A lot of attention (myself included) has been recently put on the Top 1% income and wealth. However, there is also substantial inequality in the other 99% that is worth exploring. To get an idea, if we took all the Top 1% income growth between 1979 and 2012 and distributed it among the other 99%, each of us (I assume you also belong to the other 99%...) would earn around $7000. However, the increase in the earnings gap between a college-educated and a high-school educated household is four times that in the same period. Hence, here we will focus on wage inequality among the other 99%, but particularly between the bottom 10% and top 90%, so as to exclude the very extreme cases (which deserve a different attention). But first, Figure 1 shows how wages have changed between 1963 and 2005 by wage percentile. Here we see that generally there was a much bigger increase in wages among the top half than the bottom one.
Figure 1: Change in real wages by percentile, 1963-2005.
A common measure for overall inequality is the ratio of those at the 90 and 10 percentiles. A typical issue is that the population might be changing its structure, with more people getting educated or more work experience. As this happens, the typical person in either of these percentiles might be changing, hence changing our standard interpretation of increasing inequality. Another take on inequality is to look at between-groups inequality, where the typical comparison is those with a college degree and those with a High School degree. This tries to avoid the issues of other characteristics of the population changing as in the overall inequality measure. However, another alternative look at inequality is to look within-groups, hence evaluating how much variation there is among small groups (for example: college educated, 25-30 years old, male). This three measures of inequality are displayed in Figure 2, where we see that even though the three of them have increased over the long haul, they have done so at different paces and through different paths. Particularly the college premium follows a strange path, increasing in the 1960s, decreasing in the 1970s and increasing very fast since the 1980s. This suggests that a simple, unique explanation for the recent increase in inequality is not likely to work.
Figure 2: Three measures of Income Inequality.
But has inequality changed more among the top or the bottom? An easy way to look at this is to compare the 90 and 50 percentiles (upper-tail inequality) and, separately, the 50 and 10 percentiles (lower-tail inequality). Note this still excludes the very bottom and very top. Figure 3 shows that even though lower-tail inequality grew in the 1980s, it has not grown since then. On the other hand, upper-tail inequality has increased continuously.
Figure 3: Upper- and Lower-Tail Inequality.
So what is behind this monumental increase in inequality? Identifying the cause of this change is very hard or probably impossible, but what we can do better is identify the proximate causes, meaning what seems to be closely associated with this change, even if we do not understand what led to the first thing. And this is where the skills of economists Autor, Katz and Kearney comes into play. They argue that we cannot simply think of people as belonging to one of two groups - skilled and unskilled - where the top one is associated with higher education. Figure 4 shows that from 1979 to 2005 the wages of those with a post-college education grew by a lot more than those with college degree. Moreover, the difference between those with exactly college and high-school degrees slowed down significantly since the 1980s. And finally, the difference between those with high school degrees and those without one has flattened or even decreased since the mid-1990s. All this suggests that, since the 1990s we are in a situation where the income among the very high- and very low-skilled workers has increased relative to those in the middle. Income has polarized.
Figure 4: Changes in wages by Education.
What explains this? The main hypothesis is that computerization has changed the demand for job tasks and affected the demand for skills in such a way that explains this polarization of income. Computers are good at doing routine tasks which are codifiable, like bookkeeping, clerical work or repetitive production tasks. (If you have interacted with a call-center lately, you will probably know how computers have improved in voice recognition and seem to have taken over those tasks that require gathering the same information all the time). On the other hand, abstract tasks like those performed by "high-skills" managers or educated professionals are hard to automatize since they require cognitive and interpersonal skills and adaptability. Similarly, manual tasks used in many "low-skilled" jobs like security guards, cleaners and servers are hard to computerize and hence have not been affected much by the advance of computers. Figure 5 confirms this intuition that low-skill jobs (taking the average education of those performing such jobs) usually have manual tasks. On the other end, high-skill jobs are mainly filled with abstract tasks. However, routine tasks are concentrated between the 20th and 60th percentiles.
Figure 5: Task intensity by Occupational Skill.
The conclusion is that the change in wage inequality may be substantially explained by changes in the demand of skills, which has been lately polarized by the introduction of computers. As the demand for these jobs increased, so did their wages. But why haven't workers matched the increase in demand by educating themselves more? Well, most likely this change was very hard to predict and so not enough people found higher-education to be "worth it." However, recent trends in education attainment suggest that young people are catching up to this increased demand.
