anis1_Manish RajputSOPA ImagesLightRocket via Getty Images_women labor data Manish Rajput/SOPA Images/LightRocket via Getty Images

Evidence for Equity

One might assume that, in today’s world of information overload, policymakers have ample data with which to diagnose problems, devise innovative solutions, monitor implementation, and adjust policies to maximize their impact. In fact, underinvestment in data collection is impeding progress on gender equality.

NEW DELHI – “What gets measured gets done.” It is a well-worn maxim, attributed to everyone from management guru Peter Drucker to physicist Lord Kelvin. Regardless of who said it first, the point is a crucial one: if there are no data illustrating a problem or imbalance, it is unlikely to be a consideration, let alone a priority, for those in a position to address it. And solutions, if they are attempted, are unlikely to be well targeted or efficient. This is certainly the case for gender equality.

One might assume that, in today’s world of information overload, policymakers have ample data with which to diagnose problems, devise innovative solutions, monitor implementation, and adjust policies to maximize their impact. But even in an age of big data, policymakers often lack accurate, consistent, timely, and representative information. As a result, they are working with an incomplete picture of socioeconomic conditions.

For example, data show that female labor-force participation in India has been declining since 2005. Reversing this trend, and achieving gender equality, could add $770 billion to India’s GDP by 2025, according to McKinsey Global Institute. But effective solutions would need to be informed by comprehensive data that capture the complex relationship between women’s labor-force participation and various social, political, and economic forces, and they would need to link gender equality to broader development outcomes.

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