Truck and Barter

Politics give you Gas

A recent working paper from the IMF- Political Price Cycles in Regulated Industries: Theory and Evidence. Here’s the conclusion;

“This paper presented a model of industry regulation where information asymmetries and the government-regulator’s interest in being re-elected may generate a political cycle in the regulated price. Quarterly data covering 32 countries during 1978–2004 provided strong evidence of the occurrence of a political cycle in gasoline prices.

Our model characterizes the behavior of the government-regulator as an attempt to maximize an objective function comprised of the social welfare in the regulated market and the government’s chance of being reelected. The social welfare function follows a stochastic process in which the weights attributed to consumers’ utility and firms’ profits are determined by a shock at the beginning of an election period. This shock may reflect changing political alliances or economic conditions exogenous to the regulated market. Because this shock is not immediately observable to consumer-voters, the incumbent government-regulator may have incentives to set a price below the welfare-maximizing price to signal its pro-consumer nature and thus attract more votes in the upcoming election. In fact, our model derives equilibrium regulation strategies in which some types of government-regulator will lower the regulated price in an election period, thus generating a political price cycle in the regulated industry.

We tested the implications of our model for the gasoline market in the 32 industrial and developing countries for which data were available. The choice of the gasoline market is justified by the impact it has on consumers’ utility, the visibility that fluctuations in gasoline prices have, and the various ways—besides direct price determination—through which governments can influence domestic gasoline prices. Simple statistical analysis revealed that changes in gasoline prices were, on average, 1.4 percent above changes in domestic oil costs during “normal” quarters and 0.4 percent below during periods immediately preceding an election. Focusing on real gasoline prices alone, we observed that they declined 0.3 percent, on average, during “normal” quarters and about 0.7 percent during quarters of electoral campaign. Moreover, in 15 countries of the sample, this difference exceeded 2 percentage points, whereas it exceeded 6 percentage points in seven countries.

Econometric modeling also point toward the existence of a political cycle in gasoline prices. Simple equations linking gasoline prices to changes in the cost of oil to domestic gasoline producers (which is assumed to be driven by international oil prices and the exchange rate) and dummy variables indicating periods of electoral activity were estimated using panel data techniques. Our estimates consistently yielded negative and significant coefficients on the electoral dummy variable, suggesting that after controlling for cost changes, gasoline prices are reduced during periods of electoral activity. In fact, point estimates suggest that the real price of gasoline declines by at least 0.8 percent and up to 1 percent, on average, during each of the two quarters of election campaign in the 32 countries covered by our sample, a relatively sharp decline compared to the average reduction of 0.3 percent in the entire sample period. These results were robust across various model specifications. Since we do not expect the occurrence of political price cycles in all countries at all times, it is reasonable to assume that the decline in real gasoline prices would be significantly more pronounced in the countries where a cycle is in fact observed.

Could bad weather be responsible for U.S. corruption?

Asks Peter T. Leeson and Russell S. Sobel in the following paper; Weathering Corruption

Abstract; Could bad weather be responsible for U.S. corruption? Natural disasters create resource windfalls in the states they strike by triggering federally-provided natural disaster relief. Like windfalls created by the .natural resource curse.and foreign aid, disaster relief windfalls may also increase corruption. We investigate this hypothesis by exploring the effect of FEMA-provided disaster relief on public corruption. The results support our hypothesis. Each additional $1 per capita in average annual FEMA relief increases corruption nearly 2.5 percent in the average state. Eliminating FEMA disaster relief would reduce corruption more than 20 percent in the average state. Our findings suggest that notoriously corrupt regions of the United States, such as the Gulf Coast, are notoriously corrupt because natural disasters frequently strike them. They attract more disaster relief making them more corrupt.

Related;

Disaster Relief: Type I & Type II Errors

May be report cards like this could help.

The U.S. Retail Sector Since the Mid-1970's

much of the information needed to understand the competitive dynamics of local retail markets simply does not exist in a form usable by researchers.

