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I thank without implicating, Neil Ericson, Andrew Feltenstein, Ronald Findlay, Ken Leonard, John McLaren, Edmund Phelps, Xavier Sala-i-Martin, and seminar participants at Columbia University, Florida International University, the IMF Institute, New York University, the 2002 NA Econometric Socicty Summer Meetings, Yale University and the University of Western Australia for many helpful comments. I am also grateful to Anu Kumar and Ravi Ramcharan for their research assistance.
Henry Luce, the founder of Time Magazine, is reputed to have been the first to use that phrase.
These numbers are taken from Goldin and Katz’s (1998a,b) excellent survey of America’s educational transformation at the turn of century.
From 1900 to 1917, over 14 million immigrants entered the U.S. To put this number in perspective, the U.S. population in 1900 was about 76 million, but by 1917 it had climbed to 103 million and roughly 50percent of that increase is attributable to direct immigration. The large majority of these new immigrants were unskilled laborers or were listed as having no occupation; only 160 thousand or 1% were classified as professional. In addition, most of the new immigrants during this period were male, and between the ages of 15–40. Also, of the foreign born, 56% participated in the labor market during the period 1900–1920. This compares with a participation rate of 37% for the native born [see Carter and Sutch (1997)].
Goldin (1994), Peck (1992), and Hatton and Williamson (1992) document the negative impact of immigration on the wages of the unskilled at the turn of the century.
There is also evidence that the returns to schooling were high on the eve of the civil war (Goldin and Margo (1992) and Margo (1992)). While the high school movement did not occur then, during the 1800s the U.S. did lead the world in primary schooling. The limited availability of data makes it difficult to test, but it seems reasonable to conjecture that the immigration boom before and after the Civil War may have played an important and similar role in helping engender primary schooling investment, laying the foundation for the high school movement.
This assumption simplifies the Bellman equation, but is nonbinding. See Ramcharan (2001) for a proof.
The interested reader is referred to the appendix for a derivation.
The interested reader is referred to the appendix for a more precise statement of this idea.
This is an abuse of notation. The skill premium depends not on the percent of high school graduates in the 14-17 year age group, but on the stock or number of high school graduates in the population. That said, it serves well to motivate the empirical specification.
I make the identifying assumption that at this early stage of the high school movement, expenditures per pupil were not determined by the level of high school attainment. I also assume that population movements were the main source of inter state variation in the skill premium and that the interest rate was constant across states That is, the technological and other factors behind the skill premium were constant across states.
To facilitate comparison with the existing literature, in columns 1 and 2 of Table 5 I replicate the Goldin and Katz (1997) results. There are some slight differences in the coefficient estimates, but more importantly, that framework appears to suffer from significant misspecification problems. The Ramsey RESET test (with 3 fitted terms) F-statistic is 2.45 (p-value=0.07) and the log likelihood ratio statistic is 8.44 (p-value = 0.03). That said, the impact of migration and expenditures on attainment levels (column 2) are little different compared with the specification suggested by the theory.
Borjas (1993) illustrates a variant of this point using contemporary data on ethnicity.
There are other proxies available in the literature, such as the age required for a work permit. However, these other measures produce similar results. In interpreting these results, it should be noted that cross state variation in compulsory schooling laws is only a weak proxy for the cross state effective enforcement of those laws. States with stronger laws may have much weaker enforcement.
These estimates are available upon request.
This test is based on the procedure described by Davidson and MacKinnon (1993, p. 237).
The F-test statistic for identical parameters jointly across the two regressions is 16.084; this is extremely large and leads to the rejection of identical coefficients across the two regressions at any significance level.
Goldin and Katz (1995) follow a similar strategy in their analyses of early 20th century wage structure. The interested reader is referred to those authors’ discussion of the many biases and pitfalls inherent in the use of this measure of the skill premium.
Tests for innovation of errors are only approximate in I(1) space, but provides a useful guide in practice. See Hendry and Mizon (1993).
Misspecification tests at the equation level are available upon request.
More general testing combinations were also considered. The results of these tests do not alter the conclusions; and for brevity these results are not reported, but are available upon request.
Note that this more parsimonious system encompasses the more general framework. The LR test of over identifying restrictions, which has a Chi squared distribution (15)=10.37 [p-value=0.80].