Ámbito nacional y local

Background Chapter). Full version with size, year, region and labour productivity

variables is available in Appendix 10.4.2.

Unlike in BR case, both random forests and decision trees found year variable to be the most important. Random forests ordered other variables according to their importance: labour productivity, turnover, sectors, regions, employment. Now, sector and region were found to be more influential than employment.

More specifically, the outputs showed the misclassification of SBRR. For instance, the intense mistargeting is evident in Figure 7:4, where not just micro but also medium and large firms were receiving substantial reliefs up to 100% and this misallocation particularly increased after 2009.

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With regards to the timescale, the decision tree in Figure 10:7 (p. 279) showed that none of the firms received SBRR during 2000-2004. Between 2005 and 2009, highest relief (~30%) was received by micro (according to turnover) firms in construction, retail and other services sectors in North West, East of England and South West. Further branches on these sectors show that in Yorkshire, East & West Midlands, London and South East large, medium or small firms would be more likely to receive more extensive relief than micro firms (according to employment).

Somehow similar patterns but with higher SBRR were evident for 2010-2015 data. More substantial reliefs were likely to be given to firms with a micro turnover. On average, firms with lower productivity were also likely to receive more substantial reliefs. Also, 109 micro (according to employment) firms in Yorkshire and London in catering, production and retail sectors were receiving lower SBRR than larger firms with exact circumstances. However, micro firms according to both turnover and employment were receiving higher reliefs than other firms, but SBRR seemed to be highly dependent on regions. For instance, micro firms in North East, North West, Yorkshire, East Midlands and London were receiving lower reliefs than other areas.

Regional effects were present, but depended on other variables. This is understandable given different factors such as competition or supply of the premises. Between 2005 and 2010, micro firms operating construction, retail, production, wholesale sectors were receiving 4% relief in London and North East, and Wales and Scotland just 1% on average, while in other regions ~2%. Construction, retail and other service sectors deviated more substantially with an average relief of 33% in North West, East of England and South West, 8% in Wales, Scotland and North East and 26% in other countries (in other just with employees <= 10). More recently (2010-2015), micro firms in construction, property and other services in North East, North West, Yorkshire & Humberside, East Midlands and London received 58% SBRR, whilst similar firms in other regions received reliefs of ~36% on average. On the other hand, in catering, production, retail and wholesale sectors micro firms in Scotland, North West, West Midlands, South East and South West reported reliefs of 26%, while similar firms in other regions reported 51% on average.

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7.2 T

F

P

This section describes the results from TFP estimation. It starts by providing an extensive discussion on the results from Wooldridge’s System GMM estimator (Section 7.2.1) combined with REEM trees (Section 7.2.2) and then in relation to standard econometric techniques equipped to deal with dynamic models (Section 7.2.3). The primary results are supplemented with sensitivity analysis reported in the Appendix 10.4.1. The functional forms and methodology were defined in the Research Design Chapter.

Shortly, system GMM estimator combined with REEM trees identified some relationships between SBRR and TFP. The unbiased REEM tree found cases where SBRR was having mainly negative links to TFP. Other factors such as region, rent, sector, competition indexes (HHI, PD, PS) and foreign ownership were also found to be influential. 7.2.1 Dependent Variable in REEM & Independent in Survival Analyses

Figure 7:5 (p. 197) provides the firm level evolution of estimated TFP during 2005- 2015 and links this to the receipt and degree of SBRR. TFP peaked in 2009 and subsequently declined, reflecting the generally negative economic shock. The average estimate of TFP was relatively similar whether firms were in receipt of SBRR or not. The small difference may be explained in part by the tapping nature of the relief, but there is no particular pattern in TFP. However, some differences were evident between SBRR recipients and non- recipients after 2006-2007, 2011 and 2012. These patterns might be related to either introduced or increased SBRR. Non-recipients seemed to outperform recipients before the SBRR introduction (2005) but in the following year, the recipients seemed to outperform non-recipients. Overall, it is clear there is diverse and complex variation around the average levels of productivity even among these carefully matched firms.

The TFP was estimated with Woodridge (2009) method. The matched data with imputation and without provided consistent estimates. As reported in Table 7:3 (p. 198), the coefficients stayed relatively similar after bootstrapping. The imputed dataset with bootstrapping estimated labour and capital coefficients to be 0.018 (SE=0.003) and 0.025 (SE=0.020), respectively. The capital coefficients deviate significantly across the methods. For instance, for the unmatched and unimputed data, estimates deviated significantly with labour and capital coefficients of 0.013 (SE=0.001) and -2.273 (SE=0.226), respectively. The main reason for this may be that the sample size decreases when the data is not imputed, resulting in bias. Also, as described in the Research Design, the capital was approximated. Thus, some estimation errors can be expected.

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Figure 7:5 The comparison of TFP between SBRR recipients and non-recipients. The white lines correspond to the annual averages of all