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.
A few weeks ago I wrote about the life cycle of earnings, where Guvenen, Karahan, Ozkan and Song had used over 200 million tax data observations from the Social Security Administration (between 1978 and 2010) to see how income moved with age. With that amazing data they showed that mean income would peak around the age of 50, though for the median person income would peak earlier. Given their findings I decided to look (though with worse data) at how the life cycle of earnings has changed over time.
Using Census data from the US (available through IPUMS for any other data addicts reading this), I looked at average income by age for each decade. The caveat from using this information is that if there are some cohort effects (meaning earnings are changing differently for young than for older people within one decade) I will not capture this directly, possibly leading to some confusion in the analysis. Nevertheless, the patterns are quite striking.
Figure 1 shows that average labor income used to peak a lot earlier than it does nowadays. Back in the 1960s, it used to peak around the age of 35. Income was expected to start going down after 35. However, decade by decade, this peaking point has been increasing. By the 1990 the peak seemed closer to 45, and nowadays the peak can be as high as 50. Given the results found in Guvenen's research it might be that nowadays the median worker's labor experience is much more different from the mean worker than it used to be. But why?
Figure 1: Average Income by Age, over time.
Figure 2: Relative variance of Income by Age, over time.
Source: IPUMS Census USA. Family labor income by age of head, excluding people in school or with no income.
One possibility is the increase in the share of people going to school and looking for skill demanding jobs. Back in the 1960s, the share of young people who were high school graduates was around 53%. Another 13% had graduated from college as well, hence leaving a 34% of high school dropouts. Nowadays, there are only 10% high school dropouts, while the share of college graduates has increased to around 34%. I believe this might be pushing the peaking point. For example, an engineer or lawyer probably needs to go through some lower paying job training (or internship) and needs to try many different offices until it finds the one that suits him best. Hence, they start with a quite low pay but see a high increase over time. On the other hand, a construction worker's income will probably not change much over his life. Most companies will probably pay similarly, and his wage will not change as much over his life as it will for the lawyer/engineer.
This is consistent with what is found for the variance of income. Figure 2 shows variance relative to the variance at age 26, so that we can see how it moves over life. Another interesting pattern emerges here. It used to be that income differences were quite constant until the age of 40. However, since the 2000s differences seem to have started showing up earlier. A constant increase since the age of 25 is found nowadays.
Source: IPUMS Census USA. Variance of log family labor income by age of head, excluding people in school or with no income, relative to variance at age 26.
Once again, I believe this probably might have to do with education. Back in the 1960s, more than 80% of the population would start looking for jobs around the age of 18, leaving many years until the age of 25 - where my plots start - for them to find the appropriate job.* Moreover, these types of jobs probably did not experience much wage differentials between employers. On the other hand, nowadays, 34% of young people graduate to college (and even attempt go and fail to graduate), leaving them with less years to find a job. Moreover, once again their skills probably need more time to find the appropriate employer ("matching" issues in the economics jargon).** Hence, incomes are a lot more varied nowadays since earlier stages of life. And my income peak is getting farther and farther away...
* Plots start at age 25, so as to avoid having selection issues with people who go to college and start showing up in the sample after they graduate (say at age 22). For example, if plots started at age 18, the data until age 22 would include only people who did not go to college. Starting age 22 the pool of people would change a lot, as college graduates come in. The mean income might change significantly, but this would not be due to the life cycle of earnings of the workers, just because the pool of people in the data changed. Hence, this problem is reduced by starting the analysis at age 25.
** An interesting way to evaluate this would be to look at the same data but focusing only on college graduates. Maybe another week.
Given the recent events in Ferguson - where white policeman was not indicted for shooting a black young man - that lead to protests around the US to try to stop racial discrimination, I thought it was a (unfortunately) perfect moment to see what economics has to say about this. What is the status of inequality between black and white? Using research from the Urban Institute, Figure 1 suggests that white people have 6 times more wealth than blacks, having this gap increased almost threefold since 1983. So it seems the situation has not got better over time.
Figure 1: The wealth gap in the last three decades
Source: The Urban Institute.
