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University of Bayreuth
African Studies
WORKING PAPERS
Africa Multiple connects 9
Approaching “Relationality” from Economics
A Conceptualisation, Application and Discussion
David Stadelmann and Frederik Wild, 2025
56
University of Bayreuth
African Studies
WORKING PAPERS
Approaching “Relationality” from
Economics
A Conceptualisation, Application and Discussion
CC-BY 4.0
David Stadelmann and Frederik Wild, 2025
ii
Institute of African Studies (IAS)
The University of Bayreuth African Studies Working Papers are published by the Institute of African
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Approaching “Relationality” from Economics iii
University of Bayreuth African Studies Working Papers (LVI)
Africa Multiple connects
As the Working Paper publication series of the Africa Multiple Cluster of Excellence, Africa
Multiple connects offers a forum for research conducted by researchers affiliated therewith. The
series features diverse papers such as guests’ lectures, workshop contributions, and conference
papers.
The Africa Multiple Cluster of Excellence was established in January 2019 through the Excellence
Strategy of the German Federal and State Governments. Building on the well-established
programme of African Studies at the University of Bayreuth, the Cluster pursues an innovative
agenda as expressed in its subtitle – Reconfiguring African Studies. It has almost hundred and sixty
members, from three continents, who represent a diverse range of academic disciplines and
cooperate with partner institutions in Africa, Europe, Asia, and the Americas. Our understanding
of the reconfiguration of African Studies focuses on stimulating new theoretical approaches and
the creation of new forms of interdisciplinary collaboration. In addition to the University of
Bayreuth, the Cluster is made up of four further African Cluster Centres (ACCs): the Universities
of Lagos (Nigeria), Joseph Ki-Zerbo (Burkina Faso), Moi (Kenya), and Rhodes (South Africa).
Our key concepts are multiplicity, relationality, and reflexivity. We employ them to capture the
dynamic interrelationship of diversity and entanglement that characterize African and African
diasporic ways of life and world-making. In the “Knowledge Lab”, we connect our theoretical,
epistemological, and methodological issues. Our “Digital Research Environment” integrates
analogue and digital data into a common, digital research platform. Through the Bayreuth
Academy of Advanced African Studies, the Cluster coordinates an international fellowship
programme for junior and senior researchers, including artists. Bayreuth International Graduate
School of African Studies (BIGSAS) offers research-oriented training for doctoral students.
In addition to Africa Multiple connects, there are two other subseries published within the
University of Bayreuth African Studies Working Papers. Academy reflects features research by
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BIGSASworks!, is the platform for publishing research-related articles and edited collections by
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bayreuth.de).
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africamultiple-international@uni-bayreuth.de
iv
About the Authors
David Stadelmann studied Economics (MA/BA) and Mathematics (MSc/BSc) at the University of
Fribourg (Switzerland), where he received his PhD in Economics and Social Sciences in 2010.
Since 2013, he has been a professor at the University of Bayreuth (Germany). Prof. Stadelmann is
a dedicated educator. He was a founding member of the DFG funded Cluster of Excellence EXC
2052/1 Africa Multiple. Prof. Stadelmann’s research interests span political, public, and
institutional economics, as well as growth, development, federalism, and global factor mobility.
He has authored over 120 scientific publications in journals such as Nature Communications,
Journal of Economic Behavior and Organization, British Journal of Political Science, Public Choice,
and the Journal of Comparative Economics. In addition to academic publications, he
communicates policy-relevant research findings in popular outlets including newspapers, blogs,
and magazines, with over 250 contributions. He is a frequent speaker at international conferences
worldwide and holds research fellowships at institutions such as CREMA (Center for Research in
Economics, Management and the Arts, Switzerland), BEST (Centre for Behavioural Economics,
Society and Technology, Australia), the Ostrom Workshop and Indiana University, and the IWP –
Institut für Schweizer Wirtschaftspolitik (Switzerland). Prof. Stadelmann has received multiple
awards, including the Reinhard Selten Prize from the German Economic Association, the Ludwig
Erhard Prize from the Ludwig Erhard Foundation, and the Wissenschaftspreis from the Region of
Vorarlberg. Since 2015, he has served as an editor of the peer-reviewed journal Kyklos –
International Review for Social Sciences.
Frederik Wild is a postdoctoral researcher at Stanford University and the Heidelberg Institute of
Global Health (HIGH). He earned his PhD in Economics from the University of Bayreuth and the
Bayreuth International Graduate School of African Studies (BIGSAS). During his doctoral studies,
Frederik served as a research associate for the MuDAIMa project ('Multiplicity in Decision-Making
of Africa’s Interacting Markets') within the Cluster of Excellence 'Africa Multiple' (EXC52), funded
by the German Research Foundation (DFG). His research focuses on economic development in
sub-Saharan Africa, with particular attention to education, health, and regional integration.
Frederik's work has appeared in journals including the Journal of African Economies, the Review
of Development Economics, and Economics Bulletin.
Approaching “Relationality” from Economics v
University of Bayreuth African Studies Working Papers (LVI)
Abstract
In this essay, we offer an economics-rooted conceptualisation of the term “relationality”. To
achieve the fundamental purpose of economic analysis, we argue, it is useful to conceptualise
“relationality” as a process of exploring links that explain outcomes. In alignment with the broad
objective of economic research, our perspective on “relationality” sheds light on how it can aid in
comprehending our world as is and how it came to be. We illustrate the power of our suggested
conceptualisation of “relationality” via three specific empirical applications, including the analysis
of regional economic integration in Africa. We also discuss the potential of our conceptualisation
in “Reconfiguring African Studies”.
JEL-codes: A11, A12, Z10
Keywords: Relationality, Economic Perspective, Associations, Causality, Causal Inference,
Decision Making, Understanding Livelihoods, Improving Livelihoods, Reflexivity
vi
CONTENTS
Institute of African Studies (IAS)
ii
Africa Multiple connects
iii
About the Authors
iv
Abstract
v
Approaching “Relationality” from Economics: A Conceptualisation,
Application and Discussion
1 Introduction
2
2 Purposes of Economic Research
3
2.1 Positive Analysis
3
2.2 Normative Analysis
3
2.3 Relevance of Decision-Making
4
3 An Economic Approach to Relationality
5
3.1 Conceptualising Relationality
5
3.2 Applications
7
3.3 Discussion
13
3.4 Contributing to “Reconfiguring African Studies”
15
4 Conclusion
17
5 References
18
6 Latest Publications in the Africa Multiple connects Working Paper Series
21
Approaching “Relationality” from
Economics
A Conceptualisation, Application and Discussion*
David Stadelmann † and Frederik Wild ‡
* Acknowledgements: Funding for this work was provided by the Deutsche Forschungsgemeinschaft (DFG, German
Research Foundation) under Germany’s Excellence Strategy – EXC 2052/1 – 390713894.