Based on an article by Autor, Katz and Kearney.
Figure 1: Composition of US Income Inequality.
In the last few years, substantial research from Piketty, Saez, Atkinson and others has brought the topic of inequality back to the front page of economics. They use extensive data, including tax records in some cases, to analyze the evolution of (mainly top) income inequality for a long period of time. Charles Jones has updated and summarized some these studies, which is the basis of this article. The starting question is then: How much inequality is there?
Figure 1 shows the share (and composition) of income held by the top 0.1% of the population. The first striking finding is that there is a long U-shaped pattern: (Top) Inequality was very high before the Great Depression (with the top 0.1% holding as much as 10% of the total income); Lower and steady inequality after WWII; Rising inequality since the 1970s (reaching pre-1930 levels).
Taking into account that GDP can be theoretically split into labor income (e.g. wages, salaries and business income) and capital income (capital income and gains), we can divide the analysis of inequality in a similar fashion. This shows that most of the initial decline is due to a reduction in capital income, while most of the sequential increase is due to labor income (and capital gains possibly). The returns on capital seem to have become relatively smaller for the top 0.1% of the population, while wages and business income have become more important. (A big driver of of this might be the importance of land rents in the income of this part of the population)
If you have read about Piketty's book, you may have heard about the magnitude of wealth inequality. Wealth inequality is much greater than income inequality. While the top 1% of the population hold about 17% of income, the share of wealth held by them in the US is estimated to be above 40%. The cutoff to be in the top 1% of income is 330 thousand dollars a year, while 4 million dollars are needed to be among the wealthiest 1%. Figure 2 shows the path of wealth inequality for the France, the US and the UK. It is seen that wealth inequality was a lot higher before WWI than it is today. However, this hides the fact that wealth inequality has started to increase in the 1960s. On the positive side, (at least for UK and France) it still remains smaller than in the 19th century.
Figure 2: Wealth Inequality.
So far we have discussed how inequality has behaved within labor income and within wealth. Given the importance of inequality within wealth, the remaining question is how has the share of income taken by capital evolved over time. Since most of the capital income is captured by a small number of people, a tiny change in the share taken by capital (instead of labor) can lead to substantial effects on general levels of inequality. While most of the previous plots focused on the top 1%, this is now more about the top 10% (which holds 3/4 of the wealth in the US) versus the bottom 90% (which holds the other quarter, most of which is actually held within the 50-90% range). Figure 3 shows that the share of income taken by capital had either decreased or remained stable until the 1980s. However, since then, the share of income (think of this as the share of the revenues taken by capital and property owners) taken by capital has increased in all three countries.
Figure 3: Capital share of payments.
Inequality is a big concern. However, its causes and consequences remain a puzzle. On the causation side, much research remains to be done. On the consequences side, many views are possible. Regarding the individual level, inequality might affect the chances some people have of making progress, for example through access to education. If children lack basic needs (like food), they most likely won't attend school. Regarding the aggregate level, inequality might also hinder general economic growth. For example, through reduced access to education, innovation might be damaged. However, it has also been claimed that inequality might be necessary for growth. For example, in a very poor country, if wealth is split equally no one might be able to invest. However, higher inequality might allow the richest people to be able to use their extra resources to invest and generate growth. Later, opportunities for the poor ones might flourish, leading to lower inequality. This is known as the Kuznets curve. Whatever your hypothesis is, careful thinking and proper research are probably necessary.
Based on a working paper by Charles Jones.
How much do children's social and economic opportunities depend on parents' income and social status? This is a politically correct way of asking: How doomed are children from poor parents?. The answer is essential to analyze policies that try to make every kids chances more equal. As always, a first step is to analyze what the data has to say about this. Fortunately, Chetty, Hendren, Kline and Saez (economists at Harvard and Berkley) are currently doing some beautiful analysis on this matter. Since opportunities are hard to measure, they focus primarily on income (although they also study education, crime or pregnancy) differences.
Using tax-income data on 40 million children born between 1980 and 1982 and their parents, they are going to rank people according to their income level. Parents are going to be ranked in groups from 1 to 100, according to how they do income-wise relative to other parents. Similarly, children are going to be ranked according to their incomes when they are 30-32 years old relative to the other children. Then, they are going to focus on two measures of intergenerational mobility:
1) Relative Mobility: What are the outcomes of children from low-income families relative to those from high-income ones?
