Supplementary online appendices
“Climate Shocks and Migration: An Agent-Based Modeling Approach”
10-3-2015
A1. The Agent-Based Modeling Approach
Agent-based modeling, the core analytical approach for this study, is a technique for the investigation of the short- and long-term, multi-dimensional, micro- and macro-level consequences of climate change. First developed in the computer sciences and now commonly used in geography and other disciplines, agent-based models are relatively new in the demographic sciences, with some significant exceptions (An and Liu 2010; Aparacio et al. 2011; Billari and Prskawetz 2003; Billari et al. 2006; Bruch and Mare 2006; Kniveton et al. 2011; Macy and Willer 2002). Broadly, ABMs are simulations of a population of autonomous, heterogeneous agents that interact with each other and their environment according to a set of prescribed rules. The dynamic actions of agents at the micro-level and response to the behaviors of other agents and characteristics of their environment result in regularities or emergent patterns at the macro-level.
One key feature of ABMs is that they can create a laboratory for experimental studies of social phenomena for which researchers could never undertake true experiments with real humans. As a computational technique, they allow researchers to simulate population processes over a period of time, then re-simulate these processes with a specific change in one process while holding constant everything else about the model specification. This creates an experimental situation with a true control and a true treatment simulation on the same population. In our case, we use an ABM to examine outcomes of different experimental scenarios of weather events, which makes the model particularly valuable for research on the potential short- and long-term consequences of climate change when it is inappropriate, if not impossible, to wait for these consequences to play out over time. We combine this approach with experimental testing of theoretical perspectives.
ABMs also allow for the direct incorporation of feedbacks which are fundamental to the dynamism of human and ecological systems. These feedbacks are of two types. One involves endogenous relations among key variables: for example, the risk of migration depends on household assets, which in turn depend on prior migration and remittance behavior. The other type of feedback involves interaction among agents, or, the influences that neighbors have upon each other. With feedbacks, ABMs provide the ability to analyze the dynamics of an interconnected system over time, making it possible to find emergent trajectories that would not be possible with statistical regression.
A final benefit of ABMs is that a researcher can test the effects of theoretical scenarios, which can help isolate what is driving the results of a particular experiment. For example, removing a parameter or changing a particular rule may fundamentally alter the outcomes of the model.
Figure A1. Flowchart showing overall structure of ABM
Figure A2. Flowchart showing initialization process of ABM
Figure A3. Flowchart showing step-by-step process of individual behaviors in ABM
Figure A4. Flowchart showing step-by-step process of household behaviors for each plot of land owned in ABM
A2. Description of changes to village crop yields due to climate shocks in Experiment A.
As shown in Figure 4, both floods and droughts have immediate effects on village crop yields. Rice yields respond in a clear pattern where weather shock influence decrease rice yield. However, after the weather shocks are over and the climate returns to a normal-normal pattern each year, starting in year 17, rice yields from the flood and drought scenarios do not return to parity with the reference scenario. This is initially curious. In addition, cassava and sugar yields also display patterns that might not be initially expected. We explain these unexpected patterns here.
First, considering rice yields, the lack of full recovery is because some households switched land use in response to the dramatic conditions, converting rice paddies to cassava and sugar, and did not switch back, and also because many households became poorer and could not fully fertilize their crops.
With cassava, we find a similar pattern of immediately decreasing yields during the simulated periods of drought, flood, and variability, and rebounds in production after the climate returns to normal in year 17. However, as shown in Figure 4, drought conditions decrease total village cassava yield to a much greater extent than flood conditions. Notably, in all three of the climate scenarios, total village cassava yields after year 17 are higher than in the reference scenario. This is a result of farmers switching what they grow in response to the weather; when conditions return to normal, the newly converted land is productive with the new crops and there is no motivation to switch back.
Total village sugar yields behave differently than rice and cassava. As shown in Figure 4, they actually increase during all three of the climate scenarios. After the flood or drought subsides, total sugar yields experience a second and more dramatic increase after year 17. This is similar to cassava production after the climate normalizes and is caused by households switching from rice, and cassava, into sugar production.