2.1. LED and Its Determining Factors
Since the beginning of the 1980s when local economic development was initially defined, numerous development programs, strategic planning processes, investments programs and policies have been implemented worldwide in accordance with the aims of LED. There are numerous definitions of LED (for example [
3], [
4] (p. 55), [
5] (p. 81), but no single definition is unanimously accepted. As Palavicini-Corona [
6] (p. 24) argues, from [
7] to [
3] the definition of LED evolves, but the importance attributed to the endogenous nature of the approach is notorious in all definitions. Thus, in order to better understand the LED process, we need to understand its endogenous and multidimensional nature.
According to Swinburn, Goga and Murphy [
3], the determining factors for LED are demography (including human resources and human capital), the local economy, the local business environment, infrastructure, and the regional and national policies, opportunities and competitors. Other researchers [
8] classify the determining factors of LED in six dimensions: Demography, economic structure, revenues, basic services, spatial location and governance. The multidimensional nature of LED is also mentioned by Wong [
9] (pp. 1835–1837) who classifies multiple LED determining factors in 11 generic ones: (a) Locational factors, (b) physical factors, (c) infrastructural factors, (d) human resources, (e) capital and finance, (f) knowledge and technology, (g) industrial structure, (h) quality of life, (i) business culture, (j) community identity and image, and (k) institutional capacity; the first seven factors are considered traditional factors for LED, while the last four are considered intangible factors.
2.2. Measuring and Understanding LED
The holistic nature of LED makes it difficult to fully understand and explain the logic of the relationships between various socio-economic factors that play a role in the local development process [
9]. In the last four decades, a large number of LED experiences have been documented by experts, but there are only a few impact evaluations of the programs and policies whose goal was to ensure local economic development [
6] (p. 100). Albeit most authors [
3,
4,
5,
10] define the LED process, there are only a few attempts to measure its outcomes. One explanation can be that economic development is an amorphous and vaguely defined concept, thus the effectiveness of development efforts cannot be accurately and reliably measured [
11] (p. 59); another reason is that we cannot identify a single indicator that can provide an adequate measure of LED [
9]. Using a single indicator (as for example GDP or GDP/capita) for measuring economic development for an area is considered inappropriate because it does not take into consideration the various dimensions of development, such as economic, social and environmental [
12].
The most adequate way to measure LED is to use multiple indicators as proxy measures for each dimension and then to aggregate these indicators in a composite index. However, constructing a composite index for policy analysis involves numerous methodological issues, such as the ‘selection of appropriate variables/coefficients and balancing between objective vs. subjective indicators; weighting the variables/indicators according to their relative importance; application of unbiased aggregation techniques; and making the index useful for policy purposes (i.e., in programme evaluation)’ [
12] (p. 4). Bartik [
13] suggested that the easiest way to evaluate the impact of LED policies/programs is to measure the outcomes which approximate impacts on various dimensions of business activities, including the number of business start-ups or expansions, job growth and productivity growth. However, this approach can be refuted easily; for example, Bryden [
14] draws attention to the importance of distinguishing between indicators that measure performance along a number of dimensions and indicators that help explain good or poor economic performance. The latter are more difficult to measure and interpret since both tangible and intangible factors are involved, as well as multiple interactions among them. In an attempt to help Member States, the European Commission proposed a guide for a unitary approach of the impact evaluation of socio-economic development programmes [
15], while the World Bank also offers a useful tool for using impact evaluation in practice, providing a comprehensive handbook for those who want to measure/approximate the impact (or causal effect) of a program on an outcome of interest [
16].
