The SLEM Model as an Assessment Method for Local Goods’ Competitiveness

In this paper we develop and present the SLEM model created to measure the market potential of local goods supplied by the entrepreneurs of the Cserehát region, one of the most deprived regions of Hungary. The SLEM model evaluates goods along four dimensions: supplier conditions, labour conditions, (professional) embeddedness, and market conditions. The four pillars were measured with factors that are easily accessible, and so the evaluation can be repeated with other regions as well. In case of Cserehát, we have identified the following five groups of local goods with the greatest potential: animal husbandry; honey; spices and pharmaceutical crops; nature tourism; and mineral water.


INTRODUCTION
The development of a region and the quality of life of its population in various dimensions -but primarily in an economic dimension -depend on the region's competitiveness in different markets. Especially important is the availability of its 'marketable' products and a high demand for them. Social innovation and community resources are expected to support 'local' performance that can considerably contribute to achieving social and economic goals set by the region.
A question arises, namely, what methods are to be applied and what factors are to be considered, investigated and evaluated in order to identify the economic weight points, products and services that could enhance the desired development and competitiveness of a region. In addition, it is also critical to decide what methodology is to be used for investigating the marketability of a specific 'local' product and service.
In order to answer the research questions listed above, a research study was conducted within which a model was created for identifying the range of products and services that could be of determining importance for a specific region. In addition, model calculations were also made, since our model was applied to evaluate Cserehát, a highly deprived region suffering from numerous disadvantages. In a region like this, the development of carefully selected products and services may enhance economic development of the region's social and economic environment, considerably improving the welfare of the people living there and resulting in additional favourable impacts.
A thorough literature review was conducted and a secondary study was carried out, which provided a basis for gathering, processing and summarising the national and international literature relevant to this research study and for elaborating the methodology that could be applied for performing investigations related to the marketability of any product. The secondary research was also based on the available national and international literature, but with a special emphasis on market trends. Reports on research findings, pilot projects, databases, statistical publications and informative publications of professional associations were used as sources for this research study. The most valuable findings available from these sources were gathered, selected, analysed, evaluated and summarised. Finally, the range of products to be researched was identified.
The SLEM model was created within the framework of the T-model project, concentrating on the social entrepreneurship opportunities of deprived regions. This special issue includes papers presenting research carried out on similar issues: sustainable enterprise models (Illés 2016); sustainable accounting (Demény & Musinszky 2016); establishing and operating social enterprises (Várkonyi 2016); the place of public works in the employment model of the Cserehát region (G. Fekete 2016); and route-based tourism product development (Nagy & Piskóti 2016).

