As a product of this theoretical modelling, the following equation should estimate the productivity:
𝑙𝑛𝐴𝑖𝑡 = 𝑙𝑛𝐺𝑉𝐴𝑖𝑡− 𝑙𝑛𝐿𝑖𝑡 − 𝑙𝑛𝐾𝑖𝑡− 𝑟𝑖𝑡𝛿 − 𝑆𝐵𝑅𝑅𝑖𝑡+ 𝜀𝑖𝑡 Likewise, the following function should predict survival:
𝑆𝑃 = ℎ𝑜(𝑡)𝑒∝𝐿𝐿∗∝𝐾𝐾∗∝𝑟𝑟∗∝𝑆𝐵𝑅𝑅𝑆𝐵𝑅𝑅
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However, other issues were identified in the Methodology Review that may bias estimators. The theoretical debate around capitalisation in Section 2.1 started with partial equilibrium analysis emphasising the importance of the elasticity of demand and supply.
The vast majority of models presented in this thesis also acknowledges their importance. It is evident that some factors may influence the demand and its elasticity more than others.
Similarly, some firms would be more productive than others because of their inherent characteristics. Although the identification of these characteristics is not the primary aim of this thesis, this section aims at reviewing the fundamental forces within the environment of the firms together with inside forces within them, such that these powers could be controlled for during the analysis.
4.7.1 Business Characteristics
From the Methodology Review Chapter, it is apparent that one of the primary reasons for any changes to business performance is its characteristics. The vast majority of studies in the Journal of Productivity Analysis, in one way or another, control for size.
However, this is not discussed in this section; rather discussion is on a large stream of literature directly addressing characteristics that should be controlled for.
One of the most popular methodologies employed with microdata is Gibrat’s Law of Proportionate Effect (Gibrat, 1931) claiming that firm size and growth should be independent, which is partly supported even by recent studies (Lotti et al., 2009). However, either British (Konings, 1995; Hart and Oulton, 1996) or foreign (Lotti et al., 2009) studies have concluded that Gilbrat’s Law is not necessarily valid. The more sophisticated method is introduced by Davis and Haltiwanger (1992). This method relies on descriptive analysis and includes growth and death rates to show that new firms create more dynamic jobs than older firms. It is followed by such studies as Baldwin and Picot (1995). Although more advanced techniques have been employed, further studies (Aterido et al., 2009; de Kok et al., 2011; Neumark et al., 2011) have very similar conclusions. This may be explained by Jovanovic’s (1982) passive learning model, which claims that entrepreneurs learn most when they enter the market. In contrast, Anyadike-Danes et al. (2015) maintain that neither the employment size nor age explains job growth. They believe that the only explanation is a small number of rapidly growing micro firms (see Section 4.7.2, p. 92 for an extensive discussion).
4.7.2 High Growth Firms - Entrepreneurship literature
A phenomenon of high growth firms (HGFs) is defined by OECD (2008:61): ‘All enterprises with average annualised growth greater than 20% per annum, over a
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year period should be considered as high-growth businesses. Growth can be measured by the number of employees’. This definition has been used in multiple studies (Anyadike-Danes et al., 2015; Brown and Mawson, 2015; Bravo-Biosca, 2011; Teruel and De Wit, 2011) and it seems to replace the high-growth metric invented by Birch (1987).
Anyadike-Danes et al. (2015) empirical findings are based on Birch’s (1979:8) pioneering idea that ‘small firms (those with 20 or fewer employees) generated 66% of all new jobs generated in the U.S.’ Although Birch’s theories (1979; 1987) are criticised by such studies as Dennis and Phillips (1994), small firms are recognised as the main creators of jobs (Anyadike-Danes et al., 2014, 2015; Coad et al., 2014; Cowling et al., 2015; Du and Temouri, 2015). Recently, Anyadike-Danes et al. (2009:4) estimate the contribution of HGF to job creation in the UK economy. They show that ‘11,530 high growth firms were responsible for 1.3 million out of the increase in 2.4 million new jobs in established businesses employing ten or more people between 2005 and 2008’. According to their study, around 6% of all firms create 54% new jobs in the UK. Later, these statistics are confirmed by Anyadike-Danes et al. (2015): around 6% of all firms added about 40% of net jobs by 15-year survivors. However, Butcher and Bursnall (2013) show that job creation is evenly spread across different size bands, which contradict Anyadike-Danes’s et al. (2015) results. These studies provide useful understanding about national HGF populations.
