Abstract
Purpose
This paper analyses how firm births and deaths are influenced by previous firm births and deaths in related and unrelated sectors. Competition and multiplier effects are used as the theoretical lens for this analysis.
Design/methodology/approach
This paper uses 2008–2016 Irish business demography data pertaining to 568 NACE 4-digit sectors within 20 NACE 1-digit industries across 34 Irish county and sub-county regions within 8 NUTS3 regions. A three-stage least squares (3SLS) estimation is used to analyse the impact of past firm deaths (births) on future firm births (deaths). The effect of relatedness on firm interrelationships is explicitly modelled and captured.
Findings
Findings indicate that the multiplier effect operates mostly through related sectors, while the competition effect operates mostly through unrelated sectors.
Research limitations/implications
This paper's findings show that firm interrelationships are significantly influenced by the degree of relatedness between firms. The raw data used to calculate firm birth and death rates in this analysis are count data. Each new firm is measured the same as another regardless of differing features like size. Some research has shown that smaller firms have a greater propensity to create entrepreneurs (Parker, 2009). Thus, it is possible that the death of differently sized firms may contribute differently to multiplier effects where births induce further births. Future research could seek to examine this.
Practical implications
These findings have implications for policy initiatives concerned with increasing entrepreneurship. Some express concerns that public investment into entrepreneurship can lead to “crowding out” effects (Cumming and Johan, 2019), meaning that public investment into entrepreneurship could displace or reduce private investment into entrepreneurship (Audretsch and Fiedler, 2023; Zikou et al., 2017). This study’s findings indicate that using public investment to increase firm births could increase future firm births in related and unrelated sectors. However, more negative “crowding out” effects may also occur in unrelated sectors, meaning that public investment which stimulates firm births in a certain sector could induce firm deaths and crowd out entrepreneurship in unrelated sectors.
Originality/value
This paper is the first in the literature to explicitly account for the role of relatedness in firm interrelationships.
Keywords
Citation
O'Leary, D., Doran, J. and Power, B. (2024), "The role of relatedness in firm interrelationships", Journal of Economic Studies, Vol. 51 No. 9, pp. 36-58. https://doi.org/10.1108/JES-12-2022-0631
Publisher
:Emerald Publishing Limited
Copyright © 2023, Daragh O'Leary, Justin Doran and Bernadette Power
License
Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
Firm interrelationships are instances where firm births/deaths influence future rates of firm births/deaths, see Calá et al. (2016) and Carree et al. (2011). Johnson and Parker (1994) provide a substantive theoretical framework through which these firm interrelationships can be viewed by examining the existence of multiplier and competition effects. When firm births induce future firm births (and reduce firm deaths) or when firm deaths induce future firm deaths (and reduce firm births), multiplier effects are present (Lu et al., 2008; Resende et al., 2015). Whereas, when firm births induce future firm deaths (and reduce firm births) or when firm deaths induce future firm births (and reduce firm deaths), competition effects are present (Carree et al., 2011; Pe'er and Vertinsky, 2008) [1]. The objective of this paper is to build on existing firm interrelationship work by analysing how competition and multiplier effects are influenced by firm births and deaths from related and unrelated sectors.
Little evidence exists regarding the role which relatedness plays in influencing firm interrelationships. However, economic theory suggests relatedness could influence both the competition and multiplier effect. Beginning with the competition effect, this stipulates that firm births can increase future firm deaths via competitive pressures (Gajewski and Kutan, 2018). The level of similarity between firms is considered a key factor in determining rivalry (Dejardin, 2004). Therefore, firm births from related sectors may increase future firm deaths through competitive pressure due to increased rivalry. However, non-related firms can also compete for versatile resources (Markman et al., 2009). As a result, firm births from unrelated sectors could also increase future firm deaths. This effect could also occur when firm deaths increase future firm births (Lu et al., 2008). For example, when firm deaths increase the available “market room” in a given industry, this could incentivise future firm births (Carree and Dejardin, 2020). Logic would dictate that a firm death in a certain industry would free up market room in that same industry. Therefore, firm deaths from related sectors may increase future firm births. Multiplier effects could also be influenced by relatedness. Firm births (or deaths) can increase future firm births (or deaths) via demonstration effects where the birth or death of a firm signals the value/difficulty associated with owning a firm (Dejardin, 2004). Meaning firm births could incentivise individuals to start businesses of their own (Nyström, 2007) and firm deaths could incentivise business owners to exit the market (Bartoloni et al., 2020). These demonstration effects are clearly dependent on relatedness because the economic value (or lack thereof) communicated by a firm being born (or dying) is obviously most applicable to other related firms.
Most studies of firm interrelationships, such as Arcuri et al. (2019), Gajewski and Kutan (2018) and Carreira and Teixeira (2011) do not consider the role of similarity/relatedness between firms. They analyse the degree to which firm births (deaths) impact future firm deaths (births) for the economy as a whole or for a specific sector. Dejardin (2004) and Carree et al. (2011) are two rare exceptions. They analyse how lagged firm births/deaths affect future firm births/deaths in the same and other sectors. For example, Dejardin (2004) examines how firm births in NACE sector 501 (trade of motor vehicles) are influenced by lagged births and deaths in that sector as well as in other sectors (which may be related or unrelated). While this approach accounts for firm sector, it doesn't account for relatedness between sectors. Carree et al. (2011) adopt a similar method examining how firm deaths in one industry (e.g. textiles) are influenced by lagged births and deaths in the same industry and in other industries. This approach also doesn't account for degrees of relatedness. Dejardin (2004) calls for future research to focus on designing a more detailed analytical framework to account for complementarities between economic activities. This is echoed by Guo et al. (2016) who assert that few entrepreneurial studies accurately measure technological relatedness despite relatedness between firms influencing complementary or competitive effects. This paper significantly contributes to the existing literature by examining whether the degree of relatedness between firms influences future firm births/deaths.
