differences regarding terms used in data collection and analysis. First, as indicated above, the analytic framework is flexible in that it identifies what is important to measure, but it does not prescribe the depth of substantive coverage of data collection or analysis for any particular use. It also does not hypothesize directions of causality. Both are at the discretion of those undertaking the measurement. This is deliberate. In developing our approach to measuring empowerment, we needed to identify core elements of empowerment that could be measured and used within and across a range of countries and situations. Hence, the framework focuses on domains, levels, and degrees of empowerment. However, indicators, variables, and their values must be country- and context-specific. Therefore, for the five countries in which the study was undertaken, each country team decided for themselves the levels to examine, the indicators to use for measurement, and the values placed on variables. Some analysts may be uncomfortable with this flexibility, but the scope and scale of information gathered, along with prior information on the context, will help analysts hypothesize likely associations and causal relations for testing. In addition, the availability of resources always influences the depth and spread of analysis. The ME framework provides users with clear areas of enquiry and then allows them to adapt aspects of the framework to a specific situation.

The second point is that the _level about which_ information is gathered, the _level at which_ it is gathered and the _level of pooling_ of information for analysis are conceptually and practically distinct.<sup>16</sup> _Levels about which_ data are collected refer to administrative boundaries, such as a woman’s empowerment in a household, a community, or a district headquarters. This is different from the _level at which_ data collection is undertaken, for example, interviews or other means of enquiry undertaken at the individual, household, group, community, town, or regional level. Further, both levels about and levels of data collection are distinct from pooling, or _levels at which data are analyzed._ For example, a household survey applied at the local level can ask about activities relating to the local, intermediary, or macro levels. The information and data resulting from that survey are usually pooled and analyzed at an intermediary or national level—such as in the case of an LSMS or national poverty monitoring exercise.

# 2.2 Examples of Application in Interventions

Feedback from project teams indicates that the application of the framework for structuring analysis and developing empowerment indicators is a manageable task. Lead researchers for each case study were either involved in the development of the framework or briefed on its content by the coordinating team. The lead researchers then independently instructed local researchers about its application.

The following examples from Ethiopia, Nepal, and Honduras are taken from the measuring empowerment studies in those countries.<sup>17</sup> In each case presented, methodologies have been developed but findings have not yet been fully analyzed. A

> 16 This is the case even when some tasks are undertaken simultaneously.

> 17 To date, the framework has been applied only to the evaluation of investment loans. Measuring empowerment in the context of adjustment/development policy loans has not yet been initiated but is scheduled to begin in two countries during FY04.

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