‡ University of Bayreuth (Germany). Email: david.stadelmann@uni-bayreuth.de. BEST-Centre for Behavioural
Economics, Society and Technology, IREF - Institute for Research in Economic and Fiscal Issues, Ostrom Workshop at
Indiana University, and CREMA - Center for Research in Economics, Management and the Arts.
†University of Bayreuth (Germany). Email : frederik.wild@uni-bayreuth.de. Stanford University (United States) and the
HIGH - Heidelberg Institute of Global Health (Germany). Email: fwild@stanford.edu
2
1 Introduction
Typically for an area study as an interdisciplinary endeavour, African Studies in the Bayreuth
based Cluster of Excellence Africa Multiple connects a range of academics from different
disciplines, mainly from the humanities and social sciences. A central term of the Cluster’s
research program is “relationality”, which is inherently linked to the other main conceptual terms
of “multiplicity” and “reflexivity”. Together, these three terms constitute the analytical foundation
as well as the toolset with which the aims of the Cluster are to be approached. One specific purpose
of these terms is to connect the heterogeneous fields of research as well as the diverse practices
of scholarship in order to facilitate the larger aim of “reconfiguring” African Studies. However, it
seems fair to say that the terms “relationality” as well as “multiplicity” and “reflexivity” can
currently be considered as rather elusive concepts or ideas. Within the cluster, some consider
“relationality” as an epistemological stance that views the world as consisting of relationships
rather than standalone qualities or entities. While such rather abstract perspectives have their
merits, we argue that a more tangible, or operational, conceptualization of the term is currently
lacking.
We aim to provide one such conceptualization in this essay by approaching “relationality” from
the economics discipline, i.e. offer an economics-rooted conceptualisation of it, as viewed by two
economists. We will also exhibit the usefulness of our suggested conceptualisation via three
distinct applications, including the analysis of regional economic integration in Africa. Regional
integration efforts are arguable a typical topic for research programs like the Bayreuth based
Cluster, area studies , where interdisciplinary research is highly relevant.1 Apart from generating
an understanding of “relationality” in understanding economic research, we further discuss how
our approach may be considered useful for the multitude of fields contributing to African Studies
in particular and to area studies more generally. We thereby also highlight the potential relevance
of our conceptualisation in the process of “reconfiguring” African Studies.
A definition of a term such as “relationality” cannot be judged as right or wrong per se. Rather,
definitions of terms or concepts such as these shall be purposeful, i.e. successful in fulfilling a
specific aim or purpose. At a very fundamental level, the purpose of economics research is to
understand aspects of human behaviour or human lives more generally, with the potential use to
improve human lives and the world humans live in. Thus, from an economics point of view, a
functional conceptualisation of “relationality” is rendered purposeful if it helps in understanding
human lives and ultimately, in improving them. The present inquiry is therefore relevant if it
succeeds to offer a purposeful definition or conceptualisation of “relationality” with regards to
main research objective of economists.
The remainder of the essay is structured as follows: We start by providing an intuitive account of
the purpose of economics as a social science in Section II. Section III suggests a conceptualisation
of the term “relationality” argued to be consistent with the purpose of economic research and
1 Our Cluster funded research project “MuDAIMa - Multiplicity in Decision-Making of Africa’s Interacting Markets: The
Functioning of Community Law, the Role of Market Participants and the Power of Regional Judges”, specifically
pertaining to Africas Regional Economic Communities (RECs), is a joint research effort with political scientists as well
as scholars of legal studies.
Approaching “Relationality” from Economics 3
University of Bayreuth African Studies Working Papers (LVI)
possibly numerous other research endeavours. The section further illustrates the power of our
conceptualisation along three distinct applications. Section IV offers concluding remarks.
2 Purposes of Economic Research
Economics as a field of the social sciences2 is often defined as the study of the allocation of scarce
resources among people (e.g. see Robbins 1935 for a classical contribution3 or Krugman and Wells
2013 as well as McAfee et al. 2017 for contemporary accounts). Note that absent scarcity, there
would be no significant allocation issue and, thus, most likely, no significance of economic
research as such. Studying the allocation of scarce resources and the consequences thereof is
naturally related to the behaviour and actions of interrelated agents both on an individual-, as well
as on a collective level. Economic analysis thereby examines, among other issues, which goods and
services are produced as well as questions on how they are distributed and consumed.
Intuitively, the study of economics might, in effect, be argued to serve two fundamental purposes:
(1) understanding human lives and (2) improving human lives.
2.1 Positive Analysis
The first fundamental purpose, understanding human lives, can be seen as a positive analysis. It
involves describing the “what is” is and investigates the potential drivers and mechanisms that
have led to “what is”.
A positive economic analysis might therefore try to understand how much people earn and how
income is distributed (e.g. Mincer 1958 and the large literature in labour economics), how goods
and services are consumed (e.g. Deaton 1992 and the large literature on consumption), or why
inflation occurs and what its consequences are (e.g. Friedman 1977 and a large literature in
monetary economics as a subfield of macroeconomics). As stated, understanding these processes
necessarily means to engage in an inquiry of the underlying mechanisms and the potential drivers
of them. Hence, economic analysis also includes the study of peoples’ knowledge, opinions and
preferences, among many other factors (e.g. Lusardi and Mitchell 2011 for aspects related to
financial knowledge and financial literacy).
2.2 Normative Analysis
Identifying “what is” and understanding the underlying mechanisms can be quickly, and maybe
even naturally, related to the capability of making predictions. Evidently, the ability to make
reasonably accurate predictions is a first step towards the second purpose of economic research,
2 In this essay, we hold the view that economics is a social science (e.g. Frey 1990). Note that there are also successful
schools of thought arguing that economic theory is similar to engineering. Indeed, the 2020 Nobel Memorial Prize in
Economic Sciences was awarded to Paul Milgrom and Robert Wilson for their pioneering work on auctions. Both award
winners follow an approach to solving economic problems which resembles that of engineers. Most proponents of this
approach would, however, not deny that the definition of what constitutes an economic problem is also an issue of the
social sciences.
3 Robbins (Robbins 1935; 16) notes that “Economics is the science which studies human behaviour as a relationship
between ends and scarce means which have alternative uses.”.
