Are financing conditions a threat to SMEs' performance, growth, and transformation?

Ferrando, Annalisa; Pál, Rozália Working Paper Are financing conditions a threat to SMEs' performance, growth, and transformation? ADBI Working Paper, No. 1467 Provided in Cooperation with: Asian Development Bank Institute (ADBI), Tokyo Suggested Citation: Ferrando, Annalisa; Pál, Rozália (2024) : Are financing conditions a threat to SMEs' performance, growth, and transformation?, ADBI Working Paper, No. 1467, Asian Development Bank Institute (ADBI), Tokyo, https://doi.org/10.56506/CCHD8076 This Version is available at: https://hdl.handle.net/10419/305427 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Annalisa Ferrando and Rozália Pál No. 1467 July 2024 Asian Development Bank Institute The Working Paper series is a continuation of the formerly named Discussion Paper series; the numbering of the papers continued without interruption or change. ADBI’s working papers reflect initial ideas on a topic and are posted online for discussion. Some working papers may develop into other forms of publication. Suggested citation: Ferrando, A. and R. Pál. 2024. Are Financing Conditions a Threat to SMEs’ Performance, Growth, and Transformation? ADBI Working Paper 1467. Tokyo: Asian Development Bank Institute. Available: https://doi.org/10.56506/CCHD8076 Please contact the authors for information about this paper. Email: [email protected] Annalisa Ferrando is a senior lead economist at the European Central Bank, Frankfurt am Main, Germany. Rozália Pál is a senior economist at the European Investment Bank, Luxembourg. The views expressed in this paper are the views of the author and do not necessarily reflect the views or policies of ADBI, ADB, its Board of Directors, or the governments they represent. ADBI does not guarantee the accuracy of the data included in this paper and accepts no responsibility for any consequences of their use. Terminology used may not necessarily be consistent with ADB official terms. Discussion papers are subject to formal revision and correction before they are finalized and considered published. We would like to thank Eddie Gerba and the participants of the workshop on SME performance under uncertainty at ADBI for their useful comments on a previous version of this paper. The opinions expressed herein are those of the authors and do not necessarily reflect those of the European Investment Bank or the European Central Bank. The usual disclaimers apply. Asian Development Bank Institute Kasumigaseki Building, 8th Floor 3-2-5 Kasumigaseki, Chiyoda-ku Tokyo 100-6008, Japan Tel: +81-3-3593-5500 Fax: +81-3-3593-5571 URL: www.adbi.org E-mail: [email protected] © 2024 Asian Development Bank Institute ADBI Working Paper 1467 Ferrando and Pál Abstract Availability of internal and external financing sources has an impact on firms’ investments and growth. Even profitable firms with sufficient financing sources in normal times can be affected by demand and supply shocks such as the COVID-19 lockdown, the energy crisis, or the recent tightening of financing conditions. This paper analyzes the impact of funding difficulties on firms’ investment, performance, and growth during normal periods and periods of external shocks using a regression adjustment treatment effect approach. We differentiate among structural barriers of external financing and cyclical worsening of financing conditions, controlling for other major investment barriers. We use survey data collected from the first to the eighth vintage of the European Investment Survey (EIBIS). The empirical evidence shows that micro and small firms and leading innovators are particularly vulnerable to deteriorating funding conditions. Results indicate that firms’ lagging in digitalization and green investments are facing more a structural rather than cyclical financing issue. Consequently, policy support should be oriented towards those structural financing impediments. Keywords: SMEs, investment gap, external funding, internal funding, financing constraints, uncertainty, investment barriers, firm performance, growth, digital and green transition JEL Classification: C83, D22, G32 ADBI Working Paper 1467 Ferrando and Pál Contents 1. INTRODUCTION AND RESEARCH BACKGROUND ................................................. 1 2. DATA ............................................................................................................................ 3 3. ECONOMETRIC FRAMEWORK .................................................................................. 5 4. EMPIRICAL RESULTS ................................................................................................ 7 5. CONCLUSION AND POLICY IMPLICATIONS .......................................................... 15 REFERENCES ...................................................................................................................... 16 ANNEX ................................................................................................................................... 