Moreover, whites accumulate more wealth over their lives than black (or Hispanics) do. Focusing on those born between 1943 and 1950, Figure 2 shows that this wealth gap increases over the life cycle. In 1983, whites between 32 and 40 have an average family wealth of $184,000, rising to over a million by age 59 to 67. However, blacks wealth goes from $54,000 to only $161,000 between the same ages. So whites have three and a half more wealth than blacks when they are young, but over seven times more when they are old.
Figure 2: The life cycle of wealth by race
Source: The Urban Institute.
On top of this wealth gap, an over-simplistic look at the data suggests that blacks receive worse sentences and are more likely to be suspended in school. Finally, Figure 3 shows are twice more likely to be unemployed.
Figure 3: Unemployment rate by race.
Source: Bureau of Labor Statistics.
What is behind such big gaps? Econ 101 teaches us that a properly working market system should hire and pay people according to their value. Discrimination makes no sense in competitive environment. Suppose every employer is discriminating against blacks, hence providing them with a lower wage even though they are as productive as whites. This would allow any unbiased person to take over the market. She would be able to hire these discriminated people at a wage level in between the gap (i.e. between the black and white ones) and get a better profit than everyone else, possibly kicking all racist businessmen out of the market. This Econ 101 logic is definitely too simplistic, but should let us frame our thoughts to see what it is missing out.
A possible issue is that blacks and white differ in their characteristics, beyond their color. For example, white people could be more educated. Focusing on unemployment, the question then is: When faced with observably equivalent (i.e. education, experience, etc) black and white job applicants, do employers favor the white one? Evidence goes both ways. Some suggest they do not, claiming the black-white gap stems from supply factors: African-Americans lack many skills when entering the labor market, so they perform worse. Others suggest that employers do discriminate, either by prejudice ("Taste-biased" in economics jargon) or, more usually, by what economists call "statistical discrimination": race is used as a signal for unobservable characteristics. For example, if blacks tend to be raised in worse environments (which could lead to worse productivity), employers who care about productivity (but do not about race) and cannot observe it perfectly, would use race (or ZIP codes) as a signal for it. Hence, black people would be discriminated but not (directly) because of their color.
Data limitations make it difficult to test these views. Researchers posses far less data than employers do, so even if applicants appear similar to researchers they may not be to employers. Employers can observe social skills during interviews and assess the quality behind what is stated in the typical resume information. And any racial difference in labor outcomes could easily be attributed to that. That would be a highly unsatisfactory open ending to this post.
Fortunately, Bertrand and Mullainathan designed a field experiment to try to circumvent this problem. They sent close to 5,000 resumes to more than 1,300 help-wanted ads and measured the call-back for interview for each resume. Since race cannot be explicitly written in the resume, they manipulated the perception of race by (randomly) assigning names to those resumes. Half the names used are white-sounding (e.g. Emily Walsh) while the other half is black-sounding (e.g. Lakisha Washington). A side experiment showed that the names used are associated with their respective races by more than 90% of the people. They also experimented on changing the quality of the resumes, in order to see if call-backs for black are more responsive to quality than for white (like statistical discrimination might suggest). Approximately four resumes are sent to each ad: Black-High (quality), White-High, Black-Low, and White-Low. Even though this does not go further than the call-back stage (i.e. it does not go all the way to employment), this methodology guarantees that the information the researcher and employer have is the same.
Table 1 shows the callback rate for both groups: Whites have 50% more chances of being called back. A white person would need to send 10 resumes to receive one callback, while a black one would have to send 15. Using the data on quality of the resumes (Table 5 in the paper), the return to a white name is equivalent to as much as eight additional years of experience. Moreover, there seems to be no difference depending on the industry or occupation category of the job itself. They all show differences of this sort.
Table 1: Callback rates by age.
A possible issue with this strategy is that when employers read a name like Lakisha, they may assume more than just skin color. They could interpret that the applicant comes from a disadvantaged background. In such a case, signals of quality like experience or special skills should be more important for black applicants. Similarly, ZIP codes could be used to get an idea of the social background of the applicants. If we expected statistical discrimination to be behind the gap, we should expect black applicants callbacks to respond more to either of this. However, the study suggests they don't. Higher quality of resumes improves the callbacks for white applicants but not so much for black ones. And ZIP codes don't seem to matter much either. Finally, a way of looking at this directly is by examining the average social background (proxied by mother's education) for each name used. Table 2 shows the first names used, together with their callback rate and average mother high-school completion rate. The social background hypothesis would suggest higher callback rate for higher mother education. However, no such evidence is found.
