Précis
Unemployment
vs. help-wanted
Statistical
rankings vs. movable feet
From whence
wealth?
The rate at which jobs are found (and unemployment thus reduced) depends on how many workers are looking for jobs and how many vacant jobs are available, according to the April 1999 Economic Trends newsletter published by the Federal Reserve Bank of Chicago. "The more vacancies there are (holding other factors constant), the lower the unemployment rate."
The graphic device used to illustrate this principle is the "Beveridge curve," named after the British economist who first established this empirical relationship. The Beveridge curve relationship, Economic Trends goes on to say, appears as a downward sloping line over relatively short time periods. Over longer periods, however, it has been unstable, both in the United States and elsewhere. Based on graphs of the unemployment rate against the Conference Board’s help-wanted advertising index (a rough proxy for vacancies), Economic Trends says:
"In the 1950s, for example, both unemployment and vacancies were low; nevertheless, as vacancies decreased, unemployment rose. Compare this to the 1980s, when both vacancies and the unemployment rate were much higher. Again, as vacancies declined, unemployment rose. The Beveridge curve shifted out significantly."
Since the 1980s, Economic Trends detects a new shift in the curve, this time back toward the origin of the graph. One implication of this is that a given level of "vacancies" has recently been consistent with a lower level of unemployment than had been the case in the immediate past decade.
Statistical rankings vs. movable feet
One of the really fun exercises in economic and social statistics is creating indexes for ranking people or places or things along some interesting dimension. Howard J. Wall recently took a close look at two published indexes that purport to measure the livability of American cities. Writing in The Regional Economist, a quarterly published by the Federal Reserve Bank of St. Louis, Wall was struck by the curious fact that "Although [the two] rankings consider the same general factors, their results are almost entirely uncorrelated. In the 1997 rankings, for example, there was no overlap whatsoever between [the top 10 cites in the respective rankings]."The fact that there are such big differences in the findings suggests to Wall that users should be wary. Looking at the lists of variables typically used in this sort of exercise also causes Wall some concern: Who says these are the important factors? And even if they are the right factors, are they given the appropriate weights? Given these difficulties, Wall asks if it makes any sense at all to attempt ranking places on as subjective a matter as livability.
His answer is based on the economic principle of revealed preference: If two alternative bundles of goods (or cities) are both affordable, rational consumers choose the one that will yield them the most utility (or livability). "In terms of migration decisions," Wall writes, "the principle of revealed preference says that a person’s movement from one metro[politan] area to another reveals that she prefers the new metro area to her previous one." Thus, Wall’s ranking of the livability of cities would be based on their rates of net domestic in-migration.
Applying the net domestic migration rule to the 59 largest metropolitan areas, Wall found that the top five were Las Vegas, Atlanta, Phoenix, Austin, and Raleigh-Durham. How did his rankings compare with the two systems he used as benchmarks? In one case, only two of the top ten were the same; in the other case, four of ten. Wall’s top 10 had an average rank of 31 in the first case and an average rank of 19 in the second. Interestingly, one of the benchmark ranking systems placed Las Vegas dead last.
Looking at a more detailed set of data—the Survey of Consumer Finances—led Tracy, Schneider, and Chan to take another, more careful look at household wealth. It turns out that the rising importance of equities is almost entirely recorded in the top three deciles of the wealth distri-bution. The bottom of the distribution owns virtually no real estate or cor-porate equity. The middle two-thirds holds a disproportionate share of their wealth in real estate—generally their own homes. Finally, at the top of the wealth chart, asset port-folios contained more balanced holdings of real estate and corporate equity. According to the consumer finances data, "the typical household’s real estate share far exceeds the 29 percent share attributed to real estate in the ag-gregate." In fact, say the authors, "the aggregate equity and real estate shares reported in the Flow of Funds Accounts are more characteristic of a household at the 95th percentile than a household at the midpoint of the wealth distribution."
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