However, as Anyadike-Danes et al. (2015:22) acknowledge, those firms ‘require further analysis … to understand the process of small business growth.’
The government’s policy aimed at small businesses may be discouraging high growth firms, which according to recent evidence are drivers of employment and growth.
Firms may be less likely to expand due to even more significant costs associated with a loss of SBRR in case they either improve the existing premises or relocate. Therefore, they have to restrict themselves to existing premises which may limit their investment in equipment and employment. However, it could be argued that firms receiving SBRR may be encouraged to invest more in technology, which should result in higher total factor productivity. On the other hand, recent British evidence points out that there is little evidence of the correlation between investment in R&D and both TFP growth (Harris and Moffat, 2017) and survival (Harris and Moffat, 2016).
4.7.3 Research and Development (R&D)
Researchers tend to assume that investment in R&D is vital for a business to succeed. They often use the Schumpeterian theory of creative destruction where firms have to continuously innovate to sustain their market share (the debate on Schumpeterian creative destruction is presented in Section 2.2.2.1). However, the evidence is diverse. As
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shown in the following paragraphs, it is unclear whether investments in R&D would have any effect on productivity and survival. We suggest that this might be because not all R&D projects are successful (e.g. NOKIA’s example).
Since the pioneering work of Griliches (1979), productivity studies have considered the technological spillovers to be a side product of R&D activities. The endogenous growth models of Romer (1990) and the quality ladder models of Grossman and Helpman (1991) and Aghion and Howitt (1992) theorise that innovations drive long-term economic growth and aggregate productivity. Many scholars (Medda and Piga, 2014; Ulku and Pamukcu, 2015; Franco et al., 2016) have found R&D to be very influential in the process of productivity. For instance, Franco et al. (2016) discover that service regulation reduces R&D efficiency in the manufacturing sector with knowledge production function on OECD industries with a stochastic frontier analysis. An Italian study (Medda and Piga, 2014) results in a very similar conclusion through the use of Tobit and logit regressions. They suggest that a firm’s investment in R&D results in productivity gains. However, the approach utilised in this thesis is similar to Ulku and Pamukcu’s (2015) study, which considers Turkey with a GMM estimator over the five-year period. They discover that a rise in both the foreign ownership share and technology licensing increases firms’ productivity.
It is worth noting that the conditional effect of the latter is significant only above a threshold of technological capability. It is questionable whether these results could be applied to the context of Britain because of the different economic conditions.
On the contrary, another stream of the literature (Ilmakunnas and Piekkola, 2014;
Chen and Inklaar, 2016; Goya et al., 2016) found that R&D has little or no influence on productivity. For instance, Goya et al. (2016) applies Olley and Pakes (1996) estimator to Spanish firms over the period 2004–2009 and finds that R&D expenditures do not have a direct impact on firm performance but spillovers do. Moreover, US study (Chen and Inklaar, 2016) by using a variation of Cobb-Douglas productivity function estimated by FE and GMM fails to find any evidence that knowledge spillovers within the same industry exist within the US. The similar, but more sophisticated approach of Levinsohn and Petrin (2003), is used by Ilmakunnas and Piekkola (2014) on Finnish firm-level data from 1998 to 2008.
They find that organisational activity tends to increase TFP, but R&D returns tend to be low.
More macro studies comparing different countries found similar conclusions. For instance, Luintel et al. (2014) used various estimates of R&D. They find that human capital, international knowledge spillovers and domestic knowledge stocks are the determinants of domestic productivity across nations. Another study by Cincera and Ravet (2014) looks at subsidies of large EU firms. They find a positive effect of globalisation on firms’
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productivity with R&D investment, especially in the US, while an adverse effect of industrial diversification is identified. However, they used pooled-OLS estimates and the sample consisted of mainly large companies. Therefore, it may not be applicable for this research project.
4.7.4 Foreign Ownership and Foreign Direct Investment
Another commonly included variable related to R&D is a foreign direct investment or foreign ownership. These are most often found to be influential by emerging countries as these are likely to adopt a better practice than the more developed economies. Most emerging countries like India (Malik, 2015), Indonesia (Sari et al., 2016), China (Huang and Fu, 2013; Baltagi et al., 2015) report positive effects of foreign direct investment on their productivity. Similarly, several studies in emerging markets such as Turkey (Ulku and Pamukcu, 2015) and India (Girma and Vencappa, 2014) show that foreign ownership increases productivity.
