microsimulation framework with a database that draws on harmonized household surveys with consistent definition of labor skills based on education for 104 countries(Bourguignon and Bussolo 2013 and Cruz et al. 2017). The integration of global CGE and microsimulation analysis follows several recent applications. For example, Ahmed et al. (2016a) examine the significance of SubSaharan Africa’s demographic dividend for its future growth and poverty. Devarajan et al. (2015) stress-test Africa’s recent growth and poverty performance. Indeed, our analysis indicates that the poverty angle is an important factor: AMR will result in a noteworthy increase in extreme poverty due to the disproportionate negative GDP impact of AMR on low-income countries.

The analysis covers multiple impact channels, including others not considered in earlier research. First, it takes into account the microeconomic assessments by Taylor et al. (2014) of possible human health costs in the form of increased morbidity and mortality. Second, it expands the analysis in several ways to consider lessons learned from assessments of infectious diseases based on simulation modeling and other methods. Specifically, the analysis also considers: an additional impact on labor productivity as in the case of HIV/AIDS (Kambou et al. 1992; and Arndt and Lewis 2001) and influenza (Keogh-Brown et al. 2010; Burns et al. 2006; World Bank 2006; and McKibbin and Sidorenko 2006); increased morbidity and mortality in the livestock sector, leading to lower productivity and lower supply (Laxminarayan et al. 2015); rising demands for health care due to AMR (KPMG 2014), leading to an increase in public health spending that is financed by a lump-sum tax on households to maintain fiscal balance; and possible restrictions on global food trade arising from a “fear factor,” represented by an increase in trade and transport margins, as in the recent Ebola epidemic in West Africa (World Bank 2014).

The focus of the paper is the estimation of the potential impact of AMR on the world as a whole and four income regions defined by the level of per-capita income (following the classification of the World Bank.) The income regions include countries belonging to low-income, lower middle-income, upper middle-income, and high-income groups. The list of possible policy programs and interventions against AMR are many, and it is beyond the scope of this paper to cover them in detail. O’Neill (2016) and World Bank (2017) provide excellent reviews. In fact, each infectious disease that is likely to be affected by AMR (such as HIV, tuberculosis, or malaria) will need its disease-specific support strategy (see, for example, Borowitz et al. 2003, Baris 2004, and Lindsay et al. 2004), in addition a set of AMR-specific (e.g., improved rapid diagnostics, new antibiotics for short term, and combined treatment options, etc.) and AMR-sensitive multi-sectoral policy measures and interventions (e.g., expanded health coverage, improved antibiotic stewardship, and water and environment management as discussed in World Bank 2017). Even so, our low case can be interpreted to correspond to an effective outcome of policy measures taken today and in the future; the high case for lack of policy effectiveness.

In what follows, we estimate the global impact of AMR with the features described above: a well-defined global CGE model that has dynamics and makes full use of the GTAP database, the inclusion of both microeconomic assessments on health and other channels of shock, and analysis 4