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Measuring Our Tracks

Jim took this picture when we were working on the sanitary pads project in Northern Ghana in early 2009.

The problem of measurement haunts women’s economics, especially in the arena of women-owned businesses.  Whether you are looking at how to compare salaries in the formal sector or how to evaluate motives in among sole proprietors, the challenge is considerable.

Counting and evaluating women-owned businesses is a particular puzzle for several reasons. The first is that most women’s enterprise is informal, thus unregistered and uncounted.  The second is that it is difficult to determine, even face-to-face, what is really a woman-owned business and what is not.  That’s because a woman may own the business–because of inherited money or whatever–and her husband run it.  Or, because of increasing assistance available for women-owned businesses in developing nations, a man may own the business but pretend his wife does in order to claim such benefits.  Most often, a business is owned and run by a whole family and it is hard to tell (or even to say) who is really in charge. Add to that the fact that most government registries and the like do not ask about the gender of the person in charge, and you begin to get a picture of the problem.

The Organization for Economic Development and Cooperation (OECD) has recently begun to focus on these issues, along with their generally increased attention to closing the gender gap, as I have reported here in previous posts.  There is a new report called the “Entrepreneurship at a Glance” that has a special section on women (pp. 21-35).  You can read the report here:  Entrepreneurship@Glance12.

The main measure of entrepreneurship has been the Global Entrepreneurship Monitor (GEM), which produces a special report on women.  This data is substantial and covers many countries, but, as with most large scale datasets, does not cover rural areas in developing countries well.  And, I must say it is my opinion that many of the questions in GEM are artifacts of Western prejudice.

All these datasets are as good as we’ve got for the moment and all the organizations behind them are working hard to try and make them more accountable and sensitive.  In the meantime, at the fine grain end of the spectrum, there is another urgent need, which is for metrics to measure the impact of focused interventions.  Many big corporations (ExxonMobil, Goldman Sachs) and many foundations or NGOs (Cherie Blair Foundation for Women, CARE) have extensively deployed particular interventions on behalf of women entrepreneurs and need a common language of metrics with which to evaluate them.  These programs are very often located in the poorest areas–“Bottom of the Pyramid” efforts–and so run into all the problems of cultural variation and poverty effects that can become sticky.

It might seem that the obvious interest for such programs would be in business measurements:  sales, profit, number of employees, growth.  For businesses working at this end of the scale, however, it is often difficult even to get a reasonable accounting of, for instance, sales or profit–and, believe it or not, it may be difficult to decide what counts as an employee (because neighbors and family members may pitch in or orphans may work for food, and so on).

Further, most institutions investing at this end of the economic scale intend to have social and other effects as their desired outcome, rather than a specific business measure. Institutions who invest in such programs want to see empowered women and educated children, for instance, perhaps more than they care about a particular growth rate.  So there are measures needed before and after the business achievement and those must capture elusive phenomena.  Finally, we know that issues such as domestic violence can turn even the most promising business sour–and how would an institution funding a program become aware of that intrusion?  So, it’s. . . complicated.

At the Power Shift Forum, we are going to have a session on measurement where two important players will stake out each end of this spectrum.  Mario Piacentini of OECD will present their “macro-level” system, showing the key questions, problems, and gaps, as well as showing us how OECD is developing solutions.  At the “micro” end, Noa Gimelli of ExxonMobil will discuss the ways that their programs have sought to measure impact in an arena of economics where few have travelled before them.

Other details of the Power Shift Forum are available here.


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