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The Gini coefficient, or Gini index, is derived from the Lorenz curve, and like the Lorenz curve it measures the degree of economic equality across a given population, and simplifies this reality into a single number.

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The Gini Coefficient | Intelligent Economist

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The Gini coefficient, or Gini index, is derived from the Lorenz curve, and like the Lorenz curve it measures the degree of economic equality across a given population, and simplifies this reality into a single number.

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One of the five major and common macroeconomic goals of most governments is the equitable (fair) distribution of income, which is a crucial element of a functioning democratic society. With regard to this macroeconomic goal, the distribution of income or wealth in an economy is represented by a Lorenz curve. (Note that income and wealth are distinct; one can have a high income but spend it all so that they have a low level of wealth, or a high level of wealth without any income at all.) The Lorenz curve is usually applied to economic inequality, but technically, it can be used to show the degree of unequal distribution in any context.

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The Lorenz Curve | Intelligent Economist

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One of the five major and common macroeconomic goals of most governments is the equitable (fair) distribution of income, which is a crucial element of a functioning democratic society. With regard to this macroeconomic goal, the distribution of income or wealth in an economy is represented by a Lorenz curve.

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Don’t take the readings as gospel. Just because something is printed doesn’t make it absolute truth. Be critical of what you are reading. I have chosen many readings precisely because they are provocative. If you find yourself strongly disagreeing with a reading, that’s fine. I encourage strong disagreement. However, if you disagree, you must clarify in your mind the reasons and evidence upon which you are basing your disagreement. At the same time, keep an open mind. Listen to what the readings have to say. Think about what other experiences you have had and readings you have done that might corroborate the course readings. Give yourself time to reflect on the information, insights, and perspectives offered in the readings.

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Who Pays: A Distributional Analysis of the Tax Systems in All 50 States 

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Who Pays: A Distributional Analysis of the Tax Systems in All 50 States 

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This report is the only distributional analysis of tax systems in all 50 states and the District of Columbia. This comprehensive report assesses tax fairness by measuring effective state and local tax rates paid by all income groups.

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1. What defines poverty, what causes poverty, and what are the consequences of poverty?

2. How has income inequality in America changed over time?

3. How do programs like Social Security affect poverty and income inequality?

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Define measures of economic inequality in income and wealth.

Explain sources of income and wealth inequality.

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Market outcomes can result in income inequality.

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Income levels and poverty rates vary greatly both across and within groups (e.g., age, gender, race) and countries.

The Lorenz curve and Gini coefficient are used to represent the degree of inequality in distributions and to compare distributions across different countries, policies, or time periods.

Explain sources of income and wealth inequality.

Each factor of production receives the value of its marginal product, which can contribute to income inequality.

Sources of income and wealth inequality include differences in tax structures (progressive and regressive tax structures), human capital, social capital, inheritance, effects of discrimination, access to financial markets, mobility, and bargaining power within economic and social units (firms, labor unions, and families).

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APMic Unit 6 Lesson 3 - Inequality Flashcards | Quizlet

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Start studying APMic Unit 6 Lesson 3 - Inequality. Learn vocabulary, terms, and more with flashcards, games, and other study tools.

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S2019 Presentation on US Employment (Recommended)

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S2019 Presentation on US Employment (Recommended)

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American Economic Association: AEA Richard T. Ely Lecture: Work of the Past, Work of the Future

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Economic Mobility: Introduction & Overview (EiP, part 1 of 5)

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FRBSF Senior Vice President and Associate Director of Research Mary C. Daly discusses economic mobility in the United States, the effect of a college education, and the American Dream. (part 1 of 5) Filmed live during the 2014 Meet the Experts speaker series in San Francisco, California on August 15, 2014.

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More on upward mobility

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Few scholars make as big an impact on their field as Professor Raj Chetty . As leader of the Equality of Opportunity Project , and with access to (anonymised) tax records, Chetty and his co-authors have transformed our understanding of social mobility in the U.S.

Among the many virtues of the Project's approach is a willingness to share the datasets used in each of their analyses, for use by other researchers as well as policymakers or the general public. (All this might make us sound like Chetty groupies - well, if the label fits...)

Prof. Chetty, who speaks at Brooking this week on his latest "Lost Einsteins" paper on inventions and inequality , has produced work deepening our understanding of relative intergenerational mobility, the role of place, gender gaps in opportunity and life expectancy, how colleges act to both disrupt and reinforce inequality, and the importance of good teachers in our schools and kindergarten classrooms.

