African History through the Lens of Economics: An introduction to African development and history
In the second special lecture of the Wheeler Institute’s open access online course, African History through the Lens of Economics, Elias Papaioannou, Academic Director of the Wheeler Institute for Business and Development, and Tanner Regan, Research Fellow in Economics at London Business School, gave an overview of the rich pan-African data sets now being used by researchers to supplement traditional measures of economic development such as GDP. The session was moderated by Marina Mavungu Ngoma, PhD candidate in Economics and Public Policy, Tufts University.
What are the issues with traditional GDP-based measures?
Professor Papaioannou started this special lecture with the challenge of presenting a full picture of African economic development using traditional measures based on GDP, whether in an aggregate, per capita or purchasing power parity form. The limitations of GDP as a measure of economic performance and welfare have been explored over the past decade by, amongst others, Blanchet and Fleurbaey (2013), Fioramonti (2017), Stiglitz, Fitoussi and Durand (2018) and Broadberry and Gardner (2022).
A first and significant issue with GDP is that there are regular revisions to official figures and to the conclusions that can be drawn from them. In an African context, for example, Johnson, Papageorgiou, Larson and Subramanian (2009) highlighted that Equatorial Guinea moved from being the slowest growing economy in the continent in 1975-99 to the second-fastest over the same period in consecutive versions of the Penn World Tables (6.1 and 6.2) compiled only four years apart. Second, there are issues of missing data for many African countries, particularly pre-1990, which makes long-run assessment difficult to perform. Third, data quality can be lower than we would like, particularly where national statistical agencies are under-resourced. Finally, there are more theoretical issues relating to the expansion of cross-border investment and the use of GDP rather than GNP.
Two responses to these limitations are to combine GDP with other data, such as micro-individual survey data (Young, 2012), satellite imagery on light density (Henderson et al., 2012) and other big data sets to corroborate or challenge the original figures, or to move beyond national-level GDP to focus on regional or sectoral disaggregation and on inequality between those regions and sectors, as seen in the recent work of Blanchet, Saez and Zucman (2022) on distributional national accounts for the U.S.
There are also particular reasons to want to move beyond GDP figures when considering African economic development, many of which relate to spatial variability. There is, for example, anecdotal evidence of large variations in levels of well-being – whether measured in terms of income, consumption, education, health and access to public goods – both across and within African countries. Indeed, Alesina, Michalopoulos and Papaioannou (2016) concluded that these spatial differences between administrative units and ethnic regions were largest in Africa, supporting previous research that showed larger gaps in relatively lower income countries (Williamson, 1965) and agricultural countries (Kanbur and Venables, 2005).
Another important factor is that economic policies in Africa have often had heterogeneous spatial impacts, as demonstrated by the post-independence agricultural policies pursued in a number of countries and as seen in the differing effects of trade openness and infrastructure investment and in the clustering of foreign aid and foreign direct investment. Political favouritism, often with an ethnic dimension, which has been studied by Franck and Rainer (2012) and Amodio, Chiovelli, and Hohmann (2021), provide a further mechanism for spatially heterogeneous outcomes on the continent.
How are economists improving on these traditional measures?
Modern approaches seek to utilise the variation captured by geospatial data to understand in more detail the effects of policies, interventions and history on economic development. Even just visualising this data and performing simple tabulations can yield useful information, but it is also possible to move beyond correlation and to investigate causation. For example, recent research has used this data to investigate the experimental variation between villages with and without missionary schools (Wantchekon et al., 2014), the discontinuities in historical boundaries such as those around concessions subject to forced labour (Lowes and Montero, 2021), and the artificiality of national borders in Africa (Michalopoulos and Papaioannou, 2016) as causes for present-day variability in economic performance.
