0590-A1
A.A. Kubursi[1] and L.A. Gravelines
This paper describes a simple economic impact model that integrates financial data and socio-economic indicators in the hope of underlining the importance of basing the economic modelling exercise on reliable and detailed data. It is described within the context of forest-dependent communities. Economic impact analysis is modelled as a complementary tool to financial feasibility. It captures the economic impact of expenditures on investment projects and other activities at the community and sub-national levels, integrating input-output analysis and location theory.
Communities dependent on resource extraction have traditionally suffered from the "boom and bust" cycles of fluctuating resource prices and the availability and access to resources. These cycles were particularly pronounced during the 1980's. The 1990s have compounded the 1980 difficulties with a new set of structural challenges. The Canada - United States Free Trade Agreement, Goods & Services Tax, increased environmental concerns, industrial restructuring, and a host of new technological impulses have combined to alter the operating conditions and norms of the economy. It is not clear that it is possible to disentangle cyclical from structural influences. But it is equally clear that the two components of change are now operating simultaneously, reinforcing one another.
Too many communities ignore or dismiss the need for economic renewal and diversification until after difficulties have been encountered. Although community and government interests in, and support for, economic renewal usually increases after a closure or a layoff, it is generally more difficult to kick-start the economy under these conditions of uncertainty.
Generally there are two major components to accelerating community economic renewal and diversification. The first involves creatively identifying, exploring and evaluating existing and potential economic opportunities to focus on the ones which have the highest probability of expansion and success. The second component consists of the parallel and on-going initiatives the community can undertake to create the enabling environment and conditions conducive to economic expansion and entrepreneurship. Both must be coordinated into a comprehensive and consistent work plan. This coordination process necessitates a comprehensive, up-to-date database on the local economy's economic base, interregional and international markets. The availability of an analytical economic model capable of processing available information and detailing alternative configurations of the economy as well as measuring the impacts of complementary and competing activities is critical to the success of the coordination and renewal effort. Perhaps less appreciated is the role of reliable, detailed and consistent data in the renewal effort and economic and social rehabilitation.
A variety of approaches have been used to gauge the developmental impacts of industrial expansions (contractions). These approaches can be grouped into two broad categories: comparative equilibrium models and cycle models. The comparative equilibrium models compute two static equilibria corresponding to before and after industrial expansion (contraction) and use the differences between the two equilibria as indicators of developmental impacts. The second basic modeling approach (Lowry cycle models) involves a cycle in which new employment opportunities in manufacturing or other basic industries lead to increased population and new demands for housing and services, which in turn lead to further employment. The reverse may also be true. This approach emphasizes stages, which may or may not be part of a smooth growth process. Rather, growth often proceeds in spurts with the next stage appearing only after threshold levels of available services, demand and manpower are reached.
The purpose of this paper is to present a simple economic impact model that integrates financial data and socioeconomic indicators in the hope of underlining the importance of basing the economic modeling exercise on reliable and detailed data. The recent revelations about creative accounting procedures followed by some corporations serve to underscore the need to examine carefully the typical financial flows and to organize them in a meaningful set of reliable input data for the model to process into acceptable results.
There is a general tendency to accept aggregate input data uncritically and to assign them to particular commodities and activities that may not be the appropriate ones. This tendency is rooted in two basic problems that have not hitherto received sufficient attention. First, the use of highly aggregated data makes it difficult to disentangle (to create unbundled) commodities that can allocated unequivocally to their respective classes. Second, there is the inherent difficulty of working with accounting aggregates that do not reflect actual transactions or in some extreme cases represent fraudulent transactions. The typical structure of an impact model with broad commodity classifications does not permit the user to gauge the sensitivity of the results to the problem of bundled commodities and/or the problem of accounting constructs that involve accounting niceties but no real activities.
These issues will be examined within the context of a specific forestry problem, namely forest dependent communities and measuring the extent and consequences of dependence.
The forest resource has fostered the formation of single industry communities. This is not different from any other natural resource located in remote areas. To the extent that natural resources are found and developed far away from urban centres, and to the extent that the technologies used in harvesting and processing are weight and size-reducing technologies, the economic activities involved in the development of natural resources tend to be clustered around the resource and away from higher order central places.
Communities with narrow economic basis are very vulnerable to fluctuations in the demand for the narrow products that these communities produce. Given that much of Canada's resources are exported, these communities are susceptible to foreign business cycles, particularly those in large industrialized western economies. Demand shocks are augmented by short term and long term supply shocks.
When the fortunes of the dominant industry in the community wanes, the entire life and stability of the community suffers. Fluctuations in the dominant (sometimes single) industry are transmitted throughout the entire economic structure of the community as other sectors are generally totally dependent on it. The share of the dominant industry in total community employment is often a poor estimate of its importance and role in the community.