Despite the aforementioned difficulties in measuring LED, there are several empirical studies that succeeded and managed to analyze the relationships between LED and its determining factors. Wong [
9] examined the relationship between 29 indicators representative for LED in the districts/local communities of the North West and Eastern Regions of England, in an attempt to explore the spatial development model. Using multiple regressions and factorial analyses, a district score was obtained according to the importance of the size envisaged: The big city syndrome (emphasis on traditional infrastructure, economic structure, and location), dynamic suburbs (emphasis on qualified human resources), desirable living conditions, local service center, small entrepreneurs (emphasis on entrepreneurial/business culture). According to Wong [
9] (p. 1858) the most important determining factors of LED are location (accessibility, connectivity, and proximity) and the quality of the human resource (the skills and qualifications of the workforce).
The LHDI (Local Human Development Index) proposed by Sandu, Voineagu and Panduru [
17] aggregates indicators corresponding to dimensions or capital types existing in any community: Human capital, health capital, vital capital and financial capital. The values of the indicators were aggregated into an indicator used to estimate ‘community capital’ by using multiple factorial analyses. The comparison of LHDI 2002 and LHDI 2011 with GDP/capita ratios (2001 and 2010) showed that LHDI correlates quite well with economic growth, but we have to underline that LHDI is not an indicator limited only to the measurement of economic growth as it includes a plethora of other factors. Additionally, studies show that increases in LHDI scores between 2002 and 2011 are much more pronounced in urban communities than in rural areas [
18] (pp. 111–113). Matei and Anghelescu [
19] proposed a model, based on simultaneous equations describing the evolution of local development of a municipality, using 36 variables/indicators (from which 20 are exogenous and 16 are endogenous), including economic, financial, socio-demographic and indicators of the use of public services.
Simms, Freshwater and Ward [
8] developed the Rural Economic Development Index (RECI) based on a set of six dimensions of LED (demography, economic structure, income, services, workforce, localization and governance); each dimension aggregates multiple indicators. Starting from the demographic decline of rural communities and consequently the lack of specialized workforce, the developers of RECI conclude that small communities do not have the capability to remain viable as autonomous communities and that competition among them is no longer a viable strategy; thus, the only chance for survival is collaboration. Potential strategies such as cannibalization (one community absorbs the main economic activities in the region) or amalgamation (provincial or state governments organize local governments with the objective of aggregating demand for public services) are the most likely options for these small communities [
8] (p. 361).
Assuming that connectivity is an important element in measuring economic development, the International Development Association (IDA) developed the Rural Access Index (RAI), part of a wider effort to identify key diagnostic measures to contribute to the process of community development [
20]. Since the isolation of communities is considered a major factor leading to poverty and marginalization, RAI measures the rural population living within two kilometers (typically equivalent to a walk of 20–25 min) of an all-season road. RAI is based on analysis of household surveys that include questions about access to transport and it also includes information from Living Standard Measurement Surveys (LSMS) and similar household surveys carried out between 1994 and 2003; since its inception, RAI has been established in 32 IDA countries [
20] (p. 3).
Li, Long and Liu [
21] developed an index to evaluate the degree of rurality in China at county level using national census data for 2000 and 2010 and examined the correlation between the rurality index and major socio-economic and geographical indicators. The index [
21] is based on 15 indicators/variables at county level which have been aggregated using principal components analysis. The rurality index has significant negative correlation with indicators reflecting the ability to attract investments, output and value-added capabilities, local government financial strength and residents’ income and savings levels; counties with a higher rurality degree are prone to have higher relief degree of land surface and are located at longer distances from the nearest provincial capitals, highways and railways. Therefore, counties with high levels of rurality have been marginalized both geographically and economically [
21] (p. 23); while counties with a lower rurality index have better economic performance, counties with a higher rurality index are more likely to be characterized by a lower education level of their residents, lack of professional skills and to have high employment rates in the agricultural sector (with less income), lower levels of urban development and urbanization [
21] (p. 23).