Table 2: Social background and callback rate for each name used.
If statistical discrimination is not behind this, what is then? "Taste-based" discrimination where people consciously think worse about blacks seems contradictory to other studies in the literature. In a second paper the same authors (together with Chugh) suggest that a possible explanation is that we might be unintentionally discriminating. Using a tool popular in neuroscience and sociology, the Implicit Association Test (IAT), they suggest that people have unconsciously more difficulties in associating black persons with positive words. And this is found to be harder to control in environments with time-pressure or considerable ambiguity (like looking at job applicants).
What is the best way to improve on unconscious discrimination? Is making differences between skin colors, that go as far as avoiding any topic that refers to colors which are as obviously there as any other parts of our bodies, the correct way to improve our unconscious mind? If we are raised with these concerns of what is politically correct to say, we might be doomed to unconsciously make such an unfair and damaging difference between people's skin colors.
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 do individual labor earnings evolve over the course of a person's life? If you have ever asked yourself "Should I expect my income to increase this year?" and "By how much?" this post might interest you. In a very elegant study, Guvenen, Karahan, Ozkan and Song have tried to answer these questions and more, using over 200 million tax data observations from Social Security Administration (between 1978 and 2010). If you ever thought tax data was not public, this (and my last post) might suggest that they are not. Don't worry. Only a few people are allowed to use this information, and even then they are not allowed to actually see the name of the person behind each income observation.
Looking at employed people between the ages of 25 and 60, they focus on how much earnings grow every year in average. A first look at the data is provided in Figure 1. Taking the average among all the population, yearly income peaks around 50 years old, with an increase as high as 127% from age 25.
Figure 1: Average (Log) Earnings by Age.
If you are past age 50 and have not seen such an increase, you might be wondering what's wrong with yourself. Before entering into such a depressing state of mind, please read a few more lines. This average income path hides a lot of variation across different people. More importantly, it is strongly influenced by the very top earners. Figure 2 shows that the median worker only shows a 38% increase in his earnings between age 25 and 55. It is the very top earners who influence the 127% number before. For example, the Top 1% shows 1500% increase in their earnings in that same period. More than 300 times the median increase in earnings...
Figure 2: Earnings Growth (25 to 55) by Lifetime Earnings.
Another interesting finding is that income does not peak at the same age for everyone. Even though the average person's income peaks around the age of 50, this is not the case for most people. Figure 3 shows that the median worker has almost no income growth between 35 and 45, and only the top 2% actually experience earnings growth after 45.* I hope these depressing findings for the median worker might help your self-confidence. The average numbers shown in Figure 1 are not the appropriate ones to question your life. (Figures 2 and 3 might be...)
Figure 3: Earnings Growth by Decade of Life.
Some other interesting findings in these article are that the dispersion of income growth (i.e. how much income growth differs across individuals) has a U-shape, decreasing with age up to when people are 50 years old where it spikes up again. Top earners are the exception once again, since their income dispersion grows every year of their lives.
How about the asymmetries? Is it more likely to be below or above the average increase in income? The data suggests that as people get older or richer, it is more likely to get negative shocks to income. And these seems to be due to there being more room to fall down (not less room to move up). The higher your income, the more you can lose (remember most people are not willing to pay to work, so you cannot have negative wages).
Finally, let me end with a happy note. Suppose you just saw your income go down. You might be worried that it will remain like this for a long time. The data suggests otherwise. If the decrease was very strong, it is most likely that the persistence will be very short (unless you were a very high earner). In less than a year you should see your income recover most of its previous value.
(Very Small Print Note: this does not mean you should just lay down and wait for this fact to bring your salary back to normal. No complaints are accepted if incomes do not go up.)
* Remember that if a distribution is such that there are a few outliers with extremely high income growth, we will observe an average growth much higher than the one the median worker has. Hence, focusing on the median worker might be more illustrative in these cases.
Based on an article by Guvenen, Karahan, Ozkan and Song.
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.
How much is education worth? I personally believe that education provides you much more than just a higher income in the future, but let's simplify and focus here on how much more money you would earn if you studied a few more years. This question has interested economists for a long time (at least since Mincer in the 1950s). However, this holy number that could potentially explain whether I am (economically speaking) wasting my time doing a PhD has proved very hard to estimate. But let's start at the beginning.