4
that is, the purpose of improving human lives. Positive analysis thereby informs normative
analyses.4
In economics, normative analyses are usually constituted of informed predictions about the
effects of specific interventions, including an evaluation of their (social) desirability, i.e. regarding
human well-being.5 Under certain assumptions, having knowledge about value judgements of
individuals may allow for an aggregation of these judgements in determining what might be
socially beneficial or desirable. While it has been proven that a well-defined social welfare function
does not exist under reasonable axioms (Arrow 1951), normative analysis in economics typically
aims to provide an approach to evaluate the consequences of interventions based on some
aggregation of individual preferences (e.g. Samuelson 1947). Evaluating such consequences has
given rise to an entire field named “Public Economics” that investigates, among others, how
government intervention might improve (or reduce) social welfare. Economic analysis also
highlights the problems of trying to improve social welfare, which is often linked to issues
regarding incentives of the institutional setup and in (political) decision-making (e.g. Frey and
Stutzer 2010 for a discussion on why attempts to maximise aggregate happiness as a type of social
welfare function is likely to be problematic).
2.3 Relevance of Decision-Making
Decision-makers such as politicians tend to be keenly interested in knowing how changes in
factors such as institutions, laws, regulations, etc. influence scarcity, the allocation of resources as
well as individual incentives. For instance, being cognisant of the observed relationship between
individuals’ education and income has led labour economists to explicitly model and predict
individuals’ earnings based on their levels of schooling (e.g. Mincer 1958 and the subsequent
literature investigating returns to schooling). In turn, policymakers in democracies may regard
these predictions carefully given that their electoral success depends on the approval of citizens,
which is arguably connected to their earnings or their occupational prospects.
Ideally, it is not a seemingly objective expert (or policymaker) who suggests what might be
(socially) desirable to others.6 Rather, it can be argued that people themselves reveal what is
important to them, granted that individuals’ decisions may involve errors. This is why economists
tend to have a keen interest in human decision-making, from which preferences and value
judgments are potentially revealed and normative analysis further facilitated. Of course, human
decision-making is constrained by natural, institutional, as well as social and personal
circumstances, among other factors, and constraints themselves can be a product of human
4 An interesting example for this is the field of Happiness Economics where variables related to individual life
satisfaction are analysed (e.g. Frey and Stutzer 2002).
5 For example, a petroleum tax harms buyers of petrol prima facie, given that they have to incur higher prices. However,
if that tax is used to finance and to maintain highways, and if petrol buyers and drivers are (generally) the same people,
a normative analysis may suggest that both profit, at least on average (e.g. McAfee et al. 2017 for further examples).
Policies where everybody profits are usually rather uncontroversial.
6 An expert might be seen as objective when performing a positive analysis in regard to what humans revealed to be
important to them. Knowing what humans have revealed important helps to inform normative analyses. Interventions
that are in the interest of all interested parties might then be reasonably judged as “improvements of human lives”
leading to (conceptual) unanimity (e.g. Buchanan and Tullock 1962).
Approaching “Relationality” from Economics 5
University of Bayreuth African Studies Working Papers (LVI)
decision-making. For instance, institutions, as an example of a humanly devised constraint (e.g.
North 1990), are generally an outcome of political (collective) decisions made by (a collective of)
individuals.
Given the complex interplay of preferences and constraints that govern many of the observed
phenomena in the world, economic research has been inspired by insights from the most diverse
sources, disciplines and fields (e.g. Backhouse and Medema 2009). At the same time, economic
analysis has branched out and now investigates and contributes to diverse subjects, as can be
depicted from the commonly used Journal of Economic Literature classification system7 – the so
called JEL-codes – which include, for example, “Q42 - Alternative Energy Sources”, “R14 - Land
Use Patterns” as well as the “Z11 - Economics of the Arts and Literature”.
In their endeavours, economists are often guided by pragmatic considerations. Hence, even if the
analyses performed may seem highly technical in terms of the mathematical approach,
mathematics mostly serves economists as a tool and, rather, is viewed to greatly facilitate
intersubjective communication and to reduce potential misunderstandings. It requires
formulating testable as well as refutable assumptions transparently, which is particularly relevant
to normative analyses and a discussion thereof.
In sum, economics as a social science aims to understand the complex constitution of human lives
as influenced by a multitude of underlying conditions, preferences, and constraints. It also informs
normative analyses in an attempt to improve human lives.
3 An Economic Approach to Relationality
3.1 Conceptualising Relationality
As formulated in the previous chapter, we aim to understand human lives in a first step and to
predict outcomes due to changes in factors which underlie the former in the second step.
Ultimately, this approach has the potential to improve human lives. From these purposes, a
functional conceptualisation of “relationality” follows naturally: We conceptualise it as the process
of exploring links that explain outcomes.
It is useful to use some simple mathematical notation to better understand our proposed
conceptualisation, which, as noted previously, helps in facilitating intersubjective understanding
and in highlighting our conceptualisation more transparently. Call 𝑦 our variable proxying the
outcome of interest (e.g. individual life satisfaction, the level of inflation, the level of production,
whether a country joins a Regional Economic Community, etc.). We may then relate 𝑦, say
individual life satisfaction, to potential explanatory factors 𝑋1 which could be age, 𝑋2 which could
be sex, 𝑋3 which could represent another personal characteristic, 𝑋4 which could reflect
institutional constraints such as the rule of law in a given country, 𝑋5 which could reflect
environmental factors such as temperature, etc. The choice of 𝑋𝑖 can be informed by theoretical
7 For the Journal of Economic Literature Classification System see, for example,
https://www.aeaweb.org/econlit/jelCodes.php (accessed April 28, 2022).
6
reasoning, past evidence, common sense, or creative hypotheses of the researcher. The process of
exploring links that explain outcomes would thereby imply establishing a model such as
𝑦= 𝑓(𝑋1,𝑋2,.. .,𝑋𝑖, … )
where 𝑓 is a function that stipulates the presumed relationship between the outcome 𝑦 and all
explanatory factors 𝑋1, 𝑋2, etc. Evidently, like the choice of 𝑋𝑖, theoretical modelling might try to
stipulate 𝑓 based on preceding empirical evidence or other informed priors. Thus, “relationality”,
according to our suggested conceptualisation, could be seen as the process of choosing 𝑋𝑖 and
stipulating 𝑓. In other words, “relationality” from an economics point of view entails theorising,
examining and, subsequently, re-configuring the understanding of specific phenomena, because
they are inherently envisioned as products of interconnected factors.8
Formal models such as these thereby apply logical reasoning as well as past evidence to deduce
certain relationships and to stipulate new, unexplored ones. The task of empirical economic
research is then to test these presumed relationships via, for example, qualitative or quantitative
analysis using econometric techniques (e.g. Wooldridge 2019 regarding the large scope of modern
econometrics). The expression of a relationship might be of “qualitative” nature, that is, indicating
whether there is a positive, negative or no link between 𝑋𝑖 and 𝑦. The expression might also be
“quantitative”, that is, indicating whether the link between 𝑋𝑖 and 𝑦 is comparatively strong or
weak with respect to other variables or whether it explains a relevant amount of the variation of
the outcome.