This is done by developing and explicitly accounting for related firm birth and death rate variables which can indicate how firm births/deaths in related sectors (same NACE-1 industry) and unrelated sectors (different NACE-1 industry) influence future firm births/deaths. Thus, our analysis significantly builds on previous examinations of firm interrelationships between sectors by Dejardin (2004) and Carree et al. (2011) and provides extremely novel results which have never been produced in the literature previously. The results of this analysis can provide important implications for the direction of investments in industrial policy supports for start-ups by development agencies organisations. To conduct the analysis a three stage least squares (3SLS) estimation is performed using the Central Statistics Office (CSO) of Ireland's business demography dataset from years 2008–2016. This administrative data covers all firms in Ireland and is available at the sub-county level (34 regions) and at NACE 4-digit level (CSO, 2022).
2. Literature review
The two key effects which will be examined in this analysis are the competition and multiplier effects originally set forth by Johnson and Parker (1994). This literature review will provide conceptual reasoning for why relatedness between firms would impact the mechanisms underpinning the competition and multiplier effects. We refer to relatedness here in the same way Frenken et al. (2007) refer to it in their related and unrelated variety framework. Meaning relatedness refers to cognitive proximity in a firm's operations (Crowley et al., 2021). Cognitive proximity refers to the level of similarity between firms in terms of their knowledge bases and economic interests (O'Connor et al., 2021; Criscuolo et al., 2018; Boschma, 2005). Firms which are cognitively approximate to each other may find it easier to learn from and interact with one another (Molina-Morales et al., 2014; Boschma, 2005) [2].
2.1 Competition effects and relatedness
We summarise the conceptual arguments made for relatedness influencing competitions effects in Figure 1. As per the definition of the competition effect, firm births should increase future firm deaths and decrease future firm births (Johnson and Parker, 1994). Conceptually, this can occur when firm births increase rivalry and the demand for valuable inputs which increases future firm deaths and decreases future births (Brixy, 2014; Cainelli et al., 2014). This competitive mechanism is dependent on rivalry existing between firms, so it would be expected that firm births in related industries would increase competition and induce future firm deaths. However, rivalry can also occur between unrelated firms because firms can compete with each other at various points in the supply chain due to versatile resources (Nason and Wiklund, 2018; Porter, 2011). Some resources are idiosyncratic to certain firms (Miller, 2003), but others like administrative infrastructure and human capital can be used by unrelated firms (Markman et al., 2009). Thus, it could be expected that firm births in unrelated industries increase competition which induces future firm deaths. Both processes are demonstrated in Figure 1 (Part A) by relating increased rivalry for resources and customers to increased future firm births and decreased future firm deaths.
Additionally, the competition effect should see firm deaths increase future firm births and decrease future firm deaths (Johnson and Parker, 1994). This can occur via four different mechanisms which include changes to market room, reduced rivalry, the reallocating of resources, and necessity-based entrepreneurship. As is shown in Figure 1 (Part B) firm deaths in related industries should increase “market room” which could increase future firm births and decrease future firm deaths (Carree and Dejardin, 2020). Conceptually, this process should operate via related industries. The industry in which the firms deaths took place is the industry where the “room” has been increased. Regarding the influence of reduced rivalry, firm deaths should decrease competition and demand for key inputs which should reduce future firm deaths (Cainelli et al., 2014; Dejardin, 2004). As has already been discussed, rivalry for resources can exist between related and unrelated firms (Markman et al., 2009; Porter, 2011). Thus, as is illustrated in Figure 1 (Part B) firm deaths in related or unrelated industries could reduce competition for resources and decrease future firm deaths and increase future firm births. Firm deaths also reallocate resources back into the economy where they can be used by others to start firms (Brown et al., 2013; Nyström, 2007). The versatility of resources could mean that, as is illustrated in Figure 1 (Part B), firm deaths in related or unrelated industries could reallocate resources which could result in increased future firm births and decreased future firm deaths. Finally, firm deaths can induce unemployment which motivates individuals to pursue necessity-based entrepreneurship which increases firm births (Block et al., 2015; O'Leary, 2022). Some entrepreneurs will look to start firms in industries that are related to their employment experience (Cooper and Dunkelberg, 1986; Feeser, 1987), but others may not (Åstebro and Thompson, 2011), meaning that, as is shown in Figure 1 (Part B), firm deaths in related or unrelated industries could induce push-factor entrepreneurship leading to an increase in future firm births. Given the above theoretical arguments, we propose the following three hypotheses:
Firm births (deaths) should increase future firm deaths (births) and decrease firm births (deaths).
Related firm births (deaths) should increase future firm deaths (births) and decrease future firm births (deaths).
Unrelated firm births (deaths) should increase future firm deaths (births) and decrease future firm births (deaths).
2.2 Multiplier effects and relatedness
We summarise the conceptual arguments made for relatedness influencing multiplier effects in Figure 2. According to the multiplier effect, firm births should increase future firm births (and decrease future firm deaths); and firm deaths should increase future firm deaths (and decrease future firm births) (Johnson and Parker, 1994). These results can occur via two mechanisms; demonstration/signalling effects (Dejardin, 2004) and income effects (Gajewski and Kutan, 2018). Signalling/demonstration effects occur when firm births signal the value associated with a firm and incentivise more firm births and disincentivise firm deaths (Nyström, 2007). Conversely, firm deaths could signal a lack of value which may incentivise business owners to exit the market (Bartoloni et al., 2020) and disincentivise others from entering the market (Dejardin, 2004). Demonstration effects are dependent on relatedness because the economic value (or difficulty) communicated by a firm being born (or dying) is most applicable to other related firms. This is illustrated in Figure 2 (Parts A & B) where only firm births and deaths in related industries are shown to induce demonstration effects.
