Should developing country governments prioritise investment in small towns or in big cities to stimulate development and spur poverty reduction? The common picture that comes to mind is one of the gleaming metropolis as the destination of choice for migrants from poor rural areas. This also seems to make sense since the income gap between the countryside and big cities is far greater than that between the countryside and small towns. Such reasoning naturally leads to a policy stance of investing in the big cities as the recipient of poor migrants and the engine of growth and development.
The number of cities in the developing world, with more than five million dwellers, has doubled from 36 in 1995 to 73 in 2015. However, globally the fastest growing urban centres have fewer than one million inhabitants (UN-Habitat 2016). In Africa, two-fifths of the urban population live in cities larger than one million inhabitants, but two-fifths are in small towns with fewer than 250,000 (Dorosh and Thurlow 2013). Population centres of fewer than one million account for up to 75% of urban population growth (UN-Habitat 2014). In Tanzania, the capital city Dar es Salaam dominates population agglomerations, accounting for a third of the urban population. But this proportion has not changed over the previous four decades, and small towns with populations of less than 20,000 account for an ever-increasing share of the urban population (Wenban-Smith, 2015).
Our research programme tries to understand incentives to migrate from rural areas to small towns when, on the face of it, the income gains from migrating to big cities would be far greater. We found that distance drives destination in migration decisions, explaining the seeming paradox of greater migration to small towns than to big cities. Based on this understanding, we argue for a more balanced orientation of development investment between big cities and small towns.
Our main empirical focus is on a panel data set originating in the rural area of Kagera in Tanzania (De Weerdt et al. 2021). Kagera lies at the periphery of the country and has, within its borders, only one large town – Bukoba – and no cities. The closest city is Mwanza on the southern shores of Lake Victoria, while Tanzania’s prime city, Dar es Salaam, is located at the other end of the country. In short, Kagera is a relatively remote region and that should be kept in mind when analysing urban destination choices of migrants originating from Kagera.
The Kagera Health and Development Survey (KHDS) is a study into the long-run wealth dynamics of households and individuals within North West Tanzania. This study entails the re-survey of a panel of households, originally interviewed for four rounds from 1991 to 1994. Re-surveys were organised in 2004 and 2010. A multi-topic household questionnaire is administered to all split-off households originating from the baseline households, including those that have moved out of the baseline location. This constitutes one of the longest-running African panel data sets of this nature and offers unprecedented research opportunities for examining long-run (nearly 20 years) trends in and mechanisms of poverty persistence and economic growth in rural households. As the children of the original respondents have now formed their own households, intergenerational and migration issues can also be addressed by the survey data.
The data are of particularly high quality and the 2010 round of the survey was conducted using electronic survey questionnaires administered on handheld computers, with automated skips and validation checks run during the interview when errors could be corrected at source. The core data from the KHDS are supplemented by the Tanzania Population and Housing Census. Further, in depth ethnographic research is conducted, with focus groups and semi-structured interviews designed to elucidate life histories (Ingelaere et al. 2018).
We begin with the life history of Raymond as motivation for our quantitative research. As a child in rural Kagera, Raymond saw the attractions of the capital city Dar (4.5 million inhabitants, 1650 km away): “There used to be video shows in our village and all the famous football players, like Runyamila, seemed to live in Dar. We were childish at the time and we thought that if we went to Dar we’d see all these people.”
However, speaking mainly the local language, Haya, Raymond worried about his knowledge of the national language – Swahili – and about his level of education. A major constraint was the fare to Dar at US$50 one-way at the time – a huge amount given the limited opportunities to earn cash in the village – and he did not have support on arrival in Dar. Some of the other obvious urban options for Raymond were Mwanza (700,000 people, 435 km away, one-way fare of $12) or Bukoba (100,000 people, 50 km away, one-way fare $2). Bukoba was not only closer but also more familiar – Haya could be heard everywhere on the street. Not surprisingly, Bukoba was Raymond’s first destination. In terms of the conceptual framework developed in Ingelaere et al. (2018), drawing on anthropological and sociological concepts, Raymond’s ‘action space’ was sufficiently constrained to make Bukoba the relevant destination, not the metropolis of Dar es Salaam.
Distance as a driver of destination is of course a major theme in migration research, going back as far as Ravenstein’s (1885) laws of migration. However, the focus on distance has declined somewhat in recent years, perhaps because of a view that transportation improvements have led to significant declines in transportation costs. Motivated by the story of Raymond, and ethnographic accounts from 75 respondents sampled from the KHDS itself, we began an exploration of the quantitative significance of distance in migration using the detailed data available in the KHDS panel.
If we are to understand destination choice among migrants, then it is as important to know where the migrant moved to as it is to know which potential destinations the migrant did not migrate to. Within the Tanzania census, we can identify 78 locations that were destinations for KHDS migrants. Our analysis assumes that these 78 locations are the potential destinations for our sample of migrants. They revealed themselves as practically feasible destinations for at least one of our migrants. Each of the migrants in our sample has chosen to move to one potential destination; and has therefore also chosen not to move to the 77 other potential destinations. In order to understand that choice better, we create a dyadic data set that contains 78 observations for each migrant; one observation for each potential destination. Such an approach to estimation has been followed, for example, by Fafchamps and Shilpi (2013).
The methodological details of estimation, addressing a range of econometric issues and robustness checks, are provided in De Weerdt et al. (2021). Figure 1 presents a summary of the main results, comparing the effect of wealth differential and distance on migration propensity. It is seen that distance dominates wealth overall, and in every sub-category except the upper end of the education ladder. In our sample, expected wealth at destination would have to increase by 5.7 standard deviations on average to offset a one standard deviation increase in (log) distance. The deterrent effect of distance is greatest for the poor, and for those with no education. Raymond’s story is indeed confirmed by the quantitative analysis.
Figure 1 Likelihood of choosing towns over cities
Notes: This figure shows how much more likely it is for KHDS migrants to choose a town over a city as an urban destination because of differences in distance to and wealth at the urban destination. The y-axis measures this as a predicted likelihood by plugging into the regressions estimates of Table 5 in De Weerdt et al. (2021) (i) the difference between average distance to the nearest town and average distance to the nearest city; and (ii) the difference between average town wealth and average city wealth. The solid bars labelled ‘TOTAL’ add these two effects together. The figure shows that towns are attractive because they are closer and cities because they are wealthier. The distance effect dominates the wealth effect, explaining the preference for towns, for all except the most highly educated.
Thus, in our study area of Kagera in Tanzania, much larger shares of the rural population tend to migrate to towns, despite greater income gains from moving to cities. With many of the rural migrants originally also poor, decomposition analysis in Christiaensen et al. (2019) also shows that town migration contributed more to aggregate poverty reduction than city migration for the KHDS data set, while Christiaensen et al. (2013) make the argument using cross-country regression analysis. What do these findings imply for development policy?
In low-income countries, the share of the rural population living within one hour of a town is 43%, with another 20% living within two hours; only 13% live within one hour from an intermediate city and 7% within one hour from a city (>1 million). Among the extreme poor, 80% are rural (82% in Sub-Saharan Africa; Beegle et al. 2019). Large African cities thus face a burden to act as engines of poverty reduction – they are far from where the poor live, and distance matters in migration. Broadly interpreted, the results of our research programme support the New Urban Agenda (United Nations 2017), which calls for balanced territorial development policies and plans that strengthen the role of small and intermediate cities and towns in development policy and interventions.
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