There are several notes to be made regarding the proposed conceptualisation:
◼ The process of exploring links is not limited to the exploration of whether such links
exist. In fact, it is equally relevant to explore the existence of a relationship as it is to
assess its absolute or relative importance with respect to the outcome or other
influencing factors, respectively. Literally, thousands of constraints or incentives will
matter for how decisions are made and how outcomes come about. However, not all
links will usually be of equal importance. Similarly, not all links will offer similar
explanatory power. Some relationships may be comparatively weak, while others may
be strong.
◼ Exploring links on how decisions are made and on how outcomes can be explained does
not yet lay claim to the causality of the observed relationship. Evidently, identifying
causal relationships is important when trying to explore mechanisms of how outcomes
come about. Establishing causal effects and mechanisms from observations of the world
usually requires making additional assumptions. For example, credibly causal
predictions usually require that certain side conditions remain constant.9
8 Note that the terms “links”, “relationships” or “interconnections” are regarded as synonymous.
9 In the last decades, substantial advances have been made in the ability to conduct “causal inference” via empirical
economic research. We refer to the prominent works on “natural experiments” by Joshua D. Angrist, David Card and
Guido W. Imbens for which they were awarded the 2021 Nobel Memorial Prize in Economics. These advancements tend
Approaching “Relationality” from Economics 7
University of Bayreuth African Studies Working Papers (LVI)
◼ Inherent to the process of exploring links is the attempt to observe the decisions of
humans and the outcomes of their decisions. These observations are often selective and
can be biased. To fulfil the purpose of economic research, the exploration of links must
try to avoid biases as best as possible and to give an estimate of the relevance of a
remaining bias. Indeed, explicitly integrating a probability of error or misattribution,
that is, an estimate of how likely it is that links are wrongly attributed is central to the
process of exploring links.
◼ Even if causal links have been credibly established and prediction is likely to work
accurately, we must be aware that humans observe, learn and react to information and
incentives. Thus, it is reasonable to assume that humans may integrate past predictions
of human behaviour in their own (future) behaviour.10 This implies that the process of
exploring links may influence the (established) links themselves. Such “feedback loops”
may be viewed as a special form of “reflexivity”. From an economics point of view, the
Cluster’s concepts of “relationality” and “reflexivity” are thereby inherently linked.
3.2 Applications
We demonstrate the practical value of our proposed conceptualisation of “relationality” via three
distinct applications related to our Cluster Project “MuDAIMa - Multiplicity in Decision-Making of
Africa’s Interacting Markets: The Functioning of Community Law, the Role of Market Participants
and the Power of Regional Judges”, which combines economics, law, and political science to
investigate the decision-making and living standards in Africa’s interacting markets. Specifically,
we use our conceptualisation to explore the ways in which the various links related to our project
can be examined, and thereby demonstrate our conceptualisations’ instrumental, or operational
value, particularly in understanding socio-economic livelihoods in Africa. This includes the link
between external (first-nature) factors such as temperatures and socio-economic welfare, or
even, household’s geographic proximity to trading opportunities as given by harbours, as well as
the analysis of human (second-nature) interactions such as trade, as envisioned at the core of the
MuDAIMa project.
Application 1: Temperature and socio-economic welfare
Suppose we are interested in the link between rising temperatures and welfare in African
countries (e.g. Baako-Amponsah et al. 2025)11. A reason for our interest might be to better
understand the future impact of climatic developments and climate change in particular,
to be recognized by decision-makers and international organizations. Interestingly, some area studies such as Eastern
European Studies or China Studies have also seen a move towards using these methods.
10 For instance, if humans had reacted to inflation in a way which models have predicted past occurrences, this would
not necessarily imply that these predictions also hold for future cases. Humans in general, and financial market
participants in particular, may integrate predictions about behaviour and other information rationally such that
previously established relationships may not hold anymore (e.g. the Lucas 1976 critique regarding macroeconomic
policymaking argues that it would be naïve to predict the effects of a change in economic policy solely on relationships
from past observations).
11 Baako-Amponsah, Josephine, David Stadelmann, and Frederik Wild. 2025. "Whether it’s Weather or Climate: The
Link between Temperatures and Deprivation in Sub-Saharan Africa", mimeo, University of Bayreuth.
8
especially for poorer countries where people may not have the financial capacity to protect
themselves from the expected negative consequences. Global surface temperatures have risen (on
average) 1.09°C higher during the last decade (2011–2020) compared, for instance, to the period
between 1850–1900 (IPCC 2021: SPM-5). As averages hide the variation of temperature changes
by construction, some countries have evidently experienced substantially larger increases in
temperatures than others. From our postulated conceptualisation of “relationality”, exploring
such a link requires a general understanding of temperature’s influences on past and present
living conditions. It may then help in predicting the direct welfare consequences of global
warming in the future, i.e. improve human lives.
Given that standards of living have been closely linked to production (e.g. Mankiw 2012 who
outlines ten specific principles of economics)12, economists often use countries’ gross domestic
product per capita (GDP per capita) to measure welfare at the national level. We, therefore, start
the exploration of this link by defining GDP per capita as our outcome of interest, 𝑦. Consequently,
we gather available data on GDP per capita over a selected period of time, say from 1950 onwards,
such that one country observed in one specific year constitutes a single observation. We then
match this information with measures of the, e.g., average temperatures for the selected countries
for each year (𝑋1). Note that we would naturally include other influencing variables (𝑋𝑖) which
presumably affect the link between GDP and temperature, such as precipitation (𝑋2) or latitude
(𝑋3), to isolate the pure effect stemming from temperatures. From there, we investigate the
stipulated model 𝑓 by the following simple (linear) relationship
𝐺𝐷𝑃 𝑝𝑒𝑟 𝑐𝑎𝑝𝑖𝑡𝑎= 𝛽1𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒+ 𝑿𝜷+ 𝜀.