At present there does not appear to be a readily and generally acceptable method for identifying the extent of dependence of a community on its dominant industry. Equally important is the absence of an explicit definition of the term "community dependence". The literature often confuses dependence with the share of the export base of the community. Tiebout (1956), Pleeter (1980) and Richardson (1985) classified communities as dependent on a particular industry if that industry comprises a significant portion of the direct export or economic base. Economic base theory is rooted in the notion of the basic sector. The latter comprises any activity that derives its income from sources, which are external to the regional economy. Exporting industries are the engines of growth of the community. The non-basic sectors produce only to support the basic sectors. While basic sectors generate income, non-basic sectors recirculate income.
Tiebout (1962) was the first to conceptually develop a straightforward method for measuring the economic base of a community. It involves a tally of the goods leaving the community. The value of these goods (exports) will be a measure of their contribution to the economy. More recent measures involve the use of the location quotient. This measure is based on the implicit assumption that if a community is highly specialized in an activity relative to the national or provincial average, then that portion of that industry's activity above the average is considered to be export activity. The accuracy of this technique is in doubt. There are four major problems:
There may be no national exports or indeed the nation could be a net importer or it could be self-sufficient in that industry. In either case the location quotient will under (over) estimate the export base.
Consumption patterns differ across regions and between a region and the nation. The location quotient assumes that consumption patterns are identical across regions.
To the extent that labour productivity within an industry differ across regions, differences in the location quotient may be more due to differences in productivity than in export performance.
Aggregation of heterogeneous commodities into homogeneous products undermines the accuracy of this measure.
Dependence is a relative a term, and can seldom be defined precisely. If there is a socioeconomic dependence of a community on the forest sector, it is a certain degree of dependency. Different countries appear to apply different definitions. For communities of 9,999 or less inhabitants, a Canadian Forest Service report by Pharand (1988) defines a community forest sector dependent if the percentage of the labour force in the SIC groups devoted to the forest sector was 30% or greater. For the purpose of implementing the Forest Management Act of 1976, a dependent community has been defined by the USDA Forest Service as one where (1) primary forest products manufacturing facilities account for 10% or more of the local community work force, and (2) national forest timber has accounted for at least 30% of the annual timber supply in the last five years (Waggener, 1977).
There is an extensive literature on salvaging the location quotient as an unbiased estimate of the community economic base. White et. al. (1986) and Fletcher et. al. (1991) have presented imaginative attempts to define forest dependent communities in Canada using adjusted location quotients. The adjustments revolve around using disaggregated industry data, adjusting for productivity and consumption preference differences across industries and regions and segregating local dependent industries from basic industries. These adjustments contributed towards a more profound measure of dependence, but did not resolve the original problem of relying on one single indicator to portray what is essentially a complex socioeconomic issue.
Two dependency ratio measures were developed and used in this study to broaden the perspective of the measurement. The first attempts to measure the excess of local employment in a particular basic sector over the provincial average. To the extent that the community shows a higher share of employment in basic sector j than the province, the community is said to be so much more dependent on this sector than the province. This is still in the general framework of earlier work on dependent communities alluded to above.
where
Dj = dependency ratio for sector j
Ej = provincial employment in sector j
Erj = community employment in sector j
The share of direct employment in sector j is generally not an appropriate measure of the extent of dependency and importance of sector j in the local economic base. The closure of a mill does not only deprive a community of the direct employment in the mill but would involve contractions in other sectors linked indirectly to it. This is why we present the total added employment in the community as a multiple of the added direct employment in sector j. A value of say 2.0 for this multiplier suggests that the community derives one job outside sector j for every direct job in sector j. This cannot be accomplished without a broad and comprehensive community impact model of the type discussed below. The total impacts and not the direct impacts are the true measures of the community dependency on the forest sector.
It is our contention here that these measures of dependence are very useful but need to be complemented by other measures that quantify the extent of linkages among sectors and activities. This we argue can best be ascertained within a fully integrated model that brings together economic, social and financial data.
Impact analysis provides a quantitative assessment of the economic consequences and secondary effects of economic activity and measures. To the extent that impact analysis is capable of identifying second order effects of economic projects and programs that are not typically identified and gauged by qualitative analysis, it provides a framework for analyzing alternatives.
In a way impact analysis concentrates on a number of macroeconomic measures. These are beyond the microeconomic assessment of feasibility. Rather, impact analysis should be considered as a complementary tool to financial feasibility. It goes beyond the financial aspects of a project and program to the likely amount and types of jobs that are expected and to the changes in the tax base of the community as new activities come on stream or disappear.
There is a general and pervasive tendency to adopt and to concentrate on the multipliers of impact analysis. This is fundamentally flawed. First, these multipliers are the end result of impact analysis and not the initial tools. Second, these multipliers, if they exist, are specific to an initial weighting of the expenditure stream associated with a particular activity. They are not transferable and or transportable. They apply to that particular situation only. A different vector (composition) of expenditures would generate an entirely different multiplier.
