My Research


  • Political Economy and Development studies: global (child) poverty; inequality, governance; natural disasters; economic crises; global health.
  • Economic Sociology: theory of Scarcity, Abundance, and Sufficiency; theory of science (critical realism); action theory, class and stratification.
  • Methodology: multilevel modeling, machine learning, social network analysis, topic modeling (quantitative text analysis), Bayesian statistics, R-programming.


My main research project is analyzes how external shocks – economic crises, natural disasters, and, armed conflicts – effect child poverty, and how to alleviate the adverse effects of these shocks by well-crafted policies.

My work pushes current boundaries in the field by focusing on global comparisons and by combining large-scale macro and micro databases (e.g. the Demographic Health Surveys and the Multiple Indicator Cluster Surveys). For example, I am leading a paper focusing on how natural disasters effect child poverty globally (67 middle and low-income countries). We find that children living in high-disaster countries – such as India, Kenya, Bangladesh, Ethiopia, and Thailand – run a higher risk of being knocked down to a state of absolute poverty. We find that the good governance does not help to counter the negative effects of disasters, as we have found in earlier studies we have published in World Development and Journal of South Asian Development. In the wake of climate change, this study teaches us that the world needs to address the causes of environmental degradation directly.


My Ph.D. thesis, which I finished by the spring of 2011, is titled Scarcity, Abundance, and Sufficiency: Contributions to Social and Economic theory. The thesis analyzed the concept of resource scarcity in classical (Malthus, Menger), neoclassical (Marshall, Robbins), and sociological theory (Marx, Weber). The thesis critiqued the premise of universal resource scarcity. Although the thesis was theoretically driven, it situated itself in the empirical field of food security – borrowing concepts from Sen´s entitlement theory. The overarching implication of the thesis is that scarcity should rarely be assumed, as done in most socio-economic theories, but ought to be explained – accounting for natural, cultural, and social processes.

In the near-future, I am aiming to develop empirical measures that captures the status (scarcity, abundance, or sufficiency) of resources both within and outside markets. This would be useful from a policy perspective to understand if poverty (deprivations) exists due to unequal distribution of resources, or due to actual lack of resources.

My current contribution to the field of economic sociology comes in the form of analyzing the economic orientation of the discipline of sociology. The most recent attempt has resulted in a paper where Sebastian Kohl and I use JSTOR data to measure this orientation of the discipline over the last 130 years. With the courtesy of JSTOR, we use all full-text articles available from its database to accomplish this task. This amounts to about 140 000 articles and about 160 journals between the period 1890 to 2014. Methodologically, we use topic modeling (a form of machine learning for automated text analysis) to measure the economic orientation of individual articles and journals. Our findings show that sociology is less economically oriented than what is believed.

This line of research is relevant for our understanding on what sociologists do and what their contribution is to studying economic issues. This is especially important in a time of austerity and one economics is failing to come up with alternatives.


I am passionate about exploring different avenues about how social scientist can implement old and new methodological techniques. The most recent exploration was to apply topic modeling to JSTOR-data-for-research – see above. Topic modeling is an unsupervised machine learning technique that classifies text.

I am interested to bring the field of machine learning to poverty research and development studies. Machine learning is a subfield of computer science that studies algorithms that can cluster, organize, and predict insights from data. Once deployed, the machine can produce insights (semi)automatically – from recommender systems to self-driving cars. One of the main differences between traditional statistical approaches and machine learning is that the former relies largely on deductive-hypothesis testing with well-defined static data; the latter is a dynamic system where the machine relies on a stream of new data. This means that we can, for example, create early warning systems that could track changes in food prices dynamically (e.g. on a monthly basis) and hence take policy actions earlier to protect the vulnerable, while the adverse event is still ongoing; similarly, by tracking a stream of textual media data, we could detect the onset of conflicts and thus take action earlier, before eruption. With these types of ideas in mind, the United Nation established recently an innovation initiative called UN Global Pulse exploring the power of big data and machine learning.