We have pulled together a series of charts that highlight some of the key findings from Chetty's research, with links to the relevant papers and other commentaries and analyses.

It's a Chetty-chart-fest: enjoy!

2. There is a very strong relationship between the incomes of parents and the incomes their children will have as adults. Inequality, in other words, is strongly inherited :

4. It is a different story, however, for absolute mobility - which indicates how well a person does compared to their parents in absolute terms, rather than relative ones. On this measure of mobility, the last few decades have seen a sharp decline. Most Americans born in 1940 ended up better off, in real terms, than their parents at the same age. Only half of those of those born in 1980 have surpassed their parent's family income:

8. There is a gender gap in terms of the impact of place. Boys who grow up in low-opportunity places feel the effects much more strongly than girls. Growing up in Baltimore City, for example, reduces boys' household income by 27.9 percent:

10. Education helps to boost outcomes, starting at the pre-K level. An experienced kindergarten teacher boosts earnings by $1,104 on average:

11. A college education acts as a leveler, dramatically reducing the correlation between parents' income and the adult incomes of their children . This is true for elite colleges, other four-year institutions, and community colleges:

As this brief tour suggests, the range and impact of Chetty's work is already very substantial. But as he and his team at the Equality of Opportunity Project continue to collect, connect, and analyze administrative data, we can be sure that what we have seen so far is just the beginning.

What's the overall message of this body of work? The bad news is that there are very high, structural barriers to social mobility in the U.S. The good news is that these barriers are not insurmountable, and can be lowered through the sustained application of good policies.

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Raj Chetty in 14 charts: Big findings on opportunity and mobility we should all know

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Few scholars make as big an impact on their field as Professor Raj Chetty . As leader of the Equality of Opportunity Project , and with access to (anonymised) tax records, Chetty and his co-authors have transformed our understanding of social mobility in the U.S.

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A TED Talk on Income Inequality by Nick Hanauer

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Further Discussion @ http://www.politicalyak.com/f13/nick-hanauers-ted-talk-101.html A talk on Income Inequality and the differences in taxation given by Nick Hanauer at a TED conference. Make sure you visit our website @ - http://www.politicalyak.com -! Make sure you like us on facebook - http://www.facebook.com/PoliticalYAK -! & finally follow our somewhat awesome tweets - http://www.twitter.com/politicalyak -!

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What Wasn't Said in "Wealth Inequality In America"

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Jordan Peterson Destroys the Left's Idea of Gender/Income Inequality

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If You Hate Poverty, You Should Love Capitalism

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The focus here is on inequality developed through international trade

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The focus here is on inequality developed through international trade

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The Role of Trade in Cross-Country Income Differences - Timely Topics

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B. Ravikumar, senior vice president at the St. Louis Fed, talks about the role international trade plays in cross-country income differences. He discusses the study of economic development, who wins and who loses in trade, barriers to trade and more.

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Introduction to the Opportunity Atlas.

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This article helps further explain The Opportunity Atlas and Raj Chetty's findings.

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Some places lift children out of poverty. Others trap them there. Now cities are trying to do something about the difference.

SEATTLE - The part of this city east of Northgate Mall looks like many of the neighborhoods that surround it, with its modest midcentury homes beneath dogwood and Douglas fir trees.

Whatever distinguishes this place is invisible from the street. But it appears that poor children who grow up here - to a greater degree than children living even a mile away - have good odds of escaping poverty over the course of their lives.

Believing this, officials in the Seattle Housing Authority are offering some families with housing vouchers extra rent money and help to find a home here: between 100th and 115th Streets, east of Meridian, west of 35th Avenue. Officials drew these lines, and boundaries around several other Seattle neighborhoods, using highly detailed research on the economic fortunes of children in nearly every neighborhood in America.

The research has shown that where children live matters deeply in whether they prosper as adults. On Monday the Census Bureau, in collaboration with researchers at Harvard and Brown, published nationwide data that will make it possible to pinpoint - down to the census tract, a level relevant to individual families - where children of all backgrounds have the best shot at getting ahead.

This work, years in the making, seeks to bring the abstract promise of big data to the real lives of children. Across the country, city officials and philanthropists who have dreamed of such a map are planning how to use it. They're hoping it can help crack open a problem, the persistence of neighborhood disadvantage, that has been resistant to government interventions and good intentions for years.