However, income effects provide a conceptual argument for multiplier effects occurring across any industry. Income effects occur when firm births increase levels of income/demand and thus increase future firm births (and decrease future firm deaths) to meet this new level of demand (Gajewski and Kutan, 2018; Sutaria and Hicks, 2004). The same process can increase future firm deaths (and decrease future firm births) as a result of firm deaths lowering income/demand. Individuals spend their incomes on a wide variety of goods and services (Chai et al., 2015) which means income from one sector could theoretically influence the income of any other sector (Conrad, 1955). This is illustrated in in Figure 2 (Parts A & B) where both related and unrelated firm births and deaths are shown to induce income effects. Therefore, we investigate the following three hypotheses:
Firm births (deaths) should increase future firm births (deaths) and decrease future firm deaths (births).
Related firm births (deaths) should increase future firm births (deaths) and decrease future firm deaths (births).
Unrelated firm births (deaths) should increase future firm births (deaths) and decrease future firm deaths (births).
3. Data and methods
The data for this study is business demography (2008–2016) data from the Central Statistics Office (CSO) of Ireland. We examine firm births and deaths across 568 NACE 4-digit sectors embedded in 20 different NACE 1-digit sectors pertaining to 34 regions embedded within 8 NUTS3 regions [3]. We produce fixed effects for the aggregated NUTS 3 regions to provide informative geographical results.
3.1 Constructed variables
The firm birth (death) variable is constructed by dividing the count of firm births (deaths) in year t by the total stock of businesses in year t-1. A firm is considered born if it engages in a combination of production factors, e.g. generating revenue, employing people or investing in capital [4]. A firm death occurs given the dissolution of these production factors (CSO, 2022). The firm birth and death variables are calculated in the same manner as Nyström (2007), Carree et al. (2011), and Resende et al. (2015) to capture the intertemporal elements of firm interrelationships. The notation for the construction of these two variables can be seen below in equations (1) and (2).
To measure industrial concentration and specialisation, we use the Herfindahl index and location quotient (LQ) variable respectively using standard measurements consistent with Power et al. (2019) and Antonietti and Cainelli (2011). Additionally, to indicate levels of industrial diversification related and unrelated variety variables are calculated in line with Frenken et al. (2007). Precise discussion around these variables and their calculations are provided in Appendix 4. Summary statistics for this study can be viewed in Table 1 below. Appendix 5 presents the correlation matrix between our variables.
3.2 Method of estimation
We use the three stages least squares (3SLS) methodology to estimate a system of firm birth and death equations while mitigating against potential endogeneity issues (Abdallah et al., 2015). Our use of lagged dependent variables as explanatory variables raises concerns of endogeneity where an independent variable may be influenced by a dependent variable (Abdallah et al., 2015). Specifically, this analysis may be vulnerable to simultaneity endogeneity (Wooldridge, 2010). Simultaneity can render the estimation of the impact of the independent variable (X) on the dependent variable (Y) biased because there is a reciprocal relationship between X and Y (Hill et al., 2021). Given the conceptual basis for firm interrelationships is that firm births or deaths can influence other firm births or deaths (Nyström, 2007; Johnson and Parker, 1994), it could be assumed that there is a simultaneous relationship between the X and Y in this paper's analysis. If endogeneity of this form is present, single equation methods such as two stage least squares (2SLS) can be considered preferable to OLS estimation (Bollen et al., 2007). However, 2SLS does not correct for correlation between the error terms in a structural equations system because each equation is estimated individually (Kaplan, 1988). Given that this paper analyses how firm interrelationships determine both firm births and deaths, a full system estimation method like 3SLS estimation is more appropriate because it corrects for endogeneity and estimates all the parameters jointly (Abdallah et al., 2015; Zellner and Theil, 1992). Endogeneity can be accounted for via the use of instrumental variables in a 3SLS estimation. We construct synthetic instrumental variables for all the firm birth and firm death rate variables via Bartlett's three group method (Bartlett, 1949). This method takes the endogenous variable and creates an instrumental variable which is coded as either “−1”, “0” or “1” depending on whether the value of the endogenous variable falls in the lower (−1), middle (0) or upper (1) third of the variable's distribution. This produces a suitable instrumental variable as per the criterion mentioned by Le Gallo and Páez (2013). Meaning the instrument is still somewhat correlated with the endogenous variable (allowing it to function well as an estimator) but is less correlated with the endogenous variable (thus mitigating endogeneity issues). This technique was originally used to account for omitted variable bias (Hanushek et al., 1996), but has become a wildly used method for dealing with endogeneity – see Bahlous-Boldi (2021), Doran and Fingleton (2016) and Angeriz et al. (2008).
We specify three sets of simultaneous equations systems to test our hypotheses. Each system models firm births (deaths) as a function of lagged firm births and deaths. Firstly, we estimate a model similar to the models used by Arcuri et al. (2019) and Gajewski and Kutan (2018) examining how future firm birth/death activity (t) is determined by past firm birth or death activity (t-1) with no emphasises on relatedness/unrelatedness. This model is estimated below in equations (7) and (8).
These represent the first of our firm births and deaths models. Where
Finally, we build on models (9) and (10) by adding in lagged firm birth and death rates of firms which are in unrelated sectors s-sk.
4. Results
The estimates of equations (7) and (8) are shown in the columns labelled I and II in Table 2 and show that both the multiplier and competition effect appear to influence firm births. Specifically, firm births are increased by both lagged firm births and deaths. This provides some support for both H1a and H2a. Conceptually, the multiplier effect could be attributable to income or demonstration effects (Dejardin, 2004; Gajewski and Kutan, 2018). Meanwhile the competition effect could be attributable to push-factor entrepreneurship or increased market room (O'Leary, 2022; Saridakis et al., 2016). Regarding firm deaths, the results support the competition effect. Firm deaths are increased by lagged firm births and decreased by lagged firm deaths. This provides support for H1a. This could be attributable to firm births increasing competitive pressures and inducing firm deaths as is suggested by Cefis et al. (2020) and Audretsch (1995) when referring to the displacement effect and firm deaths alleviating these same competitive pressures. These results match the results of Calá et al. (2016) and Pe'er and Vertinsky (2008) who find evidence of the presence of the multiplier effect in the case of firm deaths and Arcuri et al. (2019) and Resende et al. (2015) who find evidence of the competition effect in the case of firm deaths.