Example: If my parents income increases by one ranking point, how much is my income rank expected to increase?
(The problem with this measure is that higher mobility may be due to richer people doing worse, not poor ones doing better. Hence, the second measure might be more useful.)
2) Absolute Mobility: What are the outcomes of children from families of a given income level in absolute terms?
For example, what is the mean income of a child born from parents in the 25th percentile?
The chart below shows the national statistics of the rank-rank (relative mobility) relationship in 3 countries: Canada, Denmark and US. The slope in the US is 0.341, while the other two are half that much. This suggests that increasing one percentage point in parent rank, increases child mean rank by 0.341 percentage points. The fact that Canadian and Danish data suggest higher relative mobility should be taken with caution since this could be due to worse outcomes from the rich, rather than better ones from the poor. Interestingly, this strong correlation with parents income rank is also observed in children's college (attendance and quality) and teenage pregnancy, suggesting differences emerge well beyond the labor market. This is consistent with evidence from my previous post.
The previous chart suggests that the rank-rank relationship is highly linear. Hence, the authors are going to take advantage of this when analyzing the intergenerational mobility across different areas in the US. The question now is: Is mobility the same across the US? Or are some regions better for children to make the jump forward? Given the issues with relative mobility, we can now focus on absolute mobility: What is the mean income of a child born from parents in the 25th percentile? The heat map below shows that the Southeast shows the lowest mobility in the country, while the Great Plains, West Coast and Northeast display much higher mobility levels (the map should be read the map as darker is worse mobility). While in some regions children of parents in the 25th percentile tend to remain in the same percentile when they grow up, in other areas similar children do twice as better (in income rank terms). This pattern seems robust to controlling for children moving to other areas and cost of living or demographic reasons like marriage differences.
The obvious next question is why are regions' mobility so different from each other? Why children in some areas seem to be born with more opportunities than those in other ones? This question is not directly addressed by the authors, but they provide some correlations with local characteristics. Given econometric issues like selection and endogeneity (also explained in a previous post!), the following should NOT be interpreted as causes.* However, they show interesting descriptive information.
1) Race and Segregation: The higher the share of African-Americans, the lower the mobility observed. However, the data suggests that this holds true for the white people in those areas as well. Hence, it is not that black people tend to remain stagnant. Segregation in the area seems to be correlated with everyone's mobility. Particularly, segregation of poverty seems to be the strongest reason (isolation of rich people does not seem to be behind). Some potential reasons could be: successful role models are not present for the poorest children; worse public goods provision; or access to jobs might be harder in such areas.
2) Income: The average income level is not correlated with mobility (i.e. it is not that richer areas do better or worse). However, areas with higher income inequality show lower degrees of mobility. Interestingly, the inequality in the upper tail is not correlated with mobility. Hence, it is not about the existence of some extremely rich people. It is more about the size of the middle class. The bigger the middle class, the higher the mobility.
3) School Quality: Better schools are associated with higher mobility.
4) Social Capital: Social participation in elections, census or even religious events is positively correlated with mobility.
5) Family Structure/Stability: The higher the number of single parents, the lower the mobility. Once again, this effect extends to children who are born from parents who remained together, suggesting that the effect is not at the individual level but at the social environment one. Regions with more divorce somehow have lower mobility.
To summarize, parents income seems to be very important on children opportunities. However, there is substantial variation across different areas in the US. Some areas seem to fit much better than others the concept of "Land of Opportunity." A child raised in the Great Plains has much better chances of making a leap forward than one born in the Southeast. Segregation, inequality and family structure are highly correlated with mobility. Unfortunately, why remains a mystery.
* Families choose where they live and what institutions they support. So we can imagine that families that prefer to live in areas with better education systems or less income inequality are intrinsically different than those that prefer to live in the more segregated South of the US.
Many major economic and social problems such as crime, teenage pregnancy, dropping out of high school and adverse health conditions can be traced to low levels of skill and ability in society. The figures below show that (measurable) ability is highly correlated with having been in jail or being single with children. Economists have always been interested in the way typical goods (say agricultural or industrial ones) are produced. But if these skills and abilities are that important in life, shouldn't we focus on better understanding their production process?
