Were you not born into a rich family? Never mind, your chances to become a billionaire have never been better.
Steven Kaplan and Joshua Rauh (both from the University of Chicago) conducted one of the less typical academic researches when they studied characteristics of the richest Americans. Using the Forbes ranking which lists the top 400 wealthiest individuals in the US economy, the authors explored three simple questions: whether wealth is self-made or inherited; in what industrial activities the firms of the rich operate and to what extent technology plays a role in their business activity; and whether the Forbes 400 members have graduated from college or not.
To provide a bigger picture, they analysed four years with approximately 10-year gaps between them. Starting with 1982 and finishing with 2011’s issue they described the dynamics of the changes. The study reveals that nowadays the Forbes 400 are more likely to run their own business rather than a business established by their ancestors. In 1982 only 40 percent of the Forbes 400 members started their own business, compared to almost 70 percent in 2011. The effect of family’s wealth has also become smaller within the group of self-made billionaires. While in 1982, roughly 60 percent of Forbes 400 members grew up in rich families, nowadays, it is only around 20 percent. Equally interestingly, the share of Forbes 400 members from poor families has remained constant, around 20 percent. The figure below provides more details.
The data further indicate increasing importance of education. In particular, the percentage of college graduates among the 400 richest businessmen has grown by 10 percentage points, to 87 percent. Similarly, the rate of dropouts has increased as well. Naturally, the number of those who did not attend college at all has decreased. Considering the industry of the businesses, there are several trends worth attention. In the last 20 years, retail, technology-based industries and financial firms (hedge funds, private equity) have become more represented in the Forbes 400, whereas real estate, energy and media have experienced a decline. Overall, the structure of Forbes 400 has changed dramatically since 1982 and the position of the wealthiest individuals seems to be more open for those with no business to inherit.
Reference: Kaplan, S. N., & Rauh, J. D. (2013). Family, education, and sources of wealth among the richest Americans, 1982–2012. The American Economic Review, 103(3), 158-162. Available here.
And preferably one that doesn’t leave you
Already back in the early 60’s, David Gale and Lloyd Shapley constructed a matching algorithm which has become one of the crucial tools of modern market design. In particular, using examples of college admissions and stability of marriage, they presented a method how a market where money cannot serve as an allocation rule may reach a stable and pareto-optimal equilibrium. Other examples of such markets include organ donations, allocation of children to schools or assigning dorm rooms to college students.
Suppose the following example. There are women and men taking dancing lessons and before each lesson begins, they need to form dancing couples. Naturally, all participants will think about and compare the potential counterparts; they may even consider a list based on their preferences. As the authors prove mathematically, employing the Gale-Shapley (also called “deferred-acceptance”) procedure ensures that the solution to the problem, i.e. the couples that have formed, will be stable. By stable in this example we understand an allocation in which no two people of opposite sex would both rather have each other than their current partners, and thus will not switch partners given that their preferences do not change. After explaining the algorithm and its characteristics, the authors examine the optimality issue, which, however, is a more complex task and depends on a particular set-up of the problem.
The figure below shows an example of a stable outcome of the Gale-Shapley procedure based on the preferences of all 8 participants. The number for each pair represent the ranking of the counterpart among the four potential mates. As an example, the first cell – (1, 3) – tells us that A would be the first choice for alpha and alpha would be the third choice for A. Running the procedure yields the stable pairs whose preferences for each other are circled in the table. An interesting aspect of this particular outcome is that no participant was matched with his or her first choice. Nevertheless, the stability ensures that no participant could improve his situation while not leaving at least one other participant worse off. The results reached in this paper have had an enormously important impact on the future development of market design theory. Lloyd Shapley received a Nobel Prize in Economics in 2012 for this work and passed away in March this year; David Gale, who passed away in 2008, has not received the prize because it can only be awarded to living scientists.
Reference: Gale, D., & Shapley, L. S. (1962). College admissions and the stability of marriage. The American Mathematical Monthly, 69(1), 9-15. Available here.
Valerie Ramey and Neville Francis developed comprehensive measures of time spent doing different activities – working, doing housework (cooking, cleaning etc.), being at school and enjoying leisure time – in the U.S. since the beginning of the 20th century. The research question was initially motivated by economic theory which suggests that as the society is getting richer, we should be able to afford more leisure time and work less. This is called the income effect.
Interestingly, at the aggregate level, hours devoted to work have changed only mildly. In particular, our generation works, on average, 23 hours per capita per week, which is 4 hours less than in 1900. The overall home production does not change at all – we still work at home around 22 hours per week. Not surprisingly, there has been a huge increase in hours devoted to formal schooling by those aged between 10 and 17 years old. When it comes to leisure, the effect is of a similar magnitude as the change in work hours, but the direction is opposite. We now enjoy four hours more of leisure every week than people who lived a century ago.
