way to standardize indices and make findings easier to interpret is to separate the two dimensions. A second way to is standardize the dimensions. This is the approach adopted, for example, in the WEAI index; however, the applicability outside agriculture is limited. The empowerment classification proposed in Brody et al. (2015): economic, social, political, psychological may prove a useful subdivision. Put together, these two facets (behavior vs. attitude and dimension of empowerment) create an eightelement classification of empowerment shown in Table 1 below. Each cell of Table 1 provides examples of variables which could measure the concepts shown in the cell. For example, the _attitude_ dimension of economic empowerment could be measured by a respondent’s self-reported beliefs about her aspirations, confidence, and views on the constraints imposed on her. The behaviors dimension could be measured by the commonly used variables measuring income earning decisions as well as ones on expenditure decision making, assisted by vignettes if this proves helpful in more trials.

To support standardization, it may be helpful to define even finer sub-elements. For example, within the domain of economic empowerment, attitudes toward pathways to labor market outcomes would be separated from confidence in saving and financial skills. In this way, researchers could identify in their theory of change and/or pre-analysis research plan which subdimension of empowerment they expect to improve ex ante in the project. They could then develop a context-specific measure of each subdimension. While this approach might still be subject to the weaknesses identified in Peterman et al. (2015), it would be less ad hoc than current practice and easier to compare results across countries.

Another advantage would be the opportunity to (i) identify which aspects of empowerment a particular intervention influences,<sup>17</sup> (ii) check to see which aspects appear necessary for desired final outcomes; and (iii) identify associations/correlations between dimensions, and if possible, even identify which subdimensions are necessary for which final outcomes. For example, Upadhyay et al. (2014) did a systematic review of the associations between women’s empowerment and fertility-related outcomes. Of the 60 studies they looked at, 37 used women’s bargaining power over expenditures (as measured by the DHS) as the measure of empowerment. This was the only empowerment variable which was even mildly standardized across the studies, which they attribute to the powerful role of DHS surveys, especially for the analysis of reproductive health variables. In their conclusion, they call for more research on operationalizing women’s empowerment. They note that: _“The multidimensionality of women’s empowerment likely contributed to some of the null and counterintuitive effects of empowerment measures in this review, emphasizing the importance of further refinement and conceptualization of women’s empowerment measures, as well as the incorporation of statistical techniques (e.g., multilevel and structural equation modeling) to better capture this complex, latent construct in quantitative analyses.”_ (Upadhyay et al. 2014, page 13) In other words, if studies had measured empowerment across several domains, and used both attitudinal and behavioral measures. They might have been able to tease out which areas of empowerment mattered most for reproductive health outcomes.

> 17 Of course, this requires multi-armed impact evaluations with large samples, which has become increasingly expensive.

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