What are the factors that make a given person more or less likely to have children? How important are income, education, and optimism about the future? Is it true that “development is the best contraceptive,” as demographers like to say? And is the global population really going to double by the next century? (Probably not — in fact, one U.N. estimate finds that the population in 2100 could be lower than today.)
These are some of the questions we ask in this week’s episode, “Why Do People Keep Having Children?” (You can subscribe to the podcast at iTunes, get the RSS feed, or listen via the media player above. You can also read the transcript, which includes credits for the music you’ll hear in the episode.) Read More »
A recent New York Times article discussed a meeting being held to protest a “tiered wage” that averages $1,000 per week for performers in touring productions of Broadway musicals — compared to a “full wage” of $1,800 for the Broadway productions of the same show.
Why shouldn’t the pay be the same for the same effort? The article gets the answer correct: the pay must equal the marginal revenue product for the production to be profitable; and even compared to performances in cultural capitals like Austin, Tex., the revenue-per-seat-filled on Broadway is much higher. A touring company just cannot, as the article notes, make a profit or perhaps not break even paying the same wages as on Broadway. Perhaps not fair to the performers, but this is good economics. With this difference in pay, however, the quality of the touring companies is unlikely to be as good as the Broadway company.
We’ve blogged before about the (relatively small) effect of birth month on athletic excellence. But how does birth location affect a potential athlete? In The New York Times, Seth Stephens-Davidowitz calculated the probability of getting to the N.B.A. by Zip code. He found that players like LeBron James, born to a low-income teenage mom, are the exceptions to the rule:
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I recently calculated the probability of reaching the N.B.A., by race, in every county in the United States. I got data on births from the Centers for Disease Control and Prevention; data on basketball players from basketball-reference.com; and per capita income from the census. The results? Growing up in a wealthier neighborhood is a major, positive predictor of reaching the N.B.A. for both black and white men. Is this driven by sons of N.B.A. players like the Warriors’ brilliant Stephen Curry? Nope. Take them out and the result is similar.
A new working paper (abstract; PDF) by Paul Gertler, James Heckman, and several other co-authors examines the impressive long-term effects of a Jamaican program that taught low-income parents better parenting skills. Here’s the abstract:
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We find large effects on the earnings of participants from a randomized intervention that gave psychosocial stimulation to stunted Jamaican toddlers living in poverty. The intervention consisted of one-hour weekly visits from community Jamaican health workers over a 2-year period that taught parenting skills and encouraged mothers to interact and play with their children in ways that would develop their children’s cognitive and personality skills. We re-interviewed the study participants 20 years after the intervention. Stimulation increased the average earnings of participants by 42 percent. Treatment group earnings caught up to the earnings of a matched non-stunted comparison group. These findings show that psychosocial stimulation early in childhood in disadvantaged settings can have substantial effects on labor market outcomes and reduce later life inequality.
A new working paper (abstract; PDF) by Marianne Bertrand, Jessica Pan, and Emir Kamenica looks at gender identity and its affect on household income. Their findings will depress anyone concerned with gender equality. Here’s the abstract:
We examine causes and consequences of relative income within households. We establish that gender identity – in particular, an aversion to the wife earning more than the husband – impacts marriage formation, the wife’s labor force participation, the wife’s income conditional on working, marriage satisfaction, likelihood of divorce, and the division of home production. The distribution of the share of household income earned by the wife exhibits a sharp cliff at 0.5, which suggests that a couple is less willing to match if her income exceeds his. Within marriage markets, when a randomly chosen woman becomes more likely to earn more than a randomly chosen man, marriage rates decline. Within couples, if the wife’s potential income (based on her demographics) is likely to exceed the husband’s, the wife is less likely to be in the labor force and earns less than her potential if she does work. Couples where the wife earns more than the husband are less satisfied with their marriage and are more likely to divorce. Finally, based on time use surveys, the gender gap in non-market work is larger if the wife earns more than the husband.
A 50-year-old law professor told me yesterday that between college and law school he worked as a carpenter. I said it was great to hear that, as it must make him more productive at home. He said no, he never does carpentry work around his house now for two reasons:
- Skill depreciation: he isn’t as good at carpentry as he was when he was doing it full time.
- His requirement for quality work is such that he wouldn’t use himself as a carpenter—the income elasticity of demand for quality is positive, and his income is much higher now than after college.
He didn’t mention a third reason, which I think is important, namely that the opportunity cost of his time is too high to make carpentry a good way to spend time. (HT: TB)
New data on income inequality in the United States were just released. And they provide a useful teaching moment. The graph below, which comes from the Census Bureau, shows the evolution of the Gini coefficient since 1967. It’s pretty clear that this measure of inequality has been rising pretty much through this whole period.
p>But here’s how the Census Bureau chose to describe these data:
Based on the Gini index, income inequality increased by 1.6 percent between 2010 and 2011; this represents the first time the Gini index has shown an annual increase since 1993, the earliest year available for comparable measures of income inequality.
Say what? Read More »