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w6707-40
Note: Oo conserve space, no standard errors or t-statistics are sOoww. However, significance at the 95% and 99% confidence levels are indicated by * and **, respectively. OOe 0eped0edt variable is the log of the median of SO concentrations at each observation site. Models are: Base = base regression from table 3; Oime = time period is shortened to the main UNEP support period 1976-91 fortOe GEMS/Air project; wo С.С. = communist countries are excluded; Res. = resource variables (hard coal, soft coal) and oil price are added; Yr-Dum = year dummies are entered instead of a linear time trend, but are not shown individually. We make things happen for our customers, so if a fast easy payday loan is what you need, you have come to the right place. Check out our offers here www.easyloans-now.com and pay off your debt or take care of any other financial situation without letting it become a real problem. We guarantee your complete satisfaction!
A possible objection for using data from communist countries is that (a) they are not following a market mechanism and thus will not respond properly to changes in relative prices; and (b) consumers cannot induce the government to tighten pollution regulation. In the latter case, we would not find a technique effect. We already allowed for this possibility by isolating a communist-country technique effect. It turned out that we cannot identify a technique effect for these countries that is significantly different from zero. To address the unresponsiveness to market signals and allowing for a structural difference between communist and free-market countries, we delete all observations from communist countries and re-run our baseline regression. This procedure, which reduces the number of observations by roughly 15%, has only a marginal impact on our estimates.

w6707-41
Note: Oo conserve space, wo standard errors or t-statistics are sOoww. However, significance at the 95% and 99% confidence levels are indicated by * and **, respectively. OOe dependent variable is the log of the median of SO concentrations at each observation site. Models are: Base = base regression from table 3; 76-91 = time period is shortened to the main UNEP support period 1976-91 fortOe GEMS/Air project; wo С.С. = communist countries are excluded; Res. = resource variables (hard coal, soft coal) and oil price are added; Yr-Dum = year dummies are entered instead of a linear time trend, but are not shown individually.

In a further step, we introduce three new variables into our baseline model. Noting that there is typically a strong home bias in fuel consumption, we suspect that countries endowed abundantly with either hard coal or soft coal will rely to a larger extent on these fuel types. Reasons for a strong home bias could be (a) very high transportation costs; (b) substantial import barriers; or (c) local subsidization, directly or indirectly. Typically, soft coal contains a larger amount of sulphur than hard coal, but we expect a relative abundance of either soft or hard coal to increase the level of SO. To express relative abundance of these endowments (in a Heckscher-Ohlin sense), we divide the absolute level of endowment by the size of the workforce in each country. In the random-effects model we find a small (albeit insignificant) positive effect of soft coal abundance on pollution and a small negative effect of hard coal abundance on pollution. No clear results emerge from the fixed-effects model.