Nationwide, the variation is striking. Children raised in poor families in some neighborhoods of Memphis went on to make just $16,000 a year in their adult households; children from families of similar means living in parts of the Minneapolis suburbs ended up making four times as much.

The local disparities, however, are the most curious, and the most compelling to policymakers. In one of the tracts just north of Seattle's 115th Street - a place that looks similarly leafy, with access to the same middle school - poor children went on to households earning about $5,000 less per year than children raised in Northgate. They were more likely to be incarcerated and less likely to be employed.

The researchers believe much of this variation is driven by the neighborhoods themselves, not by differences in what brings people to live in them. The more years children spend in a good neighborhood, the greater the benefits they receive. And what matters, the researchers find, is a hyper-local setting: the environment within about half a mile of a child's home.

At that scale, these patterns - a refinement of previous research at the county level - have become much less theoretical, and easier to act on.

Image

A map used by the Seattle Housing Authority identifies neighborhoods, shaded in purple, where housing officials and researchers believe that poor children have particularly good odds of rising out of poverty. Credit Seattle Housing Authority

"That's exciting and inspiring and daunting in some ways that we're actually talking about real families, about kids growing up in different neighborhoods based on this data," said the Harvard economist Raj Chetty, one of the project's researchers, along with Nathaniel Hendren at Harvard, John N. Friedman at Brown, and Maggie R. Jones and Sonya R. Porter at the Census Bureau.

The Seattle and King County housing authorities are testing whether they can leverage their voucher programs to move families to where opportunity already exists. In Charlotte, where poverty is deeper and more widespread, community leaders are hoping to nurse opportunity where it's missing.

In other communities, the researchers envision that this mapping could help identify sites for new Head Start centers, or neighborhoods for "Opportunity Zones" created by the 2017 tax law. Children from low-opportunity neighborhoods, they suggest, could merit priority for selective high schools.

For any government program or community grant that targets a specific place, this data proposes a better way to pick those places - one based not on neighborhood poverty levels, but on whether we expect children will escape poverty as adults.

That metric is both more specific and more mysterious. Researchers still don't understand exactly what leads some neighborhoods to nurture children, although they point to characteristics like more employed adults and two-parent families that are common among such places. Other features like school boundary lines and poverty levels often cited as indicators of good neighborhoods explain only half of the variation here.

"These things are now possible to think about in a different way than you thought about them before," said Greg Russ, the head of the Minneapolis Public Housing Authority, which is also planning to use the data. "Is opportunity a block away? These are the kind of questions we can ask."

The answers shown here are based on the adult earnings of 20.5 million children, captured in anonymous, individual-level census and tax data that links each child with his or her parents. That data covers nearly all children in America born between 1978 and 1983, although the map here illustrates the subset of those children raised in poorer families. The research offers a time-lapse view of what happened to them: who became a teenage mother, who went to prison, who wound up in the middle class, and who remained trapped in poverty for another generation.

Few of the children from Northgate still live in the neighborhood, but the data traces their outcomes as adults today back to the place that helped shape them.

The patterns broadly hold true for children growing up today, the researchers believe, even though the data reflects the experience of people now in their 30s. In rapidly changing cities like Seattle, some neighborhoods will look quite different now. So in drawing their opportunity maps, the housing authorities here, working with Mr. Chetty's team, also considered indicators like poverty rates and test scores for poor students today.

The researchers argue, however, that this data that looks back over the last 30 years can reveal something about a place that's not captured in snapshots of its conditions today.

In Seattle, that picture confirmed what housing officials feared - that their voucher holders had long been clustered in neighborhoods offering the least upward mobility.

"It really struck us as, well, we are contributing to this problem, not solving the problem," said Andrew Lofton, the executive director of the Seattle Housing Authority.

Here the response means offering some of those families more choices in where to live. But that solution won't help every child, or even many of them. The larger question is how to convert struggling neighborhoods into places where poor children are likely to thrive.

In other regions, the differences between such places are more visible than in Seattle.

In the Charlotte area, Ophelia Garmon-Brown, a longtime family physician, sees in these maps clear traces of where the fewest jobs are, where the high-poverty schools are, where African-American families live.

"You could drive from your home in south Charlotte to your banking job downtown and never see poverty, because we're so segregated," said Dr. Garmon-Brown, who grew up poor herself, in Detroit. "In some of this, we have to admit that was intentional."