These approaches can operate on three levels: macro, investigating cross-country variation, meso, looking at regional variation, and micro, on the individual level. Research has looked, for example, on the macro level at ideology and political modernization (Garcia-Ponce and Wantchekon, 2021) and at the effect of the slave trade on economic development (Nunn, 2007); on the meso level at long-run effects of the Scramble for Africa (Michalopoulos and Papaioannou, 2016) and precolonial political centralisation and contemporary development (Michalopoulos and Papaioannou, 2013); and on the micro-level at colonial schools and human capital (Wantchekon et al., 2014).
What else can we use to measure development?
Four types of main pan-African datasets that are particularly well suited to investigating meso-level variation were reviewed – satellite (or GIS) data, surveys, administrative data such as censuses, and other specific datasets, such as conflict records and machine learning results. For each, Dr. Regan provided an overview of the strengths and limitations of the dataset, as well as examples of applications in research. He also encouraged the audience to remain aware of how these datasets could shape the very questions that were being asked, to think about what information was not being captured by the datasets and where other datasets might exist that could provide further insights.
Satellite data is interesting for the finer spatial resolution that it offers than aggregate statistics. For example, we can calculate which areas are more developed or are developing more quickly by observing how many lights there are in them at night or how much construction has taken place in them. Alternatively, we can test hypotheses about the impact of geography on social and economic outcomes by cross-referencing traditional economic measures against information on the local terrain or its suitability for agriculture.
We can then test conclusions that are derived from satellite imagery against data that we have gathered from surveys, whether about quantifiable measures such as household wealth, health and education, or about more qualitative data such as opinions, preferences and beliefs. There are a variety of surveys across the continent that facilitate diachronous and synchronous comparisons, the most prominent of which are USAID’s DHS and the Afrobarometer surveys. Information in these areas can be supplemented by more traditional information sets, such as national censuses or their harmonised and aggregated version IPUMS.
Finally, there are other datasets available that can provide fascinating additional colour to an analysis. For instance on the subject of conflict, some of the most widely used (UCDP-GED and ACLED) have been used in contemporary analysis of the political climate and in investigations of the long-run impact of historical events such as the slave trade on modern society. Big data and modern computational techniques are also beginning to be more widely used, with some interesting work using high-resolution satellite imagery (in 50cm2 units) and machine learning predictions of wealth in areas not covered by surveys.
How can we use these data sets most effectively?
It is in combination that these datasets can generate the most powerful analysis. For example, night luminosity data, which is good at showing progress over time, can be supplemented by DHS data to understand if there are underlying improvements in health, education and entrepreneurial activity. Likewise, survey information about trust in institutions and perceptions of corruption can be linked to data about conflicts and civil disturbances.
It is an exciting time to be conducting academic research on economic development in Africa. Where thirty years ago researchers had to rely on imprecise GDP data, they now have better data to use when considering development and inequality across many dimensions. This short special lecture provided a high-level overview of only some of the most common sources, but there are many more datasets available for researchers and many more being gathered, such as mobile data, which is being used to understand social behaviour in greater detail than ever before. As ever, it is important to remain aware of the effect that the availability of data has on the questions that are being asked, and whether there are alternative datasets that can be used to answer other questions, but we can look forward to ever increasing amounts of more nuanced analysis of African economic development in future.
Summary of datasets
Satellite (GIS) data – luminosity, landcover, agriculture and elevation
These are images from satellites that measure the brightness of a region at night as a proxy for economic development. The data have been collected by the NOAA and NASA every year since 1992, at least annually but sometimes daily, and cover the whole globe at a granularity of 1km2. These datasets can be used to watch a time series of images to understand general trends in economic growth or to perform cross-country reviews. Some fascinating examples include a case study on the effect of hyper-inflation in Zimbabwe which shows lights diminishing and then returning between 2005 and 2011 and a review of the growth of South Africa after the end of apartheid the shows the reduction in spatial inequality and the development of the country’s hinterland post-1994.
Some of the limitations of these datasets are that they capture a broad notion of development – a mixture of population and output in a given area – they suffer from technical issues such as blooming (where light emitted in one place is captured in a neighbouring area) and top coding (where the very bright lights are not captured), and they fail to pick up economic development in largely unlit areas, where there can in fact be significant variation.