That's from Ronald S. Jarmin, Shawn D. Klimek, and Javier Miranda of the U.S. Bureau of the Census, who in "The Role of Retail Chains: National, Regional, and Industry Results", use the Longitudinal Business Database -- individual retail location level data -- to assess the composition and structure of the retail industry since 1976. They're talking about the lack of product and price information, but note that the LBD does contain establishment payroll and employment, though not revenues or profits. Still, the data they analyze yield interesting conclusions:

Thus, we see that large metropolitan retail markets are characterized by fewer competitors per capita than rural and micropolitan county markets, but that competition in metropolitan markets is marked by higher firm turnover, and that this higher turnover is more pronounced among chain store retailers.

[Emphasis added]

Even more interesting is a presentation they gave in 2004 summarizing the paper. I liked slide 19 -- "Absolute Drop in Number of “Local” Stores" -- best. It notes that "The absolute number of single unit (SU) establishments has gone down. Between 1976 and 2000 the number of SUs has dropped by 2.53%." However, "Marked differences [exist] across CBSAs. Metro areas increased by 1.62% but Micropolitan and Rural areas experienced a drop of 12.40% and 17.65% respectively."

100% Employer Paid Health Insurance

Here's an interesting Discussion Paper from the Census' Center for Economic Studies: Contributions to Health Insurance Premiums: When Does the Employer Pay 100 Percent? by Alice Zawacki and Amy Taylor. The abstract:

We identify the characteristics of establishments that paid 100 percent of health insurance premiums and the policies they offered from 1997-2001, despite increased premium costs. Analyzing data from the MEPS-IC, we see little change in the percent of establishments that paid the full cost of premiums for employees. Most of these establishments were young, small, singleunits, with a relatively high paid workforce. Plans that were fully paid generally required referrals to see specialists, did not cover pre-existing conditions or outpatient prescriptions, and had the highest out-of-pocket expense limits. These plans also were more likely than plans not fully paid by employers to have had a fee-for-service or exclusive provider arrangement, had the highest premiums, and were less likely to be self-insured. [Emphasis added]

The dataset provides information on establishments and on the health insurance plans offered by each "The MEPS – IC collects data on premiums for single and family coverage, contributions by employers and employees, provider type, plan enrollment, deductibles, and copayments."

In essence, firms that contribute 100% of premia are more likely to offer higher-priced plans, but these same plans offer some contraints -- more need for referrals, higher out-of-pocket expenses, lower coverage of pre-existing conditions, and lower coverage of outpatient prescriptions. (Granted, the absolute differences don't seem all that large, even where they are statistically significant).

Which leads me to ask, what's going on here? For firms that pay 100%, higher premia appear to be buying plans with -- on average -- slightly smaller benefits. The authors note this is partly due to firm size -- smaller firms are more likely to cover 100% than larger firms. So there might be some scale effects on costs.

But the analysis also notes that 100% employer paid plans have FAR lower self-insured indemnification -- 13% of plans instead of 29% -- meaning that liability for excess medical costs is shifted from employer to health insurer far more frequently when employers are paying 100% of the premium than otherwise. In other words, employers paying 100% less frequently need to buy stop-loss insurance, and more frequently shift the risk of excess coverage to insurance companies within the health insurance contract. I'd say that's what they're paying the extra dough for, but that's just a hunch.

Interesting stuff.

(Alternate copy of the paper here).

Hoover & Siegler: McCloskey is Oh So Wrong About Statistical Significance (UPDATED)

In a November 2005 working paper, Sound and Fury: McCloskey and Significance Testing in Economics, Kevin Hoover and Mark Siegler inform us that because Deidre McCloskey still hasn't done her homework right, she continues to misrepresent the median economist as a statistical dummy. Part of the abstract:

That statistical significance is not economic significance is a jejune and uncontroversial claim, and there is no convincing evidence that economists systematically mistake the two. Other elements of McCloskey’s analysis of statistical significance are shown to be ill-founded, and her criticisms of practices of economists are found to be based in inaccurate readings and tendentious interpretations of their work. Properly used, significance tests are a valuable tool for assessing signal strength, for assisting in model specification, and for determining causal structure.

Here's a more extensive earlier draft, and a list of the full-length AER papers McCloskey and Ziliak failed to include in previous analyses.