Communities face alternatives. Resources are scarce and choices must be made. Impact analysis is an efficient tool in aiding in the evaluation of alternatives. It is one such tool of many. A multiple social accounting framework will always include impact analysis but it is not restricted to it.
The impact model used here captures the economic impact of expenditures on investment projects and other activities at the community level and the provincial level, integrating input-output analysis and location theory.
Generally, the economic impact of activities is measured from the demand side by considering the expenditures associated with the activity in the local area. Only rarely has this analysis been made from the supply-side by considering the operations of affected establishments. The system adopted here measures impact from both sides -- the supply and demand sides. Our main motivation for such an emphasis on the supply-side is based on findings in several applications that the two sides may be made to reconcile to a very small difference and the two sides present different perspectives of impact results. The demand side concentrates on expenditures of consumers, businesses, or of foreigners (exports) and as such it provides a good vehicle for measuring the differential impacts by sector (type of expenditure-machinery, construction, etc.), by origin of expenditure (Ontario, Other Provinces or foreign countries). Demand side analysis, however, does not allow the analysis of impact on local businesses by type of business, by location (central vs. non-central), by ownership (corporations, partnerships, single proprietors), or by size of business (top quartile, bottom quartile, etc.). This type of information and analysis is of fundamental importance to the development of a policy strategy capable of assessing sensitivity of different sectors and businesses. Only supply-side analysis can provide these results.
It is not sufficient that we have a supply-side approach, perhaps more important is the nature of the integration of the supply-side approach into the impact model. Frequently we find the financial feasibility analysis as a separate segment of assessing a project, industry and activity that is totally unrelated to the impact analysis or any other relevant analysis of these projects and activities. This separation is untenable and rather costly. First, impact analysis is meaningful of sustainable projects whose financial viability is assured. There are indeed exceptions to this rule when social value (benefit) exceed private benefits sufficiently to over compensate for the private losses. But the rule is still valid; impact analysis is more meaningful of projects that are feasible and viable. In addition, a thorough financial analysis will create the appropriate structure of inputs that can be used to feed directly into the impact model. This seamless integration is also beneficial in forcing a systematic interface between financial analysis and impact analysis and the use of firm and project specific data in the two analyses bringing the accountants and the economists within a common platform.
In analyzing economic impact, it is inevitably the case that policy-makers are concerned with the effects of a policy change or a decrease in a given activity - on individual households, industries, firms, and regions. Activity or policy changes are relatively abstract when viewed in terms of their aggregate impact, and estimates of aggregate effects are consequently not very relevant. It is consequently highly desirable to estimate what the impacts of a given activity or change will be on a disaggregated basis that focuses on effects on particular industries or regions.
The input-output analysis adopted in this study is a cost-efficient and effective method of assessing impact on regions and industries. Besides, by disaggregating the analysis to this extent, by integrating financial and input-output models can provide a basis for obtaining reliable estimates of the aggregate effects on total employment and other macro-variables which are potentially more accurate than if estimated wholly at an aggregate level or independent of tying them to the exact financial performance of the firm.
The motivation for using input-output models here is thus both an interest in disaggregated estimates of actual, reliable and operating financial flows and a potential improvement in the accuracy of aggregated forecasts.
It is also motivated by our desire to outline the local impacts of projects and activities. Input output analysis provides a convenient method for relating the local economic base to the provincial and national bases but only to the extent that the specific local and industrial details are gauged.
IFAM is more than an economic impact model. Rather, it strives to model the socioeconomic processes of a community anchoring the analysis on firm-specific and actual marginal data away from the average aggregates where local details are subsumed and neglected. Besides, issues of dependency on an industry, the prices of houses, the average wage, the local tax base and the wealth-income structure of the community are critical components of the system.
Fletcher, S., W. White, W. Phillips and L. Constantino. (1991). An Economic Analysis of Canadian Prairie Provinces' Forest Dependent Communities. Forestry Canada, Project no. 91-05.
Pharand, N.L. (1988). Forest Sector Dependent Communities in Canada: A Demographic Profile. Labour Market Development Branch, Canadian Forestry Service, DPC-X-23.
Pleeter, S. (1980). Methodologies of Impact Analysis: An Overview. In S. Pleeter (Ed.), Economic Impact Analysis: Methodology and Applications. Martinus Nijhoff Publishing.
Tiebout, C.M. (1956). Exports and Regional Economic Growth. 64 (2), p.160-169.
White, W., B. Netzel, S. Carr and G.A. Fraser. (1986). Forest Sector Dependence in Rural British Columbia, 1971-1981. Pacific Forestry Centre, Canadian Forestry Service, BC-X-278.
Waggener, T. R. 1977. Community stability as forest management objective. Journal of Forestry 75 (11): 710 - 714. Economic Development, New York.
| [1] Professor of Economics,
McMaster University, Hamilton, Ontario, Canada L8S 4M4. Email: [email protected] |