Stephen T. Ziliak’s research while affiliated with Roosevelt University and other places

Haiku is a distinguished (if short) form of poetry with roots dating back to 17th century Japan. Poets understand that haiku is the most efficient form of economic speech. But technical efficiency is not the only or even the main goal of writing haiku. Haiku clear a trail for enlightenment and stimulate open discussion. A wide variety of poets, from Matsuo Basho (1644-1694) to Richard Wright (1908-1960), have practiced writing haiku simply to improve their own powers of observation. To date, haiku and economics have not been explored together and certainly not at the level of principles. This article introduces a new field of inquiry, 'haiku economics', and offers tips on how to the start the journey in a classroom setting.

After William Gosset (1876-1937), the 'Student' of Student's t, the best statisticians have distinguished economic (or agronomic or psychological or medical) significance from merely statistical 'significance' at conventional levels. A singular exception among the best was Ronald A. Fisher, who argued in the 1920s that statistical significance at the 0.05 level is a necessary and sufficient condition for establishing a scientific result. After Fisher many economists and some others - but rarely physicists, chemists, and geologists, who seldom use Fisher-significance - have mixed up the two kinds of significance. We have been writing on the matter for some decades, with other critics in medicine, sociology, psychology, and the like. Hoover and Siegler, despite a disdainful rhetoric, agree with the logic of our case. Fisherian 'significance,' they agree, is neither necessary nor sufficient for scientific significance. But they claim that economists already know this and that Fisherian tests can still be used for specification searches. Neither claim seems to be true. Our massive evidence that economists get it wrong appears to hold up. And if rhetorical standards are needed to decide the importance of a coefficient in the scientific conversation, so are they needed when searching for an equation to fit. Fisherian 'significance' signifies nearly nothing, and empirical economics as actually practiced is in crisis.

In economics and other sciences, "statistical significance" is by custom, habit, and education a necessary and sufficient condition for proving an empirical result. The canonical routine is to calculate what’s called a t-statistic and then to compare its estimated value against a theoretically expected value of it, which is found in "Student's" t table. A result yielding a t-value greater than or equal to about 2.0 is said to be "statistically significant at the 95 percent level." Alternatively, a regression coefficient is said to be "statistically significantly different from the null, p ? .05." Canonically speaking, if a coefficient clears the 95 percent hurdle, it warrants additional scientific attention. If not, not. The first presentation of "Student's" test of significance came a century ago in 1908, in "The Probable Error of a Mean," published by an anonymous "Student." The author's commercial employer required that his identity be shielded from competitors, but we have known for some decades that the article was written by William Sealy Gosset (1876–1937), whose entire career was spent at Guinness's brewery in Dublin, where Gosset was a master brewer and experimental scientist. Perhaps surprisingly, the ingenious "Student" did not give a hoot for a single finding of "statistical" significance, even at the 95 percent level of significance as established by his own tables. Beginning in 1904, "Student," who was a businessman besides a scientist, took an economic approach to the logic of uncertainty, arguing finally that statistical significance is "nearly valueless" in itself.

If progressive economists are serious about changing what is valued in the economics research paper--and therefore in real-world decisionmaking in courts and hospitals and the World Bank--they should insist in their own research journals that current usage of statistical significance has no theoretical justification. If Marxist journals hurry up and change their ways they can beat to the punch the most prestigious journal of economics in the world, the American Economic Review , which, this paper shows, is filled with reports of statistical significance but not with reports of what we really want, economic or substantive significance. In empirical economics size, not fit, is what matters, but the mainstream comes up short. Likewise, testing for fit but not size is hurting Marxist analysis and the world. People standing for jobs and justice and human lives should do better. No size, we should say, no significance.

Significance testing as used has no theoretical justification. Our article in the Journal of Economic Literature (1996) showed that of the 182 full-length papers published in the 1980s in the American Economic Review 70% did not distinguish economic from statistical significance. Since 1996 many colleagues have told us that practice has improved. We interpret their response as an empirical claim, a judgment about a fact. Our colleagues, unhappily, are mistaken: significance testing is getting worse. We find here that in the next decade, the 1990s, of the 137 papers using a test of statistical significance in the AER fully 82% mistook a merely statistically significant finding for an economically significant finding. A super majority (81%) believed that looking at the sign of a coefficient sufficed for science, ignoring size. The mistake is causing economic damage: losses of jobs and justice, and indeed of human lives (especially in, to mention another field enchanted with statistical significance as against substantive significance, medical science). The confusion between fit and importance is causing false hypotheses to be accepted and true hypotheses to be rejected. We propose a publication standard for the future: “Tell me the oomph of your coefficient; and do not confuse it with merely statistical significance.â€

Books reviewed: Culture and the Labour Market, by Siobhan Austin, Boyd Black, author Stochastic Frontier Analysis, by Knox Lovell and Subal Kumbhakar, Tim Coelli, author Selected Essays on Economic Police, by Geoff Harcourt, Peter B. Dixon, author The Economics and Ideology of Free Trade: A Historical Review, by Leonard Gomes, John Aldrich, author Editing Economics. Essays in Honour of Mark Perlman, by Hank Lim, Ungsuh K. Park and G. C. Harcourt eds., Joe Isaac, author The Economics of Public Spending, by David Miles, Gareth Myles, and Ian Preston eds., Jean-Michel Josselin, author Technological Revolutions and Financial Capital, by Carlota Perez, Mardi Dungey, author Trade and the Environment: Theory and Evidence, by B. Copeland and S. Taylor, Martin Richardson, author The Ends and Means of Welfare: Coping with Economic and Social Change in Australia, by Peter Saunders, Stephen T. Ziliak, author

If replacing welfare with private charity has increased the self-reliance of the poor, the benefits would be observed in the charity organization movement of the late nineteenth century. Inebriation would subside, the heart would be cheered, earnings would rise, the broken would be complete, dependence would wither, and the classes would converge. If the benefits were large, they would have been large in Indianapolis the beacon of charity in a Coasean landscape. The hypotheses are tested in hazard models using a sample from 25 years of household-level caseworker manuscripts. The evidence is not suggestive in the direction of hope.