The data we collect can have a male bias.
Where are we going? To measuring the work that women do in low income nations.
Evidence of Male Bias
Ask someone in a developed economy about his or her job and the response relates to work away from home. Able to cite a finite number of hours, that person takes home an identifiable wage.
Ask a similar question in a low income country and you might get answers. But they obscure what really exists. Let’s say you ask the respondent to name a primary economic activity. A male would cite a job, perhaps an income generating activity outside the home. Because the woman would say she is a housewife, her other work activities are unrecognized.
Uganda’s New Questionnaire
To improve accuracy, Uganda gradually changed its statistical survey. At first they added a secondary work question. As a result, the women who called themselves housewives noted other jobs and they got a 702,000 pop in the size of the labor force. Then, several years later when they included a checklist of activities like tending the garden and fetching water from a communal source and a bigger time frame, again labor force numbers rose.
When Uganda’s types of questions, their time period, and their specificity changed, the labor force totals included an extra 1.37 million people:
Our Bottom Line: Economic Development
When women are statistically invisible, we create an inaccurate picture of production, employment and gender related problems. Only with accurate data can fiscal policy (national spending, taxes, borrowing) and development initiatives tackle the economic challenges that women face.
Our sources and more…During one of my recent walks, I listened to the BBC’s “More or Less” podcast on sexist statistics. A starting point, the podcast led me to a BBC News Magazine article, this Guardian article, and this ad from the Gates Foundation. Good for an overview of where changes need to be made, this Data2X paper has a massive number of facts. Finding the generalities in all the previous papers frustrating, I finally discovered the most productive information in this paper on Uganda.
