In the US people tip waiters almost 20% while in Argentina this number is reduced to 10%. In other countries like Japan, tipping is insulting. And then in Spain, the customer might even be considered to be tipped: if you are with a big group and brought enough business, you will probably get some digestive "chupitos" (drink shots) for free at the end of your meal. Secondly, why are servers tipped, but not doctors nor salesmen? (Actually, in Japan doctors are tipped, but not servers...) These and many other questions were very well posed by Mr. Pink in Reservoir Dogs.
This begs the first question: why tip at all? History suggests that originally 16th century Europeans tipped in advance to obtain faster service. If you were in a hurry, you would put a few coins up front, making sure you are noticed so that you get better service. Some suggest there was a sign saying T.I.P., "To insure promptitude," which originated the word "tip". Others suggest it was actually a slang word that spread around. According to Michael Lynn, a professor at the Cornell University with over 50 academic papers on the topic, tipping in the US began in the late 1800’s, when wealthy Americans traveling abroad to Europe witnessed tipping and brought the aristocratic custom back with them to “show off.”
But nowadays the social norm is that we tip afterwards. Waiters are supposed to provide good service in advance with the hope of getting tip as a reward. Nevertheless, it seems that people tip almost automatically, a rather fixed percentage (which might depend on the country). A study by Cornell University found that quality of service did not correlate much with service. So it seems we do not tip for good service. One thing that did correlate with tips was how attractive the waitress was (not for waiters though). Touching the patron's shoulder when delivering the check also seems to increase tips. So beware of touchy good-looking waitresses next time you are in a restaurant.
Second, why do you tip servers but not dry-cleaners? Why tip the hotel doorman, but not the person behind the reception desk? Why tip a baggage handler at the airport, but not the flight attendant? There really seems to be no logical explanation for this. The U.S. is empirically tip-addicted, with 31 different services being tipped. On the other hand, Canada has around 26, Scandinavian countries between 5 and 10, Japan 4 and Iceland 0. Most of the world operates on the simple premise of a service charge or a fixed price, no tip expected. But not the US.
Being now in Japan, I can see that servers deliver food promptly even without a tip. The restaurant business does not run into chaos without tips. It seems that tipping is more of a social norm nowadays, rather unrelated to service, where some countries tip and some do not. Some services are tipped and others are not. But this social norm also seems to depend to the racial group of the customer. How much is enough for a tip? How much is too much? In the US, only about a third of blacks say they tip in 15-20% range, compared to two-thirds of white. This might be mediated by their socioeconomic status (lower average education and income), but it does not explain it completely.
Let me end with an open question I recently read. Suppose tipping had never been invented and you were starting a restaurant, would you use tipping as the way to compensate your best employees? Or all your employees? Would that be the system that you would pick in a vacuum to compensate your team? I guess not. But this rather odd system can become gigantic. For example, tips have been estimated to account for around for 40 billion dollars in the US, bigger than the GDP of almost half the countries in the world.
Figure 1: Live First-Birth Rates by Age of Mothers
The 1960s were revolutionary times. As Bob Dylan - one of my favorite musicians and probably one of the most famous characters of that time - said, "there is nothing so stable as change". This was certainly true in the US at the time: The Civil Rights Movements, social unrest due to the Vietnam War, the invention of the microchip, antidiscrimination legislation, the women's movement. And the invention of Enovid, the first contraceptive pill. Yes, you read right. The contraceptive pill was a revolutionary element. And as such, it has also been studied by an economist (and by the way published in the Quarterly Journal of Economics, among the top 3 economics journals). Martha Bailey evaluated the effect the release of this little pill in 1960 had on female labor participation. Gary Becker had previously said that "the contraceptive revolution [...] has probably not been a major cause of the sharp drop in fertility". However, Bailey will show that even if fertility did not decrease because of the pill, it did delay it, allowing women to get more education and improve their labor outcomes.
Figure 1 shows trends in first-birth rates by age groups since 1940. A marked decline in childbearing among young women (focus on 20-24 years old) is seen since the pill was introduced. This lasted until 1976 when all unmarried minors were allowed obtain contraceptives under the law. Early access allowed women between 18 and 21 to get access to the pill and hence the largest decline is seen for those 18-19 years old. A first robustness check can be seen from those 15-17 years old. Since they are expected to be too young to benefit from the pill, we should and do observe no effect for them. This gives us confidence we are not just seeing a spurious result.