4.1 Intersectoral firm interrelationships
In columns labelled III through VI in Table 2 we produce extremely novel results which detail the role of relatedness of firm sector in explaining firm interrelationships through estimates of equations (9)–(12) respectively. Firm births are increased by previous firm births in related sectors (multiplier effect), providing partial support for hypothesis H2b. This could be explained by demonstration/signalling effects (O'Leary et al., 2023; Nyström, 2007). Meaning firm births communicate economic value which incentivises more related firm births. Alternatively, firm births could increase consumer demand through income effects resulting in more firm births (Gajewski and Kutan, 2018; Sutaria and Hicks, 2004). Future firm births are reduced by previous firm deaths in related sectors (multiplier effect) providing partial support for H2c. This could be conceptually attributable to demonstration/signalling effects discussed by Nyström (2007). Meaning firm deaths may disincentivise people setting up other related firms.
Firm births are increased by previous firm births and deaths in unrelated sectors (multiplier and competition effect) providing partial support for H1c and H2c. The multiplier effect could again be attributable to income effects mentioned above. Considering that the income of a given sector is a function of the income of all sectors (Conrad, 1955), it's likely the increased income would result in higher consumer demand in a variety of different sectors. Therefore, firm births from unrelated sectors could increase future firm births. The competition effect is potentially attributable to firm deaths leading to the reallocation of versatile resources which are discussed by Nason and Wiklund (2018). These resources could be seized upon by alert entrepreneurs (Adomako et al., 2018) and increase firm births.
Finally, firm deaths are increased by lagged firm births from unrelated sectors (competition effects). This finds some support for H1c and could be attributable to competitive pressures whereby new firms displace old ones (Brixy, 2014). Competition can occur between unrelated firms as rivalry is not solely determined by similarity (Markman et al., 2009). Therefore, firm deaths in unrelated sectors could increase future firm deaths.
5. Conclusions
This paper's findings show that firm interrelationships are significantly influenced by the degree of relatedness between firms. Results suggest that future firm births are increased by past firm births from related and unrelated sectors via multiplier effects. However, firm deaths are also stimulated by previous firm births from unrelated sectors via competition effects. These results are helpful in answering the call of Dejardin (2004) to establish a more detailed framework in examining firm interrelationships which accounts for complementarities between economic activities. Overall, the findings show that competition effects explain firm interrelationships between unrelated sectors. By contrast, multiplier effects explain firm interrelationships between related sectors.
These findings have implications for policy initiatives concerned with increasing entrepreneurship. Some express concerns that public investment into entrepreneurship can lead to “crowding out” effects (Cumming and Johan, 2019), meaning that public investment into entrepreneurship could displace or reduce private investment into entrepreneurship (Audretsch and Fiedler, 2023; Zikou et al., 2017). Our findings indicate that using public investment to increase firm births could increase future firm births in related and unrelated sectors. However, more negative “crowding out” effects may also occur in unrelated sectors, meaning that public investment which stimulates firm births in a certain sector could induce firm deaths and crowd out entrepreneurship in unrelated sectors.
This paper also creates avenues for future research. The raw data used to calculate firm birth and death rates in this analysis are count data. Each new firm is measured the same as another regardless of differing features like size. Some research has shown that smaller firms have a greater propensity to create new firm start-ups (Parker, 2009). Thus, it is possible that the death of differently sized firms may have a varying influence on competition effects where firm deaths induce future births. Future research could examine this.
Figures
Figure 1