The earlier research showed Charlotte as among the worst large metropolitan areas in the country in creating opportunity for poor children, a realization that prompted the community to create a task force co-chaired by Dr. Garmon-Brown. At this finer scale, parsing outcomes by race and neighborhood, poor white children in Charlotte have had more opportunity than poor black children, even when they've grown up in the same neighborhoods. In many parts of the region, however, their worlds simply don't overlap.

In other communities, what separates neighborhoods is probably tied to incarceration. Included in the new census data are neighborhood-level rates of children who were later counted in the census in prisons or jails on April 1, 2010.

About 1.5 percent of the entire cohort, adults then in their late 20s to early 30s, were incarcerated on that single day. For some neighborhoods in Milwaukee or New Haven, that number was far higher: As many as one in four poor black boys growing up in those places were incarcerated. Their neighborhoods - or something about how those neighborhoods were policed - sent more poor children into prison than out of poverty.

Underscoring how difficult it will be to transform these places, the federal government has spent billions in struggling neighborhoods over the years, funneling as much as $500 million into some individual census tracts since 1990, according to a tally by researchers of major placed-based initiatives like block grants and housing redevelopment programs.

"And yet we've never been able as a country to fully know whether and to what degree those investments were efficacious," said Kathryn Edin, a Princeton sociologist.

Ms. Edin and other researchers working with Mr. Chetty plan to re-examine those past government programs with the new data, which makes it possible to identify where children lived when they were exposed to those investments, and what happened to them afterward.

If the answers are not clear yet, there is a hint of answers coming, now that we have fine-grained data on millions of children, now that cities alarmed by the results are taking notice, now that philanthropists are lining up to help.

In Seattle, where all these pieces have converged, housing officials were recently driving past neighborhoods their map doesn't identify, into "opportunity areas" where families have begun to move.

"I believe the results of the data, but we all wish we knew what the distinguishing attributes are, so that we could build them in other neighborhoods," said Andria Lazaga, the director of policy and strategic initiatives with the Seattle Housing Authority. "That's the dream - to figure that out."
The poor children shown here were raised in families making about $27,000 a year (in 2015 dollars), at the 25th percentile of the national income distribution. Not all neighborhoods were home to such families, so researchers calculated tract-level estimates by extrapolating from the results of families at other percentiles who were present there. Data is not shown in tracts with few children. Results not shown here covering other income levels and full outcomes including incarceration are available here.
Josh Williams contributed research.

Emily Badger writes about cities and urban policy for The Upshot from the San Francisco bureau. She's particularly interested in housing, transportation and inequality - and how they're all connected. She joined The Times in 2016 from The Washington Post. @emilymbadger

Quoctrung Bui is a graphics editor and covers social science and policy for The Upshot. He joined The Times in 2015, and previously worked for National Public Radio covering economics and everyday life. @qdbui

A version of this article appears in print on , on Page A1 of the New York edition with the headline: Data Zooms In on the Springboards to Prosperity. Order Reprints | Today's Paper | Subscribe

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Detailed New National Maps Show How Neighborhoods Shape Children for Life

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Some places lift children out of poverty. Others trap them there. Now cities are trying to do something about the difference. SEATTLE - The part of this city east of Northgate Mall looks like many of the neighborhoods that surround it, with its modest midcentury homes beneath dogwood and Douglas fir trees.

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You will want to open this article in a separate window. It is from the Washington Post and has a video embedded within it.

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A revolution in genomics is creeping into economics. It allows us to say something we might have suspected, but could never confirm: money trumps genes.

Using one new, genome-based measure, economists found genetic endowments are distributed almost equally among children in low-income and high-income families. Success is not.

The least-gifted children of high-income parents graduate from college at higher rates than the most-gifted children of low-income parents.

First, consider the people whose genome scores in the top quarter on a genetic index the researchers associated with educational achievement.

Only about 24 percent of people born to low-income fathers in that high-potential group graduate from college.

That's dwarfed by the 63 percent college graduation rate of people with similar genetic scores who are lucky enough to be born to high-income fathers.

Contrast that with a finding from the other end of the genetic scoring scale: about 27 percent of those who score at the bottom quarter of the genetic index, but are born to high-income fathers, graduate from college. That means they're at least as likely to graduate from college as the highest-scoring low-income students.

The application of genetics to economics is in its infancy. Limitations abound. Most notably, researchers are forced to focus on white people. The world's genomic data comes overwhelmingly from people of European descent, and genetic comparisons across races can produce bizarre results.

But it can already begin to expose truths about the economy. The figures above come from a new, genome-based study of economic data which aims straight at the heart of the popular conception of America as a meritocracy.