In this formulation, a country’s GDP per capita is a linear function of its observed temperature and
a vector 𝑿, which includes further influencing variables such as the ones just described.13 Our
main coefficient of interest is given by 𝛽1 which quantifies the (strength of the) relationship
between countries’ temperatures and GDP per capita. 𝜀 is an error term which explicitly
acknowledges that the model cannot capture the entirety of what influences countries’ GDP per
capita. Note that the omission of other potential influences on countries’ GDP, which have to be
captured by the error term 𝜀, be it because of data availability, is unproblematic for the
interpretation of 𝛽1 as long these influences are independent of 𝛽1, that is, independent of
countries’ temperature.14
Employing this proposed equation in a regression framework, that is, applying an appropriate
estimation technique to the data, the relevance of the postulated relationship will be given by the
respective coefficient 𝛽1. This would allow us to say whether, under the set of specific assumptions
12 Economic output and growth, as measured by GDP, has been associated with improvements in various indicators of
human development and well-being, such as higher life expectancy, lower child mortality and lower malnutrition
(Deaton 2013; Weil 2013).
13 This type of setup already resembles the one used in contemporary contributions such as Burke et al. (2015) who
employ a dataset spanning much of the world. Greßer et al. (2021) apply a similar approach to subnational (regional)
data. A major difference to the simple setup above is that these authors apply so-called fixed-effects strategies, including
other controls, among other refined empirical/econometric techniques.
14 Of course, in practice, this assumption is a strong one to make.
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of the chosen estimation method, there is a link between temperature and GDP per capita for the
countries included in the sample and the period analysed. As outlined above, an estimate of 𝛽1,
particularly the coefficient’s size and its statistical significance, also allows us to make a statement
regarding the relevance as well as the strength of the proposed relationship. As we want to
understand “what is”, we are open to hypotheses contrary to our priors. Hence, although our prior
belief might be that 𝛽1 < 0, that is, higher temperatures are associated with lower GDP per capita,
our methodological implementation is generally performed in a way such that our prior beliefs do
not matter. Indeed, if we chose a very standard estimation method such as Ordinary Least Squares,
we might find that 𝛽1 < 0, 𝛽1 > 0 as well as 𝛽1 ≈0.
Note, however, as addressed in the previous section, we must be careful about making causal
claims from the observed relationship and the estimate of 𝛽1. Several assumptions are required
to establish causality. It depends, among other factors, on the “trueness” of the underlying
functional form of a model 𝑓 (we assumed a linear function for this illustration), or on the
measurability and the completeness of relevant control variables (for instance, there may be a so-
called “omitted variable bias”, i.e. unmeasured/unincluded influences that moderate and actually
define the relationship between GDP per capita and temperature). Moreover, it has been
documented that GDP data can be prone to (substantial) measurement errors.15 Our sample of
observations (we imagined using African countries for this illustration) may therefore suffer from
systematic measurement error or even manipulation, which affects the robustness of established
empirical links (e.g. Martinez 2022).
Application 2: Coastal proximity and individual living standards
Both economic theory and empirical evidence suggest that trade increases growth (e.g. Frankel
and Romer 1999). Given that harbours act as facilitators of trade, a reasonable as well as a testable
hypothesis based on these (theoretic) priors is to assume a relationship between individuals’
distance to harbours and their standards of living. Being aware of the measurement issues just
discussed, as well as the potentially omitted, moderating factors such as national institutions, a
natural extension to using aggregate data (such as GDP in Application 1) is to use more dis-
aggregated data, e.g. self-reported indicators of income or poverty from representative household
surveys. This is precisely what we do in a recent article in which we investigate household-level
data of 128,609 respondents living in 11,261 localities across 17 coastal sub-Saharan African
countries (Wild and Stadelmann 2022).
To exemplify, suppose we assume a linear link (for simplicity) between our outcome of interest,
in this instance, households’ self-reported frequency of having gone without cash income
15 Recently, data on comparable GDP measures has been shown to be prone to misreporting, particularly in developing
economies. For instance, Johnson et al. (2013) compares two versions of the Penn World Tables (PWT) which provide
comparable GDP data across countries. The authors highlight that results based on higher frequency data are not
necessarily robust to different versions of the PWT. In general, measurement, estimation and comparison of GDP is
constantly being improved (e.g. Feenstra et al. 2015 regarding the PWT).
10
(monetary droughts), and the explanatory factor, proximity to the nearest major harbour.16 We
thereby investigate
𝐺𝑜𝑛𝑒 𝑤𝑖𝑡ℎ𝑜𝑢𝑡 𝐶𝑎𝑠ℎ 𝐼𝑛𝑐𝑜𝑚𝑒= 𝛽1𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝐻𝑎𝑟𝑏𝑜𝑢𝑟+
𝛽2𝐴𝑔𝑒+ 𝛽3𝑆𝑒𝑥+ 𝛽4𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛+ ⋯+ 𝜀.
A 𝛽1 > 0 indicates a higher occurrence of monetary droughts for households living further away
from harbours, supporting existing theory, and allows us to interpret our observation as
suggestive evidence for the positive influence of trade-related factors on individual living
standards. Again, the example shows that we must necessarily take into account variables that are
potentially correlated with our analysed relationship, such as 𝛽2𝐴𝑔𝑒, 𝛽3𝑆𝑒𝑥, or 𝛽4𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛. I.e.
factors that could be correlated with distance to harbours as well as their experience of monetary
droughts. In other words, by stipulating a function f, it is reasonable to expect that living standards
are not only affected by a single variable but rather by multiple, interrelated variables at the same
time. Some of these variables, such as education, may even directly mediate the relationship
between coastal proximity and individual living standards Note that this can be related to the
concept of “modalities”, one of the four heuristic angles of the Cluster, given that the effect of
coastal distance, i.e. the effect of one’s geographic position within a country on living standards,
may inherently, or even solely, depend on its interaction with one of such “third factors” which
ultimately mediate the effect on individuals’ living standards (see Wild and Stadelmann 2022).
Application 3: Regional market integration and household welfare
As a final example of our conceptualisation of “relationality”, we provide an illustration of a
current work-in-progress in which we aim to move beyond the investigation of links and try to
establish a credibly causal relationship. Establishing causal effects is highly appealing, of course,
because they are more suitable for making predictions than mere associations.