The role of relatedness in the competition effect
Figure 2
The role of relatedness in the multiplier effect
Summary statistics for variables in estimations
| Variable | N | Mean | Std. dev | Min | Max |
|---|---|---|---|---|---|
| Firm Birth Ratet | 82,383 | 0.11 | 0.22 | 0.00 | 7.00 |
| Firm Death Ratet | 82,383 | 0.08 | 0.24 | 0.00 | 4.00 |
| Firm Birth Ratet-1 | 82,383 | 0.11 | 0.24 | 0.00 | 6.33 |
| Firm Death Ratet-1 | 82,383 | 0.08 | 0.14 | 0.00 | 3.00 |
| Related Firm Birth Ratet-1 | 82,383 | 0.10 | 0.06 | 0.00 | 5.00 |
| Related Firm Death Ratet-1 | 82,383 | 0.09 | 0.03 | 0.00 | 1.00 |
| Unelated Firm Birth Ratet-1 | 82,383 | 0.10 | 0.03 | 0.04 | 0.22 |
| Unelated Firm Death Ratet-1 | 82,383 | 0.09 | 0.01 | 0.06 | 0.14 |
| Log of Population Density | 82,383 | 4.73 | 1.65 | 3.05 | 8.42 |
| HHI | 82,383 | 119.17 | 73.34 | 2.00 | 253.23 |
| RV | 82,383 | 0.14 | 0.20 | 0.00 | 0.75 |
| UV | 82,383 | −9.80 | 2.95 | −16.72 | −2.32 |
| Log of Average Firm Size | 82,383 | 1.18 | 1.25 | −3.36 | 9.22 |
| Log of Income per person | 82,383 | 10.10 | 0.13 | 9.81 | 10.42 |
| LQ | 82,383 | 1.69 | 4.76 | 0.00 | 124.48 |
Note(s): Further discussion on the defining of these variables and their calculations are provided in Section 3 and in Appendix 4
Source(s): Author's own work
3SLS estimation results
Variables | I | II | III | IV | V | VI |
|---|---|---|---|---|---|---|
| Firm births | Firm deaths | Firm births | Firm deaths | Firm births | Firm deaths | |
| (Eqn. 7) | (Eqn. 8) | (Eqn. 9) | (Eqn. 10) | (Eqn. 11) | (Eqn. 12) | |
| Firm Birth Ratet-1 | 0.0873*** | 0.0337*** | 0.0869*** | 0.0336*** | 0.0851*** | 0.0332*** |
| (0.0033) | (0.0025) | (0.0033) | (0.0025) | (0.0033) | (0.0025) | |
| Firm Death Ratet-1 | 0.0315*** | −0.0421*** | 0.0315*** | −0.0421*** | 0.0309*** | −0.0422*** |
| (0.0055) | (0.0041) | (0.0055) | (0.0041) | (0.0054) | (0.0041) | |
| Related Firm Birth Ratet-1 | 0.0494*** | 0.0083 | 0.0114 | 0.0006 | ||
| (0.0143) | (0.0107) | (0.0146) | (0.0109) | |||
| Related Firm Death Ratet-1 | −0.0397 | 0.0079 | −0.0577** | 0.0043 | ||
| (0.0275) | (0.0205) | (0.0275) | (0.0206) | |||
| Unrelated Firm Birth Ratet-1 | 0.5822*** | 0.1199*** | ||||
| (0.0551) | (0.0412) | |||||
| Unrelated Firm Death Ratet-1 | 0.3013** | 0.0498 | ||||
| (0.1405) | (0.1051) | |||||
| Log of Population Density | 0.0056*** | 0.0041*** | 0.0055*** | 0.0040*** | 0.0021*** | 0.0033*** |
| (0.0007) | (0.0005) | (0.0007) | (0.0005) | (0.0008) | (0.0006) | |
| HHI | −0.0000 | 0.0000 | −0.0000 | 0.0000 | −0.0000 | 0.0000 |
| (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | (0.0000) | |
| LQ | 0.0008*** | 0.0001 | 0.0008*** | 0.0001 | 0.0007*** | 0.0001 |
| (0.0002) | (0.0001) | (0.0002) | (0.0001) | (0.0002) | (0.0001) | |
| RV | −0.0196*** | 0.0059 | −0.0197*** | 0.0059 | −0.0197*** | 0.0059 |
| (0.0071) | (0.0053) | (0.0071) | (0.0053) | (0.0071) | (0.0053) | |
| UV | −0.0001 | −0.0006* | −0.0002 | −0.0006** | −0.0012*** | −0.0008*** |
| (0.0004) | (0.0003) | (0.0004) | (0.0003) | (0.0004) | (0.0003) | |
| Log of Income per person | −0.0266* | 0.0007 | −0.0263* | 0.0007 | −0.0182 | 0.0024 |
| (0.0148) | (0.0111) | (0.0148) | (0.0111) | (0.0149) | (0.0112) | |
| Log of Average Firm Size | −0.0246*** | −0.0192*** | −0.0246*** | −0.0192*** | −0.0242*** | −0.0191*** |
| (0.0007) | (0.0005) | (0.0007) | (0.0005) | (0.0007) | (0.0005) | |
| B- Mining and Quarrying (Reference Category) | ||||||
| C – Manufacturing | 0.0303*** | 0.0101* | 0.0304*** | 0.0101* | 0.0282*** | 0.0096 |
| (0.0081) | (0.0061) | (0.0081) | (0.0061) | (0.0081) | (0.0061) | |
| D – Electricity, Gas, Steam and Air Conditioning Supply | 0.0477*** | 0.0029 | 0.0427*** | 0.0020 | 0.0460*** | 0.0026 |
| (0.0135) | (0.0100) | (0.0135) | (0.0101) | (0.0135) | (0.0101) | |
| E – Water Supply; Sewerage, Waste management and Remediation Activities | 0.0478*** | 0.0204*** | 0.0471*** | 0.0200*** | 0.0467*** | 0.0199*** |
| (0.0100) | (0.0074) | (0.0100) | (0.0075) | (0.0100) | (0.0075) | |
| F – Construction | 0.0327*** | 0.0186*** | 0.0336*** | 0.0183*** | 0.0317*** | 0.0179*** |
| (0.0089) | (0.0067) | (0.0090) | (0.0067) | (0.0090) | (0.0067) | |
| G – Wholesale and Retail Trade; Repair of Motor Vehicles and Motorcycles | 0.0445*** | 0.0200*** | 0.0449*** | 0.0199*** | 0.0409*** | 0.0191*** |
| (0.0087) | (0.0065) | (0.0087) | (0.0065) | (0.0087) | (0.0065) | |
| H – Transport and Storage | 0.0323*** | 0.0112* | 0.0335*** | 0.0113* | 0.0277*** | 0.0102 |