"It goes against the narrative that there are substantial genetic differences between people who are born into wealthy households and those born into poverty," said Kevin Thom, a New York University economist and author of a related working paper released recently by the National Bureau of Economic Research.

"If you don't have the family resources, even the bright kids - the kids who are naturally gifted - are going to have to face uphill battles," Thom said.

"Their potential is being wasted. And that's not good for them, but that's also not good for the economy," his collaborator, Johns Hopkins economist Nicholas Papageorge said. "All those people who didn't go to college who had those high genetic scores, could they have cured cancer?"

Thom and Papageorge's analysis builds on the findings of one of the biggest genome-wide studies yet conducted. Published by a separate team of a dozen authors in Nature Genetics in July, it's the latest result of a lengthy, ongoing effort to bring genetic analysis to the social sciences.

The Nature Genetics team scanned millions of individual base pairs across 1,131,881 individual genomes for evidence of correlations between genes and years of schooling completed. They synthesized the findings into a single score we can use to predict educational attainment based on genetic factors.

Thom and Papageorge studied the team's index after it was calculated for a long-running retirement survey sponsored by the Social Security Administration and the National Institute on Aging. About 20,000 of the survey's respondents, born between 1905 and 1964, provided their DNA along with their responses, which allowed the economists to attach genetic scores individuals' academic and economic achievements.

You already know their key finding, that being born rich trumps being born gifted. But that simple finding becomes more interesting as you come to understand what makes it unique, what it represents and the important ways in which it remains limited.

Studies fueled by huge genetic data sets likely won't upend economics like they did the biological sciences, but they do allow economists to do something new: control for the environments that people grow up in.

Previous attempts to separate academic potential from the advantages given to children of wealthy families relied on measures such as IQ tests, which are biased by parents' education, occupation and income.

Such tests can't be administered at conception, birth or infancy - before high-income parents have given their young children a head start by feeding them well, reading to them at higher rates and enrolling them in more activities.

"Two people who are genetically similar can have strikingly different IQ test scores because the richer ones have invested more in their kids," Papageorge said. When you look at the raw genetic potential of the two people, though, "you see they're actually quite similar."

The analysis doesn't hinge on a "smart gene." Such a thing doesn't exist. Genes interact in mysterious ways.

Rather than linking individual lines of genetic code to specific characteristics, scientists seek correlations along the 10 million or so steps on the double-helix ladder which explain most human diversity. They focus not on what each base pair might do, but what they might explain in the aggregate.

Geneticists, who had focused on biological attributes with clear genetic connections, were initially skeptical that an outcome as complex as education could be connected to a genetic index, Thom said. But outside tests have consistently proven the score can predict college graduation rates.

Some of the individual genetic encodings influence traits including fetal brain development and lifelong neurotransmitter secretion. Each has an infinitesimal impact on an individual's achievement. Taken together, they explain 11-13 percent of the difference in academic achievement between people.

That variation is useless if you just want to use your kid's 23andMe data to determine whether she'll get the PhD that you never did. But combined with a large population, it allows researchers to do things that, just a few years ago, seemed like Hocus Pocus.

"We are just starting to enter an era where researchers are finding for the first time really credible linkages between genetic markers and social science outcomes," Thom said.

Thom was drawn into the emerging field when colleagues got him interested in genetic markers that offered a biological measure of elevated smoking risk.

"Once I got in there, it's clear that there are all kinds of questions" we could answer with genetic analysis, Thom said.

The techniques Thom and Papageorge used can't yet be widely applied. Unlike the retirement survey they used, most economic data doesn't have a genetic component. Major economic surveys like those behind the unemployment rate aren't distributed with little saliva collection kits for genome sequencing.

And their work has limitations, in addition to being limited to white people for now. The genetic scores they used are hard to separate from their environment. They reflect the genes that were most successful when and where those individuals were growing up. And behavior that led to academic success decades ago may not be so useful when pedagogy evolves.

When the genetic index Thom and Papageorge used was tested among genomes from siblings, the results indicated as much as a quarter of the variation in score could be due to genetic code that correlates with environmental factors.

There may be genes associated with parenting behavior that creates an environment conducive to your child's success. Children with that gene would tend to succeed in school not because that gene directly helped them study, but because they received both the gene and a success-friendly environment from their parents.

It's a reminder that the study's key finding is also its key caveat: genes aren't destiny. Most achievement can't be explained by genetic factors. Environmental factors like parents' income, on the other hand?