Even though the results may seem boring as we observe slight or no changes at all, there are underlying stories which are indeed interesting. During the last century, we have witnessed significant gender convergence. While men tend to work less in market production and more at home, the opposite is true for women. As a result, the gap between male and female hours spent working in work and at home seems to disappear.
Reference: Ramey, V. A., & Francis, N. (2009). A Century of Work and Leisure. American Economic Journal: Macroeconomics, 189-224. Available here.
Is it worth it to mine bitcoins? Should I do it on my computer? The short answers would be: it depends and definitely no, respectively. To shed more light on the issue of bitcoin mining, Karl O’Dwyer and David Malone published a short study which analyses the costs of electricity used to mine bitcoins. It is commonly known that bitcoin mining is more and more difficult as there is more of the virtual currency already in circulation. Therefore, computers have to solve more complicated mathematical problems and use more electricity. As a result, mining has become more expensive.
The authors calculated that it has never been profitable to mine bitcoins even with Core i7 950 processors or with a Sony Playstation 3. Moreover, they argued that the only way to mine bitcoins profitably, taking into account only electricity usage as a cost, is to employ special hardware – Monarch BPU 600 C (ASIC) – which was constructed specifically for bitcoin mining. Note, however, the profit gap is closing even for such advanced hardware. Equally interestingly, the authors estimated how much electricity is used to mine and administrate the bitcoin currency and found out that it is comparable with the electricity usage of Ireland. The picture below depicts the development of the cost of bitcoin mining and the value of bitcoins in US dollars.
Reference: O’Dwyer, K. J., & Malone, D. (2013). Bitcoin mining and its energy footprint. In Irish Signals & Systems Conference 2014 and 2014 China-Ireland International Conference on Information and Communications Technologies (ISSC 2014/CIICT 2014). 25th IET (pp. 280-285). IET. Available here.
It is commonly accepted that a minimum wage increase has two direct effects on health. These effects result from the Grossman model, which is heavily used in economics of health. On the one hand, minimum wage increases allow individuals with low income to purchase more market goods that improve their health, for example better medical care and better food. On the other hand, it increases the opportunity cost of not working and thus makes non-market goods consumption (sport, relax) more expensive. Not surprisingly, the overall effect seems heterogeneous and differs for cohorts.
To shed more light on minimum wage effects on health, B. Horn, J. Catherine Maclean, and M. Strain analyzed data about lesser-skilled workers. As they concluded, the results fail to suggest any indisputable general improvements of the people’s health. The effect depends upon the particular group of people. While workers tend to report better health conditions after minimum wage increases, the unemployed are more likely to be negatively affected. Overall, the contribution of this study lies in providing a more comprehensive view on minimum wage policy and its consequences. Notably, the authors focused on more than just the potential decline in employment of marginal workers and recognized also additional social and medical issues.
Reference: Horn, B. P., Maclean, J. C., & Strain, M. R. (2016). Do minimum wage increases influence worker health? AEI Economics Working Paper 2016-01. Available here.
The importance of online customer-to-customer (C2C) marketplaces has been growing and nowadays Taobao, the biggest platform in China, has 500 millions of registered users. Such platforms have a common inherent issue – the presence of asymmetric information and adverse selection problems, which obstruct trade. Fortunately, the online world allows buyers to leave feedback assessing how much they were satisfied with the bought items. However, it is not that easy. It turns out that feedback has one of the most characteristic aspects of public goods – everybody would appreciate it, but only a minority of consumers are willing to provide it.
Nevertheless, as Lingfang Li, Steven Tadelis, and Xiaolan Zhoushowed showed, if consumers are motivated to leave a feedback, they do so. As a result, information asymmetry is reduced. In particular, the authors studied roughly 7 million transactions made on Taobao between September 2012 and February 2013. As a measure to sweeten online shopping, Taobao introduced a “rebate-for-feedback” reward system. This mechanism allows sellers to offer part of the paid amount to be returned back to buyer if he or she met certain conditions and left a fine feedback (not necessarily a positive one). The authors’ main results suggest that high quality sellers are more likely to ask for a review and also the reviews tend to be of higher quality (measured as the length of the rating).
Reference: Li, L. I., Tadelis, S., & Zhou, X. (2016). Buying Reputation as a Signal of Quality: Evidence from an Online Marketplace. NBER Working Paper, (w22584). Available here.
It has been a tricky question for such a long period of time: are marijuana and tobacco substitutes or complements? Leaving the economic terminology aside: do people tend to use tobacco and marijuana at the same time or do they rather alternate between the two? Since several states in the US have recently agreed to legalize (medical) marijuana use, economists were finally given the opportunity to collect and study US data about consumption habits of both.
Having gathered the data, Anna Choi, Dhaval Dave, and Joseph J. Sabia ran a difference-in-differences regression with the aim to estimate whether and to what extent the legalization of marijuana affects the consumption of cigarettes. The empirical results suggest that after the legalization of marijuana the consumption of cigarettes tends to decrease. In other words, tobacco and marijuana seem to be substitutes. If the results prove to be right, it may have important implications for policy-makers. For example, an increase in tax burden levied on tobacco might increase the demand for marijuana. Or, on the contrary, legal usage of marijuana might decrease the sales of tobacco products and thus decrease the amount of taxes collected from tobacco.