| (0.0089) | (0.0067) | (0.0089) | (0.0067) | (0.0089) | (0.0067) | |
| I – Accommodation and Food Service Activities | 0.0711*** | 0.0401*** | 0.0713*** | 0.0397*** | 0.0710*** | 0.0396*** |
| (0.0095) | (0.0071) | (0.0096) | (0.0071) | (0.0096) | (0.0071) | |
| J – Information and Communication | 0.0737*** | 0.0328*** | 0.0722*** | 0.0319*** | 0.0749*** | 0.0324*** |
| (0.0087) | (0.0065) | (0.0088) | (0.0066) | (0.0088) | (0.0066) | |
| K – Financial and Insurance Activities | 0.0281*** | 0.0248*** | 0.0267*** | 0.0240*** | 0.0288*** | 0.0244*** |
| (0.0089) | (0.0067) | (0.0090) | (0.0067) | (0.0090) | (0.0067) | |
| L – Real Estate Activities | 0.0258** | 0.0158* | 0.0246** | 0.0151* | 0.0266** | 0.0155* |
| (0.0108) | (0.0081) | (0.0108) | (0.0081) | (0.0108) | (0.0081) | |
| M – Professional, Scientific and Technical Activities | 0.0498*** | 0.0272*** | 0.0492*** | 0.0268*** | 0.0494*** | 0.0268*** |
| (0.0087) | (0.0065) | (0.0087) | (0.0065) | (0.0087) | (0.0065) | |
| N – Administrative and Support Service Activities | 0.0569*** | 0.0310*** | 0.0568*** | 0.0307*** | 0.0556*** | 0.0305*** |
| (0.0085) | (0.0063) | (0.0085) | (0.0063) | (0.0085) | (0.0063) | |
| O – Public Administration and Defence; Compulsory Social Security | 0.1132*** | 0.0504*** | 0.1088*** | 0.0495*** | 0.1102*** | 0.0498*** |
| (0.0106) | (0.0079) | (0.0107) | (0.0080) | (0.0107) | (0.0080) | |
| P – Education | 0.0671*** | 0.0144** | 0.0659*** | 0.0144** | 0.0640*** | 0.0140** |
| (0.0091) | (0.0068) | (0.0091) | (0.0068) | (0.0091) | (0.0068) | |
| Q – Human Health and Social Work Activities | 0.0430*** | 0.0151** | 0.0423*** | 0.0150** | 0.0396*** | 0.0145** |
| (0.0091) | (0.0068) | (0.0091) | (0.0068) | (0.0090) | (0.0068) | |
| R – Arts, Entertainment and Recreation | 0.0446*** | 0.0131** | 0.0432*** | 0.0127* | 0.0433*** | 0.0127* |
| (0.0089) | (0.0067) | (0.0089) | (0.0067) | (0.0089) | (0.0067) | |
| S – Other Service Activities | 0.0478*** | 0.0106 | 0.0466*** | 0.0103 | 0.0464*** | 0.0102 |
| (0.0087) | (0.0065) | (0.0088) | (0.0065) | (0.0087) | (0.0065) | |
| T – Activities of Households as Employers; Undifferentiated Goods and Services Producing Activities of Households for Own Use | 0.1345*** | 0.1387*** | 0.1356*** | 0.1399*** | 0.1299*** | 0.1388*** |
| (0.0161) | (0.0120) | (0.0162) | (0.0121) | (0.0162) | (0.0121) | |
| U – Activities of Extraterritorial Organisations and Bodies | 0.1070*** | 0.0519*** | 0.1082*** | 0.0532*** | 0.1045*** | 0.0525*** |
| (0.0161) | (0.0120) | (0.0162) | (0.0121) | (0.0162) | (0.0121) | |
| IE041 (Reference Category) (Donegal, Sligo, Leitrim, Cavan and Monaghan) | ||||||
| IE042 (Galway, Mayo and Roscommon) | 0.0028 | 0.0036 | 0.0029 | 0.0036 | 0.0018 | 0.0034 |
| (0.0032) | (0.0024) | (0.0032) | (0.0024) | (0.0032) | (0.0024) | |
| IE051 (Clare, Tipperary and Limerick) | 0.0095** | 0.0041 | 0.0092** | 0.0040 | 0.0029 | 0.0027 |
| (0.0042) | (0.0031) | (0.0042) | (0.0031) | (0.0042) | (0.0032) | |
| IE052 (Waterford, Kilkenny, Carlow and Wexford) | 0.0051 | 0.0047 | 0.0049 | 0.0045 | 0.0003 | 0.0036 |
| (0.0047) | (0.0035) | (0.0047) | (0.0035) | (0.0047) | (0.0035) | |
| IE053 (Cork and Kerry) | 0.0076* | 0.0026 | 0.0072 | 0.0025 | 0.0053 | 0.0021 |
| (0.0045) | (0.0034) | (0.0045) | (0.0034) | (0.0046) | (0.0034) | |
| IE061 (Dublin) | 0.0201*** | 0.0171*** | 0.0198*** | 0.0170*** | 0.0082 | 0.0146** |
| (0.0076) | (0.0057) | (0.0076) | (0.0057) | (0.0077) | (0.0057) | |
| IE062 (Wicklow, Kildare, Meath and Louth) | 0.0100* | 0.0091** | 0.0099* | 0.0089** | −0.0003 | 0.0069* |
| (0.0054) | (0.0041) | (0.0055) | (0.0041) | (0.0055) | (0.0041) | |
| IE063 (Longford, Westmeath, Offaly and Laois) | 0.0003 | 0.0078** | 0.0002 | 0.0076* | −0.0056 | 0.0065* |
| (0.0052) | (0.0039) | (0.0052) | (0.0039) | (0.0053) | (0.0039) | |
| 2010 (Reference Category) | ||||||
| 2011 | 0.0095*** | −0.0061*** | 0.0105*** | −0.0058*** | 0.0369*** | −0.0006 |
| (0.0029) | (0.0021) | (0.0029) | (0.0022) | (0.0038) | (0.0029) | |
| 2012 | 0.0185*** | 0.0017 | 0.0190*** | 0.0021 | 0.0415*** | 0.0064** |
| (0.0028) | (0.0021) | (0.0029) | (0.0022) | (0.0043) | (0.0032) | |
| 2013 | −0.0250*** | 0.0098*** | −0.0243*** | 0.0100*** | −0.0031 | 0.0142*** |
| (0.0029) | (0.0021) | (0.0029) | (0.0022) | (0.0038) | (0.0028) | |
| 2014 | 0.0224*** | 0.0036* | 0.0243*** | 0.0039* | 0.0524*** | 0.0096*** |
| (0.0029) | (0.0022) | (0.0030) | (0.0022) | (0.0037) | (0.0028) | |
