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.
Last week I wrote on birth of the most common measure of how the economy is doing. This measure - the Gross Domestic Product (GDP) - is produced by every country and tries to estimate how big is the total output of a country. You would imagine all economists should know how it is calculated then. My impression is that is not true. So here is my contribution today: How is GDP calculated?*
It all starts with some (almost) complete and detailed information on every activity behind the wheels of the economy: census information. These surveys almost all of the businesses with paid employees, hence covering over 95% of expenditures included in GDP. This information allows the people at the Bureau of Economic Analysis (or your favorite country's economic statistics office) to calculate their GDP very precisely for the year of the census. This estimate is usually called "benchmark" or "best-level." The problem then is that it is expensive to run a census as frequently as needed for politicians to "manage" the economy or for entrepreneurs to decide on their investments (nowadays GDP data is usually published in quarterly manner). Consequently, census data is complemented with higher frequency (and cheaper) surveys. These are less precise and not as complete as the census, but allow the statisticians to build models on how the total economy is doing based on this data. For example, a very simple model could be that if your information in the survey says the sampled businesses are producing 10% more, you could estimate the whole economy is doing 10% better.
This survey information is usually available either monthly, quarterly or annually. For example, monthly survey data covers around 35 thousand units (or less than 0.5% of businesses). The annual one covers a bit over 1.5%. And all these complement each other. Hence, the farther you are from the period of interest the more information you have on it. Therefore, GDP estimates are updated - hopefully improved - from time to time (in the US they are updated at least 3 times). One problem with survey data is that it can have significant measurement errors. For example, you may wonder why isn't consumer survey data used to estimate household consumption? Well, it is well known that people surveyed tend to report less than 25% of how much they really spend on alcohol. So this sector seems a lot smaller when you add up what people claim they spent on liquor than when you sum how much liquor stores sold. Hence consumer survey data is usually avoided. Business surveys also have problems (smaller ones) and so the models have to fix for these.
On top of measurement error, survey data is usually not enough since some sectors are not covered. For these, past trends and related data are used to estimate. For example, electricity and gas consumer spending are extrapolated using past temperatures. Only 45% of the first estimate of quarterly GDP is from components based on the monthly surveys. All the rest comes from extrapolations. Later, the share based on extrapolations is reduced to less that 6%.
Every 5 years new census data is available in the US, allowing for a big re-estimation of both previous years estimates as well as the models to reflect changes in the economy (to be used in future survey-based estimations). For example, as computers are used more as entertainment, the models are updated to reflect that when they allocate sales of computers across different sectors (home consumption versus fixed investments for example).
How good are the initial estimates relative to the final ones? Remember there is no number provided by god telling us with certainty the true state of the Economy. Hence, in serious countries you have to trust the final estimate of the statistics office (In countries like my dear Argentina, you just don't trust anyone's statistics). Initial estimates of (real) GDP correctly indicate the direction 98% of times. And its major components accuracy is as high as 88%. Taking into account the size of the project at hand (basically measuring everything produced in the country), the result is quite good.
*If you are an economist, you may know that GDP can be estimated in three different ways. I focus here on the final demand or expenditures approach. The methodology for the other two is very similar to the one described above. But if you are interested in the details you should check the paper below!
Based on an article by Landefeld, Seskin and Fraumeni.
Last week I wrote about the beauty of the most common measurement system in the world. In the Economics world there is also one measure that dominates all discussions: the Gross Domestic Product (GDP). Before the Great Depression this measure of "how the economy is doing" did not exist. People at the time felt things were going really bad, but no one could agree on how much. Back then they had numbers on how the production of some important industrial or agricultural sector was doing. Presidents Hoover and Roosevelt had to fight the Great Depression on the basis of sketchy data as stock price indices, freight car loadings or incomplete indices of industrial production. But there was no general number for the question "How bad are things?". Hence, similarly to the metric system, the base measure of "the Economy" was born during a crisis.
So the GDP was going to try to add up everything made in the country: houses constructed, beers sold, visits to doctors, etc. (In case you are picky, back then it was actually the Gross National Product, GNP, but I will avoid this difference here). And they needed someone willing to crunch all the reports and summarize them in one number. And so came an economist described by his best friends as "extremely dry": Simon Kuznets. He first needed to figure out what to count which is not as easy as it sounds since it should potentially include everything. For example, shall we include the Mafia? The US does not but some countries do. In 1987, after “black,” untaxed transactions were included, Italy showed a brilliant 18% growth and became bigger than the UK (il sorpasso ragazzi!). (I believe this was later undone) And the Italians might do it once again, given EU regulation from 2013 that requires all transactions, regardless of their legal status, to be accounted for. And another big jump forward for Italy might be ahead of us.
Another case of productive activity not usually included in GDP is unpaid work. As the IMF explains, for example a baker who produces a loaf of bread for a customer would contribute to GDP, but would not contribute to GDP if he baked the same loaf for his family (although the ingredients he purchased would be counted).