18 ADBI Working Paper [Do not enter] Ferrando and Pál 1 1. INTRODUCTION AND RESEARCH BACKGROUND The growth of SMEs is much more conditioned by access to finance than that of large firms (Fouejieu, Ndoye, and Sydorenko 2020). As firms can grow if they invest and this often requires financial sources beyond their income and savings, SMEs are less likely to be able to obtain the additional external financing they need regardless of the potential of their investment projects. Their sources of funds are often limited to their internal funds and financial support from friends and family (Ardic et al. 2013). Their financing situation can be worsened in periods of external shocks, such as the sudden drop of sales caused by the COVID-19 lockdowns, the recent increases in production costs due to high energy prices, and the tightening of external financing conditions through interest rate hikes or stricter lending conditions. The missed investment opportunities caused by the limited amount of funds are not only detrimental in the short term, especially in the present period of structural shifts towards digitalization and greening, but they might have longer-term implications for the growth, productivity, and competitiveness of the European economy. There is evidence also that the promising high growth enterprises that generates most of the employment growth in the economy face financing constraints and seek for alternative financing, additionally to the traditional bank loans (Ferrando, Pál, and Durante 2019). Moreover, the greening of the economy coincides with a similar race to adapt digital solutions at the firm level and these trends are mutually supportive. Survey-based analyses of European companies show that digital firms were more resilient during the COVID-19 pandemic, and they were less likely to reduce employment (Coad et al. 2023, EIB 2023). Furthermore, the poor degree of digitalization among SMEs and the digitalization gap between digital and nondigital firms widened for SMEs amid external shocks (Teruel et al. 2023). Without investments, such a digital divide might increase further. This paper investigate how financing difficulties affect firms’ investment decisions of different firm groups. We show that there is a greater risk of missed investment opportunities for small-sized and innovative firms than for large companies, due to their relatively higher vulnerability in periods of cyclical deterioration, which goes beyond the structurally high accumulated investment gap. With regard to the green and digital transformation, we also find evidence that those lagging behind in adopting digital solutions and those investing or planning to invest in green solutions are structurally facing more difficulties in accessing external financing. However, we do not find a significant difference in terms of financing deterioration during crises/tightening periods between transforming and nontransforming firms. Moreover, we show that the financing conditions of firms strongly impact their investments and have a strong influence on the following two years’ performance and growth. There are several studies in the literature that tackle the impact of financial constraints on investment, employment, and innovation, e.g., Butler and Cornaggia (2011) and García-Posada (2019) show that firms operating in environments with lower financial constraints are able to invest more and increase production. Fernandes and Ferreira (2017) and Duygan-Bump, Levkov, and Montoriol-Garriga (2015) stress the negative effect of financial constraints on employment. Ferrando and Ruggieri (2018) find that lower financial constraints improve labor productivity, while Gorodnichenko and Schnitzer (2013) document the negative effect of financial constraints on a firm’s innovation. ADBI Working Paper [Do not enter] Ferrando and Pál 2 By contrast, the paper of Bonanno, Ferrando, and Rossi (2023) highlights a different role of financial constraints on firms’ behaviour. Looking at the interplay between firms’ efficiency, innovation, and access to finance, the authors find that financially constrained firms tend to improve their efficiency to reduce their risk of failure and to maintain profits, regardless of the sectoral disaggregation. However, when financial constraints are binding, firms in low-tech sectors are induced to be more efficient than those in high-tech sectors to enhance their profitability. The literature also shows that financial constraints can have amplified effects during cycles: Aghion et al. (2012) find that for credit-constrained firms, R&D investment plummets during recessions and investment does not increase proportionally back during upturns, while Musso and Schiavo (2008) find that in the presence of asymmetric information and financial constraints, even small shocks may amplify business cycle fluctuations. Our paper is close to both trends of literature investigating the role of financing frictions during normal times and periods of economic downturn, as presented above, but we are able to analyse these two trends simultaneously. Moreover, in this paper, we provide deeper insights into the effect of internal and external funding conditions and types of firm characteristic s that it might improve or further deteriorate the investment trend amid the given funding