| 2015 | 0.0295*** | 0.0041* | 0.0298*** | 0.0044** | 0.0463*** | 0.0075** |
| (0.0029) | (0.0022) | (0.0029) | (0.0022) | (0.0041) | (0.0030) | |
| 2016 | 0.0326*** | 0.0128*** | 0.0326*** | 0.0129*** | 0.0446*** | 0.0152*** |
| (0.0030) | (0.0022) | (0.0030) | (0.0022) | (0.0039) | (0.0029) | |
| Constant | 0.3053** | 0.0478 | 0.3008** | 0.0468 | 0.1330 | 0.0124 |
| (0.1502) | (0.1122) | (0.1502) | (0.1122) | (0.1508) | (0.1127) | |
| Observations | 82,383 | 82,383 | 82,383 | 82,383 | 82,383 | 82,383 |
| R-squared | 0.0463 | 0.0322 | 0.0465 | 0.0322 | 0.0483 | 0.0324 |
| χ2 | 4003.14 | 2742.97 | 4016.09 | 2743.97 | 4182.84 | 2755.95 |
| p-value | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
Note(s): Standard errors in parentheses, ***p < 0.01, **p < 0.05, *p < 0.1. Eqn. means Equation
Source(s): Author's own work
Expected coefficient signs for multiplier and competition effects
| Multiplier | Competition | |
|---|---|---|
| + | – | |
| + | – | |
| – | + | |
| – | + |
Note(s): Where
Source(s): Table is based on work from Johnson and Parker (1994)
Regions representatives
| Region | % | Region | % |
|---|---|---|---|
| Carlow | 2.58% | Cork County | 3.75% |
| Dublin City | 3.85% | Kerry | 3.16% |
| South Dublin | 3.57% | Limerick City | 2.88% |
| Dublin Fingal | 3.18% | Limerick County | 2.96% |
| Dun Laoghaire-Rathdown | 2.99% | North Tipperary | 2.79% |
| Kildare | 3.32% | South Tipperary | 2.61% |
| Kilkenny | 2.85% | Waterford City | 2.57% |
| Laois | 2.53% | Waterford County | 2.62% |
| Longford | 2.27% | Galway City | 2.79% |
| Louth | 3.09% | Galway County | 3.29% |
| Meath | 3.31% | Leitrim | 2.19% |
| Offaly | 2.63% | Mayo | 3.07% |
| Westmeath | 2.89% | Roscommon | 2.54% |
| Wexford | 3.07% | Sligo | 2.65% |
| Wicklow | 3.21% | Cavan | 2.64% |
| Clare | 3.18% | Donegal | 3.23% |
| Cork City | 3.27% | Monaghan | 2.47% |
Source(s): Author's own work
NACE 1-digit sectors representatives
| NACE 1-digit industries | % |
|---|---|
| B- Mining and Quarrying (Reference Category) | 1.05% |
| C – Manufacturing | 26.43% |
| D – Electricity, Gas, Steam and Air Conditioning Supply | 0.63% |
| E – Water Supply; Sewerage, Waste management and Remediation Activities | 1.63% |
| F – Construction | 5.08% |
| G – Wholesale and Retail Trade; Repair of Motor Vehicles and Motorcycles | 21.97% |
| H – Transport and Storage | 3.71% |
| I – Accommodation and Food Service Activities | 2.01% |
| J – Information and Communication | 5.05% |
| K – Financial and Insurance Activities | 3.63% |
| L – Real Estate Activities | 1.04% |
| M – Professional, Scientific and Technical Activities | 4.68% |
| N – Administrative and Support Service Activities | 7.57% |
| O – Public Administration and Defence; Compulsory Social Security | 1.25% |
| P – Education | 2.86% |
| Q – Human Health and Social Work Activities | 3.07% |
| R – Arts, Entertainment and Recreation | 3.46% |
| S – Other Service Activities | 4.34% |
| T – Activities of Households as Employers; Undifferentiated Goods and Services Producing Activities of Households for Own Use | 0.27% |
| U – Activities of Extraterritorial Organisations and Bodies | 0.27% |
Source(s): Author's own work
Variable definitions and descriptions
| Variable | Description | Calculation |
|---|---|---|
| Herfindahl index | The Herfindahl index measures concentration in a particular industry. A higher value indicates higher industrial concentration in a region, while a lower value indicates a lower level of industrial concentration in the region (van Egeraat et al., 2018). The index of | Where HHIi is the Herfindahl index for region i; yij is the number of firms in region i in NACE four-digit industry j; and yi is the number of firms in region i |
| The location quotient (LQ) variable | The LQ variable compares the concentration of employment in a sector in a region with the concentration of the same sector nationally | Where |
| Related variety (RV) and unrelated variety (UV) | RV and UV are variables which proxy levels of industrial diversification within regions. Higher values of these indices indicate higher levels of unrelated variety or higher levels of related variety. These calculations of these variables are consistent with those used by Frenken et al. (2007) | Where |
| Population Density | The density is measured via persons per squared km | Persons per km2 |
