Finding ways to create more, better and inclusive jobs is at the core of ending extreme poverty and reducing inequality. But doing so has proved extremely difficult. We’ve been living with an assumption that economic growth is sufficient, and that jobs will follow. But the evidence is showing us that either this assumption from history is incorrect or that the time lag is politically and morally unacceptable. Moreover, aggregate jobs numbers are masking major challenges facing particular groups.
While these challenges are not new, there are new techniques emerging which better help us to diagnose why the challenge is so stubborn and thus to help find new solutions. As Kaushik Basu, Chief Economist of the World Bank noted in a recent presentation to the GLIM IZA Conference held at the World Bank HQ in October, “labour is the heart of the twin goals. You have to attend to labour. But it is not easy – there is not one solution to this. Beneath many of the crises around the world lies the changing tectonic structure of global labor. And so we need to use a multitude of methods to attend to this.”
Using a multi-disciplinary approach to analyzing labor markets is reaping fascinating results, in a way that traditional macro developmental economics might miss. Many of these approaches are being trialed by colleagues here at the World Bank as well as leading academics. They are using techniques borrowed from psychologists, sociologists, historians and even big data computer scientists to arrive at insights into some of the largest jobs challenges that we face. At the heart of this is a deepening understanding that a job is the sum of many parts, from both the supply side and from the demand side. Looking at these challenges through a jobs lens is vital and that lens must be multifocal. The jobs challenges cannot be seen from just one dimension.
Closing the gap between female and male wages and participation rates is a key developmental goal. Women are underrepresented in the workforce and they tend to be at the lower end of pay scales. There is also a separate demographic challenge in the field of female fertility. Lowering female fertility levels in developing countries tends to lead to positive development outcomes. But how should countries try to achieve both these aims? Andrei Levchenko at the University of Michigan, has recently shown that countries that have a strong concentration of industries in which women work, also have lower levels of fertility. The development implication from this is that if countries want to increase female participation in the work force, lower their overall fertility rates and create jobs, they should have industries in which female participation is strong.
Many women however lack the skills and training to get into the workforce in the first place. This is particularly true in regions where culture prevents them from leaving their communities. A recent study by Jacob Shapiro from Princeton looked at how to get more women to take up skills training in Pakistan. His study shows that crossing the boundary line of the village has a huge negative impact on whether women will enroll in basic skills training. The only way to fully study this phenomenon is through using socio-ethnological techniques, not found in traditional economics.
Dino Merotto, the lead economist within the Jobs Group at the World Bank is working to improve the stock of spatial analysis techniques available to prioritize cross-cutting policy reforms and investment solutions for more, better and inclusive jobs. He and his colleagues are developing micro analytic techniques that combine information on locations with evidence about firm entry, growth and job creation across economic sectors on the demand side, and with evidence of migration patterns, occupational mobility, and growth in household labor incomes on the supply side of labor.