Computable General Equilibrium Models for Policy Evaluation and Economic Consequence Analysis

Abstract

This chapter reviews recent applications of computable general equilibrium (CGE) modeling in the analysis and evaluation of policies that affect interactions among multiple markets. At the core of this research is a particular approach to the data and structural representations of the economy, elaborated through the device of a canonical static multiregional model. This template is adapted and extended to shed light on the structural and methodological foundations of simulating dynamic economies, incorporating “bottom-up” representations of discrete production activities, and modeling contemporary theories of international trade with monopolistic competition and heterogeneous firms. These techniques are motivated by policy applications including trade liberalization, development, energy policy and greenhouse gas mitigation, the impacts of climate change and natural disasters, and economic integration and liberalization of trade in services.

Climate Mitigation Investments: An Economic and Policy Outlook

“…Tariff hikes do not have a uniform effect on different sectors of the economy (Countryman & Narayanan, 2017;Sue Wing & Balistreri, 2018). The modelling done in this article is an attempt to validate the same.…”

“…For policymakers, practitioners and other stakeholders, it is vital to know such impacts, and therefore, this study measures the effect and magnitude of tariff changes across regions and industries and provides an answer as to how these translate into shocks to the economy through forward and backward linkages, with some sectors being more severely affected than others. The literature has mostly used CGE models for such types of assessment (Böhringer et al, 2020;Sue Wing & Balistreri, 2018).…”

Earnings, EITC, and Employment Responses to a $15 Minimum Wage: Will Low-Income Workers Be Better Off?

“…Thus, the model operates in the spirit of a circular flow of income and spending in the local economy during the past full year in which data are available. The models rely on elasticity parameters derived from prior economic research that express average industry- and sector-specific producer and consumer responses—elasticities—to changes in prices and income (Burfisher, 2017; Treyz & Stevens, 1985; Wing, 2004).…”

“…For example, Mitra-Kahn (2008) argued that CGE model builders could assuage many concerns by revealing elasticity values used in their models, as well as how these values are derived. The complexity of CGE models and the relatively unknown nature of some parameters often makes it difficult to trace results to specific features of their databases, input parameters, or algebraic structure (Wing, 2004). While REMI incorporates all current District of Columbia area macroeconomic data, it cannot run historical forecasts or control for historical variables.…”

Technology Strategies for Low-Carbon Economic Growth: A General Equilibrium Assessment

“…Our focus is on elucidating the tradeoff between the abatement and productivity benefits of improved energy efficiency and the opportunity costs of acquiring expensive relatively expensive technology-specific capital. To that end, we strive to keep the analysis as transparent as possible by choosing to represent the remainder of the economy in a simple and straightforward fashion, following the canonical model in Sue Wing and Balistreri (2014). In sectors where discrete technology options are not represented, the production function is specified according to the nested CES structure of the non-technology component identified by the dotted area in Figure 2.B.…”