Ever been in Jail by 30 years old, by ability (males)
Probability of being single with children (females)
Would it be simplifying but relatively fair and innocuous to summarize all our skills into one word like ability or intelligence? Or is it important for us to recognize that there is more than one skill? For example, the No Child Left Behind Act concentrates attention on cognitive skills (math, reading) through achievement test scores, not evaluating a range of other factors. However, noncognitive (personality, social, emotional) skills seem to be very important as well. They contribute to performance in society at large. Gaps in skills seem to be present early in the lives of children, being family environments very good predictors of them. The chart below shows that Children's cognitive skills gaps are present as early as 3 years old and are strongly related related to mother's education.
Mean Cognitive Score by Maternal Education
Can early intervention fix these differences? Economically speaking, are these skills better "produced" when children are one year old or can we later remediate them when they are older (through primary and high school)? These issues are essential when analyzing public policies to improve education or adult socioeconomic behavior. Current policies, like reducing pupil-teacher ratios, focus on later remediation. But shall we improve schools? Or is it better to focus in educating parents so that they can take better care of their children in the first three years of their lives?
Heckman has started a major project in order to be able to understand these important questions. But (good) economists like using data carefully in order to answer questions objectively. It's important to recognize that we don't have direct data on these skills. People don't carry a number with them saying "I have cognitive skills of level 2 and noncognitive ones of level 5". So Heckman and his coauthors are going to do some heroic econometric work to get around this.
They assume that these unobserved factors are related to children parents (through their also unobserved skills, income, education, etc) and their "investments" in their kids (reading them, taking them to museums, etc). But how can Heckman estimate the effects of things we don't observe? Basically they are going to assume that these (unobserved) skills are related to test results and later outcomes in life like education, crime, early pregnancy and many others. Taking into account how these multiple outcomes and investments correlate with each other, will allow them to estimate these two set of skills and give them the information they are after. Notice we need very large amounts of information on the same children and their parents at many periods of their lives. So where does the data come from? It may be hard to believe, but many countries have such data. For example, over 10 thousand American children (and their mothers) have been followed since they were born, which will let Heckman estimate what matters in child development and how we should distribute our efforts in order to improve it?*
Let me give you an idea of the amount and type of questions these families answered when they were interviewed, usually every two years. From these surveys, we know: children's gestation length weight at birth, memory for locations, picture vocabulary, standard test results (on reading and algebra), friendliness, sociability and behavior problems; whether their parents read them, how many books they have, how often the family eats together and whether they go to museums or concerts; their mother's arithmetic skills, self-esteem and their family income and savings. And many many more. A crazy large amount of information is collected in a regular manner from these same people. A by product of this study is that we can find out what seems to work best in improving children's skills. Interestingly, how often the mother reads to the child or if they eat together during the first year seems to be some of the most important factors. Similarly, once the children grow older (6+ years), going to museums or concerts seem to become very important for their development as well. (If you are thinking about applying this nowadays, you should take this with a grain of salt. Keep in mind these children grew up in the 1980s so they did not ask to go to Miley Cirus concerts. Music was probably better then.)
So what about the production process of these skills? Is it better to invest in the first years or we can achieve the same results by "fixing" children's bad initial years when they get older through better education systems? Heckman's results suggest we should focus on the first three years of their lives. Parental investment in these years has a much greater impact than later ones. Moreover, during these early years improving one set of skills seems to increase the quality of the other one. Skills beget skills.
And are the two skills equal? No. Children's cognitive skills tend to stabilize early in life (say around 6 years old) and are difficult to change later on. On the other hand, social skills seem to flourish when children are between 6 and 14 years old. In case you are wondering whether economics has gone mad, let me say that this seemingly crazy study suggests that what happens in these early years of life can explain over 50% of the years of education, criminality or teenage pregnancy. This is very relevant. Moreover, it suggests that if governments were interested in improving any of these outcomes, they should try to invest very early (before schooling years) in the disadvantaged. Possibly educating parents on the importance of reading to their children or taking them to social activities might help. How to approach this parental education is the next issue at hand.
* They are actually going to use only 2000 first-born white children in their estimates, in order to avoid issues related to my last week posts. They want children to be as similar as possible, in order to avoid capturing wrong effects in their estimates. They also allow for endogeneity and measurement error in their estimation process.
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