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• W2528937578 abstract "Isono and Kumagai (2016) estimate the impacts of Association of Southeast Asian Nations' (ASEAN) Master Plan on ASEAN Connectivity (MPAC) and Economic Research Institute for ASEAN and East Asia's Comprehensive Asia Development Plan 2.0 (CADP) using a geographical simulation model (GSM). The paper provides an overview of the MPAC, and the CADP describes the GSM and its specifications, presents the estimation results, and concludes with some policy implications. The estimation uses the IDE-GSM, which captures changes in the geographical distribution of industries and population as a result of infrastructure development and trade and trade facilitation policy measures. In the model, firms choose the route and transport mode that minimizes costs in terms of money and time. To estimate the potential impacts of the MPAC and CADP, Isono and Kumagai take the differences in gross domestic products (GDPs)/regional gross domestic products between a baseline and an alternative scenario. The baseline scenario assumes minimal additional policy measures by 2015. The alternative scenarios assume different combinations of policy measures which are fully implemented by 2030. The estimation results reveal that both the MPAC and CADP can have positive economic impacts at the aggregate level, particularly when infrastructure projects are complemented by other measures that reduce NTBs and promote industrial development. However, the magnitude of this impact will differ greatly, both across and within countries. The results also underscore how different combinations of policy measures can produce different results in terms of economic growth and income distribution. Isono and Kumagai urge policymakers to be more cognizant of any possible trade-offs between growth and equity and proposes a more strategic approach to combining “projects for higher economic growth and projects for narrowing development gaps.” My first comment relates to short-run versus long-run impacts as determined by factor mobility. The overall analysis is described as “long-run” in nature, which is not surprising given the time horizons involved. An important determinant of the impact of infrastructure development in the long run relates to factor movements or reallocation of capital and labor across industries and borders in line with changing relative prices as a result of lowering of trade and transport costs. This is a key finding of Warr et al. (2010), for example, in relation to the impact of a bridge connecting Mukdahan in Northeastern Thailand and Savannakhet in Lao PDR. The absolute gain in welfare that arises in the long run is larger than the short-run impact by a factor of 22 in Mukdahan and 28 in Savannakhet, pointing to a huge difference between short-run and long-run impacts as a result of factor mobility. But in Isono and Kumagai, “international migration is prohibited” (p. 12), and it is unclear if capital reallocations are allowed. Ignoring these factor reallocations across borders is likely to significantly underestimate the total gains from infrastructure improvement, as well as its spatial and distributional outcomes. For the CADP estimates, the overall shock is even more complicated. The infrastructure scenario seems to assume road improvements in 2020 and not 2015, and details are sparse (MPAC is much clearer). The Non-tariff barrier (NTB) reduction scenario assumes yearly reductions from 2016 to 2025 for selected countries, but in terms of an improvement in global rankings, it is a little difficult to grasp in terms of magnitude. But then it is also noted later that for all scenarios, “Tariffs, non-tariff barriers and services barriers are changing on the basis of FTA/EPAs currently in effect” (Isono and Kumagai, 2016, p. 12).This is confusing. The special economic zone (SEZ) scenario assumes improvements in productivity for specific SEZ sites in CLMVs, but there are unexplained differences in the magnitude of the improvements and the years when these take place (i.e. 5% improvement in some sites in 2015, 5% improvement in others in 2020, and a 50% improvement only in Dawei, Myanmar in 2020). Clearly, the SEZ shocks explain a significant portion of the overall results for these countries, accounting for 78% in Cambodia, 51% in Lao PDR, 36% in Myanmar, and 46% in Vietnam, respectively, of the total change. Given their importance in explaining the overall result, some explanation for the assumed improvements in productivity is warranted. The analysis by Warr and Menon (2015) on SEZs in Cambodia, for instance, suggests that while the Phnom Penh SEZ has been quite successful in attracting new types of FDI, the others are still struggling to do so. But from page 24, all the SEZs in Cambodia that are listed (which is incomplete and missing quite a few) are all provided with the same 5% improvement in 2015 shock." @default.
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• W2528937578 date "2016-07-01" @default.
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• W2528937578 title "Comment on “Economic Impacts of Improved Connectivity for ASEAN: An Application of the Geographical Simulation Model”" @default.
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• W2528937578 doi "https://doi.org/10.1111/aepr.12149" @default.
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