The process of establishing causal effects in empirical economic research requires the
identification of a proper empirical setting. This often involves the analysis of exogenous shocks
such that the analysis performed depends on comparatively few defendable side-conditions and
assumptions.17 One prominent methodological approach is the so-called difference-in-differences
method, which, in the most general case, compares two specific units (e.g. countries, states or
individuals), as a first difference, over two specific periods of time, as the second difference. The
difference-in-differences method is often implemented to investigate the effects of specific policy
changes, whereby only one of the observed units is “treated”, that is, one unit implements a policy
change such as minimum wage, a change in tax rates, etc. From a slightly technical standpoint, the
fundamental assumption underlying this analysis is that absent policy change, the two units
would have followed “in parallel”, i.e. their trajectories regarding observed outcomes would have
remained consistent with the patterns observed prior to the policy implementation.
16 Wild and Stadelmann (Wild and Stadelmann 2022) use the Afrobarometer’s geo-referenced datasets (BenYishay et
al. 2017; Afrobarometer 2019) and create a continuous variable measuring individuals’ geodesic (ellipsoidal) within-
country distance to the nearest major harbour.
17 Angrist and Pischke (2015) provide a comparatively intuitive introduction to modern methods of causal inference.
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Correspondingly, deviations from this anticipated trajectory are then plausibly attributed to the
investigated policy change, which constitutes the difference-in-differences estimate.
In our specific application, we treat the re-establishment of the East African Community (EAC) in
2001 as a regional policy intervention which had differential effects on individual households
depending on their geospatial location within the countries (see Eberhard-Ruiz and Moradi 2019;
Wild 202418). Again, recent advances in theoretical as well as empirical economic literature
inform our priors on how we may model the relationship between trade liberalisation and
household welfare, that is, how we stipulate f. While trade liberalisation has been shown to
increase countries’ economic growth in the aggregate (e.g. Frankel and Romer 1999), there can be
substantial variation in the distribution of benefits within countries, particularly across regions
and households (Brülhart 2011; Pavcnik 2017). Suppose then, that we aim to investigate the
hypothesis that households living closer to internal EAC borders profit more intensely from
regional economic integration; a theoretical result for which evidence has been provided in both
developed (e.g. Brülhart et al. 2012) and in developing settings (e.g. Hanson 1994, 1997). A
potential (difference-in-differences) formulation of the link between the re-establishment of the
EAC and monetary droughts as an indicator of economic well-being (see Application 2) is then
𝐺𝑜𝑛𝑒 𝑤𝑖𝑡ℎ𝑜𝑢𝑡 𝐶𝑎𝑠ℎ 𝐼𝑛𝑐𝑜𝑚𝑒= 𝛼+ 𝛽1𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝐸𝐴𝐶 𝐵𝑜𝑟𝑑𝑒𝑟+ 𝛽2𝑂𝑏𝑠𝑒𝑟𝑣𝑒𝑑 𝑎𝑓𝑡𝑒𝑟 𝑡ℎ𝑒 𝐸𝐴𝐶+
𝛽3(𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝐸𝐴𝐶 𝐵𝑜𝑟𝑑𝑒𝑟∗𝑂𝑏𝑠𝑒𝑟𝑣𝑒𝑑 𝑎𝑓𝑡𝑒𝑟 𝑡ℎ𝑒 𝐸𝐴𝐶) + ⋯+ 𝜀.
Here, 𝛽3 captures the moderating relationship between distance to internal EAC Borders and the
EAC’s re-enactment, that is, it captures the differential change in economic outcomes of
households (e.g. occurrence of monetary droughts) over time (before and after the establishment
of the EAC) for households living further away from internal EAC borders compared to those living
closer to them. A positive difference-in-differences estimate, 𝛽3 > 0, suggests increasingly
negative welfare effects (a higher occurrence of monetary droughts) after the re-establishment of
the EAC for households living further away from internal EAC Borders than for households closer
to them. As such, if we presume an increasing exposure to a trade shock for households living
closer to the border, as our priors, 𝛽3 represents the pure effect of a trade agreement on
households such as the EAC. The key assumption behind this causal claim is that nothing but the
re-establishment of the EAC influenced the difference in welfare outcomes (e.g. monetary
droughts) between households living closer and households living more remote to internal EAC
borders (first difference) before and after 2001 (second difference). Exploring such a relationship
places specific demands on the data, such as the availability of geo-referenced household surveys
conducted before and after the establishment of the EAC, that is, before and after 2001.
In this specific example, our conceptualisation of “relationality” is key not only in stipulating the
proposed link between regional economic integration and household welfare but also in
empirically identifying a credibly causal effect. In other words, not only does the relative position
of a household to internal EAC Borders matter, but also its relative position to other households
18 Wild, Frederik. 2024. Development in Sub-Saharan Africa: New Micro-Level Evidence on Education, Geography, and
Trade. PhD dissertation, University of Bayreuth. https://epub.uni-bayreuth.de/id/eprint/7682/.
12
and the comparison of these relationships over time.19 Deliberative processes such as these
adequately encapsulate what our economics-rooted conceptualisation of “relationality” requires
and what it means in practice. We are now precisely in the process of exploring links that explain
outcomes. As can be seen, such an investigation of links is regularly concerned with the untangling
of a complex web of potential links between the outcomes and the underlying mechanism which
also means to incorporate further aspects relating to these phenomena such as space and time in
order to respect the multifaceted nature of the phenomena under study. Evidently, it is practically
impossible to consider all expected and actual influencing circumstances, which further highlights
the relevance of systematically thinking about potential errors and the necessity of a clear
statement of assumptions when exploring such relationships.
Figure 1: Regional market integration and household welfare in the EAC
Notes: Own illustration using the Afrobarometer datasets of Round 1 and 2.
19 Note also that when considering this application, our conceptualization of “relationality” can be argued to integrate
and reflect aspects of the Cluster’s heuristic angles of “modalities”, that is, by the way in which the observed households
and individuals relate, as well as “spatialities” and “temporalities”, that is the spatial and temporal aspects which
determine the phenomena observed.
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Figure 1 provides a schematic illustration of Application 3.20 It depicts a potential prediction made
from a difference-in-differences estimate analysing the geospatial response of household welfare
before and after the EAC. The shading of the maps in Figure 1 serves to illustrate a prediction of a
𝑦, “gone without cash income” (monetary droughts), across space (i.e. across distance) with
darker levels indicating a higher incidence of monetary droughts. The left panel of Figure 1
displays absolute levels of having gone without cash income before the establishment of the EAC,
the right panel illustrates the change of this reported outcome in surveys conducted after the EAC
was established, brighter levels of shade representing larger reductions in monetary droughts
than darker levels of shade. Such a prediction could be made by using data sampled in rounds 1
and 2 of the Afrobarometer’s geo-referenced household surveys (depicted as dark coloured dots),
which were conducted closely before and closely after the re-establishment of the EAC (BenYishay
et al. 2017; Afrobarometer 2019). Note that Kenya was not sampled in survey round 1 of the
Afrobarometer, which is why we don’t illustrate survey enumeration areas in the left panel of
Figure 1 for Kenya.
