Global market power dataset of the primary foods industry

The study of market power has garnered interest from academia, policymakers, and industry in recent times since the publication of de Loecker et al. (2020). This paper introduced a novel methodology to estimate the markup, a proxy commonly used to denote market power. Using said methodology, they found that the markup has been increasing nearly continuously since the 1980′s. Rising markups have been connected to a myriad number of negative economic developments, yet most papers are constrained to study these effects on specific industries related to manufacturing and service. Furthermore, even though data exists for a considerable number of countries globally, the quality and reliability is reduced when examining low-income economies.11 Note: this paper makes references to “high-income” and “low-income” countries. The income groups mentioned are based on the four-income classification system proposed by the World Bank and is explained in more detail here.To circumvent these problems, the authors have devised an alternate approach to calculate the markup, not by using firm-level data but by using macroeconomic data and an estimation procedure based on Generalised Maximum Entropy (GME). The methodology permits the estimation of markups for virtually every country in the world and a substantial number of industries.The dataset provides estimates of the markups for 170 countries in the world for the so-called Primary Foods industry (comprising agriculture, hunting, fishing, and logging). It was calculated by aggregating two datasets: the EORA input-output tables and the UN FAO-Stat database. The merged dataset produced a panel from which the markup estimates were then calculated.The publication potential of this dataset is very high, as no other source exists that captures this detail of information across countries with different income levels. The Primary Foods industry is also crucial for the development of poorer countries, as it often accounts for a large portion of their economy. This dataset opens up avenues of research finding ways to reduce the markup, thereby making economies more efficient and potentially improving the welfare of agents within the economy.The usage of macro-data opens up additional avenues of research not available to micro-data, including measuring the impact of Global Value Chains (a form of globalization), institutional quality, and more on markups.