Nonetheless, this is a useful dataset that has been put to interesting use, including by Chiovelli, Michalopoulos and Papaioannou (2021), who looked at economic development in Mozambique in areas where landmines have been cleared, using luminosity as the proxy for development, and by Michalopoulos and Papaioannou (2021), who mapped contemporary economic development on pre-colonial ethnic institutions.
A fine-grained measure of the share of land that was built up in 1975, 1990, 2000 and 2014 available from the European Commission’s GHSL (Global Human Settlements Layer) project, this dataset can be used to analyse the growth of cities and towns over time and the patterns of urban development. It is possible, for example, to visualise the massive growth of Lagos since 1975 in terms of scale, form and shape. An interesting example of research in this area is Baruah, Henderson and Peng (2017), which looked at how colonial legacies shape contemporary urban development, with former British colonies having more fragmented development that former French colonies, which more frequently used a grid-layout in their cities.
The SRTM dataset records height above sea level across the world at a single point in time and is available from the US Geological Survey. Elevation is an important determinant of economic outcomes as a predictor of agricultural adoption, likelihood of flooding, and even conflict. Nunn and Puga (2017) use this data to create a measure of ruggedness – or hilliness – which they show made regions less susceptible to slaving raids, which, as will be seen in the lecture on the slave trades, have long-run effects on economic outcomes.
The University of Wisconsin-Madison dataset shows the fraction of land suitable for agriculture, allowing us to identify the areas of Africa that are more or less amenable to farming. Michalopoulos (2012) uses this data to demonstrate that geographical variability is a fundamental determinant of ethnolinguistic diversity.
Survey data – Demographic and Health Surveys (DHS) and Afrobarometer
Demographic and Health Surveys (DHS)
These surveys, available from USAID, have since 1985 addressed various aspects of African development, including fertility, education, household structure, assets and wealth, immunisation and access to public goods. They cover samples of the populations of different countries at different times, which ensures wider coverage but introduces some limitations, particularly where the same locations are not repeated, sample questions change and there is a focus on large urban areas.
Again, though, notwithstanding these limitations, the datasets provide numerous interesting insights. For example, using the pan-African coverage for the period 2010-20, we can plot development in areas such as household wealth and access to piped water; or, focusing on Nigeria and the surveys from 2003 and 2018, we can see a persistent pattern of greater wealth in coastal regions. The DHS data has been used widely in research, including the impact of concessions in the Congo free state on education, health and wealth outcomes (Lowes and Montero, 2021), the links between legal origins and rates of female HIV (Anderson, 2018) and detailed analysis of African growth (Young, 2012).
Afrobarometer is a survey that seeks to understand the opinions, preferences and beliefs of Africans. Seven rounds of samples have been conducted between 1999 and 2019 across 37 countries. As with DHS data, there are some limitations resulting from small sample sizes, sometimes sparse coverage of countries, changing coverage of people and locations over time, and incomplete coverage in conflict areas, but these do not prevent interesting research. Some examples of the information we can get from this survey include pan-African and intra-national opinions on how residents would describe the present economic condition of their country. We can use the responses to questions about institutions in a country to investigate levels of trust in traditional leaders compared to national parliaments, courts and local assemblies.
Research that has made use of Afrobarometer data includes Nunn & Wantchekon (2011), an analysis of how exposure to the slave trade created lower current levels of trust in institutions, Depetris-Chauvin, Durante and Campante (2020), who looked at how shared experiences of football strengthens a sense of national identity, and Michalopoulos and Papaioannou (2015), a study of precolonial political centralisation and the effect on current development.
Administrative data – country censuses, IPUMS census and administrative boundaries
Individual country statistics bureaus conduct censuses of the entire population every ten years covering education, household structure, employment, birthplace and mobility, births and deaths, and other matters, such as housing material. The caveats associated with these datasets are that they are spatially coarse, infrequent, not standardised across countries and difficult to access.