As depicted in the left panel of Figure 1, in this stylised example, households living closer to
internal EAC borders are estimated to report monetary droughts as more frequent (darker
shading) before the EAC. This is consistent with 𝛽1 < 0. Note, however, that this relationship is
altered as can be depicted the right panel of Figure 1, which suggests a relative decrease in the
occurrence of monetary droughts for households living closer to EAC borders compared to those
living further from them after the establishment of the EAC.21 Note that a difference-in-differences
estimate 𝛽3 > 0 would represent the direct quantification of such a depicted change, and a 𝛽3 >
0 which also satisfies |𝛽3| > |𝛽1| would indicate a complete reversal of the pattern observed
before the EAC (in the left panel), as it would estimate a difference in the effect of distance to
borders between survey rounds 1 and 2 that is larger than the estimated effect of distance before
the EAC (𝛽1). Note that any estimated 𝛽3 > 0 for which |𝛽3| > |𝛽1| is not satisfied would still imply
a positive effect of the re-establishment of the EAC on households although not leading to a
reversal in the sign of the combined distance coefficient after the EAC (𝛽1 + 𝛽3). While the
intuition behind the application is comparatively easy, implementing a reliable difference-in-
differences estimate is challenging in practice and requires careful analysis.
3.3 Discussion
We argue that our suggested conceptualisation of “relationality” is purposeful in describing the
fundamental process and purpose of economic research, as is exemplified by the above
applications. The process of exploring links that explain outcomes is therefore relevant for
understanding the world, i.e. the “what is”, as well as in trying to improve the world by
understanding how it came to “what is”. In the process of establishing links, systematic reflection
20 Note that this is an example for the purpose of illustration of this specific application, only. That is, the values depicted
in the illustration are not based on estimation results. Rather, it is a stylised illustration which aims to aid the
understanding of this application.
21 To facilitate the interpretation of the difference-in-differences estimate, the prediction disregards the actual level of
the reported outcomes at localities such that only the gradient of the shading should be interpreted rather than the
actual level of the tone. Note also that important controls such as an indicator of living close to capital cities (which
would result in areas around capital cities to be shaded “brighter”) are intentionally missing from this stylised example
for simplicity.
14
must be put into how f is stipulated. A large reservoir of established empirical and theoretical
methods and other findings help to identify a potentially suitable f. As seen, the process requires
to transparently state, justify and defend the assumptions made in the research endeavour.22
While they can be seen as reasonable when judged by others, they necessarily remain refutable
assumptions.
It is noteworthy that applied at the individual level, our proposed conceptualisation of
“relationality” has the potential of taking account of aspects of intersectionality. Intersectionality
commonly aims at understanding how different aspects of individual characteristics, views or
identities matter to create different modes of discrimination (Runyan 2018). To see this, choose
an indicator of discrimination at the individual level as 𝑦. Then investigate variables like sex,
gender, age, skin colour, etc. as different 𝑋𝑖. Thereby, discrimination can be modelled by multiple
variables (influences) at once instead of looking at them in isolation.23 Moreover, their absolute
as well as relative importance can be analysed, going beyond a qualitative analytic framework. It
is also noteworthy that economic research has long been interested in discrimination for equity
and efficiency reasons (e.g. Stiglitz 1973; Becker 1995), and suggestions for reducing
discrimination have been drawn from such research.
When thinking about establishing links, errors need to be explicitly acknowledged. Even if a
researcher holds a fully deterministic view of the world, the number of side conditions (reflected,
among others, by the 𝑋𝑖) in the real world is practically too high such that errors will inherently
be part of the modelled relationship. Our conceptualisation of “relationality”, therefore, requires
thinking about errors in the postulated relationship and, ideally, thinking about how much of the
stated link could be due to errors. In this regard, self-reflexivity is a key part of “relationality”
conceptualised as an analysis of links that explain outcomes. It is clear that the values of
researchers also matter in the arts and science of economics, as in any other field (e.g. Van Dalen
2019), such that the choice of f, 𝑋𝑖 and the research question itself may depend on values, too. To
some extent, we would argue that competition among researchers for the best methods (the best
f and the best way of accounting for 𝑋𝑖) to describe the world as accurately as possible and to
make as correct predictions as possible is a relatively good way of reducing potential biases linked
to researchers’ values and in achieving largely accurate results (see also Stadelmann and Gottal
2019).24
Lastly, when trying to understand, and especially when trying to predict human behaviour, one
must consider that humans react upon information, that is, they are knowledgeable, and that
established knowledge may affect the human decision-making process, in turn. Thus, the process
of establishing links may alter the links that have been previously discovered themselves. As an
example, think of stock market traders: They incorporate specific models and predictions based
22 The process of stipulating f also involves to present working papers (work-in-progress) to peers even at early project
stages.
23 Modelling interactions, that is, multiplicative relations between, for example, age and sex, would allow to consider
further heterogeneities.
24 From our own interdisciplinary research experience, we would argue that researchers from different disciplines
competing to improve our understanding will tend to uncover similar links, at least in the long-term.
Approaching “Relationality” from Economics 15
University of Bayreuth African Studies Working Papers (LVI)
on them in their forecasts. The incentive for stock market traders to do so is clear, as a correct
prediction of stock market movements offers large profit opportunities. However, by
incorporating models and predictions into their choices, they could actively alter these previously
established links. The integration of models in the decision-making process thereby figures
“relationality” as the process of exploring and anticipating links which may itself influence the
established links. Seen as a manifestation of reflexivity, this is a challenging problem in practising
economic research. Conceptually, one way to think of bringing together this type of “reflexivity”
with “relationality” might be to stipulate 𝑦= 𝑓(𝑋1,𝑋2,… , 𝑦) where the observation of an outcome
depends on the outcome (or the expectation of the outcome) itself.
3.4 Contributing to “Reconfiguring African Studies”
If “configuration” refers to the specific arrangement of the components in a particular system, a
“reconfiguration” will refer to a change of the arrangement of this same system. Applying this view
to African Studies might imply that the way of performing research is changed while the main
purpose of the research endeavour itself remains.
