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About RECI

The Developers

The web application for RECI was conceptualized and implemented by:

Alvin Simms PhD, Dept. of Geography, Memorial University
David Freshwater PhD, Agricultural Economics, University of Kentucky, OECD
Jamie Ward MSc, Dept. of Geography, Memorial University

ccwebworks, Department of Computing and Communications, Memorial University

David Cantwell Web Design and Development
Dale Conway Web Design and Development



RECI is an acronym for the “Regional Economic Capacity Index”.  RECI is based on regional economic concepts for the geographic study of the allocation of public services (i.e. health and education), industries, resources and retail business within a region. This approach does not provide an exact description of economic activity but rather identifies tendencies or trends. Within this context the important factors that contribute to the economic well being of a place are: [1] demography of the population and labour market, [2] employment opportunities and participation rates, [3] dependencies on primary or secondary industries, [4] the cost of transportation or the distance between communities within the region and [5] the accessibility to public services such as education and health care. In addition, other factors that influence labour markets and the regional economy are distance to larger regional centres, opportunities for employment and markets for products.

RECI is also linked with the functional integration principle whereby communities that are linked to employment centres should form a region. However, the constraint for this model is that the linkages are based on daily journey to work patterns and not weekly or monthly commutes. Although, the model acknowledges the potential impact of inter regional economies on the intra regional labour market it is the assessment of the intra regional labour market and economic well being that ultimately reveals the long term viability of a region and its ability to sustain an intra regional labour market.

The following sections will briefly describe the inputs to RECI and how these inputs are scored and interpreted.

RECI Inputs

The 5 main groups of inputs are: [1] Demographics (Labour Market), [2] Economic Structure, [3] Service Level, [4] Governance and [5] Spatial Location. There are 3 other and somewhat overlapping inputs and they are: [1] Labour Supply, [2] Labour Demand and [3] Income. These major groups represent composite scores based on multiple variables combined into a single relative score. The scoring is a combination of pluses (i.e. at or above regional average) and minuses (i.e. below regional averages) and in some cases the inverse score is reported. An inverse score is used when an input scores negative and the negative value indicates a good economic outcome or workforce utilization with a positive impact on the economy. For example, in some regions having a low number of people collecting EI may score negatively because it is below the regional average: in this case, the scores are inverted to make negative scores positive.  The objective function is to “sum” all the scores such that those having the highest relative score have a competitive advantage. The scoring methodology (to be published in 2012) is based on fuzzy set theory, index weighted overlay models and comparative advantage techniques. The RECI model is very flexible and additional “relevant” inputs can be added in future upgrades to the model.  RECI’s information is limited by the availability of data. Note inputs are used in all or one of RECI’s 3 main menu items, View Community Data, Pseudo Comparative Advantage and View Community Age Structure.


The Demographic indicator has 8 inputs and they are:

[1] Age Structure – this input measures the relative sizes of different age groups in the labour force against demographic growth models and this measure is a + indicator such that the higher the number the more viable the workforce.

[2] Age Structure Range – this indicator is used exclusively in the View Community Age Structure menu whereby a histogram of the community’s age cohort is plotted and compared against 3 demographic growth models (Low, Median and High). Given the observed demographic structure a comparative analysis is done with the 3 growth models and a diagnostic is provided on the condition of the community’s age structure.

[3] Participation Rate – measures the percentage of eligible people who are in the workforce. This is a + indicator and the higher the score the more attractive the labour market.

[4] High School Completion – is a + indicator that measures the percentage of the workforce that has a high school diploma and is associated with labour market quality.

[5] Total Population – the size of a community represents labour market capacity as well as the ability of the labour market to support local services.

[6]Working Age Population – represents the percentage of population between the ages of 16 and 65.  The higher the + score on this input suggests a more immediate employment potential for the workforce.

[7] Education Level – refers to the degree of educational diversity within the labour force. It is a measure of the degree of homogeneity between workers who did not complete high school, completed high school, finished college/trades school, earned a bachelor’s degree and those with graduate degrees.  A high + score indicates a well-balanced and trained labour market.

[8] Non University but Post Secondary – refers to the percentage of workforce that has a college or trades diploma.  This is an important factor for potential employers.

Economic Structure

Economic structure provides information on the stability of the local economy, the utilization of the workforce and the entrepreneur potential within the community as well as proximity to larger retail centres within the region.  The inputs used for the Economic Structure composite score are:

[1] Percentage Primary versus Secondary Industries – this input refers to the percentage of the workforce employment in resource extraction and manufacturing industries. The score is inverted for both the web display and the Economic Structure score.  It is acknowledged that while primary industries are a very important source of labour market income for the economy a low score in this model suggests an overexposure to resource availability and market volatility. This can lead to reduced stability in the labour force (boom and bust scenarios).

[2] Self-Employment Ratio – is a percentage of the labour force that is self-employed. Generally, it provides a measure of local creativity and internal growth.  Recent studies have demonstrated that entrepreneurship can be an important factor when assessing the viability of smaller rural communities.

