Hughes, et. al.: Long-Term Socio-Economic Modeling

Hughes, Barry. (2004). "Long-Term Socio-Economic Modeling". unpublished IFs working paper on the Frederick S. Pardee Center for International Futures website: Denver, CO. http://www.ifs.du.edu/reports.htm.

The purpose of this paper is two-fold: The first desire of the authors is to document the socio-economic sub model. The second purpose is to provide an analysis of how IFs can be used as a tool to understand social support systems (iii).

The paper beings by outlining the changing nature of social, economic and political interactions globally. These changes are referred to as globalization, and IFs is seen as a tool that will be helpful in understanding the possible effects of these changes. This introduction is brief.

The model is then introduced generally. Much of the data has changed since the original writing of this document (the authors refer to it as a living document).

An approach to understanding the socio-economic side of the model is put forth. I was not familiar with this approach. It is summed up in Table 2.1 (8). From this table. There are three key aspects of the socio-economic sub-model. These are demographic, goods and services and financial. These three columns can then be broken down into six rows: organizing structure, stocks, flows, key aggregate relationships and key agent-class behavior relationships.

The authors then briefly situate the IFs model within the broader modeling literature. Firstly, they say that it has characteristics of systems dynamics models (stocks and flows, etc.), but that it isn’t limited to systems dynamics. Then, they highlight agent-class interactions, but they claim that what they are not doing is micro-level modeling. There are three different systemic/structural elements to the model: the agent-class relationships, the market of goods and services (as created through the production function) and the financial flow element.

The SAM is then examined, and the IFs SAM is situated vis-à-vis the literature on SAMs. The authors contend that they differ from the SAM literature in 5 ways: 1.) the universality of the SAM representation in IFs; 2.) the connection of this universal SAM to the global financial system; 3.) there is a representation of both stocks and flows which is driven by a construction of the interaction of assets and liabilities; 4.) is a temporal addition through the connection of the SAM to the broader, long-term model of IFs; and 5.) additional sub-models that are horizontally tied to the SAM in the interest of long-range forecasting.

The IFs Preprocessor makes a brief appearance in this paper, as it is important to understand the mechanism of how data is translated and run through the model, sub-model by sub-model. The preprocessing begins cleaning and filling data holes. Then, it moves to calculate demographic data in age-cohort structures. Then it calculates both agricultural and energy numbers, both of that are used in the economic calculations. The economic sub-model is the next to be calculated.

Page 16 represents a helpful mini-legend of commonly used IFs subscripts.

Other key formulas used in the model are discussed briefly, as they will be important in the rest of this text.

Chapter 3: The Goods and Services Market Foundation

The IFs economic sub-model draws on two different modeling traditions: the dynamic growth model of classical economics and the general equilibrium model of neo-classical economics (20). The goods and services market builds on the production function and the demand market created by the Cobb-Douglas production function as well as the endogenously created MFP. This then is situated within a larger SAM. The goods and services market creates and produces supply and demand features for households, firms and governments as well as embedding the production function within six sectors of production.

Growth in the goods and services market responds directly to endogenous labor supply growth, endogenous growth the stock of capital as well as MFP.

In terms of equilibrium seeking, this aspect of the goods and services market is promoted through price changes by sector that attempt to reconcile supply with demand. “Prices respond to stock levels” (20). There are three mechanisms that IFs uses to maintain supply and demand: price-driven changes in domestic demand, price-driven changes in trade and stock-driven changes in investment by destination.

IFs is not an equilibrium seeking model in each year, but rather an equilibrium chasing model over time. This is similar to the GLOBUS model and the SARUM model.

The production function is established starting with a Cobb-Douglas function, and then building upon that based on the work of Solow in 57. Solow saw that much economic growth could not simply be addressed by additions of capital and labor, and introduced technology change. This becomes multifactor productivity in IFs. This concept had been exogenously modeled in previous models, but, with the work of Romer in 1994, it became endogenized in IFs.

Convergence is also a crucial aspect of MFP in IFs. There are four factors that can positively or negatively influence convergence. They are the following: the convergence base, knowledge creation and diffusion, human capital quality, social capital quality and physical capital quality.

After calculating value added for each sector, IFs goes on to determine gross production and intersectoral flows. This is done by imposing an exogenously determined imput-output matrix on the model.

Trade is the next critical component to be calculated (though there are other components calculated in the interim: labor supply, government demand, GDP at PPP, etc.). This is done by creating an international supply/demand matrix. Imports and exports respond to relative prices. On the production side, the export base and export ceiling is computed. The difference between trade levels and domestic prices is imposed on the model through standard elasticity numbers. Import demands are tied to final demands and intersectoral flows. These are responsive to changes in incomes and prices relative to elasticities.

The computation of stocks is then examined. As is noted throughout the literature, IFs is a general equilibrium seeking model. This, however, is not the full story. IFs is also referred to as a “chasing equilibrium” model because it does not look for market clearing behavior in any given year. Instead, inventories are the key for keeping the model from clearing in every year. Prices, on the other hand, drive the market towards equilibrium.

The paper then goes on to examine how consumption, expenditures, transfers and economic interactions are computed for governments, firms and households. This is accomplished and accounted for in the SAM structure. The actual details of this computation involves each agent/class to interact their needs/desires with material constraints. For example, government pension transfers are accounted for based on the size of the population cohorts over 65 coupled with the tax revenues derived from firms and households. Additionally, firms establish their levels of production and investment based on their supply of capital as well as the relative attractiveness of different sectors. Households also are responsive to their relative economic positions and are distinguished into skilled and unskilled, as well as either saving or consuming based on the relative levels of interest rates and prices. “The SAM structure in IFs is really a combination of an accounting system and an equilibrating system” (58).