Around the end of the 1950s, Mincer proposed - after building a rather simple model of human lives - doing a regression of (log) income to years of education and years of work experience. If you did this you would basically find that one year of schooling increased your income by between 10 and 14%. However, recent analysis by Heckman (and his army of co-authors) shows that returns to education are not that easy to estimate. Particularly, the effect of each year of education may not be the same across levels (e.g. the year you finish high-school is worth a lot more than any of the previous) and that education might change your future income growth due to years of experience (e.g. if you finish high school each year of work may bring you a higher increase in income than if you did not finish high school). This is seen in Table 3a below for white men both in 1940 and 1990 (bottom row is Heckman's and top row is Mincer's). Each column refers to a different year of education: 10-12 is finishing High School and 14-16 is completing college.
Another interesting finding here is that this would suggest that finishing high school in the 1990s is worth a lot more than it used to be in the 1940s (50% vs 24% income increase). Are we learning more nowadays? Possibly. But possibly not as well. This could come from a well known issue to applied economists: selection bias.
Let me introduce this concept with a (hopefully) clearer example that comes from the health sector: the number of deaths and hospitals. For every 100 people hospitalized for diagnosis in the US more than 2 die every year. On the other hand, for every 100 people in the US only 0.8 die every year.* Hence, comparing the two pools of people, anyone ill might think: "Wow! If I go to the hospital I increase my chances of dying by more than 100%. Then, I should stay away from hospitals and try to get better on my own." But this clearly makes no sense (at least not if you have health insurance!). You are comparing a pool of people who are sick (hospitalized) with a healthy one (everyone else). If the first one stayed out of the hospital, we would expect that more of them would die. And the same logic applies to education.
Focusing on the 10-12 column, these regressions are (intuitively) comparing people who finished high school with those who did not. But are these people equal in all other terms besides having finished school? Most likely not. We can imagine that people who don't finish high school have had a worse childhood, come from worse neighborhoods and are generally raised in a more distressed environment ("sick" in the hospital example above). This would suggest that even if this people did go to school they might do differently (worse?) in the labor market later on. Similarly we can imagine that the people who did finish high school had families with a better economic background who could more easily provide job opportunities to their children, hence increasing their labor income independently of schooling choices. Basically, the two groups of people cannot be compared directly. Hence, the increase in the observed returns could be because the pool of people who don't finish High School nowadays is (relatively) worse than the one in the 1940s. Most people finish high school nowadays, while this was not the case 60 years ago. In other words, the selection bias could have gotten worse over time.
What economists might like to do to solve the enigma of schooling returns is to randomly assign people to different education levels. Someone would be flipping coins and deciding everyone's education. This way we would be able to make sure that all kinds of people are equally distributed across the different education levels. And so the income levels of the different groups could be easily compared. Fortunately, economists are not allowed to dictate people's lives that much. And the best solution so far has been to look for uncontrolled events that make (some) people more likely to go to school (but are not related to their wages in the labor market directly). And then we compare this group to some other (similar) people who were not affected by such an event. A nice example comes from Seth Zimmerman and his estimation of returns to college admission.
Zimmerman focuses on a large public university in Florida (FIU), which was particularly easy to get in when compared to other universities (kind of like a last-resort university. Apologies to any FIU students reading this!). This way he can be more confident that if someone was not admitted there, they would not be admitted by another school. But, how does he separate people randomly into the two groups (admitted versus not admitted)? His trick is to take people just around the GPA admissions threshold. Figure 4 shows that people right above it are 23% more likely to be admitted to this university than students just below it (and more likely to attend as well).
Assuming that people are not able to control their GPA at this particular university, this would provide him with people being "randomly" assigned to "admitted" and "not-admitted" groups.** Hence, we can now compare the income across the two groups worrying less about selection. Figure 8 suggests that being admitted to college (i.e. from being just above the threshold) increases your income by around 22%.
