An interesting article (hugely relevant to India) in voxeu by Roland Hodler and Paul Raschk.
They point to how politicians favor their regions:
Many political leaders favour their preferred regions. An extreme example is Zaire’s former dictator Mobutu. In his remote ancestral home town Gbadolite, he built a huge palace complex costing $100 million, luxury guesthouses, an airport capable of handling Concords, and the country’s best supply of water, electricity, and medical services. But Mobutu is no exception. There is a large literature on distributive politics documenting regional favouritism. Golden and Min (2013) review the literature on redistributive politics based on an inventory of more than 150 empirical studies. They notice that most studies focus on a single democratic country, and a single policy outcome.
We have many such news around India as well. There has been a lot of news recently on how India’s new PM will revitalise city of Varanasi. Though it is not his place of origin but as he has fought elections from Varanasi, it has become his preferred region. Similarly how SP leader Mulayam Singh has tried to develop the area around his hometown of Saifai in UP. There are many many others.
So how can one figure this regional bias?
In a recent article (Hodler and Raschky 2014), we complement this literature on distributive politics by taking a systematic look at regional favouritism in a large and diverse sample of countries that includes democracies as well as autocracies, and by employing a broad measure of regional favouritism that captures the aggregate distributive effect of many different policies. In particular, we use information about the birthplaces of political leaders and satellite data on nighttime light intensity to study whether subnational administrative regions have more intense nighttime light when being the birth region of the current political leader.
Our analysis is based on a panel dataset with 38,427 subnational regions in 126 countries, and annual observations from 1992 to 2009. The dependent variable is the logarithm of average nighttime light intensity, which is recorded by US Air Force Weather Satellites and provided by the National Oceanic and Atmospheric Administration. Henderson et al. (2012) document a strong relationship between nighttime light intensity and GDP at the country level, and propose the use of nighttime light intensity as a measure of economic activity at the subnational level. Using regional GDP data by Gennaioli et al. (2013), we find a similarly strong relationship between regional nighttime light intensity and regional GDP. Our main explanatory variable is a dummy that equals one for the birth region of each country’s current political leader, and zero for all other regions.
Our results suggest that being the leader region increases nighttime light intensity by around 4%, and GDP by around 1% on average.
Rise in GDP was expected but night time light intensity too rises..Nice way to capture the impact.
Though this does not lead to sustainable development. Once the elections are lost, out goes the leader and the growth..