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.
After a long long time devoted to education, economists do need to look for a job. But they (generally) do not do it in the standard way: calling, sending CVs and so on. There is something called the Job Market that takes place every year early in January. Obsessed with efficiency, the Economics Job Market has a particular advantage: applications and initial interviews are centralized. Most of them take place at the American Economic Association annual meeting. And after some very stressful days, interested employers call back and schedule fly-outs for February-March. After meeting them, going for drinks and dinner, and also presenting your research, job offers are determined. But the question I have is what determines the outcome of this stressful process? As a person that will hopefully eventually go through this ordeal, I wondered if there was any data about it.
Even though all economists have experienced this, I wasn't able to find much research about the job market unfortunately. But I found one paper where they asked what aspects of education are associated with good outcomes in the job market. They collected data on graduates from Top departments (Harvard, MIT, Princeton, Stanford and Chicago) and checked what was associated with the best job outcomes. Obviously, the sample of graduates coming from those departments is not representative of all economics graduates. Since they were accepted in such departments, they are most likely representative of the very top of the distribution of applicants to PhDs. Studied by academics, another caveat is that job placements in the business sector were generally assigned a much lower ranking than university ones (a good business sector job was similar to a university in the 200-250 rank). But well, the questions are:
0) What is the typical PhD graduate? 1990-1999.
He (only 25% female) is a foreigner (63% non-US) who might come from a foreign undergrad school (49%). Most likely he does not come from a top undergrad school though (22% coming from top-15) nor does he have a masters degree (24% with masters). 3 out 4 admitted students do graduate the PhD. And around 26% of (this very selective group of) graduates end up in a Top-20 school. The sample is a bit old and selective unfortunately and some things might have changed. Unfortunately, one has definitely not. It is still mainly male students.
1) Do admissions requirements matter for grades?
Before entering, a standardized exam called GRE is required. This has three parts: math, verbal and analytical. GRE math and analytical grades - even within this group of people with really high ones - are highly positively associated with good core grades in the PhD program. I always thought this was more of a filter requirement: once above it, all students would be pretty similar. But it seems not.
Coming from a Top-15 US university is not associated with better grades. A masters degree helps slightly. And coming from a foreign school is correlated with better grades. But this may be due to a much more selective procedure for students coming from abroad. Or from them being more devoted since they are willing to leave their home countries.
2) Do grades matter for graduation?
First, grades are highly correlated: if you do well in one, you also do well in others. I find this very interesting since we are looking at people who will later on focus on a very very tiny part of the world of economist, so we could have expected that people doing great in one Micro would not do well in Macro, or viceversa. Let me clarify that grades are only a small part of the PhD. Most of it is actually doing research, which is what most graduates will do afterwards in their careers. But core micro and macro - sorry econometrics! - grades certainly seem to matter for graduation. Even (sort of) when restricting to those who passed the courses requirement, goods grades were associated with graduation.
3) And finally, what matters for job placement?
A) Observable before starting the PhD.
Once again, coming from a foreign university is positively associated with landing a Top 20 job. Coming from a Top school in the US is also good. GRE not so much anymore. (Being a man or a woman does not seem important either, so maybe we have a hope.)
B) Observable after starting the PhD.
Micro and Macro core grades are good predictors of job placement - sorry econometrics again. Admissions rank does not seem relevant, which might question the capacity of departments to rank students. Conditional on grades, coming from a foreign school does not seem to matter as much. But coming from a Top US school still does. I wonder if a language or culture bias could be behind this...
The questions that remains are why are some characteristics much stronger predictors of grades than of job placements? If what really matters for the outcome of PhD students or the evaluation of the department is the placement, why does the admission procedure seem quite ineffective in predicting it? And, finally, what's wrong with econometrics?
Based on article from AEA.
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.
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.
Planet Money did an excellent episode on how much university majors pay. Some great data:
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