LITERATURE REVIEW
Measuring competitiveness is a complex task. According to Losoncz (2004), there are more than 10,000 definitions of competitiveness in use. A widely accepted definition that can be equally applied to micro-, mezzoand macro-level competitiveness defines the phenomenon as "the ability of firms, industries, regions and nations to permanently generate a relatively high level of income, and sustain a relatively high level of employment, while competing with international (global) competitors" (Lengyel 2000, p. 43). A similar definition was adopted by the US Competitiveness Council: "the ability to produce goods and services that meet the test of national and international markets while citizens earn a standard of living that is both rising and sustainable over the long run" (OECD 1997, p. 35).
Longman's Advanced American Dictionary (2000) simply states that competitiveness is "the ability of a company or a product to compete with others and the desire to be more successful than other people". If this ability broken down to business functions, the competitiveness of the firm can be interpreted as its ability to do better than the competitors in sales, market share, and profitability (Lall 2001). Szerb et al., who recently conducted very comprehensive competitiveness research focused on Hungarian firms, define small enterprise competitiveness as "a closely interconnected system of competencies such as human capital, financing, cooperation, offered product, administrative routines, competition strategy, applied technology, marketing, internationalisation, and online presence, that enable the firm to efficiently compete with other businesses, and to offer products that are valued highly by the customers" (Szerb et al. 2014, p. 8).
Although some prominent experts claim that competitiveness primarily lies in products and businesses (e.g. Krugman 1994), a more aggregated level of approach also makes sense. Regional competitiveness can simply be interpreted as a sum or combination of competitive firms, but on mezzo-and macro-levels other factors have an important impact as well. Such conditions as a business-friendly economic environment, productivity, or high level of education are all important components of competitiveness (Liargovas &Skandalis 2008, p. 5). Cooperation links among businesses within a region is also significant; competitiveness is also defined by the extent to which a region is called a "learning" region, or an "innovation system" (OECD 1997, p. 36).
We built our own model on Porter's five forces (Porter 1979) and his diamond (Porter 1990) model. Porter is another expert who focuses on product-and industry-level competitiveness, although he does not deny the importance of institutional factors. He accepts that the government is a factor that affects the five forces, for example (Porter 2008). The five-forces model identifies the key components of the competitiveness or attractiveness of an industry in the following five factors: threat of new entrants: profitable industries yield high returns that attracts competitors; threat of substitutes: good substitute products from other industries can push customers to switch to alternatives; bargaining power of buyers: the high bargaining power of buyers can push prices down over time; bargaining power of suppliers: suppliers of parts, materials, labour, or services can push up the price of inputs, and shrink profits if they have high bargaining power; industry rivalry: high levels of competition creates the incentives for innovation in the industry (Porter 1979).
Porter published his diamond model (1990) to answer the question of why certain industries rose in certain regions. The diamond model therefore is more related to regional/macroeconomic competitiveness. By taking a look at cluster development in ten trading nations, it names four main determinants. They may be influenced by the government or some other random conditions: factor conditions: including human, knowledge, physical, and capital resources, and infrastructure; demand conditions: competition levels and the sophistication of customers, which pressure firms to innovate faster; related and supporting industries: these industries not only provide cost-effective inputs, but they also help in the innovation process of the final product offered by the sector; firm strategy, structure and rivalry: rivalry, as in the five-forces model, creates incentives to innovate, and well-set strategies help in exploiting competitive advantages (Porter 1990). Porter excluded natural resources-driven industries (e.g. the oil industry) from his analysis. His decision is understandable, but given that our research is focused on a mostly agricultural region, natural resources will have to be considered as well. The five forces and the diamond model can be integrated. The integrated model shows the interconnections among the factors of the two models. The bargaining power of suppliers affects factor conditions, while the bargaining power of buyers affects demand conditions. Supporting industries, on the other hand, influence the intensity of competition and rivalry (Némethné 2010).

Figure 1. The pyramid model of regional competitiveness
We end the literature review by presenting Lengyel's pyramid model (2003). The pyramid model interprets competitiveness at a regional level. Given that we focus on the competitiveness of goods of a certain region, the categories introduced by Lengyel are relevant in our analysis as well. Lengyel agrees that the ultimate goal is to provide relatively high standards of living for the people living in a certain region. The standards of living are determined by factors that Lengyel splits into three categories: fundamental concepts, fundamental factors, and success factors (see Figure 1). Competitiveness can be measured through the fundamental concepts: labour efficiency, income, and employment. Fundamental factors are factors that influence competitiveness in the short run. The competitiveness of the region can be boosted by wisely managing the fundamental factors. Success factors, on the other hand, have a long term influence on competitiveness, but according to Lengyel they cannot be changed by the decision makers (Lengyel 2003).