condition. To analyze the investment behavior of firms in relation to funding difficulties, we cross-check alternative variables like a longer-term accumulated investment gap, of the dropping of investment projects recently, planned investment drops, and changes in the investment rate. We also show that the presence of external finance difficulties considerably affects profitability and asset growth on average two years later. Interestingly, even firms with no accumulated investment gap or planned investment drop are affected in the long term by external funding conditions, resulting in lower performance and growth. From a policy perspective, our analysis points to the importance of firm-level policy support both in normal times and in periods of crisis and transition, not only for short-term survival and stabilization but also for longer-term targets. Recently, it has been shown that firms that benefitted from the policy support during the COVID-19 pandemic tend to be more optimistic about their investment plans, especially those in digital technologies (Harasztosi et al. 2022). In this paper, we argue more generally that policy makers should provide special attention to SMEs and leading innovators not only due to their structurally higher external financial constraints but also because of the impact of cyclical deteriorations of financing conditions. With respect to the green and digital transformation, we found less evidence of the impact of crisis. This is in line with previous findings suggesting that recent crises might act as a push for digitalization, especially during the COVID-19 crisis, and for greening and increasing energy efficiency, especially during the energy price shock, as a strategy for survival. Nevertheless, we do find significant results indicating greater funding difficulties structurally across years among those firms lagging in digitalization and green investments. The paper proceeds as follows. The next two sections describe the data and the empirical methodology. Section 4 outlines the empirical results, while Section 5 concludes and presents some policy recommendations. ADBI Working Paper [Do not enter] Ferrando and Pál 3 2. DATA For our analysis, we rely on the pooled first to eighth vintages of the European Investment Bank Investment Survey (the EIBIS 2023), combined with Moody’s ORBIS database. The EIBIS database contains information on more than 12,000 nonfinancial firms annually in the EU for the period 2016–2023. EIBIS is an EU-wide survey that gathers qualitative and quantitative information on investment activities by nonfinancial corporations, both SMEs (5–250 employees) and larger corporates (250+ employees), their financing requirements, and the difficulties they face when running their business. Using stratified sampling, EIBIS is intended to be representative across all 27 member states of the EU, within countries, four firm size classes (micro, small, medium, and large), and four sector groupings (manufacturing, services, construction, and infrastructure).1 For each firm, the survey replies are linked to information derived from the annual financial statements obtained from Moody’s ORBIS database. More importantly for our analysis, the survey contains information about changes in internal and external financing conditions, difficulties in obtaining any external financing, long-term investment barriers, types of investment (ranging from fixed tangible assets to intangible assets and innovation types new to firms, new to companies, or new to the global markets), realized investments, accumulated investment gaps compared to their needs/opportunities, and investment plans for the near future. The alternative investment variables enable us to conduct a profound analysis of the financing-investment relationship. Moreover, in terms of the link to the financial statements of firms, we are able to check the performance and growth dynamics of firms. Our main variables of interest are the encompassing indicators of external and internal financing difficulties. For the indicator of external funding difficulties, we also define separately the cyclical and the structural funding difficulties. The structural barriers of firms’ access to finance are indicated by the level of the development of the financial sectors and other firm-specific characteristics, like their transparency, credibility, level of tangible assets, profitability, etc. To capture such elements on the supply side of financing, in this category we consider those viable firms that needed a loan but were either discouraged or rejected (fully constrained) or received less than they needed (quantity constraints) or it was too expensive (price constraints). We check viability by not registering losses for three consecutive years, to be certain that constraints are due to external financing conditions and not to the weak financial performance of the firm. To eliminate the time-varying cyclical component of this variable, we take the average value of the firm level variable across years. We capture separately the tightening financial cycles, regardless of the level of development and characteristics of the financial system and firms. For this, we consider the perception of firms regarding the changes in their external financing conditions. To eliminate internal (like the successfulness/viability of the business strategy) versus external drivers, we exclude from this category firms that register losses for three consecutive years (financially weak firms regardless of the cycle). The encompassing external funding difficulties indicator combines these two sources of structural and cyclical financing impasse. Figure 1 presents both the structural and cyclical external funding difficulties variables for the financial period 2015–2022 covered by the 2016–2023 EIBIS waves. 