19 widely argued that IRAs are needed to control the pricing behavior of monopolies. Two notable exceptions to the noninteraction of the dummy variables are in the prices of local calls and of industrial electricity. In both of these cases, the interaction is negative and stronger than the direct impact of each policy. In other words, in these two cases, the evidence suggests that the regulator indeed limits the opportunistic pricing behavior of operators.

The interactions between corruption and the reform dummies are once more the most interesting of the effects. The effect of reforms on the price consequences of corruption is, in general, less significant than suggested by theory. The introduction of an IRA influences only some of the prices of concern to the industrial consumers, reinforcing the impact of corruption on average electricity prices. In telecoms, the presence of an IRA reduces the adverse impact of corruption on household connection charges (line 5). The introduction of PPI has no impact on the price effect of corruption (line 6). It does, however, offset the impact of corruption on local call prices and on industrial connection fees in telecoms. Lines 4 to 6, considered together, suggest that the two policies never combined to affect prices significantly during the period of analysis.

# **6. Impact of reforms on quality**

This section focuses quality. As in sections 4 and 5, we first explain how and why we picked our proxies in each sector. We then move to a basic data analysis before discussing the econometric results.

## **6.1 Selection of quality indicators**

The measurement of quality proved to be the most challenging of the three dimensions of performance. Quality may be assessed from the technical point of view or from a service perspective. The first angle is generally based on quantitative measures, often generated by engineers. The second tends to rely on qualitative measures, often based on surveys of users. Quantitative measures are less subjective, even if they are usually reported by the utilities or providers based on self-assessments. Examples include electric outages and reported phone faults. Qualitative measures often capture the _perceptions_ of respondents. Regardless of the merits of the two types of measures, we could not find qualitative datasets with large enough coverage for our purposes.

In electricity, we rely on power transmission and distribution losses as percentages of total output as our measure of technical quality. These are defined by IEA as “losses in transmission between sources of supply and points of distribution and in the distribution to consumers, including pilferage. Shares may not sum to 100 percent because other sources of generated electricity (such as geothermal, solar, and wind) are not shown.”<sup>21</sup> Theoretically, power transmission and distribution losses are a suitable proxy for the quality of service because the higher the losses, the higher the probability

> 21 We found only one working paper, Vagliasindi (2004), on the effects of reform policies on technical quality in electricity; it, too, used the IEA measure. The paucity of academic literature on electricity quality is in part due to data limitations.