Census data can be used to show us, for example, the progress in educational achievement in Mozambique between 1997 and 2007, where levels around Maputo are high and rising. Recent research using these datasets includes investigation of the French and British colonial legacies in Cameroon, where men are more educated on the side of the partition formerly controlled by the British (Dupraz, 2019), Nunn and Watchekon (2014) on the slave trade and origins of mistrust (2011), and a review of elite control of society in Sierra Leone (Acemoglu, Reed and Robinson, 2014).
Integrated Public Use Microdata Series (IPUMS)
IPUMS are standardised censuses from many countries, conducted every ten years (in most cases) and covering aspects of development including education, household structure, employment, birthplace and nativity. Drawbacks of this dataset are that it is based on samples of around 10% of the population, not complete censuses, can be at a less detailed level, and it is not entirely standardised across locations and boundaries.
Alesina, Hohmann, Michalopoulos and Papaioannou (2021) have used this dataset to study intergenerational mobility in education, considering, for example, household structure, i.e. how well children are educated relative to their parents, and the effect of place. Further areas of research have been in the relationship of transport costs, trade and urban growth (Storeygard, 2016) and in early life circumstances and adult mental health (Adhvaryu, Fenske and Nyshadham, 2019).
Administrative boundaries (GADM)
A final useful dataset is on administrative boundaries and provided by GADM. It covers the whole world at one point in time and is useful for disaggregating national areas. It is worth noting that countries have different levels of localisation in their internal boundaries and that because the dataset is at one point in time, it may not reflect the latest realities on the ground.
Other datasets – conflicts, institutions, imagery, and machine learning
There are two notable sources of data about conflicts: the Uppsala University UCDP-GED database of major and minor civil wars from 1989 to 2019, which is based on a standard definition of a conflict and records the number of casualties, and the ACLED record of riots, protests, battles and civil disturbances, which covers 1977 to 2022 and is based on an aggregation of news sources.
An example of how we can use this dataset is in comparing two recent periods in Ethiopian history, 2018-19 and 2020-21, which have seen a significant increase in the levels of conflict. These datasets have been used to look at the long-run effects of the Scramble for Africa on ethnic homelands that were partitioned by European powers (Michalopoulos and Papaioannou, 2016), at how mineral resources fuel conflicts (Berman, Couttenier, Rohner and Thoenig, 2017), and at how mobile technology supports political mobilisation (Manacorda and Tesei, 2020).
High-resolution satellite images (VHR)
Modern satellite imagery can provide details at a fine scale of around every 50cm, meaning that local development can be measured very accurately. These datasets are expensive and hard to scale, but have been used, for example by Henderson, Regan and Venables (2021) to estimate the economic costs of historical land rights and slower development in Nairobi between 2004 and 2015.
Modelled wealth and income
A combination of satellite imagery, survey data and machine learning, this technique has been used by Yeh et al. (2020) to ‘fill in the gaps’ in other data sets and to estimate the wealth and income in Nigeria. This is based on proprietary techniques and is not easy to implement from first principles, but offers an interesting possible future avenue of development.
African History through the Lens of Economics is an open-access, interdisciplinary lecture series to study the impact of Africa’s history on contemporary development by the Wheeler Institute for Business and Development. This course is led by Elias Papaioannou (London Business School), Leonard Wantchekon (Princeton University), Stelios Michalopoulos (Brown University), and Nathan Nunn (Harvard University and supported by CEPR, STEG and the European Research Council. The course runs from February 1 to April 13 of 2022 and has received more than 27.000 registrations. For more information visit the course website.
David Jones (MBA 2022) is a Classics graduate and has worked as a teacher in Malawi, an accountant at Deloitte and in the finance function at the Science Museum in London. He completed an internship with the Wheeler Institute’s Development Impact Platform in Zambia over summer 2021 and is now continuing as an intern for the Wheeler Institute, contributing to the creation of content that amplifies the role of business in improving lives
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