1 EIBIS has been shown to be a reliable data source with no systematic sampling bias (Brutscher et al. 2020). ADBI Working Paper [Do not enter] Ferrando and Pál 4 Figure 1: External Funding Difficulties – by Structural and Cyclical Components Our second main variable, the internal funding difficulties, is defined as those viable firms declaring that their internal finance conditions have deteriorated. To exclude the impact of firms with financial problems (zombie firms), which are less probable to invest, we consider only those that do not register losses for three consecutive years. Furthermore, our analysis relies on several variables from EIBIS 2016–2023 and Orbis 2015–2022 as described in Table 1 in the Appendix. We distinguish four different dependent variables: 1) investment gap; 2) drop in realized investment; 3) drop in planned investment; 4) net investment rate. The first three variables are derived from the survey responses and are constructed as dummies. Each is equal to 1 if: (1) firms declare that investment over the last three years was too little to ensure the success of their business going forward (investment gap); (2) firms report less investment than in the previous year (realized investment drop); 3) the total investment spent in the current or next year is expected to be less than in the previous year (planned investment drop). The last variable is the net investment rate, which is defined as the difference in fixed assets between two subsequent years, over lagged fixed assets. Table A1 also includes the definitions of several control variables, like size classes, sectors, and a set of financial ratios (leverage, profitability, cash holding), as well as several dummy variables on the obstacles to investment activities. We also control for digital and green investments. Table 1 displays several characteristics of the firms in our dataset. Around 14% of firms report having some external funding difficulties, while 12% say that these difficulties are related to cyclical conditions and around 5% to structural issues (some overlap is likely between the two variables). The percentage of firms signaling internal funding difficulties is slightly lower (12%). An investment gap is reported by 15% of firms, while the difference between the drop in realized investment (21%) and the drop in planned investment (27%) is 6 percentage points. 0% 5% 10% 15% 20% 25% 2015 2016 2017 2018 2019 2020 2021 2022 structural cyclical ADBI Working Paper [Do not enter] Ferrando and Pál 11 Table 6: Determinants of Investment – Impact of External/Internal Funding Deterioration and Increase in Uncertainty, Marginal Effects (1) (2) Variables Investment Gap Planned Investment Drop External funding difficulties (lag) 0.041*** 0.032*** (0.008) (0.008) Internal funding difficulties (lag) 0.058*** 0.148*** (0.009) (0.009) Cash holdings (lag) –0.064*** –0.063*** (0.023) (0.020) Profitability (lag) –0.309*** –0.017 (0.031) (0.022) Financial leverage (lag) –0.003 0.100*** (0.016) (0.014) leading_innovators1 –0.020 –0.034*** (0.013) (0.011) Small –0.019* –0.044*** (0.011) (0.009) Medium –0.043*** –0.072*** (0.011) (0.009) Large –0.052*** –0.110*** (0.011) (0.010) Construction –0.026*** –0.025*** (0.009) (0.008) Services –0.027*** –0.012 (0.008) (0.008) Infrastructure 0.003 –0.044*** (0.008) (0.007) South –0.084*** –0.018*** (0.007) (0.007) West and North –0.058*** 0.017*** (0.008) (0.006) D2020 –0.010 0.178*** (0.009) (0.008) Obstacle – uncertainty (lag) 0.032*** 0.052*** (0.009) (0.007) Obstacle – lack of demand 0.012* 0.028*** (0.007) (0.006) Obstacle – lack of skilled staff 0.020** –0.021*** (0.008) (0.007) Obstacle – digital infrastructure 0.007 –0.009 (0.007) (0.006) Observations 15,000 30,967 Standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. ADBI Working Paper [Do not enter] Ferrando and Pál 12 With regard to firm characteristics, we find that firms with greater financial leverage are more likely to invest less, while cash savings and profitability clearly serve as a positive buffer for investments. Leading innovators are less likely to decrease their planned investments, despite the relatively worse external funding conditions. This can be explained by the strong reliance on internal or alternative sources as well as on the fact that R&D investments, once launched, are planned for a longer period of multiple years. Across size classes, larger-sized firms have a significantly lower accumulated investment gap than micro firms. Further, they are also less likely to drop their future