As the diffusion of the pill increases, the distribution of age at first-birth also changes. Figure 2 plots the fraction of women first giving birth by age groups and cohorts. Among women born before the 1940 who were too old to benefit from early access to the pill, around 62% report having children before age 22. For those born around 1955, this had dropped by 25%. Notice that both figures suggest that these effects were not due to preexisting trends. Also no changes are seen between 1955 and 1960, when all women would have already had access to the pill.
Figure 2: Distribution of Age at First-Birth, by Cohorts.
And where does the economics come in? Early access to the pill was reflected in female labor force participation. Before 1940 the increase in women's participation had been driven by married women over 30 years old, who returned after their children had grown. On the other hand, for those born in 1955 the "fertility dip" is not observed any more. Participation rates were 25% higher at age 25.
Figure 3: Labor Force Participation, by Age and Cohort.
But how can we disentangle the effect of the pill from all the other things going on the 1960s that I mentioned above? Here is were econometric tools come in. The expansion of the pill was different across states, which individually changed the legal rights of individuals ages 18 to 21. Indirectly, this effect empowered women to get early access to the pill, without parental consent.* This exogenous variation will allow Bailey to compare the effect of the pill on women's life cycle labor force participation. Just to fix ideas, the methodology is like taking two states that were previously equal. But one state decides to extend legal rights to younger individuals and the other does not. Consequently, only one state allows young women to get access to the pill. Then, the difference in the labor force participation of the women between the two states will be coming from the pill. More than two states and more controls are used to obtain the results, but the intuition of the technique is in the previous simple example.
A first thing to check is whether early access to the pill had an effect on fertility. Table 1 shows the baseline estimate (column 2) is that it reduced the probability of giving birth by age 22 by 14%. Interestingly, early access to abortion does not seem to drive the results (column 3). As expected, it did not reduce the number of children before 19, since women did not have legal access to the pill without parent consent before that age. Finally, as other people had reported, the pill did not reduce the number of children women had, suggesting it just delayed it.
Table 1: The effect of early legal access to the pill on fertility.
What effect did this have on labor outcomes? Bailey shows that early access to the pill increased labor force participation of women ages 26-30 by 7%, and also increased those of ages 31-35. They also seem to work more hours, hence getting closer to male labor outcome averages. For women under 25 years old, results suggest that the pill increased their enrollment in school. Changing career trajectories - resulting from delay in childbearing - was the primary mechanism this little pill increased female labor-force participation.
* Bailey goes into some detail to justify that this extension of rights was not related to states characteristics that could be directly related to the variables of interest. Most of the changes are suggested to have to do with discrepancy under federal law of being old enough to be drafted to the Vietnam war by age 18, but not being able to vote. At the state-level, legislation was extending rights to 18 year old men and women.
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.
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 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.
With the recent constant appearance of Alibaba on the news, the increasing relevance of Chinese exports to the world is extremely clear. Low-income countries accounted for just 9% of US manufacturing imports in 1990. But by 2007, they had more than tripled its share. And who do you think was behind this? China accounted for as much as 89% of this increase.
In this period, China's transition to an open economy included a massive 150 million people migrating from rural to urban areas. Imagine reallocating around half of the United States population geographically, with a particular focus on manufacturing production. Add to this formula novel access to foreign technologies as well as capital and Chinese exports growth to seem reasonable. However, did this come to the expense of anyone? This is the main objective of this post. One group being threatened by Chinese takeover of manufactures is obviously the manufacturing workers in the rest of the world. As these goods are easily tradable, we could expect job losses in these sectors. The figure below shows that as Chinese increased its relevance in US imports, the share of the population working in the manufacturing sector in the US decreased by one third.
However, many things could explain this decline. For example, it could be that Americans themselves were getting more educated and moving to other sectors. Alternatively, the service sector could be becoming more productive in the US, offering higher wages and hence draining employees from the manufacturing industries. These (and many other alternatives) do not involve China's exports growth. Moreover, they could be causing the increase in Chinese exports themselves. (Imagine US decides to get out of the production of manufactures, leaving a lot of unsatisfied demand which leads the Chinese to produce more). Hence, in order to make sure we are capturing the correct effect, modern econometric techniques come to the rescue! Autor and Dorn (AER, 2013) basically exploit the differences in the exposure to import competition across cities in the US. For example, it would be expected that - if the leading cause comes from the Chinese side - an area where manufacturing employed 25% of the people to be more affected by Chinese exports than an area that only employs 10% in manufacturing. Particularly, they will differentiate areas by how specialized they are in each division within manufacturing, and how imports from each of these changed over time. And these differences will give us the information we are after.