African Studies is often seen as the study of Africa, including its demography, religions, politics,
economy, and languages, among others. As economists, we believe that at least some of the
purpose of African Studies lies in understanding the manifold ways of human lives as well as
human livelihoods of the African continent. The process of exploring links that explain outcomes
may often require specific cultural, historical, and institutional knowledge.25 Hence, our
conceptualisation of “relationality” can be an appealing view in bringing together researchers
from diverse disciplines. We suggest viewing a “reconfiguration” as the continuing process of
thinking about the multitude of these interrelated factors and the continuing change of knowledge
regarding these established links when investigating Africa. Indeed, the dynamic and multifaceted
nature of various phenomena may necessitate an ongoing process of reconfiguration to account
for new and evolving understandings. We suppose this view is also an appealing endeavour for
many other individuals, not only academics.
Our conceptualisation of “relationality” therefore explicitly seeks to develop and empirically
support theories that may allow drawing connections across different areas and fields. At the
same time, it is also consistent with views that aim to develop contextualised knowledge of Africa
in a joint effort between social scientists and humanists. Consequently, while the central purpose
of economic research with a focus on Africa would be to understand human livelihoods of the
region, if the process of exploring links allows deriving general mechanisms that are likely to hold
elsewhere, so much the better. If, by contrast, the external validity of results across geographic
areas is not assured, this is not necessarily important for the people in the region. Put differently,
25 Our application regarding regional economic integration and household welfare provides a case in point: The general
expectation of international donors would be that Regional Economic Communities such as the East African Community
increase trade and thereby also improve living standards. While this is generally supported for aggregate indicators,
not everyone will benefit (to the same extent) and this may have to do with other economic or non-economic reasons
and preconditions, stemming from historical, political, institutional as well as legal contexts. Addressing questions in
context such as the EAC therefore requires a joint research effort together with political scientists and legal scholars,
among others.
16
while our aim may be to uncover universal relationships and mechanisms, insights that apply, for
instance, in a specific setting in Africa only, are, of course, still valuable for the people living there.
Economics, and nowadays also relevant parts of political science, as well as some parts of
sociology, often tend to follow rational choice theory. According to our own perceptions, some
scholars of African Studies seem to perceive the rise of rational choice theory as a potential threat.
This need not be the case. If, without any specific input with regard to history, culture, and
institutions, among other factors, rational choice theory could accurately predict, for example, the
behaviour of politicians, firms or individuals, then contextualised information would no longer be
necessary, at least in making predictions. In other words, the “what is” is explained, and
predictions can be accurately made. However, it is highly unlikely that this will be the case in many
settings or circumstances. Thus, the inclusion of contextualised information will generally greatly
improve the predictive capacity. From that point of view, our conceptualisation of “relationality”
is inclusive of various different disciplines, viewpoints and researchers, which is particularly
relevant in “reconfiguring”.
Some scholars active in African Studies interrogate current epistemological approaches and
theories by trying to insert what may be viewed as African-centred ways of thinking. As exhibited,
our view is pragmatic such that Africa is placed as neither exotic nor exceptional or banal: If
alternative epistemological approaches help in explaining the world and, in particular, African
livelihoods better than current approaches, then these approaches are precisely the ones which
are likely to be quickly embraced.
Approaching “Relationality” from Economics 17
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4 Conclusion
At a very fundamental level, the purpose of economic research may be viewed as understanding
human lives. The complexity of this task requires careful consideration of a multitude of aspects
which influence human lives and human action. Such aspects include, among others, the historical,
geographical, political and cultural background linked to the phenomena under study.
We conceptualise “relationality” as the process of exploring links that explain outcomes. We argue
that this conceptualization inherently acknowledges the complexity of research of the social
sciences in general, and in economics in particular. We thereby view “relationality” not as an
abstract concept, but as a fundamental perspective of the economics science and as lived practice
in contemporary economic research, where one must continuously try to untangle a complex web
of interrelated influences.
Our exposition of three distinct applications illustrates how our conceptualisation of
“relationality” is conducted in practice. We saw, for instance, that an approach aimed at
understanding (causal) relationships, such as the trade-welfare nexus, necessarily involves the
recognition and formal conceptualisation of spatial as well as temporal aspects, among other
factors. “Relationality” in our conceptualisation will therefore naturally require engaging with a
broad set of methods and results of different disciplines, including, but not limited to, other social
sciences.
Therefore, “relationality” from an economics point of view is specific enough to facilitate the
attention on context-specific relations, while also capable of establishing universal relations. Seen
as such, we argue that our approach and conceptualisation of “relationality” directly contributes
to the aims of the Bayreuth based Cluster of Excellence Africa Multiple in “reconfiguring” African
Studies.
18
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Approaching “Relationality” from Economics 21
University of Bayreuth African Studies Working Papers (LVI)
6 Latest Publications in the Africa Multiple connects Working Paper
Series
Title
Editor(s)
Year of
Publication
Issue
Luttes honnêtes : transformation
structurelle, finances publiques et
récurrence de la crise de la dette
au Sénégal.
Kai Koddenbrock
2024
55(8)
Zur epistemischen Rahmung von
Gerüchten im Covid-19 Diskurs
Kameruns.
Martina Drescher
2024
52(7)
Doing ethics. An outline of a
constructivist and
phenomenological approach of
moral communication.
Martina Drescher
2024
49(6)
Live as African: On the Relevance
of Thomas Sankara’s Agenda for
Economic Liberation
Ndongo Samba Sylla
2022
33(5)
beaucoup de peur et de
questionnement: Une étude
comparative des discours sur le
Covid-19 en Côte d'Ivoire et au
Cameroun
Martina Drescher,
Oumarou Boukari, and
Carline Liliane Ngawa
Mbaho
2022
29(4)
COVID-19 and (Im)mobilities in
West Africa
Andras Breuer, Martin
Doevenspeck, Kamal
Donko, and Ouedraogo
Serge
2021
26(3)
African Studies and the Question of
Diasporas
John Ayotunde (Tunde)
Isola Bewaji
2021
25(2)
Figuring out how to Reconfigure
African Studies
Rüdiger Seesemann
2020
24(1)
David Stadelmann and Frederik Wild. 2025. Approaching “Relationality” from
Economics: A Conceptualisation, Application and Discussion. University of Bayreuth
African Studies Working Papers 56, Africa Multiple connects 9. Bayreuth: Institute of
African Studies.