Building the SLEM Model
Based on Porter's and Lengyel's concepts we have built the SLEM model used to measure the competitiveness of goods and services coming from the Cserehát region. We built the model around three main criteria: it should comply with the most respected theories of the field; it should only have a complexity level that enables us to highlight the most important influencing factors; and it should be realistic in the sense that it can be used for primary analysis. Given that the ultimate goal is to ensure a relatively high level of income, and standards of living, we have chosen four pillars of competitiveness: Supplier conditions: the ability of businesses producing a certain good or providing a certain service to rely on local supplier networks. An important aspect of competitiveness is that the products compare favourably with those of international competitors. One such group of internationally competitive products is "Hungaricums". SLEM focuses on Hungaricums with its supplier conditions category. Labour conditions: the ability of businesses to rely on qualified labour available in the region. The Cserehát region is characterised by high levels of unemployment, and at the same time by the shortage of qualified labour. Developing businesses that require labour with the skills and competencies that are locally available is therefore a key to the region's future growth. Embeddedness: the ability of businesses to rely on their partners and other stakeholders from the region to engage in cooperation focused on innovation. Competitiveness is greatly boosted if businesses can form networks with each other, and with other stakeholders. Although networks can be artificially Economic structure; Innovation culture; Regional accessibility; Labour quality; Social structure; Decision centers; Quality of environment; Social cohesion of the region Success factors created as well, usually the tradition and culture of cooperation in a given industry is the one factor that decides if a network is functional or not. Market conditions: the ability of businesses to rely on sound and high quality market demand. This final factor is self-explanatory; income may only be generated if the products are sold. SLEM, the name of our model, is derived from the abbreviations of the four above factors. These factors are quite close to the ones found in Porter's diamond model. S for Supplier conditions is the equivalent of related and supporting industries (and it is also connected to the bargaining power of suppliers, an element of the fivefactor model). L for Labour conditions is an element of factor conditions (although factors like capital or physical factors are also included in the diamond model). E for Embeddedness is a representation of Porter's firm strategy, structure, and rivalry factor. M for Market conditions is identical to Porter's demand conditions. Lengyel (2003) would put all four elements of SLEM into the success factors category (see Figure 1). In other words the SLEM model ranks local products according to the extent to which they correspond to the long term success factors of a region.

Operationalising the SLEM Model
Finding indicators to properly measure the SLEM factors is not easy. Some of the most important criteria for selecting an indicator are the following: relevance: the indicator measures exactly what the model means by a certain factor, and changes in the indicator value only occur when there is a change in the characteristics of the factor as well; easy to measure: the cost of producing the indicators should be low; easy to access: in case the measurement is done by a third party, accessibility of the indicators is very important; reliability: if possible, indicators with objective measurements should be given priority; strictly monotonic: only those indicators can be used for reliable measurement whose value change can be interpreted consistently; e.g. GDP per capita is consistent, since an increase always signifies a positive change; the calories consumed per capita is not strictly monotonic, since an increase canhave a negative effect after reaching a certain tipping point; wide scale: the indicators should apply to a wide range of industries or countries, so that the analysis can be repeated in many circumstances. The indicators chosen for the measurement of the SLEM model are described below. They fulfil five of the above criteria completely, and there is partial correspondence between the relevance criteria and the chosen indicators. The relevance could be increased if custom-made statistics were created through surveys. This, however, would make the measurement very expensive and narrow in scale. 1. S -Supplier conditions: to measure the supplier conditions a simple indicator was chosen that only has two values. If a given business can be attached to the value chain of a product that can be found on the list of Hungaricums (CHV 2016), the indicator has a value of 1. In any other case it has a value of 0. This measurement method fulfils the criteria of being easy to measure and easy to access, the indicator is obviously strictly monotonic and is wide scale. The relevance is questionable, since a number of other indicators could have also been chosen, each of which would have had to been measured by us. Simplicity was favoured over higher relevance here. 2. L -Labour conditions: to calculate the indicator for labour conditions, the regional statistics database of the Hungarian Central Statistical Office (KSH 2016a) was used. The latest available data belongs to 2011. Since district-level employment data is provided, three districts were chosen (Edelény, Encs and Szikszó) to represent the Cserehát region. Most of the settlements of these three districts indeed belong to Cserehát, therefore the distortion is only minimal. Sectorial employment numbers (the number of employees working in (1) agricultural and forestry, (2) industry and (3) services), and total employment numbers are both accessible. The ratio of the sectorial and the total employment numbers is used as the indicator for labour conditions. This way the indicator value of vegetable production for example, is the share of people working in agriculture compared to the total number of people employed. The relevance of this indicator could be improved if employment data were produced in a more detailed structure. 3. E -Embeddedness: a similar measurement was used as for labour conditions. The regional statistics database (KSH 2016a) offers district-level data on the number of registered enterprises. 2013 is the year with the latest available data. The ratio of registered enterprises operating in (1) agriculture and forestry, (2) manufacturing, (3) the construction industry, (4) catering and hospitality, or (5) health care services, and the total number of registered enterprises is the indicator for embeddedness. The indicator value for rural tourism for example is calculated by dividing the number of enterprises operating in the catering and hospitality industry by the total number of enterprises. This indicator has the same advantages and disadvantages as that for labour conditions. Thanks to the more sophisticated structure of the enterprise data, however, its relevance is stronger.