investments than micro/small firms. Uncertainty, a lack of demand, and a lack of skilled staff are significant barriers to investments, thus accumulating investment gaps. Nevertheless, firms with a lack of skilled staff are more resilient in terms of investment plans. This is in line with the idea that a lack of skilled staff reported by firms as an obstacle to investment may be thought to incentivize other types of investments, for instance in digital or AI to substitute for skilled workers, rather than in trainings to enhance the skill of already employed workers. However, in other studies (EIB 2024) it is found that high-growth firms, which invest relatively more than average firms, are more often constrained by the unavailability of qualified workers. In an additional exercise, we split the external funding difficulties into structural and cyclical components. As previously stated, among firms that show structural difficulties in obtaining external finance, we consider viable firms that needed a loan but were either discouraged, did not receive it, or received less than they needed. We group in the category of firms signaling cyclical difficulties those that, regardless of their current external financing possibilities, believe that external financing conditions are worsening. In the econometric analysis presented in Table 7, the investment gap is likely to increase due to both structural and cyclical components, with a higher impact of the former (Column 1). The estimated drop in planned investment indicated in Column 2 is driven instead by the cyclical component, while the negative coefficient of the structural component tends to mitigate the overall impact. This we may explain by the fact that those firms that structurally face difficulties in accessing external financing rely more, or exclusively, on internal financing, so they are less indebted in general and consequently less impacted by tightening of the financing conditions. Impact of External Financing Difficulties on Performance and Growth In this section we present the results on the ex post effect of the presence of external funding difficulties on firm performance and growth. The propensity score is run using the specification described in Section 3. Table 8 shows the distribution of the sample across firms with external funding difficulties and investment gaps (Panel a) and drops in planned investment (Panel b). ADBI Working Paper [Do not enter] Ferrando and Pál 13 Table 7: Determinants of Investment Gap/Planned Investment Drop to Identify the Impact of Structural External Financing Difficulties, Cyclical Funding Deterioration, and Internal Funding Deterioration (1) (2) Variables Invgap Planned Inv Drop Structural external financing difficulties 0.090*** –0.042*** (0.010) (0.010) Cyclical external financing difficulties 0.035*** 0.047*** (0.009) (0.009) Internal funding deterioration (lag) 0.058*** 0.146*** (0.009) (0.009) Cash holdings (lag) –0.066*** –0.060*** (0.023) (0.020) Profitability (lag) –0.297*** –0.022 (0.031) (0.022) Financial leverage (lag) –0.017 0.111*** (0.017) (0.014) leading_innovators1 –0.022* –0.033*** (0.013) (0.011) Small –0.017 –0.044*** (0.011) (0.009) Medium –0.040*** –0.073*** (0.011) (0.009) Large –0.048*** –0.111*** (0.011) (0.010) Construction –0.027*** –0.025*** (0.009) (0.008) Services –0.026*** –0.012 (0.008) (0.008) Infrastructure 0.002 –0.043*** (0.008) (0.007) South –0.082*** –0.020*** (0.007) (0.007) West and North –0.053*** 0.014** (0.008) (0.006) D2020 –0.009 0.176*** (0.009) (0.008) Obstacle – uncertainty (lag) 0.031*** 0.052*** (0.009) (0.007) Obstacle – lack of demand 0.011 0.028*** (0.007) (0.006) Obstacle – lack of skilled staff 0.021*** –0.021*** (0.008) (0.007) Obstacle – digital infrastructure internal funding difficulties (lag) 0.008 –0.009 (0.007) (0.006) Observations 14,992 30,944 Standard errors in parentheses. ADBI Working Paper [Do not enter] Ferrando and Pál 14 Table 8: Sample Distribution of Firms with External Financing Difficulties and Investment Issues Panel A: Investment Gap Investment Gap 0 1 Total External funding difficulties 0 68% 14% 82% 1 13% 4% 18% Total 81% 19% 100% Panel b: Planned Investment Drop Planned Investment Drop 0 1 Total External funding difficulties 0 60% 22% 82% 1 11% 6% 18% Total 71% 29% 100% We estimate the propensity score, which results from the conditional probability of a firm signaling external funding difficulties, given the value of the observed firms’ characteristics and different subsamples of with or without investment difficulties (investment gap or planned investment drop). The set of explanatory variables chosen must satisfy the balancing property, which requires that after the matching, the distributions of the covariates and the propensity score between the treated and the control groups are similar. Figure 4 confirms that the propensity score distribution after the matching is similar for the treated and control groups. Figure 4: Propensity Score Distribution Before and After the Matching Source: EIBIS-Orbis 2016–2023. Table 9 displays our main findings on the (ex post) effect of access to external finance on a firm’s growth based on the propensity