Looking at wages, the effect found of imports from China is negative. An increase in the imports per worker of around three thousand dollars (which was the average change from 1990 to 2007), would explain a decline of around 2.25% (0.76 times 3). More interestingly, this effect is stronger among men and people without college education. It is important to remark that this can only be calculated for the employed. Hence, if we expected workers with lower ability and earnings to be more likely to lose their jobs after the Chinese expansion, the effect on wages above would be understated. And so wages would have fallen even more for the whole sector, it's just that the effect could be hidden by the increasing number of people losing their jobs.
And what if we divide the effect between sectors? I would have expected the wage effect to be stronger in the manufacturing sector itself. But well, the data seems to suggest the opposite: wages seems to have been unaffected in this sector. However, the manufacturing sector was particularly affected by a major reduction in employment (predicting a decline of 12% due to China's increase in exports).
So most of the effect on wages mentioned for the whole economy seems to come from the non-manufacturing sectors. How can this be possible? Well, (adaptive) story telling is a prerequisite for any upstanding economist. And here is the one that seems most appropriate given the results: the increase in imports from China led to firing of the lower skilled workers in the manufacturing sector but had no effect on their average wages (note this could still involve a decrease in the wages of the ones that remained employed). Having no new paychecks, these newly unemployed decided to reduce spending and so decreased their purchases of services that have to be provided locally (like a haircut or a dentist). This reduced this local sector's revenue. Moreover, the newly unemployed also fled to other sectors looking for jobs. Having lower revenues and seeing lots of people of willing to work for less, other sectors reduced their wages.
Based on an article by David Autor and David Dorn (AER, 2013).
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."
70 years ago this wasn't the case. How did the US become the printer of the currency of choice? While (almost) every other country needs a foreign currency to trade, the US can just press a button and this beautiful green paper comes out. And then they can give us these pieces of paper and we all give them real goods. Quite a privilege. Luckily, they don't take as much advantage of this privilege as some of my fellow countrymen would.
How did this happen? Let me set the environment first. It all started at the end of the 2nd World War. A beautiful hotel in Bretton Woods (New Hampshire, USA). 44 countries got together to restore the economy. The gold standard was out, and every country was trying to protect its own industries (in other words, no trade). Currency values were flying around (well, not as much as some future post-Soviet or Latin American countries would experience, but a lot for the time). How can you trade when you don't know what the other currency will be worth tomorrow? Well, as any person with an "Argentinean survivor" degree knows, the answer is "you can't." So a tiny group of people were trying to put an end to this by deciding on the exclusive right to create money out of nothing. But lots of alcohol and cigars, a beautiful Peruvian singer and not much sleep may have interfered...
There were really just two sides: England vs US. Keynes vs a John Doe. (All the rest seem to have been just drinking and partying). But you can imagine by now who won: This John Doe, a number cruncher, timid, antisocial, actually named Harry White. But forgotten by history.
Both wanted an international currency for trade. Keynes wanted a new currency created by a worldwide global bank (named "bancor" from the French "banc d'or" to please the Frenchmen, but based in London of course to cheer the Royal pockets...Tricky John Maynard). White of course wanted another universal currency, the US dollar. How did he win? He started writing in all the documents "gold convertible currency." But who had all the gold? The US (80% of it). And finally, after all that drinking, the people got confused about what "gold convertible currency" meant. Keynes was at another meeting, and his British replacement - either not very bright or quite hangover - suggested that for "clarity" they could simply call it "US dollar." He thought this was just a technicality. Bollocks. Keynes was ill (he even collapsed and some journals even reported he was dead), so he was never able to check the document he finally signed (nor kill his second man). And, hence, after a seemingly irrelevant "clarification", every country now uses dollars to do business.
In 1999, Time magazine included Keynes in their list of the 100 most important and influential people of the 20th century. Well, I feel the role of the US dollar has been quite relevant, so shouldn't Harry be on that list? Maybe this absence has to do with what later happened to Harry White. Accused (and later convicted) by FBI Director Hoover for being a spy for the Soviet Union, he was also questioned for passing Treasury plates to print Allied money, sparking a black market and serious inflation throughout Germany. Quite a turnaround for the American hero (a real life Walter White?). If all this shouldn't be a movie, I don't know what should.
Based on another great episode from Planet Money.
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