Figure 1: Percentage of Fully Immunized Children (District Level)

As soon as I mapped the data a clear pattern emerged. The districts in the northern regions were lagging behind in terms of immunization. When I mapped the next indicator, percentage of district level population living at a low standard of living, a similar pattern was visible: many regions with low immunization rates had a high prevalence of populations with a low standard of living (Figure 2). (I reviewed the same indicators from a similar survey conducted 5 years prior. Though development indicators had improved in some regions over time, I could clearly identify regions that were continuously lagging behind.)

The next step for this analysis was to overlay the locations of World Bank project activities on these indicator layers. For Figure 2, darker regions or regions in higher quartiles, indicate a high percentage of the district population qualified as having a low standard of living.

Figure 2: Percentage of Population with a Low Standard of Living

In the map of district immunization rates (Figure 1), the active World Bank projects cover the Northern districts in the second lowest quartile, but districts in the bottom quartile (lowest immunization rate and lightest shade) in adjoining regions have no active World Bank projects. On the other hand, there are projects in the Southern states, like Tamil Nadu, which fall in the third and fourth quartile (high rates of immunization). The map with the standard of living indicator (Figure 2) presents a slightly different picture. The World Bank active projects cover the majority of Northern districts in the quartile with the highest percentage of the population belonging to the low standard of living category (the darkest region).

This initial analysis therefore suggests several interesting questions for researchers How effective is the targeting of these projects? If the projects were mapped alongside a different set of indicators, rather than these two, what sort of picture would emerge? What if we looked at the trends in key indicators over time, relative to the start/end date of these projects, rather than a simple snapshot? Geo-enabling aid activity information makes this kind of spatial analysis much more feasible.

Anustubh Agnihotri is a graduate student at the University of Texas.

Around the world, rising food prices have forced millions of people back into poverty, spurring political unrest and complicating the global economic recovery. Now, the tragedy unfolding in the Horn of Africa has once again put a spotlight on questions related to food security. One longstanding question is how donors should address the tension between meeting urgent humanitarian needs in times of acute food insecurity versus long-term investments in agricultural productivity. Food aid and short-term safety net programs save lives, but do not solve the root causes of chronic food insecurity.

We also looked at UN Food and Agricultural Organization data on overall global food production, and from what we saw, the trends are encouraging. After accounting for population data, which we got from the World Bank, we found that per capita agricultural yield is up, including dramatic increases in Sub-Saharan Africa, which has increased food production nearly 24% since 2000.

The growth in food production could be a sign that the renaissance of agricultural aid (after the post-Integrated Rural Development-era pessimism) is enjoying some success. But despite these positive trends, the global food crisis continues, and climate change severely undermines prospects for increasing food production. According to the World Bank’s Hunger Clock, more than 900 million people are undernourished, and the number continues to tick upward.