score analysis. From Column 1 of Table 9, we see that the presence of external funding difficulties has a negative and statistically significant impact on the subsequent profitability and growth. Firms that faced difficulties in obtaining external financing were less profitable (–1.26 percentage points) and grew relatively less (1.1%) than firms that did not face this kind of problem. In the fifth column we see that the losses in terms of profitability are even higher among the subgroup of firms that report having had some investment gaps in the past (–1.74 percentage points). By contrast, the distinction between firms with and without future plans to drop investment does not add any further additional information on future ADBI Working Paper [Do not enter] Ferrando and Pál 15 performance. We should bear in mind that there is a gap between planned and realized investment gaps; firms tend to be more pessimistic when declaring their plans. In terms of future asset growth, the loss is lower for firms that, on top of financial problems, also signaled no past investment gap (Column 3). This finding supports the idea that these firms, having already expanded according to their business needs in the previous years, might hold back from investing further when external finance is not easily available. Results might also suggest that besides the direct impact of funds availability for investments, there is a significant negative impact of external financing difficulties on performance and growth. Nevertheless, the coefficient of this second group is not significant, given the low sample size (4% and 6% of the total sample, respectively). Table 9: Differential Growth Rates of Firms With Funding Gap Versus Firms With No Funding Gap by Investment Decisions – Propensity Score Results (for More Details See Table A3 in the Appendix) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) 2-year Average Growth Rate All Firms T-stat No Investment Gap T-stat With Investment Gap T-stat No Planned Investment Drop T-stat Planned Investment Drop T-stat ROA –1.26 –6.14 –1.23 5.1 –1.74 3.53 –1.214 4.4 –1.206 3.52 Total Assets –.011 –2.56 –0.02 2.83 –0.01 1.28 –0.02 2.3 –0.01 1.52 5. CONCLUSION AND POLICY IMPLICATIONS In this paper we provide novel evidence of the negative impact of internal and external funding conditions on firms’ investments and growth. We document that micro and small firms and leading innovators are more likely to face both internal and external funding difficulties, especially in cyclically worsening periods. We show that the presence of external funding difficulties has a long-term impact on future firms’ performance and growth. Firms indeed have more difficulties generating additional financial flows in their investment in total assets in the subsequent two years when they had previous difficulties collecting external finance. The losses are even higher when the same firms had also signaled some investment gaps in the past. We argue that policy support should focus on firms that are more vulnerable to tightening and deteriorating funding conditions, especially if internal and external funding conditions are deteriorating simultaneously, which has been the case recently for many micro and small firms. Viable firms, even with high growth potential, and leading innovators might be forced to stagnate by canceling their investments due to a lack of, or unaffordable, funding. Increasing the alternative financing solutions would fit particularly the financing need of small and innovative firms, especially those with high potential to grow. In the current period of structural shifts towards digitalization and greening, financing conditions might play an important role in transforming European firms. Results indicate more of a structural rather than a cyclical financing issue among firms that are lagging in digitalization and green investments. Consequently, policy support should be oriented towards those structural impediments that prevent firms from transforming. Targeted policy support of these specific investments is needed to close the digitalization and greening gap among EU firms, thereby accelerating the green and fair transition. ADBI Working Paper [Do not enter] Ferrando and Pál 16 REFERENCES Abadie A., D. Drukker, L. J. Herr, and G. W. Imbens. 2001. Implementing Matching Estimators for Average Treatment Effects in Stata. Stata Journal 1(1): 1–18. Aghion, P., N. Berman, L. Eymard, P. Askenazy, and G. Cette. 2012. Credit Constraints and the Cyclicality of R&D Investment: Evidence from France. Journal of the European Economic Association 10(5): 1001–1002. Ardic Alper, O. P., M. Hommes, P. Stein, and W. Benno. 2013. Closing the Credit Gap for Formal and Informal Micro, Small, and Medium Enterprises. Washington, D.C.: World Bank, January 2013 Group. Bonanno, G., A. Ferrando, and S. P. S. Rossi. 2023. Do Innovation and Financial Constraints Affect the Profit Efficiency of European Enterprises? Eurasian Business Review 13: 57–86. Brutscher, P.-B., A. Coali, J. Delanote, and P. Harasztosi.2020. 