[3] Employment Insurance Ratio – refers to the percentage of earnings in a community derived from employment insurance.  Again this score is inverted for both the model and the web application. Thus a low score indicates low labour force utilization.

[4] Distance to Retail Centre – this input is based on road distance and proximity to retail centres.  This score is inverted such that a high score indicates a community is proximate to a retail centre and a low score would suggest a more isolated community with limited access to services and employment opportunities.

[5] Three Largest Employers Share – is an indicator to assess the reliance of communities on its 3 largest employers. The initial calculation is based on the percentage of workers who are employed by the 3 largest firms. The final score is inverted and a low score represents potential instability and high risk in the event of an industry downturn or closure.


Within RECI, income is simply a measure of market base income and government transfer payments. The scoring is computed so that communities that score higher on income derived from market sources generally have higher workforce utilization. The two income inputs are:

[1] Market Income – refers to the percentage of a community’s income that is derived from market sources. Higher scores indicate a higher utilization of the workforce.

[2] Transfer Payment Income – this input is based on the percentage of a community’s income that is derived from government transfer payments.  The final scores are inversed such that the lower the score the lower the utilization of the workforce.

Service Level

Service level in RECI version 1.0 is constrained to 3 basic services distance to a post office, high school and hospital. The 3 inputs are described as follows:

[1] Distance to Post Office – a basic service for many communities is access to a post office and as population declines to a predetermined threshold the postal service is generally discontinued within a community. The proximity to a post office, for this version of the model, is viewed as a competitive advantage for a community. For those communities near a post office the score will be high and as the score decreases it indicates decreasing accessibility.

[2] Distance to High School – proximity to a high school is considered a competitive advantage and the scores are inverted so being near a high school will produce higher scores and lower scores mean lower accessibility to a high school.

[3] Distance to Hospital – proximity to a hospital not only provides access to health care but recent studies indicate that hospitals also generate significant spinoffs to a local economy. Therefore, communities near a hospital have a competitive advantage over those with lower or no accessibility.  Again the scores are inversed so that the higher scores indicate greater accessibility to a hospital.

Spatial Location

Spatial location is designed to incorporate geography in the model, particularly the distance factors that have an impact on regional economies. It is also acknowledged that communities can overcome locational disadvantages if they have a service or product that is unique to their region and the demand is high enough to mitigate travel distance or the cost of transportation. In the current version of the model, 3 distance factors are used to calculate the Spatial Location score and they are:

[1] Distance to St. John’s or Corner Brook – measures access to the province’s largest urban centres as well as all the amenities and opportunities associated with the centres.  It is well documented that smaller communities that are proximate to larger urban centres have a distinct competitive advantage over communities that are more distanced from these centres.  The scores are inversed and communities scoring higher have a competitive advantage as well as a higher degree of proximity to a large urban centre.

[2] Distance to Trans Canada Highway – this measure represents the degree of access a community has to the province’s primary transportation network and the opportunities that are associated with it.  The score is inversed and higher scores represent a competitive advantage and a higher degree of proximity to the Trans Canada Highway.

[3] Distance to Tourist Destination – measures proximity to provincially listed tourist destinations.  Nearness to a tourist destination identifies a potential to capitalize on spinoffs generated by tourists visiting the region.  The score is inversed and higher scores indicate a competitive advantage.


Governance in this application can be seen as a measure of the viability of intra-regional community councils and NGOs and whether these groups are proactive in intra-regional development.  The 4 factors used in this study are:

[1] Grants Received – the score is calculated from the value of grants that a municipality applied for and received from other levels of government. In the context of this model it is a measure of the degree of entrepreneurship associated with municipal governments.  Higher scores are associated with proactive governments.

[2] Elected Officials Turnover – this is a composite score that assesses whether a municipality participated in the most recent election and, if yes, what was the voter turnout. This score is used to assess voter interest in local issues. Higher scores indicate a high participation rate.

[3] Part of Multi Community Organization – the score is a sum of the number of multi-community organizations that a municipality participates in. The higher the scores, the more viable a region is for sharing services and other government sponsored development initiatives.

[4] Volunteer Organizations – is a measure of how proactive a community is in attracting volunteers from the existing workforce.  A high score on this factor indicates a high participation of citizens in their local government and local service and development agencies. Generally, communities scoring high on this factor tend to have both strong intra and inter regional socio-economic linkages with other agencies.


RECI is a work in progress. Version 1.0 is a static database and is generally a means to present some of the findings of our functional regions study.  In this version the user cannot add or delete indicators from the model or update the database. Future versions of RECI will be more dynamic: users will be able to integrate data from the Newfoundland and Labrador Statistics Agency Community Accounts as well as do population forecasts and determine the impacts of proposed economic developments as well as assess downturns in regional economies. In addition, the future versions of RECI will use Google Earth as a mapping platform so that spatial and temporal queries can be performed on the data. The ability to run spatial models for region building and transportation cost analysis will also be included. The current model is a decision support tool and should be used with other sources of information such as the Community Accounts web application.