Reference: Choi, A., Dave, D., & Sabia, J. J. (2016). Smoke Gets in Your Eyes: Medical Marijuana Laws and Tobacco Use (No. w22554). National Bureau of Economic Research. Available here.
In one episode of The Big Bang Theory, Sheldon Cooper let dice to make decisions for him. A similar experiment was conducted by Steven Levitt. However, instead of studying decisions that affected him and his life, he investigated the decision process of people in a field experiment. In particular, he collected data about more than 20 000 people who faced an important decision (quitting a job, leaving a spouse or going back to school) and let them toss a coin. As a rule, when a head appeared the subject was asked to make the change, whereas a tail stands for maintaining the status quo for at least two months. After 2 and then also 6 months the participants were given a survey asking, among others, about their happiness.
As the author claims himself, there are two main research questions: (i) do the participants obey the coin and do as it says; and (ii) is there any impact on their reported level of happiness? Assuming that only the marginal agents join this experiment and thus a half of them are expected to take an action if there were no coin toss and the coin is fair, means that if there is no real effect of the coin then actions of 50% the participant should coincide with the coin suggestions. However, in this case, as the author reports, significantly more than 50% of participants follow the coin recommendation. In particular, while in more important issues it is around 55%, the ratio is even higher for the less important issues – 67%.
When it comes to the causal effect on happiness, using slightly more advanced statistical methods to combat the endogeneity problem, Steven Levitt argues that those individuals whose coin came up heads (take a change) report being happier than those with tails (status quo). It can be because having a head motivates to take a change which he/she would rather postpone or do not take at all (status quo biased observed in normal life). Overall, even though the conclusion may be biased for several reasons, it plants a seed of doubt if we would not be better off letting (at least the less important) decision were taken randomly.
Reference: Levitt, S. D. (2016). Heads or Tails: The Impact of a Coin Toss on Major Life Decisions and Subsequent Happiness (No. w22487). National Bureau of Economic Research. Available here.
How difficult is it to find a fund manager to whom you would entrust your money; to assess his quality before it is too late? Apparently, one way is to look at his family background as it has been recently shown to be a decent signal of future performance. In particular, Chuprinin and Sosyura (2016) found that fund managers from poor families outperform those coming from a wealthier background.
To explain this phenomena, the authors argued that wealthy family background makes it easier to move up into a managerial position as the applicants face less barriers. In contrast, for an applicant from a poor family it is heavy going. As a result, to succeed as a manager with a poor family background, one needs to possess top skills. In other words, the selection process causes that while the rich applicants do not need to meet the highest criteria, the poor ones do.
The authors also claimed that it would necessarily mean that there is more dispersion among the fund managers from rich families as not all of them satisfy the highest criteria. On the contrary, performance of the successful applicants from less rich families are likely to be more similar. As a matter of fact, they found higher volatility in the results among the fund managers with wealthier background and thus provided evidence in favor of their arguments. The picture shows the distribution of income of the general male population and that of the managers’ fathers.
Reference: Chuprinin, O., & Sosyura, D. (2016, May). Family Descent as a Signal of Managerial Quality: Evidence from Mutual Funds. In University of Miami, School of Business Administration, 6th Miami Behavioral Finance Conference. Available here.
The presence of universities in a particular region is argued to have a positive effect on GDP per capita. To test this claim empirically, Valero and Reenen (2016) made use of a huge database of almost 15000 universities in 1500 regions. Their empirical exercise suggests that there is a positive and significant impact of the existence of a university in a region on the region’s GDP per capita and this conclusion seems to be robust to different specifications. They found that a doubled number of universities is associated with a 4% increase of GDP per capita.
Additionally, the authors identified four main channels through which the positive effect is likely to influence the economy. In particular, unsurprisingly, more universities increase human capital which is believed to increase productivity and thus the GDP per capita as well. Apart from human capital, universities and their environment strengthen innovation activity, measured by the number of registered patents. Further, it is widely known that institutions represent a key determinant of economic growth. Specifically, some of the institutions such as democracy and political culture are claimed to be necessary for growth, especially in developed countries. Finally, there is a direct effect of higher economic activity from the existence of the university in a particular region (construction of the buildings etc.) and higher demand from professors and students. The empirical results indicate that growth is driven by both human capital and innovation, though the effects of these are small in magnitude. The impact of democracy and institutions also seems to be positive although rather in the long term. To sum up, the authors conclude that the presence of universities impacts growth also in other ways than simply via the increase in demand caused by higher economic activity.
Reference: Valero, A., & Van Reenen, J. (2016). The economic impact of universities: Evidence from across the globe (No. w22501). National Bureau of Economic Research. Available here.