It is important to notice a few limitations of this kind of studies. These econometric techniques don't come for free. This number is the return to college admission only for people who, for various reasons, are near the threshold. And once again, this people might be very different than the ones who had no trouble being admitted. So the 22% rate should be understood as the return to this particular group of people and not for everyone else. Nevertheless, this number might be the relevant one if you are thinking about a policy that changes the requirements for admission. Such a policy would affect this particular group and not the general population. Moreover, another drawback is that this return does not consider "General Equilibrium" effects: If such a policy were applied in all the country we would expect to have lot more people graduating in the next few years, which might affect the wages of college graduates. Hence, the returns to education might change.
Economics research can (sometimes) be extremely difficult when compared to physics and other such sciences. In these sciences nature's rule is well defined. It may be hard to understand but it is there, and all the data you observe is from such a rule. In economics, data observed is driven from people's different lives, crazy personalities, complicated families and interestingly different regions of the world. And on top of these differences (which we can't observe in the data), individuals are making choices which manipulate the data we economists try to work with. Hence, understanding humans and outcomes related to them can be very complicated. Like Stephen Hawking once said,
"While physics and mathematics may tell us how the universe began, they are not much use in predicting human behavior because there are far too many equations to solve. I'm no better than anyone else at understanding what makes people tick, particularly women."
* I would like to have the number of deaths of people not hospitalized to make the comparison with education but I wasn't able to find that number easily.
** Note that GPAs are computed differently by various universities. So students would need to preview that they want to apply to this particular school and be able to control their results very precisely to affect their result around the threshold. People way above the threshold can be easily thought to be very different, but people just above and just below the threshold are probably quite similar.
Charging taxes on income is hard. Worldwide experiences show that less developed countries have difficulties raising revenue from income taxes. Below I have plotted GDP per capita and Income (and capital) tax share of total government revenue for 2005. It is reasonable that most countries have a hard time making people and firms pay income taxes, but richer countries clearly tend to do it more.
Source: World Development Indicators.
The income tax has not been common throughout history. For example, a century ago the income tax almost did not exist in the US. Most government revenue was from trade tariffs and consumption taxes. These are much easier to collect. You just need someone at the port of trade, or some random controls at shops. Look at the sale value and take a share. But income tax is harder. You need people to be capable of keeping track of their income and sum it. And then you also need them to be honest and report it. Finally you need to enforce it, with a system potentially capable of checking every person. However, in spite of all these difficulties, income tax now account for over 55%. How did this happen?
Well, as usual, first came a government in financial trouble: wars are the starting point of most taxes. The idea first floated during the 1812 war with the UK, but it was unsuccessful. Later, the civil war was bad enough to ensure the introduction of the income tax (and the beloved IRS!), focusing on rich individuals. How did they enforce people to pay? By encouraging people to report their neighbors to the IRS if they were driving a Ferrari (or the horse equivalent of the time). Some people even claim that the income tax was key for the victory of the north. So the income tax might have even stopped slavery!
But this was not enough. The war ended and so did many of the pressing needs. You may think it would have been reasonable to keep the tax to build a safety fund? Wrong. As soon as the war ended, rich people didn't want to keep paying. Moreover, they could afford the lawyers and the Supreme Court agreed with them. Surprising, ha? But you can always count on new government deficits. They currency and stock market crisis of 1907 meant funds were needed. So just before the First World War the constitution was changed, allowing the government to collect income tax. But it was still focused just on the rich. Less than 2% of the people paid taxes.
Once the Second World War arrives to the American coast, more money is needed. So they decided to expand the income tax to the middle class. They needed someone beloved, with credibility, charming to promote the tax: Donald the Duck. Yes, you read correctly. Here is the video:
And this is how the income tax came to be in the US. With an approval rate usually above 80%, it surpasses any politician I know. As Walt Disney appropriately said: "If you can dream it, you can do it."
Do people get paid more if they are better looking? Freakonomics did a podcast interviewing beauty economist Hamermesh who explained that:
If you are an economist you may think the statistics above could be problematic. For example, better looking people could be more charming or better communicators, which are more "standard" sources of income differences. However, even when looking at quarterbacks - where looks may not matter as much, except for merchandise/publicity revenue which is not included here - Berri finds that beauty (based on symmetrical faces) pays as well.
So, if surgeries cost less than $10k, and we can increase our lifetime income by over $150k, shouldn't we all get a surgery? It seems like a great investment. Well, data (from China and the surgery mecca of "North Korea") says this does not help...
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