Michalek and Zarnekow [
12] developed a multidimensional rural/rural development index (NUTS 4) in Poland and Slovakia which can also be used at the regional level (NUTS 2); the index was preponderantly focused on quality of life. The authors initially took into account 991 indicators considered relevant for rural development and quality of life in Poland and 340 in Slovakia in the 2002–2005 period. The dimensions of rural development included in the construction of the index (based on a Principal Component Analysis) were: Economic, social, environmental, demographic, local public administration and infrastructure. Among the top 10 variables/coefficients positively contributing to quality of life in rural regions in Poland the most important were personal income, availability and quality of new residential buildings, access to selected technical infrastructure, the share of the private sector in the service sector and the spatial accessibility of rural enterprises [
12] (p. 19). In Slovakia, the most important variables/coefficients positively contributing to local rural development were those associated with: (a) Population structure (high share of population at a productive age within the total population), (b) the share of private enterprises and natural persons in total units, (c) the level of consumption, (d) spatial access of rural population to social infrastructure (such as swimming pools, sports facilities, telephone lines, local communication, etc.), (e) the structure of the local business, (f) the share of enterprises in the area, and (g) variables/coefficients associated with favorable climate and natural conditions [
12] (p. 19). In Romania a study from the Academy of Economic Studies [
1] focused on the socio-economic development potential of rural areas: A total of 25 indicators were grouped (classified) in five dimensions, related to endogenous potential, environmental factors, human capital, economic activities and technical-urban equipment. The study provided a diagnosis of the Romanian rural space, respectively a hierarchy of communes on the basis of their index of socio-economic potential; based on this index, seven of the top 10 communes in Romania are located at the border of regional growth poles and the other three are adjacent to some of the largest and most developed cities in Romania, outside the growth poles [
1].
Although multiple authors [
22,
23,
24,
25,
26,
27,
28,
29] studied the link between economic development (at the national, state or regional level) and infrastructure, only a limited number of studies focused on the impact that investments in local infrastructure have on LED or local rural development. Rives and Heaney [
11] tried to see if there is any link between infrastructure and the LED level of 178 communities in Iowa (USA) by building an index consisting of indicators structured in four main dimensions: Economic development, infrastructure, location and education. The aforementioned study confirms the existence of a link between infrastructure and local economic development; furthermore, public policies aimed at ensuring infrastructure maintenance works (especially highways that ensure the connectivity of small communities) can then further increase LED. Other important findings of Rives and Heaney [
11] refer to the following: (a) The location of the community is another factor that significantly influences economic development; (b) higher levels of taxation discourage development; (c) human capital/resources and the share of the population employed in industry have a significant impact on development, and (d) the only independent variable that does not seem to influence economic development is the size of the population.
Janeski and Whitacre [
30] evaluated the impact of the federal program for funding water and sewer infrastructure projects in the rural area of Oklahoma between 1990 and 2000. They measured the economic growth over both short (1 to 10 years) and long (10 to 20 years) term in 564 communities that have been divided into two categories, beneficiaries (N = 143) and non-beneficiaries (N = 421).
t-tests of the mean growth rates showed that most of the economic growth measurements do not differ between communities, but that the growth rate in the percentage of households with earnings is significantly higher for treated communities on both short and long term [
30] (p. 30). Both Ordinary Least Squaresand Average Treatment Effect methods revealed similar results: in the short term, the increase in indicators (eight dependent variables) is not associated with participation in the water and sewerage financing program, while in the long run they confirm that only median house values increased in the communities that received funds for water/sewer infrastructure. ATE results allowed the authors to claim that increased growth in median house values in Oklahoma communities that received financial support for water and sewer infrastructure is mainly caused by these investments [
30] (p. 33).
Thadaboina [
31] showed that investments in Information Technology and Communication networks, databases and personal computer operating courses have reduced commercial costs for the rural population, the time and costs of accessing public services, while also increasing productivity in the agricultural sector and improving the overall quality of life. Thus, the development program analyzed by Thadaboina [
31] succeeded not only in increasing the employment rate of the rural population, but also provided spill-over benefits, such as increasing the level of participation and involvement of the population, facilitating access to health, education and financial facilities.