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ADBI Working Paper [Do not enter] Ferrando and Pál 18 ANNEX Table A1: Descriptions and Definitions of the Main Variables Variable Description Main dependent variables Investment gap Firms declaring that investment over the last three years was too little to ensure the success of their business going forward Realized investment drop Firms with less investment than in the previous year Planned investment drop Firms for which total investment expected for the current or next year is expected to be less than in the previous year Net investment rate Difference of fixed assets between two subsequent years, over previous fixed assets Main variables External funding difficulties Firms with either structural or cyclical funding difficulties structural Those viable firms that needed a loan but were either discouraged, did not receive it, or received less than they needed. Not registering losses for three consecutive years cyclical Firms expecting their external financing conditions to deteriorate. Not registering losses for three consecutive years Internal funding difficulties Firms declaring that their internal finance conditions have deteriorated. Not registering losses for three consecutive years. Main control variables Firm size Four size classes: micro (23% of observations), small (34% of obs), medium (29% of obs), and large (15% of obs) Sector Broad sector groups (dummy variables): manufacturing (28% of observations), construction (22% of obs), services (26% of obs), and infrastructure (23% of obs) Country group Countries are clustered into three groups: “Center and East,” “South,” and “Northwest.” “Center and East”: BG, CZ, HR, HU, LT, LV, PL, RO, SI, SK; “South”: CY, ES, FR, GR, IT, MT, PT; “Northwest”: AT, BE, DE, DK, EE, FI, IE, LU, NL, SE Profitability Cash flow (profit plus depreciation) over average of total assets (current and preceding year) Financial leverage Sum of loans and long-term debt over total assets Cash holdings Amount of cash and cash equivalents over total assets Cash flow Net income minus changes in working capital over total assets ROA ROA is calculated by dividing a firm's net income by the average of its total assets, multiplied by 100 Firms’ growth Difference of total assets between two subsequent years, over previous total assets Labour productivity Labor productivity is calculated by dividing the total output by the total number of employees Obstacle – uncertainty The extent to which uncertainty about the future is an obstacle to investment activities Obstacle – lack of demand The extent to which demand for product and services is an obstacle to investment activities Obstacle – lack of skilled staff The extent to which availability of staff with the right skills is an obstacle to investment activities Obstacle – digital infrastructure The extent to which access to digital infrastructure is an obstacle to investment activities Leading innovators Firms with (substantial) R&D and products new to the country or the global market Digital Firms that have implemented digital technology in parts of their business or organized their entire business around it Green Already invested or plan to invest to tackle the impact of weather events or carbon emissions ADBI Working Paper [Do not enter] Ferrando and Pál 19 Figure A1: Investment Trends – Computed with ORBIS Data Source: Orbis 2015–2023. Figure A2: Investment Trends – Computed With EIBIS Data Source: EIBIS 2016–2023. -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 2003 2006 2009 2012 2015 2018 2021 Percentage change of investments net investment gross investment 8% 9% 10% 11% 12% 13% 14% 15% 16% 2000 2003 2006 2009 2012 2015 2018 2021 Cash holdings to total assets 0% 20% 40% 60% 2016 2017 2018 2019 2020 2021 2022 2023 Share of firms with investment drop (realized and planned) Investment drop in previous year Planned investment drop for the current or next period ADBI Working Paper [Do not enter] Ferrando and Pál 20 Figure A3: External Funding Difficulties and their Impact on Investments – Annually External funding difficulties and their impact on accumulated investment gap External funding difficulties and their impact on planned investment drop Note: Bars represent confidence interval at 95% level. Source: EIBIS-Orbis 2023. Figure A4: Expected Probability of Investment Gap and Planned Investment Drop With and Without External Funding Difficulties – Annually Investment gap Planned investment drop Note: Bars represent confidence interval at 95% level. Source: EIBIS-Orbis 2023. 0% 5% 10% 15% 20% 25% 0% 5% 10% 15% 20% 25% 30% 2016 2017 2018 2019 2020 2021 2022 2023 Impact of external funding difficulties in previous year on investment gap, secondary axes Share of firms with external funding difficulties 0% 5% 10% 15% 20% 25% 0% 5% 10% 15% 20% 25% 30% 2016 2017 2018 2019 2020 2021 2022 2023 Impact of external funding difficulties on planned investment drop, secondary axes Share of firms with external funding difficulties 0% 5% 10% 15% 20% 25% 30% 35% 40% 2017 2018 2019 2020 2021 2022 2023 No external funding dificulties - lagged Firms with external funding difficulties - lagged 0% 10% 20% 30% 40% 50% 60% 2016 2017 2018 2019 2020 2021 2022 2023 No external funding dificulties Firms with external funding difficulties