distance to PRB coal and then decrease as more alternative low-sulfur coals become available. The distance variable and low-sulfur coal price premium are both included as proxies for lowand high-sulfur coal prices.

Specifications in (31) establish correlations between state restructuring activity and emission level. Previous studies (Joskow, 1997; White, 1996) suggest that a major motivation for electricity restructuring was to remedy the problem of high electricity prices in the Northeast and California. Under ROR regulation, PUCs set the price of electricity based on the determination of recoverable costs. If dirty and less efficient coal power plants in the Northeast contributed to regional high electricity prices by passing on high environmental control costs to consumers, which in turn induced restructuring, regulatory variables _RetailAccess_ and _Transit_ may be endogenous to emission rates. A fixed effects model would be able to address the endogeneity concern. A fixed effects model levels out the inherent variations in unit operation that potentially correlated with the emission rates, and therefore we can still test how changes in price volatilities changes emission rates for the same unit across time periods.<sup>26</sup> Finally, results could be affected by sample attrition issues due to plants being removed from the reporting database. In fact, starting in 1998, many plants were divested to non-utilities and dropped from the sample because cost data of nonutility generating facilities are not reported to the public. If divestiture decisions were driven by unobservable unit specific characteristics, which make them systematically different from non-diverstitured plants, for example, if divestitured plants tend to be more competitive and produce fewer emissions given a level of output, then the estimation would also be biased. To assess if divestiture creates an attrition bias, I obtain the estimation results from a balanced sub-panel composed of units that remain in the database through 2004. The sample selection problem would be most severe in this specification if observations were not missing at random.

The stochastic disturbances in the estimating equations are assumed to be correlated across observations.<sup>27</sup> To obtain robust standard errors, I adjusted standard errors for clustering by unit in the following estimations.

# **IV. (iii) Data**

I began construction of the dataset with all privately and public owned Phase I coal-fired generating units. For these units, I constructed a panel dataset beginning in 1996, the first year for which coal prices are available, and ending in 2004, the last year for which the allowances trading data were updated. The data are collected and merged from several data sources to obtain information concerning annual aggregate productions, quality and quantity of coal used, and SO2

> 26 Fixed effects model is not sufficient to avoid endogeneity bias if restructuring policies are adopted in response to the trend in operating performance. Given that restructuring decisions were generally made before 2000, I conducted reduced sample estimation based on observations from 2000 to 2004. This alternative specification does not change the magnitude and significance of the results from the full sample; therefore, suggest that the endogeneity of regulatory statuses variables may not be a particular concern.

> 27 Estimated average first-order autocorrelation coefficients indicate _uit_ is likely to be serially correlated. In the emission rate equation, the coefficient is 0.26 in the fixed effects model; in the percentage change of annually banked allowance model, the coefficient is -0.38 in the fixed effects model. The likelihood ratio test also shows evidence of cross-sectional heteroskedasticity.

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