construct a simple global computable general equilibrium (CGE) model with one sector in each region to analyze the effects of labor supply changes. Their database comes from disparate sources: 10 social accounting matrices (SAMs) mostly from the International Food Policy Research Institute (three for Sub-Saharan Africa, six for Latin America, and one for the Middle East and North Africa)<sup>2</sup> plus an aggregated input-output table for a high-income region derived from the World Input-Output Database.<sup>3</sup> This information is scaled to match the macroeconomic aggregates of the pertinent regions and linked by trade flows from the Directions of Trade database of the International Monetary Fund. The resulting SAMs do not distinguish producing sectors, comprising mainly of aggregate output, a single intermediate consumption, and one labor category for each region. In particular, the study does not make use of a more disaggregated and consistent database like that of the Global Trade Analysis Project (GTAP) of Purdue University, which has become standard for global CGE modeling.<sup>4</sup> Keogh-Brown et al. (2009) use a global model with sector detail to evaluate the labor supply and productivity effects of AMR. Unlike Taylor et al. (2014), they use a model with multiple sectors (GLOBE, see McDonald et al. 2007) and a globally consistent database of GTAP. Rather than use detailed meta-studies to determine the appropriate labor supply shocks, they use a sigmoid curve parameterization of the frequency of resistance to estimate the impact of reduced use of antibiotics. Also, their analysis relies on a static version of the GLOBE model which does not describe how the impact of AMR evolves over time.

To improve on the previous modeling efforts, the analysis of the global impact of AMR in this study uses GLOBE_DYN (McDonald et al. 2013), a multi-sectoral, multi-country and multiagent dynamic recursive CGE model that is consistent with neoclassical growth theory and uses GTAP data. A range of possible AMR incidence scenarios is compared to a baseline scenario, with the marginal effects of AMR measured as the difference in the evolution of economic variables between the scenarios with AMR incidence and the baseline. This ex-ante approach follows the rich tradition of using CGE analysis to examine policy issues in developing countries,<sup>5</sup> such as trade reform in India (Go and Mitra 1999), carbon tax and climate change in South Africa (Deverajan et al. 2010), or policies to speed up progress on the Millennium Development Goals (Lofgren et al. 2013). Such models and analysis are also increasingly applied to examine the forward-looking effects of complex global issues, such as the long-term effects of international trade agreements like the Doha Development Round (Hertel et al. 2009) or global migration (Ahmed et al. 2016b).

To analyze the effects of AMR on global poverty, the results of the GLOBE_DYN model are passed on to the Global Income Distribution Dynamics (GIDD), a World Bank global

> 2 Listed in Appendix C, _Ibid_ .

> 3 Ibid; Timmer (2012).

> 4 See <u>www.gtap.org</u> for more information.

> 5 See, for example, the survey in Devarajan and Robinson (2013).

3