The persistent urbanising effect of refugee camps: evidence from Tanzania, 1985–2015

ABSTRACT

With the rise of forced displacement, attention has turned to the economic impact of refugees. However, few studies investigate long-term impacts. We use data for Tanzania for the period 1985–2015 to examine the effect of camps on urbanisation and local development, exploiting a unique satellite-derived dataset of high spatial resolution and temporal frequency. We show a modest but significant effect of refugee camps on built-up area up to a 100 km distance. We then match camp locations to regional gross domestic product, local consumption spending and employment patterns. Output in areas with camps grew at a faster rate during camp operation, but closure of camps was associated with change in economic activity. Activity induced by camps is largely in non-tradeable goods and services rather than inducing longer run structural transformation.

KEYWORDS:

• refugee camp
• urbanisation
• satellite imagery
• consumption
• spatial

JEL:

• J61
• O15
• R12
• R14

ACKNOWLEDGEMENTS

The authors thank Philip Verwimp at the Université libre de Bruxelles, Annemie Maertens, Barry Reilly and Andy McKay, and other colleagues at the University of Sussex for helpful comments on earlier drafts. Francois Maystadt, Zhou Yang-Yang and the UNHCR office in Tanzania answered many questions and shared data on refugee camps there. Mattia Marconcini of the German Aerospace centre (DLR) graciously provided the data used in this paper. Thanks are due to James Muthoka, Ran Goldblatt and Rick Lawrence for introducing the authors to, and advising on the use of, remote sensing data. Nsababera would like to thank the University of Sussex for funding her PhD, on which this article is based. Any errors of interpretation in the data rest with the authors.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1 Two recent papers examine the impact of camps on the local environment: Salemi (2021) examines the impact on deforestation; and Maystadt et al. (2020) examine the replacement of land with cropland.

2 At a national level, the phenomenon of urbanisation without structural transformation has been documented in Africa (Fay & Opal, 2000; Fox, 2017; Gollin et al., 2016).

3 Outside the economics literature, satellite data to study urbanisation in the context of conflict in Africa have been used by Kranz et al. (2015), Pech and Lakes (2017) and Pech et al. (2018). The most frequently used dataset elsewhere is available only every 15 years and at a relatively low resolution. Images acquired at a single date or at long intervals hamper classification accuracy. A problem with low-resolution datasets is the ability to detect small settlements in rural areas, such as in much of Tanzania. On these issues, see Goldblatt et al. (2018), Marconcini et al. (2019) and Palacios-Lopez et al. (2019).

4 A limitation of the data used here is that, although of high resolution, they do not allow us to differentiate types of urban activity. We also experimented with the use of nightlight data as a means of identifying economic activity. Although Alix-Garcia et al. (2018) had some success in analysing activity around refugee camps in Kenya using this approach, we did not find sufficient differences in luminosity in the data to validate this approach. In areas of low development, it is likely that the low intensity of man-made lights cannot be distinguished from background noise given the low resolution of the satellite. It should also be noted that the night-time satellites have a much lower resolution (approximately 1 km) compared with the built-up data used in this paper (30 m).

5 These are comprised of 31 administrative regions. There are, on average, 1200 grids in a region.

6 This method referred to as the ‘degree of urbanisation’ was endorsed in March 2020 by the UN Statistical Commission. It was jointly developed by the European Commission, International Labour Organization (ILO), Food and Agriculture Organization (FAO), Organisation for Economic Co-operation and Development (OECD), UN-Habitat and the World Bank. For further discussion of the advantages and limitations of this approach in the Tanzanian context, see Wenban-Smith (2014): the alternative of a population density-based measure of urbanisation is limited in this context since such data are not available in every year of the present study.

7 Population density is obtained from European Commission, Joint Research Centre (JRC) and Columbia University, Center for International Earth Science Information Network (CIESIN) (2015).

8 We also interacted treatment with coverage of a national villagisation policy (Ujamaa) of the 1970s as a proxy for local socio-economic conditions pre-camp since provision of social services such as education, health and water supply were a key pillar of the policy (Coulson, 2013). To measure coverage of the policy, we followed previous work and used the percentage of a district’s population in the 1978 census that lived in planned villages (Silwal, 2016). Results (available from the authors upon request) show no statistically significant differences by coverage of the policy. However, this may be because these data are available at a higher resolution (district level) than our localities, hence offering little variation.

9 The road network in Tanzania is correlated with major towns (African Development Bank, 2013; Tanzania Roads Agency, 2018).

10 Since the size of the cells is larger than in the previous analysis as noted above, cells that do not have a camp within their boundary are by default far from camps by 50 km or more and are therefore in the control group. The analysis was repeated where the definition of ‘treated’ was expanded to include cells sharing a border to a cell with a camp; in this case control cells are farther than 100 km. Results (available from the authors upon request) are similar but smaller in magnitude.