The impact of institutional proximity, cognitive proximity and agglomeration economies on firm-level productivity

Abstract

PurposeThis paper analyses the impact of institutional proximity, cognitive proximity and geographical proximity (in the form of agglomeration economies) on the firm-level productivity of foreign-owned firms in Ireland. The analysis of agglomeration economies, consisting of internal economies of scale, localization economies, related variety and urbanization economies, has a strong pedigree in regional economics literature. Increasingly, however, alternative explanations of firm-level productivity performance have been explored with institutional and cognitive proximity often identified as other important determinants of performance. This paper presents an analysis of the importance of agglomeration economies (based on geographical proximity) versus institutional and cognitive proximity (which may be a-spatial).Design/methodology/approachA series of measures capturing regional level agglomeration economies are generated as well as measures of institutional and cognitive proximity. The impact of these effects on foreign-owned firm-level productivity is analysed using data from the Irish Census of Industrial Local Units 2009. The estimation method employed is general method of moments (GMM) which allows for the potential endogeneity of variables within the system of analysis.FindingsThe results reveal that institutional proximity has a positive impact on productivity. A possible reason for this result is that local units of the same nationality are sharing knowledge in relation to successfully conducting business in Ireland. However, cognitive proximity is found to be statistically insignificant. Agglomeration economies are also important with urbanization economies and the availability of skilled labour having a positive effect on productivity.Originality/valueThe key contributions of this paper are as follows; firstly, the paper provides the first test of the institutional and cognitive proximity hypotheses on productivity while also controlling for a series of internal and external agglomeration economies. Secondly, the analysis considers, firm level, regional level and national level indicators as determinants of firm's productivity. In combining micro and macro level indicators, the paper attempts to answer the call of Van Oort et al. (2012) for such analyses. Thirdly, the paper provides the first detailed examination of the role of ‘proximity’ on foreign-owned manufacturing firms in the Irish context.

Unravelling urban advantages - A meta-analysis of agglomeration economies

“…Koster et al (2014) is an edge case that we code "yes", in which the dependent variable measures commercial property rents and the model controls for the size and quality of the building. K/L ratio "Yes", where the model controls for capital intensity, that is, the ratio of capital to labour inputs (see, e.g., Noonan et al, 2020;Rigby and Brown, 2015), including those that use proxies for capital intensity per worker (see, e.g., Soroka, 1994).…”

“…Most studies use simple measures, such as the total number of inventors, patents, or simple derivatives thereof-such as patents per capita or per worker (see, e.g., Artis et al, 2012;Beugelsdijk et al, 2018;Feser, 2002;Lobo et al, 2014;López-Rodríguez and Faíña, 2007;van Oort and Bosma, 2013). In contrast, van Dijk (2007), López-Rodríguez, Faiña et al (2011) and Drucker and Feser (2012) consider expenditure on research and development, whereas Noonan et al (2020) consider research investment per sector.…”

The role of culture as a determinant of tourism demand: evidence from European cities

“…As such, an IV generalised method of moments (GMM) estimator will also be estimated to include instruments for potentially endogenous variables. The instruments are constructed using the three-group method commonly used in economic literature; see for example, Noonan, 2021;Noonan et al, 2021. This involves separating the endogenous variable into three groups of equal size and then creating an instrumental variable which take values of À1, 0 and þ1 depending on whether the observation is in the lowest, middle or highest group of observations (Kennedy, 2008, p. 160). The Difference-in-Sargan test (C statistic) is calculated after the IV GMM regression to test for endogeneity.…”

“…European Union (2018b, p. 2) state that the variables included in the index “were selected with respect to statistical coherence, country coverage and timeliness”. In fact, it is not uncommon in econometric analysis to include variables from different reference periods; see, for example, Kim et al (2000), Alkay and Hewings (2012), Noonan et al (2021).…”

Are Non-Special Dimensions of Proximity in Local Clusters Related? An Analysis of 99 European Clusters

“…Based on Boschma's [9] (p. 67) theory, institutional proximity is related to the "institutional framework at the macro-level" and refers to respecting similar rules and regulations in a particular group. First, following standard rules and regulations can prevent profiteering [9] and facilitate interactive learning [30]. On the other hand, social proximity creates trust between firms [24], and mutual trust creates Institutional proximity [31].…”