Global Economic Growth and Agricultural Land Conversion under Uncertain Productivity Improvements in Agriculture

Abstract

We study how stochasticity in the evolution of agricultural productivity interacts with economic and population growth at the global level. We use a two-sector Schumpeterian model of growth, in which a manufacturing sector produces the traditional consumption good and an agricultural sector produces food to sustain contemporaneous population. Agriculture demands land as an input, itself treated as a scarce form of capital. In our model both population and sectoral technological progress are endogenously determined, and key technological parameters of the model are structurally estimated using 1960-2010 data on world GDP, population, cropland and technological progress. Introducing random shocks to the evolution of total factor productivity in agriculture, we show that uncertainty optimally requires more land to be converted into agricultural use as a hedge against production shortages, and that it significantly affects both optimal consumption and population trajectories.

The Expansion of Modern Agriculture and Global Biodiversity Decline: An Integrated Assessment

“…According to the Intergovernmental Panel on Climate Change, which has collected together over a hundred projections of global GDP this century, real global mean GDP per capita will increase by 350 per cent between now and 2100 (Clarke et al, 2014). 1 Together these imply an expansion of the modern agricultural system in order to meet growing dietary needs and food preferences (Alexandratos and Bruinsma, 2012;Lampe et al, 2014;Lanz et al, 2017a). Not only do more mouths require more food, there is also a strong positive (if concave) relationship between income per capita and various measures of food consumption (Tilman et al, 2011).…”

Modeling Uncertainty in Large Natural Resource Allocation Problems

“…While these models are undoubtedly helpful for understanding the broad implications of uncertainty on the intertemporal allocation of land resources, they fail to account for the e↵ect of uncertainty in supply and demand drivers on the optimal allocation of land resources in the long run. Our study is perhaps most closely related to the recent work of Lanz et al (2017) who develop a two-sector stochastic Schumpeterian growth model with the endogenous allocation of global land use. They find, like our paper does, that optimal allocation of global land use requires more cropland conversion when the uncertainty in agricultural productivity is present.…”

“…They find, like our paper does, that optimal allocation of global land use requires more cropland conversion when the uncertainty in agricultural productivity is present. Lanz et al (2017), however, focused on endogenous population dynamics, labor allocation, and technological progress, whereas our paper is concerned about the endogenous allocation of multiple types of land use and corresponding land-based goods and services. Our paper also advances on methodological grounds by introducing a novel algorithm that overcomes the computational di culties of solving multidimensional stochastic land use models, which made Lanz et al (2017) significantly…”

Determination Of State Economic Activities - Developing Countries In East Asia & Pacific Using Classical Approaches

“…If this is allowed to continue, then it will worsen the economic condition of a country. Therefore, technology certainty is needed to maintain the preservation and adequacy of productive land in each country (Lanz, Dietz, & Swanson, 2018).…”