M -Market conditions: the System of National
Accounts section of the Hungarian Central Statistical Office's database (KSH 2016b) was used to calculate this indicator. This database offers data on the distribution of Hungarian country-level household consumption. We calculated the average rate of change of household consumption between 2009 and 2013 (the latest available data) in the following areas: (1) alcoholic drinks; (2) food; (3) housing products and services; (4) non-alcoholic drinks; (5) tourism. The change rates were then normalised using the minmax method (see formula (5) presented below), and the normalised value was used as the indicator for market conditions. Yet again, this indicator has exactly the same advantages and disadvantages as for E.
By calculating the average of the four indicator values, an index can be derived that has a minimum of 0 and a maximum of 1. Values near zero indicate a low potential; values near 1 indicate a high competitiveness product in the Cserehát region. Since no weights are used, the four factors are considered to have the same relevance in determining the competitiveness of a product. The formulae used in the calculations are the following: (1) S = 1 or 0, depending on the type of the product

Testing the SLEM Model
By applying the methodology and using the statistical data related to Cserehát, this study determined the subcomponents of the index measuring the competitive advantage potential of the region under investigation. After summarising the obtained results related to twentyfour products, the consolidated SLEM index was calculated.

Supplier Conditions
As for the supplier potential, seven out of twenty-four products were identified as 'superlative products', namely Hungaricums (fruit palinka, grape-pomace palinka, mineral water, nature tourism, spices and pharmaceutical crops, honey and livestock) that could be produced, supplied and marketed. People engaged in these activities could act as producers, suppliers or even service providers and gain a considerable competitive advantage. Table 1 shows products that could become Hungaricums in the region. Food products from fattened geese Source: own elaboration

Embeddedness
In the methodological description, the share of enterprises in the sector of national economy compared to the total number of enterprises was measured in the professional embedded sub-index in the Cserehát region. As many as five sectors of national economy were focused on the basis of which all twenty-four products can be classified into groups. The sectors are as follows: 1. Agriculture, forestry and fishery 2. Processing industry 3. Construction 4. Accommodation and food service 5. Human health and social work.
The total number of registered enterprises in the districts of Edelény, Encs and Szikszó amounts to 8,784. The enterprises operating in the five sectors of economy under investigation account for 5,094 and 668 of these enterprises are partnerships. Their distribution by economic sectors is provided in Table 2. Based on the sub-index methodology, the professional embeddedness is measured by comparing the number of enterprises in the national economy with the total number of enterprises operating in the region. The degree of embeddedness of the major economic sectors in the region is illustrated in Figure 2.
Source: own elaboration The diagram clearly shows that agriculture, forestry and fishery are dominating sectors of economy in the Cserehát region. Their index value of 0.413 indicates that almost half of the enterprises operating in the region pursue agricultural activities. The importance of the remaining four sectors is significantly lower. Construction enterprises amount to 6%. The number of processing, accommodation and food service enterprises is nearly the same and accounts for about 4%. Enterprises engaged in human health and social work have the lowest degree of embeddedness, with their 0.025 sub-index.

Labour Conditions
In order to quantify the indicator showing the preparedness and professional knowledge of the available workforce, the twenty-four selected products were grouped by professional knowledge required from the workforce for producing and supplying these products.
Three groups were created based on workforce competencies: workforce employed in agriculture, and forestry (A), in industry and construction (I), and in commerce, trade and services (C). Then the products were classified in line with this principle (the capital letters in the brackets indicate the workforce competencies): fruit palinka (I), grape-pomace palinka (I), wine products (A), apple wine (A), mineral water (I), rural tourism (C), rural accommodation services (C), cultural tourism (castles, manors, historical industrial sites) (C), nature tourism (forest schools, cave tours) (C), leisure tourism (horse riding, fishing and cycling) (C), retirement homes (C), therapeutic and recreational tourism (hot water wells and springs) (C), fruit products (wine) (A), vegetables (A), cereals (maize) (A), folder products (lucerne, clover) (A), industrial crops (sunflower) (A), organic food (A), spices and pharmaceutical crops (A), honey (A), grass energy (A), logging (I), animal husbandry (A), cheese and dairy products (A) and construction products (I).
In the second step of calculating the sub-index, the number of employees possessing various professional knowledge was determined from the statistical data related to the Cserehát region (Table 3). After this, the indicator value assigned to the selected products (Figure 3) was calculated.
Source: own elaboration indicates that these products are the most reliant on professional knowledge. The indicator value of products produced by employees engaged in commerce, trade and services amounts to 0.152, which is a half of the previous value. This means that the labour intensity of these products has considerably decreased. The indicator value of the products made by the workforce employed in agriculture and forestry is extremely low, since it accounts for only 0.030, which indicates that the production of agricultural products does not require a high number and highly qualified workers.

