in a given geography
Why do indicies vary overtime?
Wealth vs income inequality- income refers to money received/earned on a continuous basis for work/investments. Wealth implies money or valuable possessions, like property that’s accumulated overtime. Wealth inequality tends to be more stark & pronounced than income inequality. Most systems measure income not wealth inequality as it’s much harder to measure especially if illiquid.
Measuring income can be complicated- investment income, pensions etc. because there’s not non-salary sources.
Pre or post gov taxation or redistribution. Also can take into debt or not which will affect a measure of wealth or income
Case study- Chile/Korea
Measured in 3 different ways:
Original is unadjusted income.
Gross income from wages & salary plus other forms of income like pensions, interest, dividends, and rental incomes
Disposable income is the amount of money you have for spending and saving after your tax and any money provided by the state.
Impact the taxation- Countries vary in their Gini coefficient but also in the gap between inequality using their pre & post-tax income. Shows how much taxation is contributing to reducing economic inequality in various places.
E.g. in Chile there’s not much difference between the Gini coefficient before and after taxes and redistribution- pretty similar. Same with Korea.
Look at European countries, European taxation is doing a lot to reduce income inequality as measured by the Gini coefficient.
Demand for redistribution e.g. the US
US household incomes in 2015.
See the meanness to the left of the median. There’s this long tail which represents the wealthy. Note the distribution is truncated. So this is actually these are $250,000 and over. So this if plotted out it would be much longer if in equal bins. So the tail is actually a whole lot longer than is reflected.
Another one of these measurement things to be aware of. Often when we take sort of official gov data on income it’s truncated. So we’re actually not necessarily getting a full picture of the extremes of inequality as we’re not even, we’re just putting all of those super wealthy people at $250,000 and over. That long tail is pulling the median up relative to the mean.
Demand for redistribution e.g. the UK
After redistribution you can see the distribution is positively skewed but is less pronounced in the UK than it is in the US.
But, it’s still relatively unequal.
We still have this median to the left of the mean and this long tail. And I’m sure the data is truncated too, because this is £80,000 which is actually not a lot in the context of wealthy people in the UK so should go out a lot further. This makes the UK look more equal than it actually is