| Log of Average Firm Size | Log of average firm size acts as a control variable for the scale of businesses within the sub-county regions | The natural log of average firm size within sub-county regions |
| Log of Income per person | Log of income per person acts as an economic control variable | The natural log of income per person within sub-county regions |
Source(s): Author's own work
Correlation matrix
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | FB Rate | 1 | ||||||||||||||||||||||
| 2 | FD Rate | 0.22 | 1 | |||||||||||||||||||||
| 3 | FB Ratet-1 | 0.12 | 0.06 | |||||||||||||||||||||
| 4 | FD Ratet-1 | 0.12 | 0.06 | 1 | ||||||||||||||||||||
| 5 | RFB Ratet-1 | 0.01 | 0.01 | 0.12 | ||||||||||||||||||||
| 6 | RFD Ratet-1 | 0.06 | 0.00 | 0.25 | 1 | |||||||||||||||||||
| 7 | UFB Ratet-1 | 0.08 | 0.06 | 0.13 | 0.10 | |||||||||||||||||||
| 8 | UFD Ratet-1 | 0.04 | 0.02 | 0.08 | 0.03 | 1 | ||||||||||||||||||
| 9 | IVFB | 0.53 | 0.12 | 0.52 | 0.11 | 0.05 | ||||||||||||||||||
| 10 | IVFD | 0.01 | 0.02 | 0.03 | 0.04 | 0.34 | 1 | |||||||||||||||||
| 11 | IVRFB | 0.11 | 0.64 | 0.09 | 0.05 | 0.03 | 0.04 | |||||||||||||||||
| 12 | IVRFD | 0.06 | 0.04 | 0.10 | 0.05 | 0.33 | 0.18 | 1 | ||||||||||||||||
| 13 | FB Rate | NA | 0.23 | 0.12 | 0.07 | 0.01 | −0.01 | 0.08 | ||||||||||||||||
| 14 | FD Rate | 0.03 | 0.02 | 0.04 | 0.06 | 0.17 | 0.26 | 0.52 | 1 | |||||||||||||||
| 15 | FB Ratet-1 | 0.54 | 0.14 | 0.15 | 0.11 | 0.06 | 0.04 | 0.10 | 0.07 | 1 | ||||||||||||||
| 16 | FD Ratet-1 | 0.10 | 0.59 | 0.08 | 0.06 | 0.03 | 0.05 | 0.07 | 0.05 | 0.30 | 1 | |||||||||||||
| 17 | RFB Ratet-1 | 0.11 | 0.66 | 0.08 | 0.05 | 0.03 | 0.04 | 0.06 | 0.04 | 0.27 | 0.89 | 1 | ||||||||||||
| 18 | RFD Ratet-1 | 0.11 | 0.66 | 0.08 | 0.05 | 0.03 | 0.04 | 0.06 | 0.04 | 0.27 | 0.89 | 1 | 1 | |||||||||||
| 19 | UFB Ratet-1 | 0.10 | 0.59 | 0.08 | 0.06 | 0.03 | 0.05 | 0.07 | 0.05 | 0.30 | 1 | 0.89 | 0.89 | 1 | ||||||||||
| 20 | UFD Ratet-1 | 0.10 | 0.59 | 0.08 | 0.06 | 0.03 | 0.05 | 0.07 | 0.05 | 0.30 | 1 | 0.89 | 0.89 | 1 | 1 | |||||||||
| 21 | Log of PD | 0.04 | 0.03 | 0.04 | 0.05 | 0.19 | 0.21 | 0.49 | 0.50 | 0.10 | 0.08 | 0.07 | 0.07 | 0.08 | 0.08 | 1 | ||||||||
| 22 | HHi | −0.03 | −0.02 | −0.03 | −0.04 | −0.14 | −0.16 | −0.38 | −0.43 | −0.10 | −0.08 | −0.06 | −0.06 | −0.08 | −0.08 | −0.43 | 1 | |||||||
| 23 | LQ | −0.05 | −0.06 | −0.05 | −0.07 | −0.02 | −0.04 | −0.01 | −0.01 | −0.08 | −0.08 | −0.08 | −0.08 | −0.08 | −0.08 | −0.04 | 0.02 | 1 | ||||||
| 24 | RV | −0.02 | 0.01 | −0.03 | 0.03 | −0.12 | 0.00 | 0.01 | −0.01 | 0.05 | 0.10 | 0.08 | 0.08 | 0.1 | 0.10 | −0.05 | 0.04 | −0.07 | 1 | |||||
| 25 | UV | 0.04 | 0.03 | 0.03 | 0.03 | 0.16 | 0.15 | 0.42 | 0.31 | 0.09 | 0.07 | 0.06 | 0.06 | 0.07 | 0.07 | 0.53 | −0.34 | −0.03 | −0.08 | 1 | ||||
| 26 | Log of Inc | 0.04 | 0.03 | 0.04 | 0.04 | 0.20 | 0.18 | 0.52 | 0.44 | 0.12 | 0.10 | 0.08 | 0.08 | 0.10 | 0.10 | 0.70 | −0.59 | −0.03 | −0.07 | 0.67 | 1 | |||
| 27 | Log Av size | −0.14 | −0.14 | −0.12 | −0.08 | 0.01 | −0.01 | 0.06 | 0.08 | −0.12 | −0.11 | −0.11 | −0.11 | −0.11 | −0.11 | 0.15 | −0.09 | 0.37 | −0.04 | 0.07 | 0.13 | 1 | ||
| 28 | Region | 0.01 | 0.00 | 0.01 | −0.01 | 0.02 | 0.01 | 0.04 | −0.01 | −0.03 | −0.03 | −0.02 | −0.02 | −0.03 | −0.03 | −0.21 | 0.12 | 0.01 | 0.02 | −0.04 | −0.12 | −0.06 | 1 | |
| 29 | Year | 0.04 | 0.02 | −0.01 | 0.00 | 0.00 | −0.04 | 0.01 | −0.26 | 0.05 | 0.02 | 0.02 | 0.02 | 0.02 | 0.02 | 0.00 | 0.02 | 0.00 | −0.02 | 0.27 | 0.15 | 0.02 | 0.00 | 1 |
Note(s): “FB” and “FD” can be read as “Firm Birth” and “Firm Death” respectively
“RFB” and “UFB” can be read as “Related Firm Birth” and “Unrelated Firm Birth”
“RFD” and “UFD” can be read as “Related Firm Death” and “Unrelated Firm Death”
“IV” can be read as “Instrumental Variable” meaning “IVFB”, for example, refers to the instrumental variable created for the Firm Birth Rate variable. The instrumental variables were created using Bartlett's three group method
Source(s): Author's own work
Notes
See Appendix 1 for the expected signs associated with multiplier and competition effects.
From a measurement perspective, firms can be considered related to each other if they operate in the same NACE 1-digit industry and unrelated if they don't.
See Appendixes 2 and 3 for a percentage breakdown of regions and NACE sectors in the model respectively.
If a dormant unit is reactivated within two years, this event is not considered to be a birth.
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Acknowledgements
This research was funded by the Irish Research Council.