We design, detail and test an approach which allows optimizing policy instruments to maximize welfare over results emerging from a Computable General Equilibrium (CGE) model. To technically enable policy optimization over a CGE model, we train a deep learning-based surrogate model that approximates the behavior of the CGE model. The final policy optimizing is then subject to the surrogate model. To show case our approach, we optimize the timing of emission abatement and a carbon tax recycling strategy for Tanzania from 2018 to 2050 with a detailed recursive-dynamic single-country CGE model. Our proposed approach and the provided code to train surrogate models for CGE models is quite generic, allowing its application to differently structured CGE models and associated policy instruments. Even though generation of the observation sample is computing time intensive, the surrogate model enables policy optimization not possible by using the CGE model directly. The trained neural network replicates the simulation behavior of the CGE model quite accurately with an average R2 of 99.99% over the outputs. Besides optimization, such a surrogate model representing the key input-output relations of a CGE model could also be easily integrated into other modelling frameworks.
Over the past fiveyears, the Global Trade Assistance and Protection(GTAP)Data Base has been expanded to include 16 additional individual African countries, while datasets for 10 existing African countries have been updated. Most of these efforts collaboration with experts from African National Statistical Offices and the Center for Global Trade Analysis (CGTA), alongside contributions from other partner institutions. As a result, the number of African countries represented individually in the GTAP Data Base has reached 42, enabling researchers and policymakers to conduct more detailed and policy-relevant analysis on priority issues facing African economies. This article documents the progress achieved so far and outlines next steps to address remaining data gaps for African countries in the GTAP Data Base. It also highlights key experiences, lessons learned, and challenges encountered during the process. By linking the work of statisticians and economists, the paper provides valuable insights for researchers, practitioners, and policymakers who rely on the GTAP Data Base for empirical analysis but may be less familiar with the underlying data construction process. Strengthening country-level coverage in the GTAP Data Base is essential to support evidence-based policymaking and unlock Africa's development potential.
This paper introduces MPSGE.jl, a free and open-source package in Julia that facilitates CGE model building from tabular definitions. The package's programmatically generated equations reduce redundant and repetitive code, errors, and development time. The built-in functions simplify troubleshooting, analysis, and reports. Embedding within a general-use scientific language enables streamlined integration with general-use functions and tools. The design combines computational efficiency with intuitive syntax and model formation. The open-source foundation allows for greater access, and user contributions to ongoing development. This work is a contribution to the open-science movement, with the aim of increasing access, robustness, transparency, and collaboration. We give an overview of how to use the package, its construction, and highlight some of its features. We include example models of different forms and scale as illustrations. First, we employ a simple toy model to introduce the basic structure. Then, we illustrate how the flexibility of MPSGE in Julia can facilitate functionality not possible in the GAMS progenitor. With a third example model, we demonstrate the package used at scale by evaluating tariff effects using a model with five household types for each U.S. state. We link to the package and its documentation for further reading and utilization.
Since the 1990s, there have been rapid increases in concentration ratios in many industries in the U.S., Australia and, we suspect, in other countries. Despite this, applications of GTAP continue to be based on pure competition or Melitz-style Large-Group Monopolistic Competition (LGMC). In either case, all firms are small, there is free entry, and industries make zero pure profits. Markusen challenges modellers to move to Small-Group Monopolistic Competition (SGMC) in which industries have high levels of concentration and firms are aware of the likely behaviour of their rivals. By making two generalizations of Melitz-LGMC specifications, we create a version of GTAP in which some industries are modelled as SGMC. First, we treat the demand elasticities perceived by firms for their products as variables. In our SGMC specification, markups over marginal costs, which depend on perceived elasticities, rise when these elasticities are reduced (in absolute terms) by anti-competitive practices. Second, we allow for sticky adjustment of the number of firms in an industry and simulate situations in which entry is blocked or partially blocked, allowing incumbent firms to make positive pure profits. As illustrated in our simulations, the emergence of pure profits has the potential to suppress real wage rates.
This paper provides an overview of the Global Trade Analysis Project (GTAP) Data Base, version 12 (hereafter referred to as GTAP 12). This latest version distinguishes 145 countries and 18 aggregate regions for 7 reference years (2004, 2007, 2011, 2014, 2017, 2019, and 2023) and details the annual value of economic flows, within and between economies, across 65 goods and services sectors at pre-and post-tax valuation. Central to this database are the bilateral trade and international transportation margin flows that link all countries/regions in the world. GTAP 12 also marks the first instance in which the land use and land cover (LULC) data, classified into 18 agroecological zones (AEZs), are incorporated into the standard database construction process, thereby resulting in consistent land information across all GTAP databases. This, along with the greenhouse gas emissions satellite data, will greatly facilitate the use of the GTAP framework in economy-wide studies of trade and environmental issues at the global and regional levels.
