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The socioeconomic effects of height
Rising wage inequality
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The socioeconomic effects of height
Does it literally pay to be tall? It appears so, according to “Life at the top: the benefits of height,” a paper by Angus S. Deaton and Raksha Arora (NBER Working Paper Series, National Bureau of Economic Research, June 2009). Deaton and Arora use data from the Gallup-Healthways Well-Being Index polling to study the effect of height on income, happiness, and other factors that enter into quality of life. The authors analyze men’s and women’s responses separately.
Men and women who were polled rated their life as a whole on a scale of 1 to 10, a score of 1 representing “the worst possible life for [the respondent],” and score of 10 representing “the best possible life for [the respondent].” Each additional inch of height was found to raise the reported evaluation of life by the same amount as a 4.4-percent increase in family income for men and by the same amount as a 3.8-percent increase in family income for women. When regressions were run separately by race and ethnicity, the results of Whites and Hispanics were very similar to the overall results, but among Blacks and people of Asian descent, height was not found to improve people’s evaluations of their own lives.
The poll also asked people whether they experienced much enjoyment, happiness, sadness, anger, stress, or physical pain during the previous day. Taller respondents were less likely to report pain and sadness and more likely to report happiness and enjoyment. However, taller respondents experienced more stress and anger than their shorter counterparts, although this effect is reversed when the researchers control for race and ethnicity. Whites and Blacks average about the same height, whereas Hispanics and Asians tend to be shorter. It is because white people reported more stress than Asian, black, or Hispanic people that higher levels of stress were found among taller people.
The authors of the paper also calculated the average height of people in each of 11 categories of monthly income. On the whole, the average heights of people in the higher paying categories were greater than those of people in the lower paying categories. The researchers did a similar analysis for six categories of education level and found that a higher level of education is always associated with a greater mean height. Because controls for education and income diminish substantially most of the positive effects of height, the authors conclude that the benefits of height can be explained almost completely by the positive association between height and both education and income.
Many researchers have documented a rise in wage inequality in the United States over the last several decades. The research often points to the increase in low-skilled service employment during the period and the simultaneous decline in manufacturing jobs as contributing factors. In a recent paper entitled “Inequality and specialization: the growth of low-skill service jobs in the United States” (NBER Working Paper Series, National Bureau of Economic Research, July 2009), economists David H. Autor and David Dorn use Census Bureau data to study the rise in wage inequality at the level of local labor markets over the period from 1950 to 2005.
The authors find that during the 1980s wages and employment declined sharply in low-skill occupations and increased in high-skill occupations. During the 1990s, however, employment shares and relative earnings increased in both low-skill and high-skill occupations, leading to a “U-shaped” pattern of wage growth that has sometimes been termed “polarization.” Controlling for other factors, Autor and Dorn isolate a “single proximate cause” for the change at the lower end of the wage scale: employment and wages in low-skill, “in-person” service occupations have been increasing sharply since the 1990s. These low-skill service jobs include such occupations as food service workers, security guards, janitors, gardeners, domestic workers, home health aides, childcare workers, hairdressers and beauticians, and recreation workers. As employment in these occupations grew, it declined in other “blue-collar” jobs, such as production, craft, and repair occupations and operators, fabricators, and laborers. The growth in the low-skill occupations in the 1990s parallels that of managerial and professional specialty occupations, which require the highest level of skill and education.
A key insight of the Autor-Dorn analysis is that the nature of the changes in wages and employment over the 1980–2005 period “suggests that demand shifts must play a key role in any economic explanation of the changing structure of wages and employment in both decades.” Using statistical models, the authors explore several hypotheses, including the role played by technological change and automation, which varies by occupation. Some jobs, such as bookkeeping, clerical work, and repetitive production tasks, have become largely automated in recent years, whereas the physical and interpersonal skills required for “in-person” service jobs have proved much more difficult to computerize. As the authors note, the “output” from such jobs is not storable or transportable and thus cannot be outsourced. The primary focus of their empirical analysis is the rise of service employment at the level of local labor markets, with automation and technology more strongly affecting those areas that have higher concentrations of routine job activities.
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