That's all in section 5.1, starting at page 31 (37) of the working paper. I was with H&S much of the way in that section-- especially about the subjectivity required to construct the evaluations, and the inconsistency across the two reviews -- until they conflate the refusal of M&Z to re-produce a representative sample of the now lost paper-to-dataset mappings with a refusal to "share" them. This serves to imply that the mappings are hidden in Ziliak's sock drawer, or thereabouts.... and the authors lose my respect with what I fear is not just poor word choice. Still, the paper is as interesting as it is fierce.

From page 34 (40):

Unfortunately, Ziliak has informed us that such records ["that indicate precisely which passages in the text warrant particular judgments with respect to each question."] do not exist. And McCloskey and Ziliak declined our requests to reconstruct these mappings retrospectively for a random selection of the articles (e-mail McCloskey to Hoover 11 February 2005). Absent such information, including any description of procedures for calibrating and maintaining consistency of scoring between the two surveys, we cannot assess the quality of the scoring or the comparability between the surveys.

McCloskey’s reason for not sharing the mappings appears to be, first, that they are utterly transparent and, second, that the relevant information is contained in the scores themselves:

Think of astronomers disagreeing. We have supplied you with the photographic plates with which we arrived at our conclusions [i.e., the question-by-question scores for the 1990 survey]. . . The stars [i.e., the articles in the American Economic Review] are still there, too, for you to observe independently. [e-mail McCloskey to Hoover 19 February 2005]

[Emphasis added]

UPDATE 1/25: After reading Dr. Ziliak's comment, and carefully reading the sections of their paper dealing with M&Z's AER work, I must say that I'm disappointed in H&S. I don't think H&S have much new to say other than the problem is not as bad as M&Z claim. However, this is an empirical question, that in my mind, H&S fail to address thoroughly -- in fact, not even in a cursory fashion.

H&S caught my attention by insisting their data were better than the original. So I figured they would try to reproduce results -- which granted, is pretty hard and thankless work! What I really wanted to know from their paper were the results of a sensitivity analysis that should have been performed: given a) the expanded and more comprehensive dataset (allegedly 20% larger over the original), and b) a revised protocol (they didn't seem to like the multi-faceted M&Z questionnaire), how often do the M&Z results still hold? How frequently do published papers focus on measuring and sizing up economic impact? H&S didn't answer these questions. Hence, I found the paper of Hoover and Siegler pointless from the standpoint of my interests. And their selective detailed review of several papers in section 5 demonstrates nothing to me.

In Size Matters, M&Z found that the percent of full-length AER papers that didn't distinguish economic from statistical significance grew from 70% in the 1980's to 82% in the 1990's.

But H&S claim to have found new data: 15 papers in the 80's and 56 papers in the 90's that M&Z failed to include in their previous analyses. Since H&S don't perform one, let me create my own sensitivity analysis, measuring the potential impact of new data (though not the impact of a revised questionnaire).

First, the original M&Z data, percent of papers not distinguishing economic from statistical significance:

1980's: 127/182=70%
1990's: 112/137=82%

Second, I'm looking to make a lower bound: assume previous identifications of M&Z are correct, but that every single paper H&S have discovered does measure oomph:

1980's: 127/197=64%
1990's: 112/193=58%

In other words, under the extremely unlikely scenario that every single paper H&S have identified distinguishes economic from statistical significance, a majority of top AER papers STILL don't! And that 6% drop over the period, by itself, is not important to the profession.

For a more likely (though not most likely), mid-range estimate, assume half of all newly discovered papers measure oomph:

1980's: 135/197=69%
1990's: 140/193=73%

And finally, what if none of the new papers measure oomph:

1980's: 142/197=72%
1990's: 168/193=87%

In interval form, the new estimates for the share of papers not distinguishing economic from statistical significance range from 64%-72% for the 1980's and 58%-87% for the 1990's.

In sum: A majority of papers in the AER in the 1980's and 1990's did not distinguish economic and statistical significance, although trends in the share are not yet determinable.

(Of course, what is really called for is another observer to categorize the raw data using a different protocol, but that will have to wait for somebody without a blog).