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Analysis | It's better to be born rich than gifted

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A revolution in genomics is creeping into economics. It allows us to say something we might have suspected, but could never confirm: money trumps genes. Using one new, genome-based measure, economists found genetic endowments are distributed almost equally among children in low-income and high-income families. Success is not.

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What is causing inequality to worsen?

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Lansing EL Consequences of Rising Income Inequality

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How do we measure inequality?

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Gini in a Bottle Some Facts on Income Inequality - Page One Economics - St. Louis Fed

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Sales tax

Sales taxes are applied consistently to purchases regardless of consumer income level. The tax is considered to be regressive because low-income consumers experience greater consequences from the tax and so end up paying a larger percentage of their income as a result of it.

For example: If two people spend $150 on shoes and pay a 5% sales tax (just as an example), they both pay $7.50 in taxes on these shoes. But if one of these people (Person 1) makes $5,000 per month, and the other only makes $1,500 (Person 2), there is a significant difference in the percentage of income that they end up paying in sales tax on this item. The sales tax rate on Person 1's $150 shoe purchase is 0.15 percent, but the sales tax rate on Person 2's purchase of the same size is 0.5 percent, more than three times as much that of Person 1.

Propety tax

Property taxes can be regressive similarly to sales taxes, but this isn't typically the case in practice. We'll explain why. The amount of property taxes is based on the value of a given property, rather than the income or overall wealth of the owner of that property. So a low-income and a high-income property owner who each own properties of the same total value will end up paying the same amount for their property tax. This is what makes property tax regressive. In practice, though, high-income individuals will typically purchase higher-value properties, so they end up paying a greater total amount in property taxes most of the time.

For example: Let's say Person 1 earns an annual income of $30,000 and Person 2 earns a monthly income of $60,000; additionally, each person owns a home valued at $100,000. There's a 1% yearly property tax, so they both pay $1,000 per year in taxes on their homes. But for Person 1, that property tax amounts to roughly 3.3% of their income, while for the higher-income Person 2, it's half that, about 1.7%. However, in most cases, someone with twice the income might spend twice as much on their home. In reality, Person 2 would be more likely to have, say, a $200,000 home, which means then that they would end up paying 3.3% of their income in property taxes as well.

Sin tax

These kinds of taxes are taken for goods that are seen as harmful to individuals and society as a whole. Most commonly, this includes tobacco and alcohol. The taxes on such goods are higher than for other goods in an effort to discourage people from consuming large amount of harmful substances. As with sales taxes, the tax makes up a greater percentage of the incomes of low-income people than of high-income people, which makes it regressive.

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Sales taxes are applied consistently to purchases regardless of consumer income level. The tax is considered to be regressive because low-income consumers experience greater consequences from the tax and so end up paying a larger percentage of their income as a result of it.

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The Opportunity Atlas is an initial release of social mobility data, the result of a collaboration between researchers at the Census Bureau, Harvard University, and Brown University. While the estimates in the Opportunity Atlas are not provisional, we are still testing aspects of the research product, including census.gov integration, planned annual data refreshes, and variable additions. Any opinions and conclusions expressed herein are those of the authors and do not necessarily reflect the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. The statistical summaries reported in this paper have been cleared by the Census Bureau's Disclosure Review Board release authorization number CBDRB-FY18-319.

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Map the childhood roots of social mobility

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A presentation based on OurWorldInData.org

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A presentation based on OurWorldInData.org.

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Don't forget to go to this Padlet for links to quizzes and questions.

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The Economics of Inequality- The Gender Pay Gap- Part 1

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techniques for assessing and visualising the degree of income inequality in an economy

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AP Micro: POL (BI), POL‑2 (EU), POL‑4 (EU), POL‑5 (EU)

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About this unit

In this unit, we start exploring the arguments for and against government intervention in an otherwise competitive market. We examine the conditions for allocative efficiency, using the marginal social benefit and marginal social cost principle, and the ways in which externalities, public goods, and the market distribution of income create market failures even in competitive free-market economies. In addition, we study the effectiveness of government policies such as subsidies, taxes, quantity controls, and public provision of goods and services, which are designed to correct market failures and achieve allocative efficiency.

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Market failure and the role of government | Khan Academy

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In this unit, we start exploring the arguments for and against government intervention in an otherwise competitive market. We examine the conditions for allocative efficiency, using the marginal social benefit and marginal social cost principle, and the ways in which externalities, public goods, and the market distribution of income create market failures even in competitive free-market economies.