In general, advanced economies tend to have lower effects. One UK study (Harris and Moffat, 2015) fits their GMM model into plant-level-panel data from the Annual Respondents Database covering 1997–2008. They find that most foreign ownership groups have higher than average TFP, but the effect is marginal. They argue that it is because of law number of the foreign firms, which is also expected in the sample used in this thesis as discussed in the Research Design Chapter (Section 6.1.3).
4.7.5 Employees characteristics
Building on the dynamic capabilities theory introduced in Section 2.2.2.3, it is clear that employees and employers may be the driving force behind any increases in TFP and survival. The previous researchers were using several angles and methods to at least partly account for them, more often than controlling for these, they focused on one of the characteristics. Several studies have investigated whether employees’ bonuses, nationality and age have any effect on productivity. For instance, Akay and Dogan (2013:123) partly extend Jones (1971:3–21) theory, who suggests that an increase in the volume of labour in the economy will raise the output in all industries. Akay and Dogan add that ‘the magnitudes of the increases in some industries are more than others depending on the value of the elasticity of substitution along with factor intensities between industries.’ They use a generalised form of the standard specific factors model using 25 US industries. More advanced economies such as the Spanish (Gómez-Déniz and Pérez-Rodríguez, 2015) and the Italian (Bettin et al., 2014) are investigating migrant workers. For instance, the Spanish study (Gómez-Déniz and Pérez-Rodríguez, 2015), observing labour productivity, shows
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that immigrants increase Spanish labour productivity significantly. Arguably, this methodology may not be the most appropriate because it does not consider such inputs as materials and capital forming total factor productivity.
4.7.6 Implications for the Analysis
These two Subsections (4.6 and 4.7) attached to the framework suggested that the analysis should control for some key characteristics to not over(under)estimate the causal relationships between the variables. It is evident that some factors may influence the demand and its elasticity more than others. Similarly, some firms would be more productive than others owing to their inherent characteristics. Although identification of these characteristics is not the primary aim of this thesis, this section reviewed the fundamental forces within the environment of the firms together with inside forces, so that these powers could be controlled for during the analysis. The discussion found that the accuracy of the framework is likely to depend on the competition. Furthermore, it emphasised the importance of controlling the key business characteristics, their growth patterns, R&D investment, foreign ownership and foreign investment as well as employees’
characteristics.
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5 M ETHODOLOGY R EVIEW
The theoretical framework was introduced in the previous chapter. This chapter aims to uncover how this thesis could employ the empirical analysis shaped by the framework. More specifically, to investigate how other researchers approached the productivity and survival analyses so that the approach applied in this thesis would not just be appropriate for the data but also forward-looking. It is worth recalling that the overall aim of this thesis is to understand whether Small Business Rates Relief (SBRR) has any effects on firms, in particular with regard to their productivity and survival. Total factor productivity (TFP) is the portion of output not explained by the amount of inputs used in production. Whilst, survival analysis in this setting is a method for analysing the expected duration of time until closure.
As illustrated in Figure 5:1, this chapter is divided into three major sections. The first (Section 5.1) is devoted to productivity and its estimation. It starts by reporting the results from the systematic literature review on methodologies (Section 5.1.1). It then attempts to find an appropriate functional form for productivity (Section 5.1.2) and discusses how these functions were estimated by other researchers (Section 5.1.3) as well as showing empirical examples appropriate for the microdata (Section 5.1.4). As a result, the chapter establishes that an appropriate functional form is a logarithmic Cobb-Douglas productivity function and it should be estimated with control functions and system-GMM approaches. Then, the focus turns to survival analysis (Section 5.2). This subsection is divided into basic mechanisms (Section 5.2.1), definitions (Section 5.2.2) and some recently applied techniques (Section 5.2.3.1). The preferred method is selected to be the Cox proportional hazard model (Section 5.2.3.2). Finally, the chapter develops into machine learning extensions (Section 5.3). It defines, introduces (Section 5.3.1) and gives examples (Section 5.3.2) of how these approaches were applied in previous studies.
Figure 5:1 Overview of the Methodology Review Chapter
TFP is estimated with Wooldridge one-step estimator and then affects are measured with unbiassed REEM trees. The results are supplemented with the dynamic estimator Survival is estimated with both CPH and ST
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