Well, suppose you have figured what to count out. Now, how do you count it? If it is produced and sold in the US but some of the inputs come from abroad you need to subtract that part (Tough job that requires some sort of input-output tables). Then you have all the goods produced in your country that you want to add up. If you were ever told you cannot add apples and oranges, Kuznets would tell you are wrong. That part was easy. You just need to transform them into a common unit, money, and add that up.
After 6 years of work (almost too late for the Crisis?), Kuznets published his best seller: National Income, 1929–35. Believe it or not, it sold out. And every country then joined the party and started putting numbers to the health of their economies. A first quantitative picture of the economies around the world was made. And the figure below shows how bad the situation was back then and how things have improved. Economists tend to look at GDP per person as a better measure of life standards (otherwise all big countries are going to be better than smaller ones). This chart shows the terrible effect of the Great Depression and the wars in Germany, USA and UK (PPP is to put everything in a common scale of prices).
Figure: GDP per capita (PPP, log scale)
Note: Scale gives the equivalent GDP value in 2012 US dollars.
Source: Maddison Tables and World Development Indicators.
But even if the GDP measure was too late for the Great Depression, it would come useful later on. The biggest test for this measure came in the 1940's with World War II. They needed to know how many planes and bullets could be made before prices would go up and goods in the cities would become scarce. The army thought they could make a lot. Roosevelt himself gave a speech in 1942 saying they would make 60 thousand planes and 45 thousand tanks. But Kuznets picked up his calculator and told the president it was not possible given the number of factories and steel mills. Of course the economist was right and Roosevelt backtracked. Did the confidence of the US on how to use its resources help win the war? Very likely. And all thanks to a very dry data-cruncher economist.
After this experience many economists thought that if they could measure it, they could control it. They thought it was like physics. If they understood which variables were moving the economy, they could manage them and drive us to the right place. After a century of several bad worldwide crisis experiences, it is probably safer to say if you can't measure it, you can't control it. But I guess that if they had been able to really control it, all us economists would be unemployed. Any mistakes done may just be for the well-being of me and my fellow colleagues.
(In the 17th century William Petty made some progress on some of the concepts behind the national accounts but his estimates did not allow to identify the magnitude of a crisis, the yearly growth nor the size of some particular product in a country's production, which are the main use of GDP estimations nowadays. Petty's objective was mainly to show that landlords did not make as much income as thought. He claimed labor was the source of 3/5 of national income and so if tax revenue was the objective, consumption and labor income taxes should be taken into account.
GDP is still a very messy object to calculate. Next week I will write on how GDP is actually calculated. Hang tight!)
How do you know how much a kilogram of coffee is? You probably use a scale. But how do you know all scales are the same? How do you know my scale and the scale used by Colombian sellers are the same? Fortunately there is a list of standard measures (including for example how long, heavy or hot something is) that keep all of us under the same standard. In this new post I will talk about the story behind the measures we all use nowadays. You may think I have gone way out of my field and you are probably right. But I just can't imagine any form of Economics without measurement. And I can't imagine measurement without thinking about the beautiful metric system.
Most definitions of these measures are actually quite complicated. For example, what is a second? The Bureau International des Poids et Measures defines is as:
Before then measures were based on Charlemagne’s ideas. Many were simply borrowed from human body, like the pied du roi (or king’s foot) or the toise (the distance across a man's outstretched arms). But what if men were bigger in one part of the world than in another? Hence measure were quite uncertain and clearly not fixed: they varied from town to town, between occupations as well on the type of object to be measured. So agreements on measures were hard.
What gave room to the Metric System we have today? An economic crisis of course. The famine of 1780s meant that food should get more expensive. But bakers were worried about increasing prices (lots of revolts were happening), so they started baking smaller loaves. People started noticing loaves were smaller, but no one could universally check their weight! And the French revolution set the reform environment which started with a new standard. They wanted a system based on nature, that avoided national vanities and could be used by all nations.
And so first came the meter: They took a quarter of the circumference of the Earth and divided it by 10 million. That's a meter. And this gave birth to the kilogram. To define the unit of mass they preferred water to other bodies (such as gold) because it was easy to get anywhere in the world. They divided the meter in 10, formed cubes of that size, filled them with water and voila! The kilogram. And from the kilo they defined other 4 base units...
This object in France makes sure that whenever I buy one kilo of bread from a shop, we can all agree how much that is. Well, unless you go to typical corner store where a kilo of bread may be less than a kilo. But even in that case we can actually determine the real weight and formally complain about it. This is supposedly the story of Poincare (also in France but in the 1900s). From Allen Downy's Think Stats book:
Let me finish with the story of the first kilogram. This "perfect" object has been used as a prototype to build a few other kilogram sub-prototypes (called sisters) over the world. And these have themselves being used to build others, all the way to our day to day scales. Every time each one of us checks his own weight, this can be traced back to this little object in Paris from the 18th century. And the most interesting thing is that recently it was found that (comparing it to its sisters) the perfect kilogram was losing weight! The funny thing is that even though it lost weight - since itself defines weight - the object is actually still one kilogram! Which brings us to the bummer conclusion that we have all gained weight in the meantime. As the definition of a kilogram got lighter, we all got heavier.
Follow me on twitter at @diedaruich
News and posts for an active mind.