Market Conditions
The last element of the SLEM index assesses supply conditions by taking into account changes in household consumption expenditure. The products can be classified into five expenditure groups: food products, nonalcoholic drinks, alcoholic drinks, furnishing products, household goods and accommodation provision services. The standardised indicator value enables us to characterise product groups.
Source: own elaboration  Figure 4 shows the increase in private consumer spending resulting from the increase in the demand for accommodation provision services, food products and non-alcoholic drinks in the period mentioned earlier. The low value of alcoholic drinks, furniture and household goods results from a low demand for these products.

Consolidated SLEM Index
By averaging four sub-indices, a consolidated index value for products was obtained. The objective set in this research study has been achieved, since it has become possible to determine the economic potential of the twenty-four products available in the Cserehát region. The ranking provided in Figure 4 allows the direction of primary research studies to be determined more accurately, placing greater focus on research areas.
Source: own elaboration The SLEM index enables us to identify five products as potentially market competitive products (animal husbandry, honey, spices and pharmaceutical crops, nature tourism and mineral water) since their index values are high and amount to 0.5. Four products (logging, apple wine, wine, energy grass and construction products) which have lower index values than 0.2 can be neglected.

Conclusions, Recommendations
Since the SLEM model only offers a rough measurement method, there are several options to make it more sophisticated. A potential way to develop it is if we carry out a target study for the region examined. During this, we can explore the existing typical products, and the corporations producing them in the given geographical area. Furthermore, we can examine if there are suitable skills available on a local level, and how robust the professional and entrepreneurial embeddedness is.
To sum up, the findings obtained with the help of the created model can be improved still further if the potentially competitive products are further investigated by quantitative research methods. The first recommendation is to conduct an expert interview with the 'supply side' and sample as wide range of target audience as possible: product producers, farmers, players actively participating in trading with local products and market operators. The second recommendation is to investigate the 'demand side' by performing focus group interviews. While selecting samples for interviews from the pool of potential consumers of local products, not 0,00 0,10 0,20 0,30 0,40 0,50 0,60 0,70 0,80 0,90 1,00 spice and pharmaceutical crops production honey production animal husbandry nature tourism (cave tours, forest tours) mineral water fruit palinka grape-pomace palinka fruit products (grapes) vegetable production cereals production (corn) forage crop production (medick, cloverleaf) industrial crop production (sunflower) organic food production milk and cheese production rural tourism, rural accommodation services cultural tourism (castles, manors, historical industrial… leisure tourism (fishing, horse riding, biking) therapeutic and recreational tourism (thermal sources) retirement homes logging industry wine products apple wine energy grass production construction materials production 0,60 0,60 0,60 0,55 0,54 0,37 0,37 0,35 0,35 0,35 0,35 0,35 0,35 0,35 0,30 0,30 0,30 0,30 0,29 0,18 0,15 0,15 0,11 0,09 only local inhabitants but also people living in other areas should be interviewed, since they may also become potential consumers in the region under investigation. While selecting samples, attention should be paid to conventional family roles in order to avoid overrepresentation of females in the sample and to attract an audience from several generations and various life cycles.
Although it could be fine tuned, even in its current form the SLEM model can be successfully applied to explore the comparative advantages of a given region even in its current form. These results can be particularly useful when a development strategy is formulated for the given region, as development priorities should be based on these relative strengths. The SLEM model provides useful information when decisions on the distribution of European Union and Hungarian development funds are made. Information on high potential products/businesses of the region is also useful for non-governmental organisations aiming to develop the region.