Telecommunications and Outmigration (China)

This paper uses a unique natural experiment in Chinese villages to investigate whether access to telecommunications-- in particular, landline phones -- increases the likelihood of outmigration. By using regional and time variations in the installation of landline phones, the difference-in-differences estimation shows that access to landline phones increases the ratio of out-migrant workers by 2 percentage points, or about 50 percent of the sample mean in China. The results remain robust to a battery of validity checks. Furthermore, landline phones affect outmigration through two channels: information access to job opportunities and timely contact with left-behind family members. The findings underscore the positive migration externality of expanding telecommunications access in rural areas, especially in places where migration potential is large.

That is from this paper by Yi Lu, Huihua Xie, & Lixin Colin Xu.

Landline phones is not be the first thing that comes to mind as a determinant of migration. It is surprising to see how significant that is though, and it makes sense. Leaving home knowing that one can keep in touch with relatives makes a difference. I can't imagine how it feels to leave family behind knowing that communication will be very difficult, and that is precisely the situation that many immigrants have faced. Phones reduce the costs of leaving. 

If we follow the argument we should see an increase in outmigration from poor regions due to cheaper Internet communication. Internet however have other effects such as outsourcing which increases employment in developing countries and as a consequence might reduce outmigration. 

Green Revolution and Mobile Phones and Development

Two interesting articles from the newest number of the Journal of Development Studies: 

Why were So Many Social Scientists Wrong about the Green Revolution? By Orr. 

Most social scientists once took a negative view of the socio-economic consequences of the Green Revolution. Events have since proved them wrong. Using Bangladesh as an example, we offer three reasons why social scientists were mistaken. One is the focus on village studies at the expense of nationally representative surveys. Another is insufficient appreciation of the technical limits of the new rice technology. The third is a misleading model of agrarian change. The inability of village studies to validate generalisations, the reluctance to abandon the historical model of de-peasantisation, and opposing beliefs about how to evaluate socio-economic consequences created a Rashomon Effect that made the controversy hard to resolve.

Mobile Phones and Economic Development in Rural Peru. By Beuermann, McKelvey & Vakis.

We estimate the effects of mobile phone coverage on different measures of economic development. We exploit the timing of mobile coverage at the village level merging it with a village-level panel dataset for rural Peru. The main findings suggest that mobile phone expansion has increased household real consumption by 11 per cent, reduced poverty incidence by 8 percentage points and decreased extreme poverty by 5.4 percentage points. Moreover, those benefits appear to be shared by all covered households regardless of mobile ownership.

Unfortunatelly I did not find the full papers online. The abstracts are self-explanatory. 

Related graphs:

What is surprising is that fixed lines are still being used.

Source of graphs. 

Mobile Phones, Networks, and Migration (Uganda)

Personal networks can help rural workers find urban jobs. When information flow increases due to the expansion of mobile phone use, the new information flow may strengthen existing types of personal network, such as ethnic networks, or provide opportunities to those who were previously outside of these networks. We examine the combined impacts of mobile phone use and personal networks by using panel data from 856 households in 94 communities in rural Uganda, where the number of communities with mobile network coverage increased from 41 to 87 communities over a 2-year period between the first and second surveys, conducted in 2003 and 2005, respectively. We find first that the possession of mobile phone handsets at the household level increases an individual's chance of leaving his or her rural village to find a job, and second, that mobile phone use increases the chance that an individual will choose migration to a greater degree for individuals who belong to a smaller ethnic group than to a larger group in the capital city, Kampala.

That is from the paper "The impacts of mobile phone and personal networks on migration: evidence from Uganda" by Megumi Muto, published in the recent number of the Journal of African Economies (November 2012). A draft is here. 

That number of the Journal is a special issue on impact evaluation (IE) in Africa. See a good post by Justin Sandefur on IE here. 

HT: @m_clem

Mobile phones

In this paper, we document how a new technology - mobile phones - reduces transaction costs and enables Rwandans to share risk quickly over long distances. We examine a comprehensive database of person-to-person transfers of mobile airtime and find that individuals send this rudimentary form of “mobile money” to friends and family affected by natural disasters. Using the Lake Kivu earthquake of 2008 to identify the effect of a large covariate shock on interpersonal transfers, we estimate that a current-day earthquake would result in the transfer of between $22,000 and $30,000 to individuals living near the epicenter. We further show that the pattern of transfers is most consistent with a model of reciprocal risk sharing, where transfers are determined by past reciprocity and geographical proximity, rather than one of pure charity or altruism, in which transfers would be expected to be increasing in the wealth of the sender and decreasing in the wealth of the recipient.

That is from the paper by Fafchamps, Blumenstock, and Eagle, "Risk and Reciprocity Over the Mobile Phone Network: Evidence from Rwanda" (September 2011). This is a neat visualization of the call traffic in Rwanda ("the earthquake occurs at roughly 9:30am, about 25 seconds into the video"). 

Blumenstock has also used mobile-phone call-records to infer pattern of migration in Rwanda (see a visualization here).